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Peer-reviewed

Research Article

The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Health Sciences, University of York, York, England, United Kingdom

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Roles Investigation, Methodology, Supervision, Writing – review & editing

Affiliations Centre of Health Economics, University of York, York, England, United Kingdom, Luxembourg Institute of Socio-economic Research (LISER), Luxembourg

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

Affiliations Department of Health Sciences, University of York, York, England, United Kingdom, Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada

Roles Conceptualization, Investigation, Supervision, Writing – review & editing

  • Darius Erlangga, 
  • Marc Suhrcke, 
  • Shehzad Ali, 
  • Karen Bloor

PLOS

  • Published: August 28, 2019
  • https://doi.org/10.1371/journal.pone.0219731
  • Reader Comments

7 Nov 2019: Erlangga D, Suhrcke M, Ali S, Bloor K (2019) Correction: The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review. PLOS ONE 14(11): e0225237. https://doi.org/10.1371/journal.pone.0225237 View correction

Fig 1

Expanding public health insurance seeks to attain several desirable objectives, including increasing access to healthcare services, reducing the risk of catastrophic healthcare expenditures, and improving health outcomes. The extent to which these objectives are met in a real-world policy context remains an empirical question of increasing research and policy interest in recent years.

We reviewed systematically empirical studies published from July 2010 to September 2016 using Medline, Embase, Econlit, CINAHL Plus via EBSCO, and Web of Science and grey literature databases. No language restrictions were applied. Our focus was on both randomised and observational studies, particularly those including explicitly attempts to tackle selection bias in estimating the treatment effect of health insurance. The main outcomes are: (1) utilisation of health services, (2) financial protection for the target population, and (3) changes in health status.

8755 abstracts and 118 full-text articles were assessed. Sixty-eight studies met the inclusion criteria including six randomised studies, reflecting a substantial increase in the quantity and quality of research output compared to the time period before 2010. Overall, health insurance schemes in low- and middle-income countries (LMICs) have been found to improve access to health care as measured by increased utilisation of health care facilities (32 out of 40 studies). There also appeared to be a favourable effect on financial protection (26 out of 46 studies), although several studies indicated otherwise. There is moderate evidence that health insurance schemes improve the health of the insured (9 out of 12 studies).

Interpretation

Increased health insurance coverage generally appears to increase access to health care facilities, improve financial protection and improve health status, although findings are not totally consistent. Understanding the drivers of differences in the outcomes of insurance reforms is critical to inform future implementations of publicly funded health insurance to achieve the broader goal of universal health coverage.

Citation: Erlangga D, Suhrcke M, Ali S, Bloor K (2019) The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review. PLoS ONE 14(8): e0219731. https://doi.org/10.1371/journal.pone.0219731

Editor: Sandra C. Buttigieg, University of Malta Faculty of Health Sciences, MALTA

Received: March 19, 2018; Accepted: July 2, 2019; Published: August 28, 2019

Copyright: © 2019 Erlangga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The search strategy for this review is available in Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In recent decades, achieving universal health coverage (UHC) has been a major health policy focus globally.[ 1 – 3 ] UHC entitles all people to access healthcare services through publicly organised risk pooling,[ 4 ] safeguarding against the risk of catastrophic healthcare expenditures.[ 5 ] Low- and middle-income countries (LMICs) face particular challenges in achieving UHC due to particularly limited public resources for health care, inefficient allocation, over-reliance on out-of-pocket payments, and often large population size.[ 5 ] As a result, access to health care and the burden of financial cost in LMICs tends to be worse for the poor, often resulting in forgone care.[ 6 – 8 ]

Introducing and increasing the coverage of publicly organised and financed health insurance is widely seen as the most promising way of achieving UHC,[ 9 , 10 ] since private insurance is mostly unaffordable for the poor.[ 11 ] Historically, social health insurance, tax-based insurance, or a mix of the two have been the dominant health insurance models amongst high income countries and some LMICs, including Brazil, Colombia, Costa Rica, Mexico, and Thailand.[ 12 ] This is partly influenced by the size of the formal sector economy from which taxes and payroll contributions can be collected. In recent decades, community-based health insurance (CBHI) or “mutual health organizations” have become increasingly popular among LMICs, particularly in Sub-Saharan Africa (e.g. Burkina Faso,[ 13 ] Senegal[ 14 ] and Rwanda[ 15 ]) as well as Asia (e.g. China[ 16 ] and India[ 17 ]). CBHI has emerged as an alternative health financing strategy, particularly in cases where the public sector has failed to provide adequate access to health care.[ 18 ]

We searched for existing systematic reviews on health insurance in the Cochrane Database for Systematic Reviews, Medline, Embase, and Econlit. Search terms “health insurance”, “low-middle income countries”, and “utilisation” were used alongside methodological search strategy to locate reviews. Seven systematic reviews were identified of varying levels of quality, [ 19 – 26 ] with Acharya et al.[ 27 ] being the most comprehensive. The majority of existing reviews has suggested that publicly-funded health insurance has typically shown a positive impact on access to care, while the picture for financial protection was mixed, and evidence of the impact on health status was very sparse.

This study reviews systematically the recent fast-growing evidence on the impact of health insurance on health care utilisation, financial protection and health status in LMICs. Since the publication of Acharya et al. (which conducted literature searches in July 2010), the empirical evidence on the impact of health insurance has expanded significantly in terms of quantity and quality, with growing use of sophisticated techniques to account for statistical challenges[ 28 ] (particularly insurance selection bias). This study makes an important contribution towards our understanding of the impact of health insurance in LMICs, taking particular care in appraising the quality of studies. We recognise the heterogeneity of insurance schemes implemented in LMICs and therefore do not attempt to generalise findings, but we aim to explore the pattern emerging from various studies and to extract common factors that may affect the effectiveness of health insurance, that should be the focus of future policy and research. Furthermore, we explore evidence of moral hazard in insurance membership, an aspect that was not addressed in the Acharya et al review.[ 27 ]

This review was planned, conducted, and reported in adherence with PRISMA standards of quality for reporting systematic reviews.[ 29 ]

Participants

Studies focusing on LMICs are included, as measured by per capita gross national income (GNI) estimated using the World Bank Atlas method per July 2016.[ 30 ]

Intervention

Classification of health insurance can be complicated due to the many characteristics defining its structure, including the mode of participation (compulsory or voluntary), benefit entitlement, level of membership (individual or household), methods for raising funds (taxes, flat premium, or income-based premium) and the mechanism and extent of risk pooling [ 31 ]. For the purpose of this review, we included all health insurance schemes organised by government, comprising social health insurance and tax-based health insurance. Private health insurance was excluded from our review, but we recognise the presence of community-based health insurance (CBHI) in many LMICs, especially in Africa and Asia [ 18 ]. We also therefore included CBHI if it was scaled up nationally or was actively promoted by national government. Primary studies that included both public and private health insurance were also considered for inclusion if a clear distinction between the two was made in the primary paper. Studies examining other types of financial incentives to increase the demand for healthcare services, such as voucher schemes or cash transfers, were excluded.

Control group

In order to provide robust evidence on the effect on insurance, it is necessary to compare an insured group with an appropriate control group. In this review, we selected studies that used an uninsured population as the control group. Multiple comparison groups were allowed, but an uninsured group had to be one of them.

Outcome measures

We focus on three main outcomes:

  • Utilisation of health care facilities or services (e.g. immunisation coverage, number of visits, rates of hospitalisation).
  • Financial protection, as measured by changes in out-of-pocket (OOP) health expenditure at household or individual level, and also catastrophic health expenditure or impoverishment from medical expenses.
  • Health status, as measured by morbidity and mortality rates, indicators of risk factors (e.g. nutritional status), and self-reported health status.

The scope of this review is not restricted to any level of healthcare delivery (i.e. primary or secondary care). All types of health services were considered in this review.

Types of studies

The review includes randomized controlled trials, quasi-experimental studies (or “natural experiments”[ 32 ]), and observational studies that account for selection bias due to insurance endogeneity (i.e. bias caused by insurance decisions that are correlated with the expected level of utilisation and/or OOP expenditure). Observational studies that did not take account of selection bias were excluded.

Databases and search terms

A search for relevant articles was conducted on 6 September 2016 using peer-reviewed databases (Medline, Embase, Econlit, CINAHL Plus via EBSCO and Web of Science) and grey literatures (WHO, World Bank, and PAHO). Our search was restricted to studies published since July 2010, immediately after the period covered by the earlier Acharya et al. (2012) review. No language restrictions were applied. Full details of our search strategy are available in the supporting information ( S1 Table ).

Screening and data extraction

Two independent reviewers (DE and MS) screened all titles and abstracts of the initially identified studies to determine whether they satisfied the inclusion criteria. Any disagreement was resolved through mutual consensus. Full texts were retrieved for the studies that met the inclusion criteria. A data collection form was used to extract the relevant information from the included studies.

Assessment of study quality

We used the Grades of Assessment, Development and Evaluation (GRADE) system checklist[ 33 , 34 ] which is commonly used for quality assessment in systematic reviews. However, GRADE does not rate observational studies based on whether they controlled for selection bias. Therefore, we supplemented the GRADE score with the ‘Quality of Effectiveness Estimates from Non-randomised Studies’ (QuEENS) checklist.[ 35 ]

cRandomised studies were considered to have low risk of bias. Non-randomised studies that account for selection on observable variables, such as propensity score matching (PSM), were categorised as high risk of bias unless they provided adequate assumption checks or compared the results to those from other methods, in which case they may be classed as medium risk. Non-randomised studies that account for selection on both observables and unobservables, such as regression with difference-in-differences (DiD) or Heckman sample selection models, were considered to have medium risk of bias–some of these studies were graded as high or low risk depending on sufficiency of assumption checks and comparison with results from other methods.

Heterogeneity of health insurance programmes across countries and variability in empirical methods used across studies precluded a formal meta-analysis. We therefore conducted a narrative synthesis of the literature and did not report the effect size. Throughout this review, we only considered three possible effects: positive outcome, negative outcome, or no statistically significant effect (here defined as p-value > 0.1).

Results of the search

Our database search identified 8,755 studies. Five additional studies were retrieved from grey literature. After screening of titles and abstracts, 118 studies were identified as potentially relevant. After reviewing the full-texts, 68 studies were included in the systematic review (see Fig 1 for the PRISMA diagram). A full description of the included studies is presented in the supporting information ( S2 Table ). Of the 68 included studies, 40 studies examined the effect on utilisation, 46 studies on financial protection, and only 12 studies on health status (see Table 1 ).

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Utilisation of health care

Table 2 collates evidence on the effects of health insurance on utilisation of healthcare services. Three main findings were observed:

  • Evidence on utilisation of curative care generally suggested a positive effect, with 30 out of 38 studies reporting a statistically significant positive effect.
  • Evidence on preventive care is less clear with 4 out of 7 studies reporting a positive effect, two studies finding a negative effect and one study reporting no effect.
  • Among the higher quality studies, i.e. those that suitably controlled for selection bias reflected by moderate or low GRADE score and low risk of bias (score = 3) on QuEENS, seven studies reported a positive relationship between insurance and utilisation. One study[ 36 ] reported no statistically significant effect, and another study found a statistically significant negative effect.[ 37 ]

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Financial protection

Overall, evidence on the impact of health insurance on financial protection is less clear than that for utilisation (see Table 3 ). 34 of the 46 studies reported the impact of health insurance on the level of out-of-pocket health expenditure. Among those 34 studies, 17 found a positive effect (i.e. a reduction in out-of-pocket expenditure), 15 studies found no statistically significant effect, and two studies–from Indonesia[ 59 ] and Peru[ 62 ]–reported a negative effect (i.e. an increase in out-of-pocket expenditure).

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Another financial protection measure is the probability of incurring catastrophic health expenditure defined as OOP exceeding a certain threshold percentage of total expenditure or income. Of the 14 studies reporting this measure, nine reported reduction in the risk of catastrophic expenditure, three found no statistically significant difference, and two found a negative effect of health insurance. Only four studies reported sensitivity analysis varying changes in the threshold level,[ 59 , 62 , 75 , 76 ] though this did not materially affect the findings.

  • Two studies used a different measure of financial protection, the probability of impoverishment due to catastrophic health expenditure, reporting conflicting findings.[ 77 , 78 ] Finally, four studies evaluated the effect on financial protection by assessing the impact of insurance on non-healthcare consumption or saving behaviour, such as non-medical related consumption[ 79 ], probability of financing medical bills via asset sales or borrowing[ 40 ], and household saving[ 80 ]. No clear pattern can be observed from those four studies.

Health status

Improving health is one of the main objectives of health insurance, yet very few studies thus far have attempted to evaluate health outcomes. We identified 12 studies, with considerable variation in the precise health measure considered (see Table 4 ). There was some evidence of positive impact on health status: nine studies found a positive effect, one study reported a negative effect, and two studies reported no effect.

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Type of insurance and countries

Considering the heterogeneity of insurance schemes among different countries, we attempted to explore the aggregate results by the type of insurance scheme and by country. Table 5 provides a summary of results classified by three type of insurance scheme: community-based health insurance, voluntary health insurance (non-CBHI), and compulsory health insurance. This division is based on the mode of participation (compulsory vs voluntary), which may affect the presence of adverse selection and moral hazard. Premiums are typically community-rated in CBHI, risk-rated in voluntary schemes and income-rated in compulsory schemes.

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In principle, CBHI is also considered a voluntary scheme, but we separated it to explore whether the larger size of pooling from non-CBHI schemes may affect the outcomes. Social health insurance is theoretically a mandatory scheme that requires contribution from the enrolees. However, in the context of LMICs, the mandatory element is hard to enforce, and in practice the scheme adopts a voluntary enrolment. Additionally, the government may also want to subsidise the premium for poor people. Therefore, in this review SHI schemes can fall into either the voluntary health insurance (non-CBHI) or compulsory health insurance (non-CBHI), depending on the target population defined in the evaluation study. Lastly, we chose studies with high quality/low risk only to provide more robust results.

Based on the summary in Table 5 , the effect on utilisation overall does not differ based on type of insurance, with most evidence suggesting an overall increase in utilisation by the insured. The two studies showing no effect or reduced consumption of care were conducted in two different areas of India, which may–somewhat tentatively–suggest a common factor unique to India’s health system that may compromise the effectiveness of health insurance in increasing utilisation.

Regarding financial protection, the evidence for both CBHI and non-CBHI voluntary health insurance is inconclusive. Furthermore, there is an indication of heterogeneity by supply side factors captured by proximity to health facilities. Evidence from studies exploring subsidised schemes suggests no effect on financial protection, even a negative effect among the insured in Peru.

Lastly, evidence for health status may be influenced by how health outcomes are measured. Studies exploring specific health status, (examples included health indexes, wasting, C-reactive protein, and low birth weight), show a positive effect, whereas studies using mortality rates tends to show no effect or even negative effects. Studies exploring CBHI scheme did not find any evidence of positive effect on health status, as measured either by mortality rate or specific health status.

This review synthesises the recent, burgeoning empirical literature on the impact of health insurance in LMICs. We identified a total of 68 eligible studies over a period of six years–double the amount identified by the previous review by Acharya et al. over an approximately 60-year time horizon (1950—July 2010). We used two quality assessment checklists to scrutinise the study methodology, taking more explicit account of the methodological robustness of non-experimental designs.

Programme evaluation has been of interest to many researchers for reporting on the effectiveness of a public policy to policymakers. In theory, the gold standard for a programme evaluation is the randomised control trial, in which the treatment is randomly assigned to the participants. The treatment assignment process has to be exogenous to ensure that any observed effect between the treated and control groups can only be caused by the difference in the treatment assignment. Unfortunately, this ideal scenario is often not feasible in a public policy setting. Our findings showed that only three papers between 2010 and 2016 were able to conduct a randomised study to evaluate the impact of health insurance programmes in developing countries, particularly CBHI [ 38 , 75 , 103 ]. Policymakers may believe in the value of an intervention regardless of its actual evidence base, or they may believe that the intervention is beneficial and that no one in need should be denied it. In addition, policymakers are inclined to demonstrate the effectiveness of an intervention that they want implemented in the most promising contexts, as opposed to random allocation [ 104 ].

Consequently, programme evaluators often have to deal with a non-randomised treatment assignment which may result in selection bias problems. Selection bias is defined as a spurious relationship between the treatment and the outcome of interest due to the systematic differences between the treated and the control groups [ 105 ]. In the case of health insurance, an individual who chooses to enrol in the scheme may have different characteristics to an individual who chooses not to enrol. When those important characteristics are unobservable, the analyst needs to apply more advanced techniques and, sometimes, stronger assumptions. Based on our findings, we noted several popular methods, including propensity score matching (N = 8), difference-in-difference (N = 10), fixed or random effects of panel data (N = 6), instrumental variables (N = 12) and regression discontinuity (N = 6). Those methods have varying degree of success in controlling the unobserved selection bias and analysts should explore the robustness of their findings by comparing initial findings with other methods by testing important assumptions. We noted some papers combining two common methods, such as difference-in-difference with propensity score matching (N = 10) and fixed effects with instrumental variables (N = 8), in order to obtain more robust results.

Overall effect

Compared with the earlier review, our study has found stronger and more consistent evidence of positive effects of health insurance on health care utilisation, but less clear evidence on financial protection. Restricting the evidence base to the small subset of randomised studies, the effects on financial protection appear more consistently positive, i.e. three cluster randomised studies[ 39 , 75 , 76 ] showed a decline in OOP expenditure and one randomised study[ 36 ] found no significant effect.

Besides the impact on utilisation and financial protection, this review identified a number of good quality studies measuring the impact of health insurance on health outcomes. Twelve studies were identified (i.e. twice as many as those published before 2010), nine of which showed a beneficial health effect. This holds for the subset of papers with stronger methodology for tackling selection bias.[ 39 , 49 , 89 , 103 ] In cases where a health insurance programme does not have a positive effect on either utilisation, financial protection, and health status, it is particularly important to understand the underlying reasons.

Possible explanation of heterogeneity

Payment system..

Heterogeneity of the impact of health insurance may be explained by differences in health systems and/or health insurance programmes. Robyn et al. (2012) and Fink et al (2013) argued that the lack of significant effect of insurance in Burkina Faso may have been partially influenced by the capitation payment system. As the health workers relied heavily on user fees for their income, the change of payment system from fee-for-services to capitation may have discouraged provision of high quality services. If enrolees perceive the quality of contracted providers as bad, they might delay seeking treatment, which in turn could impact negatively on health.

Several studies from China found the utilisation of expensive treatment and higher-level health care facilities to have increased following the introduction of the insurance scheme.[ 41 , 44 , 45 , 88 ] A fee-for-service payment system may have incentivised providers to include more expensive treatments.[ 43 , 83 , 88 ] Recent systematic reviews suggested that payment systems might play a key role in determining the success of insurance schemes,[ 23 , 106 ] but this evidence is still weak, as most of the included studies were observational studies that did not control sufficiently for selection bias.

Uncovered essential items.

Sood et al. (2014) found no statistically significant effect of community-based health insurance on utilisation in India. They argued that this could be caused by their inability to specify the medical conditions covered by the insurance, causing dilution of a potential true effect. In other countries, transportation costs[ 69 ] and treatments that were not covered by the insurance[ 59 , 60 ] may explain the absence of a reduction in out-of-pocket health expenditures.

Methodological differences.

Two studies in Georgia evaluated the same programme but with different conclusions.[ 50 , 51 ] This discrepancy may be explained by the difference in the estimated treatment effect: one used average treatment effect (ATE), finding no effect, and another used average treatment effect on the treated (ATT), reporting a positive effect. ATE is of prime interest when policymakers are interested in scaling up the programme, whereas ATT is useful to measure the effect on people who were actually exposed to insurance.[ 107 ]

Duration of health insurance.

We also found that the longer an insurance programme has been in place prior to the timing of the evaluation, the higher the odds of improved health outcomes. It is plausible that health insurance would not change the health status of population instantly upon implementation.[ 21 ] While there may be an appetite among policymakers to obtain favourable short term assessments, it is important to compare the impact over time, where feasible.

Moral hazard.

Acharya et al (2012) raised an important question about the possibility of a moral hazard effect as an unintended consequence of introducing (or expanding) health insurance in LMICs. We found seven studies exploring ex-ante moral hazard by estimating the effect on preventive care. If uninsured individuals expect to be covered in the future, they may reduce the consumption of preventive care or invest less in healthy behaviours.[ 108 , 109 ] Current overall evidence cannot suggest a definite conclusion considering the heterogeneity in chosen outcomes. One study found that the use of a self-treated bed nets to prevent malaria declined among the insured group in Ghana[ 54 ] while two studies reported an increase in vaccination rates[ 62 ] and the number of prenatal care visits[ 55 , 62 ]among the insured group. Another study reported no evidence that health insurance encouraged unhealthy behaviour or reduction of preventive efforts in Thailand.[ 66 ]

Two studies from Colombia found that the insured group is more likely to increase their demand for preventive treatment.[ 47 , 49 ] As preventive treatment is free for all, both authors attributed this increased demand to the scheme’s capitation system, incentivising providers to promote preventive care to avoid future costly treatments.[ 110 ] Another study of a different health insurance programme in Colombia found an opposite effect.[ 48 ]

Study limitations.

This review includes a large variety of study designs and indicators for assessing the multiple potential impacts of health insurance, making it hard to directly compare and aggregate findings. For those studies that used a control group, the use of self-selected controls in many cases creates potential bias. Studies of the effect of CBHI are often better at establishing the counterfactual by allowing the use of randomisation in a small area, whereas government schemes or social health insurance covering larger populations have limited opportunity to use randomisation. Non-randomised studies are more susceptible to confounding factors unobserved by the analysts. For a better understanding of the links between health insurance and relevant outcomes, there is also a need to go beyond quantitative evidence alone and combine the quantitative findings with qualitative insights. This is particularly important when trying to interpret some of the counterintuitive results encountered in some studies.

The impact of different health insurance schemes in many countries on utilisation generally shows a positive effect. This is aligned with the supply-demand theory in whichhealth insurance decreases the price of health care services resulting in increased demand. It is difficult to draw an overall conclusion about the impact of health insurance on financial protection, most likely because of differences in health insurance programmes. The impact of health insurance on health status suggests a promising positive effect, but more studies from different countries is required.

The interest in achieving UHC via publicly funded health insurance is likely to increase even further in the coming years, and it is one of the United Nation’s Sustainable Development Goals (SDGs) for 2030[ 111 ]. As public health insurance is still being widely implemented in many LMICs, the findings from this review should be of interest to health experts and policy-makers at the national and the international level.

Supporting information

S1 table. search strategies..

https://doi.org/10.1371/journal.pone.0219731.s001

S2 Table. Study characteristic and reported effect from the included studies (N = 68).

https://doi.org/10.1371/journal.pone.0219731.s002

S3 Table. PRISMA 2009 checklist.

https://doi.org/10.1371/journal.pone.0219731.s003

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  • v.2017; 2017

Supervised Learning Methods for Predicting Healthcare Costs: Systematic Literature Review and Empirical Evaluation

Mohammad amin morid.

a David Eccles School of Business, University of Utah, Salt Lake City, UT, USA

Kensaku Kawamoto

b Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA

Travis Ault

c niversity of Utah Health Plans, Murray, UT, USA

Josette Dorius

Samir abdelrahman.

An important informatics tool for controlling healthcare costs is accurately predicting the likely future healthcare costs of individuals. To address this important need, we conducted a systematic literature review and identified five methods for predicting healthcare costs. To enable a direct comparison of these different approaches, we empirically evaluated the predictive performance of each reported approach, as well as other state-of-the-art supervised learning methods, using data from University of Utah Health Plans for October 2013 through October 2016. The data set consisted of approximately 90,000 individuals, 6.3 million medical claims and 1.2 million pharmacy claims. In this comparative analysis, gradient boosting had the best predictive performance overall and for low to medium cost individuals. For high cost individuals, Artificial Neural Network (ANN) and the Ridge regression model, which have not been previously reported for use in healthcare cost prediction, had the highest performance.

Introduction

The United States’ national health expenditure (NHE) grew 5.8% to $3.2 trillion in 2015 (i.e., $9,990 per person), which accounted for 17.8% of the nation’s gross domestic product (GDP) 1 . In seeking to control these unsustainable increases in healthcare costs, it is imperative that healthcare organizations can predict the likely future costs of individuals, so that care management resources can be efficiently targeted to those individuals at highest risk of incurring significant costs 2 . Key stakeholders in these efforts to manage healthcare costs include health insurers, employers, society, and increasingly healthcare delivery organizations due to the transition from fee-for-service payment models to value-based payment models 3 . For any given individual, insurers generally have the most comprehensive information on healthcare costs as they pay for care delivered across various healthcare delivery organizations.

Predicting healthcare costs for individuals using accurate prediction models is important for various stakeholders beyond health insurers, and for various purposes 4 . For health insurers and increasingly healthcare delivery systems, accurate forecasts of likely costs can help with general business planning in addition to prioritizing the allocation of scarce care management resources. Moreover, for patients, knowing in advance their likely expenditures for the next year could potentially allow them to choose insurance plans with appropriate deductibles and premiums.

Despite the importance of healthcare cost prediction, to our knowledge there has been no review of the literature on this important topic. Therefore, we conducted a systematic literature review. Moreover, in order to enable a direct comparison of approaches on a common data set, we evaluated each of the identified approaches on a health insurance data set from the University of Utah Health Plans. We also evaluated additional state-of-the-art methods not previously evaluated in the literature.

Literature Review

Adapting a search strategy from a previous systematic review 5 , we searched Google Scholar and MEDLINE. The latest search was performed on February 21, 2017. We used a combination of the following search terms: healthcare cost prediction, medical claim cost, pharmacy claim cost; healthcare expenditure prediction; healthcare risk score prediction; and patient cost prediction.

In conducting the systematic literature review, we sought to answer the following questions. Because the answer to the first question identified that using features of prior costs to predict future costs performed as well as or better than approaches that also used clinical data for cost prediction purposes, all subsequent questions were focused on approaches that used prior cost features to predict future costs (referred to henceforth as “cost on cost prediction”).

  • What are the types of healthcare cost prediction approaches reported in the literature?
  • What are the input features that have been used for cost on cost prediction?
  • What are the supervised learning methods that have been used for cost on cost prediction?
  • What are the performance measures and evaluation results for cost on cost prediction?

Direct Comparison of Alternative Cost Prediction Methods using a Health Insurer Data Set

Approach . This study was approved by the University of Utah Institutional Review Board (Protocol # 00094358). We used a health insurance data set to directly compare the performance of cost on cost prediction approaches identified in the literature, as well as other state-of-the-art supervised learning techniques.

Data . Our data set consisted of 6.3 million medical claims and 1.2 million pharmacy claims from approximately 91,000 distinct individuals covered by University of Utah Health Plans from October 2013 to October 2016. Available data included demographic information (e.g., age, gender, age), clinical encounter information (e.g., place and date of service, provider information), diagnosis and procedure codes, pharmacy dispense information, and cost information (e.g., paid, allowed and billed amount). This data was filtered to individuals with insurance membership for the whole three years period, which resulted in approximately 3.8 million medical claims and 780,000 pharmacy claims from 24,000 patients.

The data set was divided into two time periods: an observation period and a result period. The former time period was from October 2013 to September 2015 (i.e., two years), which was used to predict individuals’ cost in the result period ranging from October 2015 to October 2016 (i.e., one year). Table 1 shows all input features used in this study. All features used in this study were cost related features extracted from Bertsimas et al. 6 , which had the largest and most complete set of cost related features among the reviewed manuscripts. If a member did not have any cost for a specific month it was considered as zero; therefore, there are no missing values in this dataset.

Table 1.

Features used to develop the prediction models.

The range of paid amounts in the result period showed that 80% of the overall cost of the population came from only 15% of the members. Therefore, aligned with the literature on cost bucketing, to reduce the effects of extremely expensive members, the data set was partitioned into five different cost buckets. This partitioning was done so that the sum of members’ costs in each bucket was approximately the same in the observation period (i.e., the total dollar amount in each bucket was the same). For instance, 84% of members are in bucket 1 with the same total cost amount as the members in bucket 5, which contains about 2% of the population.

Classifier . Classifiers evaluated included Linear Regression, Lasso 7 , Ridge 8 , Elastic Net 9 , CART 10 , M5 11 , Random Forest 12 , Bagging 13 , Gradient Boosting 13 , SVM 14 , and ANN 15 . Except for CART, the other classifiers had not been previously evaluated for cost on cost prediction. All models were optimized on their parameters to get their best parameter setting on 30 percent of the data set. Models were evaluated with the following parameter settings: number of hidden layers, number of nodes in each layer, learning rate, and momentum were varied for the Neural Network; kernel type along with the corresponding parameters of each kernel type were varied for the Support Vector Machine; minimum split and minimum number of sample in each leaf were varied for the M5 and CART; learning rate and loss function for the Gradient Boosting; and alpha was varied for the Lasso, Ridge and Elastic Net.

A brief description of all the models used in this study (except linear regression) is provided below.

Lasso : This is a linear regression model enhanced with variable selection and regularization, which is given by the L1-norm (the loss function is the linear least squares error) 7 .

Ridge : This is a linear regression model where the regularization is given by the L2-norm (the loss function is the linear least squares error). L2-norm equips the model to have non-sparse coefficients, which means many coefficients with zero values or very small values with few large coefficients 8 .

Elastic Net : This is linear regression model that linearly combines the L1-norm and L2-norm penalties of the Lasso and Ridge models 9 .

CART : This is a regression decision tree, where on each node the algorithm chooses the split that minimizes the sum of squared errors for regression of the node. The important quality is that the algorithm uses the sample mean of the instances in each node for regression 10 .

M5 : Similar to CART, this algorithm is also a regression tree, where a linear regression model is used for building the model and calculating the sum of error as opposed to the mean 11 .

Random Forest : This is an ensemble learning algorithm that fits a number of regression decision trees on several subsamples of the data. The mean value of the outcomes of the regression tree is generated as the final prediction of the algorithm 12 .

Support Vector Machine : This is a support vector regression model implemented based on libsvm 14 which uses kernels to find the regression lines.

Bagging : This is an ensemble learning algorithm that fits each base regression model on random subsets of the data that are generated by a bootstrapping sample method. Aggregation of the individual predictors is performed by averaging to form the final prediction 13 .

Gradient Boosting : This is an ensemble learning algorithm, where the final model is an ensemble of weak regression decision tree models, which are built in a forward stage-wise fashion. The most important attribute of the algorithm is that it ensembles the models by allowing optimization of an arbitrary loss function. In other words, each regression tree is fitted on the negative gradient of the given loss function, which is set to the least absolute deviation 13 .

Artificial Neural Network (ANN) : This is a large collection of processing units (i.e., neurons), where each unit is connected with many others. Neural networks typically consist of multiple layers and the goal is to solve problems in the same way that the human brain would 15 .

20-fold cross validation was employed as the evaluation method on 70% of the data set. For statistical significance, we first applied the Friedman’s test to verify differences among multiple classifiers. If significant at an alpha level of 0.05, pairwise comparisons were made with the Wilcoxon Signed-Rank test. This statistical approach was aligned with the method recommended by Demsar 16 .

1. What are the types of healthcare cost prediction approaches reported in the literature?

There are three kinds of methods that have been reported for cost prediction: rule-based, statistical and supervised learning. The disadvantage of the rule based methods (e.g. Kronick et al. 17 ) is that they require a lot of domain knowledge, which is not easily available and is often expensive 18 . Although statistical models, mainly multiple regression models, are powerful tools for capturing the relationships between the predictors and the dependent variable, they have two important challenges 18 . One is that working with several independent variables often causes multicolinearity, which is caused by the presence of significant correlations among predictors. Moreover, their performance is challenged by the skewed nature of healthcare data, where cost data typically feature a spike at zero, distributions are strongly skewed with a heavy right-hand tail 19 , and extreme values can be present, all of which make them inefficient in small to medium sample sizes if the underlying distribution is not normal. Although several advanced statistical methods have been proposed to accommodate the skewness observed in healthcare data, this type of prediction method is not able to outperform supervised learning methods 20 . Therefore, this paper is devoted to the use of supervised learning methods for cost prediction, and the remainder of the literature review excludes other types of prediction methods.

There are generally three types of literature that use supervised learning for cost prediction. In the first type, the goal is to predict cost using medical predictors. In this type of literature, the main goal is to show the effect of medical factors such as chronic disease score on cost prediction 21 . In the second type of literature (which is limited), cost predictors with or without medical predictors are used to predict cost. In the last type of literature, researchers bucket individuals’ costs and predict an individual’s cost bucket rather than his or her actual costs. This last type of research applies nominal predictive models rather than numerical predictive models.

Cost prediction using non-cost predictors. Lee et al. 22 provided one of the earliest works on predicting cost by using non-cost predictors. They selected a small sample of 492 patients from a hospital in Korea and compared the performance of ANN and a classification and regression tree for cost prediction. Demographic information, diagnosis codes, number of laboratory tests, the number of admissions and number of operations were the predictors of their analysis. The results showed the superiority of ANN.

Powers et al. 23 evaluated several regression statistical modeling approaches for predicting prospective total annual health costs (medical plus pharmacy) of health plan participants using Pharmacy Health Dimensions (PHD), a pharmacy claims-based risk index. Their models included ordinary least squares (OLS) regression, log-transformed OLS regression with smearing estimator, and 3 two-part models using OLS regression, log-OLS regression with smearing estimator, and generalized linear modeling (GLM), respectively. The results showed that most PHD drug categories were significant independent predictors of total costs. The OLS model had the lowest mean absolute prediction error and highest R 2 . The main conclusion was that the PHD system derived solely from pharmacy claims data can be used to predict future total health costs.

Analyzing the impact of multimorbidity (i.e., co-occurrence of more than three chronic disease conditions) on health care costs, König et al. 21 interviewed 1,050 randomly selected primary care patients aged 65 to 85 years suffering from multimorbidity in Germany. A conditional inference tree algorithm was used as the classifier. The results showed that Parkinson’s disease and cardiac insufficiency were the most influential predictors for total costs, and that the high total costs of Parkinson’s disease were largely due to costs of nursing care.

Cost bucket prediction. Lahiri et al. 4 predicted the rise in patient care costs as a binary classification problem. They used a data set with more than 114,000 patients for a span of three years (2008-2010) to investigate which patients experienced increases in inpatient expenditures between 2008 and 2009. Using stacked generalization, they ensembled six classifications algorithms including gradient boosting machine, conditional inference tree, neural networks, SVM, logistic regression and Naive Bayes. This achieved 80% recall, 78% accuracy and 76% precision. One of the contributions of the paper was that they initially had 12,400 features, most of them arising out of diagnosed conditions and drugs taken, and selected 44 of them according to their information gain. This helped the authors to identify major factors which were crucial in determining whether an individual was going to incur higher healthcare expenditure going forward. In a similar study, Guo et al. 24 tried to predict patients’ transition from one cost bucket to another bucket in the following year. To do so, they applied multiple methods (each for a single type of transition) to improve the prediction performance. The results showed that they could improve the performance for 21% comparing to baselines. Moreover, they found that the proposed method can help health care entities achieve efficient resource allocation while improving care quality. Reviewing all papers in this category, we found no studies on categorical cost prediction that used cost-based features as the input.

Cost prediction using cost predictors (cost on cost prediction). Bertsimas et al. 6 provided one of the first evaluations in the area of health cost prediction using supervised learning techniques. They used a combination of medical, demographic and cost related features from August 2004 to July 2006 as the input and applied regression decision tree and clustering to predict total patient costs in 2007, as measured by insurance payments including medical and pharmacy payments. The results showed that utilizing just 22 cost related features as input and a CART regression decision tree as the classifier gave almost the same performance as adding the medical and demographic information (total of around 1500 features) or applying clustering techniques. Performance was reported in terms of Mean Absolute Error, Hit Ratio, R 2 and a penalty based evaluation designed by the authors. Bucketing was also used to evaluate the prediction results to assess the accuracy. This evaluation showed that while the method is strong at predicting low cost buckets, it had a weak performance on higher cost buckets.

Following the above study, Sushmita et al. 18 evaluated the use of a regression tree, M5 model tree and random forest for cost prediction and showed that M5 had the best performance. The results also confirmed that prior healthcare costs alone can serve as a good indicator for future healthcare costs. To predict patients’ cost for the next year, they used the Medical Expenditure Panel Survey (MEPS) data set coming from responses to panel surveys given to households and their employers, medical providers, and insurance providers over two year periods.

Duncan et al. 2 compared several different supervised learning and statistical models to predict patients’ cost including M5, Lasso and boosted trees. They applied their experiments on 30,000 patients where the information from 2008 was used for training and the total allowed amounts in the claims from 2009 were used for testing. They involved a variety of predictors as input including the previous year’s total cost, total medical cost, total pharmacy cost, demographic information, total visits and chronic conditions (83 different conditions). The results showed that boosted tress and M5 were the most effective classifier in terms of R 2 and Mean Absolute Error (MAE) respectively, and that cost predictors were the strongest predictors. Moreover, confirming previous literature results, this paper showed that statistical methods are not as good as supervised learning techniques.

Kuo et al. 25 attempted to show the significance of pharmacy-based metrics as opposed to diagnosis-based morbidity measures in predicting patients’ costs and outpatient visits. They used data from 2006 to predict patients’ billed costs in 2007. To achieve this, they applied linear regression on the data set. Evaluation was done based on Mean Absolute Error and R 2 . Although the purpose of the study was to explore the capability of the pharmacy-based metric in cost prediction, the results confirmed that using cost based features for cost prediction has almost the same accuracy as adding other types of features to the input. This paper did not incorporate sophisticated cost features and just used a single cost feature from 2006. Frees et al. 26 studied the ability of linear regression to predict individuals’ costs in terms of healthcare insurance payments. They used self-rated physical health and self-rated mental health, provided by participants, using demographic and survey-based information as their input. Getting a reasonable performance (i.e., R 2 =0.27), they found that cost, self-rated mental health and self-rated physical health are the most important predictors.

Collectively, and in particular in the study by Bertsimas et al. 6 , these studies found that cost on cost prediction can match the performance of predictions made using clinical input factors or clinical plus cost input factors.

2. What are the input features that have been used for cost on cost prediction?

Input features are one of the essential parts of a supervised learning task. Numeric cost prediction studies have benefited from a variety of features as input, which are summarized in Table 2 . As seen, Bertsimas et al. 6 evaluated a wide range of cost inputs and reported the performance of cost inputs separately. Their results showed that prediction using a superset of 1542 features, including clinical features, had the same performance as using just the 21 cost predictors. This finding was confirmed by other researchers in subsequent work 2 , 18 .

Table 2.

Input features used for cost on cost prediction in the literature

3. What are the supervised learning methods that have been used for cost on cost prediction?

There are a variety of supervised learning methods that have been used in this area. Table 3 summarizes all different methods that have been reported as successful methods for cost on cost prediction. These methods include Lasso, which is a type of linear regression, gradient boosting on regression decision trees, M5 regression decision tree, random forest, linear regression and CART regression tree. Table 3 also shows the target type of the cost that was studied in each paper. Billed amount is the total amount that is charged by the health care provider and the paid amount is the amount that is paid by the insurance company.

Table 3.

Supervised learning methods used for cost on cost prediction in literature

4. What are the performance measures and evaluation results for cost on cost prediction?

MAE : This shows the average error of the model on prediction of the actual cost values and is calculated as follows:

where a i and p i are the actual and predicted costs of member i in the result period respectively.

Mean absolute percentage error (MAPE) 25 : This is a modified version of absolute error in which the MAE is divided by the mean of the cost, so that the MAE could be compared across the models with different means of cost:

MAPE = - MAE is dependent on the data set, such that different models from different studies cannot be directly compared using that measure. MAPE is a relative measure and does not have this limitation.

R 2 : This shows the Pearson correlation between the actual and predicted cost values:

Hit Ratio : This measure shows the percentage of the members for whom a model forecasts the correct cost bucket:

Penalty Error 6 : This is a performance measure for cost prediction based on domain knowledge. Penalty error penalizes models for underestimating high cost members more than overestimating low cost members, which is motivated by the estimated opportunity loss. Table 4 shows the penalty table for the five-cost-bucket scheme. The final value of the penalty error is calculated from the average forecast penalty per member of a given sample.

Table 4.

Penalty table based on the predicted and actual cost buckets

Table 5 summarizes the evaluation measures used in different papers. The reported performance measures in this table correspond to the whole data set used in each study. This study reports the experimental results in terms of all five performance measures except MAE, which is not reported given the sensitivity of absolute cost data.

Table 5.

Performance measures and outcome for cost on cost prediction in literature

Tables 6 to ​ to9 9 show the performance comparison between different supervised learning models on training and validation data sets. As seen, Gradient Boosting had the highest performance in terms of all measures in all buckets except bucket five. Here, ANN was superior. Also, the Ridge model showed a comparable performance compared to ANN, especially for low cost buckets.

Table 6.

Performance comparison among different supervised learning models for numeric measures on the training data set. Models that are annotated with ( l ) have been used in the cost on cost prediction literature before (see Table 3 ), while those annotated with ( n ) are new to this study.

Table 9.

Performance comparison among different supervised learning models for categorial measures on the validation data set. Models that are annotated with ( l ) have been used in the cost on cost prediction literature before (see Table 3 ), while those annotated with ( n ) are new to this study.

Summary of findings . This study reviewed the literature of healthcare cost prediction and found that cost on cost prediction performs as well or better than cost prediction using clinical data or clinical data plus cost data. Moreover, supervised learning methods were found to be superior in predictive ability. Moreover, we found that gradient boosting provides the best cost on cost prediction models in general, with ANN providing superior performance for higher cost patients. The evaluations show consistency between training and validation results.

Strengths . An important strength of this study is that we combined both a systematic literature review and a head-to-head empirical evaluation of different supervised learning methods reported in the literature. An additional strength is that we evaluated state-of-the-art supervised learning methods not previously evaluated in the literature for cost on cost prediction in health care.

Limitations . The main limitation of this study is that we just used one data set. More experiments on different data sets from different institutions and regions could provide more solid evidence on the comparative performance of different algorithms. The second limitation of this study is that we just used cost features. Although previous studies showed that medical features did not improve the performance of the cost models, we could potentially still benefit from such features for two reasons. One is that the new supervised machine learning methods may benefit from the medical features. Second is that the medical features have more explanatory power that may help decision makers understand the root causes of members’ costs.

Future studies . This study was devoted to the paid amount of the medical claims. An interesting venue of research would be analyzing the billed amount as well as the out-of-pocket amount paid by patients to see which approaches work best for each type of cost metric. Another future research direction would be to explore the use of more advanced supervised learning methods such as deep learning and structure analysis to improve the performance of cost prediction methods. Finally, adding medical features and benefiting from their predictive and explanatory power can be another future research direction, which has already been started in our team.

The literature indicates that the preferred approach to healthcare cost prediction is cost on cost prediction using supervised learning methods. Empirical analysis of alternate approaches using data from a single health insurer found that gradient boosting provides the best cost on cost prediction models in general, with ANN providing superior performance for higher cost patients.

Table 7.

Performance comparison among different supervised learning models for categorial measures on the training data set. Models that are annotated with ( l ) have been used in the cost on cost prediction literature before (see Table 3 ), while those annotated with ( n ) are new to this study.

Table 8.

Performance comparison among different supervised learning models for numeric measures on the validation data set. Models that are annotated with ( l ) have been used in the cost on cost prediction literature before (see Table 3 ), while those annotated with ( n ) are new to this study.

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  • Center on Health Equity and Access
  • Health Care Cost
  • Health Care Delivery
  • Value-Based Care

Fourteen years of manifestations and factors of health insurance fraud, 2006–2020: a scoping review

  • José Villegas-Ortega   ORCID: orcid.org/0000-0001-7947-111X 1 , 2 , 3 ,
  • Luciana Bellido-Boza   ORCID: orcid.org/0000-0003-0825-9271 3 &
  • David Mauricio   ORCID: orcid.org/0000-0001-9262-626X 1  

Health & Justice volume  9 , Article number:  26 ( 2021 ) Cite this article

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Healthcare fraud entails great financial and human losses; however, there is no consensus regarding its definition, nor is there an inventory of its manifestations and factors. The objective is to identify the definition, manifestations and factors that influence health insurance fraud (HIF).

A scoping review on health insurance fraud published between 2006 and 2020 was conducted in ACM, EconPapers, PubMed, ScienceDirect, Scopus, Springer and WoS.

Sixty-seven studies were included, from which we identified 6 definitions, 22 manifestations (13 by the medical provider, 7 by the beneficiary and, 2 by the insurance company) and 47 factors (6 macroenvironmental, 15 mesoenvironmental, 20 microenvironmental, and 6 combined) associated with health insurance fraud. We recognized the elements of fraud and its dependence on the legal framework and health coverage. From this analysis, we propose the following definition: “Health insurance fraud is an act of deception or intentional misrepresentation to obtain illegal benefits concerning the coverage provided by a health insurance company”. Among the most relevant manifestations perpetuated by the provider are phantom billing, falsification of documents, and overutilization of services; the subscribers are identity fraud, misrepresentation of coverage and alteration of documents; and those perpetrated by the insurance company are false declarations of benefits and falsification of reimbursements. Of the 47 factors, 25 showed an experimental influence, including three in the macroenvironment: culture, regulations, and geography; five in the mesoenvironment: characteristics of provider, management policy, reputation, professional role and auditing; 12 in the microenvironment: sex, race, condition of insurance, language, treatments, chronic disease, future risk of disease, medications, morale, inequity, coinsurance, and the decisions of the claims-adjusters; and five combined factors: the relationships between beneficiary-provider, provider-insurance company, beneficiary-insurance company, managers and guānxi.

Conclusions

The multifactorial nature of HIF and the characteristics of its manifestations depend on its definition; Identifying the influence of the factors will support subsequent attempts to combat HIF.

Corruption and fraud are embedded in health systems (HS), and they are motivated by abuse of power and dishonesty (García, 2019 ) that harm the user population, generating economic and even human losses (World Bank, 2018 ). The different aspects of corruption seriously weaken the access and performance of the HS; among the most affected, equity, quality, response capacity, efficiency and resilience should be mentioned (W. H. Organization, 2016 ). In the world, more than seven billion dollars are spent on health services from those,  between 10% and 25% of spending is directly lost as a result of corruption, an amount that exceeds the annual estimate for 2030 in providing universal health coverage (Jones & Jing, 2011 ; World Bank, 2019 ). In addition, there is a constant increase in healthcare spending, healthcare professionals seeking to maximize their profits, and health insurance seeking to contain costs (Dumitru et al., 2011 ; Wan & Shasky, 2012 ).

Fraud in the HS is often included in the discussion of corruption since these practices generally involve abuse of power (Vian, 2020 ). Health insurance fraud (HIF) is a substantive component of the HS crisis (Manchikanti & Hirsch, 2009 ). The HIF mainly affects developing countries with fewer resources (Perez & Wing, 2019 ), weakened health systems and a lack of quality, causing significant losts and inefficiencies (Kruk et al., 2018 ). Losses caused by HIF in some high-income countries range between 3 and 10% (Rashidian et al., 2012 ), and its main motivation is the search for money by fraudsters, to which other individuals, organizational or contextual factors are added (Busch, 2012 ; Wan & Shasky, 2012 ). HIF is a problem that ranks second after violent crimes in the United States (USA) (Sparrow, 2008 ) and can be committed by medical providers, policyholders and health insurers (Busch, 2008 ). In this sense, it is essential to identify and understand the factors that influence HIF and its manifestations to combat them and reduce losses in HS.

The public health programmes of the different countries of the world propose interventions to prevent and detect HIF, many of which lack effectiveness in their results. Although the interventions include multiple deterrence efforts and strategies based on data mining, they are insufficient to show effective results to combat HIF (Abdallah et al., 2016 ; Bayerstadler et al., 2016 ; Hassan & Abraham, 2013 ; Joudaki et al., 2015 ; Kang et al., 2010 ; Kelley et al., 2015 ; Kose et al., 2015 ; Li et al., 2008 ; Lin et al., 2013 ; Ormerod et al., 2012 ; Perez & Wing, 2019 ; Rashidian et al., 2012 ; Shi et al., 2016 ). Likewise, scientific evidence indicates a shortage  of studies that address how to deal with fraud effectively in the health sector; however, it identifies some promising interventions, such as the actions of an independent agency, prohibitions, internal control practices, transparency, accountability, among others, but it is unknown whether or not they contribute to reducing corruption (Gaitonde et al., 2016 ). In this same sense, the evidence shows that despite the efforts made to reduce HIF, it is a complex problem difficult to address.

HIF, as a fraud, is multifaceted, multidimensional and interrelated, mainly caused by the insufficiency of theories that can explain its complexity (Huber, 2017 ). Part of the complexity of the HIF is supported by the dynamic behaviour of fraudsters, which generates the need for human interaction to identify suspected cases (Travaille et al., 2011 ), given the scarce specialization in detection interventions, which are limited to opportunistic verifications of previously known patterns and detections by coincidence (Bayerstadler et al., 2016 ). On the other hand, complexity is supported by the lack of a standardized definition of the HIF, which does not have a consensual definition; however, it could refer to deception or intentional misrepresentation used to obtain illegal benefits, making it difficult to distinguish from abuse, waste or error (Hyman, 2001 ; Joudaki et al., 2015 ; Lee et al., 2020 ; Rashidian et al., 2012 ) In addition, its manifestation will depend on regulation and market behaviour (Bayerstadler et al., 2016 ; Green, 2007 ). The proposal of a definition seeks to contribute to the development of better strategies (Kacem et al., 2019 ).

Given the absence of a standardized definition of HIF, this scoping review could contribute to filling a gap in knowledge, providing a definition with a homogeneous language, which can dispel ambiguity and facilitate its understanding. In this sense, the objective of our scope review is to define the HIF, identify the causes or factors that influence, and the consequences or manifestations that occur; for which we will answer the following questions: What is health insurance fraud? How is health insurance fraud manifested? Furthermore, what factors influence health insurance fraud?

The results obtained are intended to promote future studies that more effectively channel the interventions that prevent, detect, and provide responses to combat HIF and be a reference for decision-making in countries’ public health.

To answer our research questions, we conducted a scoping review, using a rigorous literature review method, which establishes conceptual limits, following the considerations of the document “PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation” (Tricco et al., 2018 ). In Additional file  1 , we include the Checklist for Scoping Reviews (PRISMA-ScR).

Scoping reviews allow answering broad questions such as those posed by our study, while systematic reviews allow answering clearly defined questions (Tricco et al., 2018 ). The scoping review aims to identify gaps in knowledge, analyze the literature body, clarify concepts, or investigate the nature of a problem (Munn et al., 2018 ). In this sense, our research questions seek to examine and clarify the definition used in the literature on HIF and show how this theoretical definition becomes tangible and shown in reality through the manifestation. As fraud is a complex problem, we also seek to identify and inventory the associated factors that influence it. Our contribution provides new elements of judgment to confront the HIF, it will facilitate the redesign and innovation of practical strategies that help combat fraud, and we hope that other studies will join the few that have demonstrated effectiveness in their interventions.

Eligibility criteria

We reviewed studies according to our objective, and we included those reviewed by peers and are indexed to international databases since they are considered validated knowledge. We limited our search from January 1, 2006, to July 31, 2020, taking into account that the U.S. Department of Health and Human Services issued in 2006, the final rule that establishes civil monetary sanctions, procedures for investigations and hearings, for violating the Health Information Portability and Privacy Act (Tovino, n.d. ). In addition, we observed that starting in 2006, monetary recoveries from HIF sanctions increased by 40% compared to the previous 20 years (Helmer Jr, 2012 ). The search had no geographic or language restrictions. However, we exclude conferences, proceedings, posters, editorials, letters, misprints and books, as they do not provide reliable scientific evidence. We also excluded studies with meanings ascribed to health abuse related to drugs, diseases, suicide, racism, food fraud, security, network, web, and  electronics device; all of them for being away from the health insurance environment.

Information sources

To identify the documents, we conducted a scoping review search in ACM, EconPapers, PubMed, Science Direct, Scopus, Springer and Web of Science between January 1, 2006, and July 31, 2020.

Search strategies

To guarantee not to lose potential studies, we applied an iterative approach; we initially used the studies that met the inclusion criteria in PubMed and WoS. The search strategy included a combination of keywords and medical topic headings (MeSH for PubMed), terms related to “Fraud healthcare” (concept A) and “Health Insurance” (concept B). Subsequently, we used the results of this search to identify keywords and MeSH terms, which were adapted in the search strategies of the other databases, as reported in Additional file  2 . A health librarian reviewed the search strategy.

Data process, evaluation and quality of the study

Once we applied the search strategies and identified the potential studies, two authors made the review separately as follows: we designed a matrix in Excel, in which we listed all the studies characterized by name, code, among others (Additional file  3 ). In order to perform the first screening in the database, we ordered the studies by titles and authors, and eliminated duplicates, both authors in consensus. In a second selection, we reviewed the titles, abstracts and conclusions of the selected studies; for this, each author considered the eligibility criteria of the study, in the end, both authors agreed on the list. Once the possible studies had been chosen, the two authors read the full text, which allowed us to select those that contribute directly to the research questions (Theoretical Fraud, Practical Manifestation, Factors), and we identified them in the matrix; finally, the authors reached at a consensus. Subsequently, we evaluated the content and explicit references to answer the research questions; We have avoided conjectures or interpretations. In addition, we considered the theoretical or experimental contribution of each study for the factor inventory and showed the influence (if the factor increases or worsens the HIF, we used the positive sign, while if the factor reduces the HIS, we registered the negative sign; a result could also indicate an ambivalent influence so we used both signs). We have considered a consensus greater than 95%, and the differences were discussed and cleared for both autor based on screening, eligibility, and final inclusion of the studies. However, we excluded studies that did not contribute to any questions.

We reviewed 67 pairs of articles included in the study, both qualitative and quantitative, and with the help of the matrix, we discussed the results and reached a consensus on the information extraction. To evaluate the quality and rigour of the studies, we used a tool for integrative reviews, which is based on four factors: type of study, sampling method, detail of the data collection method, and analysis. The possible score generated by this tool varies between 4 (qualitative design, sampling and collection of unexplained data, and narrative analysis) and 13 (quantitative experimental design, random sampling, explained data collection and inferential statistics) (Olsen & Baisch, 2014 ; Pfaff et al., 2014 ). The details of the quality scores of the included studies can be seen in Additional file  4 . To ensure the strength of the evidence and the studies’ quality, the authors independently graded the articles in rounds, and disagreements were resolved through discussion until an agreement more significant than 95% was achieved. In addition, we evaluated the SCImago journal rank (SJR), which measures the scientific influence of academic journals according to the number of citations of the journal to which each of the included manuscripts belonged, which is justified by the high quality of publications over the years (Ardito et al., 2015 ).

Selection of studies

A total of 944 studies were identified following the selection criteria. Subsequently, 84 duplicate studies were eliminated, and then the titles, abstracts and conclusions of 860 studies were examined, from which 89 full texts were recovered, to which we incorporated two relevant studies identified from other sources (a thesis referred by the authors selected from 2003 and another reference document on fraud issues of the National Health Care Anti-Fraud Association of 2018). In the process, we excluded 24 studies related to HIF detection techniques, data mining models, processes, activities or other aspects not related to the factors and manifestations of HIF. Finally, we included 67 studies (Fig.  1 ).

figure 1

PRISMA flow diagram

RQ1: definition of health insurance fraud

We identified six definitions of HIF and the key elements of each of them to integrate them into a single consensual definition, as shown in Table  1 . General definitions of fraud that included intensity of desire, risk of apprehension, violation of trust, rationalization (Ramamoorti & Olsen, 2007 ), also defined as the obtaining of a financial advantage, or the cause of a loss through an implicit or explicit deception using a mechanism through which the fraudster obtains an illegal advantage or causes an unlawful loss (Levi & Burrows, 2008 ).

Based on what is described in Table 1 , we have identified and classified five key elements that incorporate the six definitions shown, and subsequently, we have integrated them, as shown below:

It is deceptive, and the people involved tend to deceive, lie, hide and manipulate the truth. Five definitions affect the term “deception”, which is associated with an act linked to misrepresentation and deception.

It is intentional; Fraud is not the result of simple error or negligence but involves deliberate attempts to obtain an illegal advantage; thus, it induces a course of action predetermined by the perpetrator (Pickett & Pickett, 2002 ) 47. Five definitions affect the term “intentional”, which is associated with a deliberate act.

Obtains a benefit, profit or advantage; Usually, the benefit is economic, which implies that there is a victim and that the action produces losses of individual, organizational, and even national resources.

It is illegal, and some definitions describe it as a criminal act or severe federal crime. To establish an illegal act, you must break the law. Some practices may be legal in some countries, but not necessarily in others; it will depend on the rules and regulations of each country or state.

Health insurance coverage (HIC), taken from the definition of health insurance, allows us to circumscribe the scope. The synonyms of HIC used are “health coverage”, “medical care coverage”, and “health benefits” (Elwyn et al., 2000 ) 48.

To have a single definition of HIF, we have again integrated what is described in A, B, C and D, from which we obtained that “Fraud is a deliberate deception to obtain unfair or illegal profits”, a statement that is complemented by described in E. We specified that the absence of one of five key elements identified puts at risk the comprehensive definition of HIF. Finally, we arrived at the following definition:

“Health insurance fraud is an act based on deceit or intentional misrepresentation to obtain illegal benefits concerning the coverage provided by health insurance.”

To illustrate the elements that comprise the definition of HIF, we showed Fig.  2 , in which we show the relationship they have with its factors (RQ2) and manifestations (RQ3).

figure 2

Elements of the definition of health insurance fraud, its factors and manifestations

RQ2: manifestations of health insurance fraud

We found that fraud manifests itself in multiple ways, such as performing unnecessary services, falsifying records, separating invoices, and misrepresented coding. Therefore, we classified the manifestations by actor (Li et al., 2008 ; Sheffali & Deepa, 2019 ). In this sense, we present the manifestations by a) health service providers, including hospitals, laboratories, and health care professionals; b) insurance underwriters, including patients; and c) public or private health insurance companies, which include state-subsidized programmes.

In 23 studies, we identified 13 manifestations of fraud by the provider; the three most common manifestations are phantom billing based on claims presented for medical services not provided, mentioned in 14 studies, the falsification of administrative or clinical documents in 10 studies and the proportion of unnecessary care in 9 studies. In 16 studies, we identified seven manifestations of fraud by the subscriber; the three most frequent are identity fraud mentioned in 8 studies, manipulating the eligibility information and manipulation of documents, both noted in 4 studies. Finally, in two of the studies, two manifestations of fraud of the agent or insurer were identified: false declarations of benefits or services and falsification of reimbursements. Each manifestation was briefly described, and one or more examples were provided. We observe that each study could contribute to more than one manifestation. For a better understanding, we showed Table  2 .

RQ3: health insurance fraud factors

In this study, we define the factors as elements that can originate or influence the HIF. They can be categorized in multiple ways, such as by actors: internal staff, patient, intermediary and insurer (Yusuf, 2010 ); and by environment: context, organization and individual (Vahdati & Yasini, 2015 ; Vian, 2020 ), or internal and external (Akomea-Frimpong et al., 2016 ). These categorizations do not consider the relationship between each category, although factors influence more than one category, as in the doctor-patient interaction. Therefore, we added a new category called the collaborative environment, which includes interaction factors between different categories, either classified by actor type, or environment. For the presentation of the factors related to the HIF, we considered the macroenvironment categories if the factors are motivated by external influences (Lesch & Baker, 2013 ); mesoenvironment, if the factors are inspired by the context of the organizations; microenvironment, if the factors are associated with demographic and individual characteristics; and the collaborative environment.

We identified 47 factors that influence the occurrence of health insurance fraud, categorized into macroenvironment (6), mesoenvironment (15), microenvironment (20) and collaborative (6). For each study, we denoted with a positive sign (+) when the factor increased the HIF, and a negative sign (−) if the factor reduced the HIF; when used a single sign, it indicated that the study proved a theoretical or narrative contribution. A factor can show both signs simultaneously (+−), which means that its influence is ambivalent. In contrast, a double sign indicated that the study had an applied validation based on a method de experimentation or quasi experimentation.

Macroenvironment factors

A total of 14 studies explain 6 factors, 8 studies refer to norms and regulations, 3 studies to economic, political and social issues, and 3 studies to cultural issues. We can observed that some applied studies involve more than one factor. Culture is the only factor that contributes to increasing HIF (Zourrig et al., 2018 ); rules and regulations (Lesch & Baker, 2013 ) and geography (Manocchia et al., 2012 ) show an ambivalent influence, conditioned on the environment in which they were studied. At the theoretical level, it is found that complexity, infrastructure and economic, political and social conditions influence the HIF.

Mesoenvironment factors

We identified 26 studies that explained 15 factors, the most referenced factors are audit, supervision and control, with 8 studies, while 6 studies explain the general characteristics of the provider. The factors supported by applied studies that have shown influences in favour of the HIF occurring are the general characteristics of the provider (Herland et al., 2018 ; Kang et al., 2010 ), in favour of the HIF decrease (Vian, 2020 ), ambivalent (Massi et al., 2020 ); the management and policy of complaints show results that contribute to HIF (Vian, 2020 ) and ambivalent results (Lesch & Baker, 2013 ); while that the reputation shows ambivalent influence (Tseng & Kang, 2015 ), the audits, supervision and control contributes to reducing the HIF (Kang et al., 2010 ).

Each Macroenvironmental and Mesoenvironmental factor was briefly described, and one explanation was provided. We can observed that some studies involved more than one factor. For a better understanding, we present Table  3 . In the next session, we analyze the studies according to the quality methodology proposed in this work to specify our findings in greater detail.

Microenvironment factors

A total of 35 studies contributed to explaining 20 factors of the microenvironment (see Table  4 ). Applied studies show that the two most referenced factors are prescription medications, and ethics and morals, both with 11 studies each. Other relevant factors are those related to demographic characteristics, among which sex and age stand out, with 4 studies each. In a study conducted in the state of Florida, USA, we found statistically significant results that encourage HIF: the western region, being a woman, being white, having health insurance, predominantly English language, having a condition sensitive to health, greater future risk of illness, health condition (Manocchia et al., 2012 ). Another factor that positively influences HIF is the prescription, dispensing, cost and consumption of medications (Aral et al., 2012 ; Herland et al., 2018 ; Lin et al., 2008 ; Liou et al., 2008 ; Weiss et al., 2015 ). In addition, factors related to users’ perceptions of health services such as inequity, injustice (Lesch & Baker, 2013 ), high deductibles, and coinsurance (Lammers & Schiller, 2010 ) can condition the HIF. Regarding the attitudes of claims adjusters, their decision is fickle, and has been demonstrated experimentally ambiguous (Tseng & Kang, 2015 ). Finally, the values that regulate human behaviour, such as ethics and morals, determine fraud.

Collaborative factors

A total of 10 studies contributed to explaining 6 collaborative factors (see Table 4 ), in which the most referenced factor was the relationship between the provider and the patient, with 3 studies. Relationships between the consumer provider, provider insurer (Lin et al., 2008 ), consumer insurer (Manocchia et al., 2012 ), the influence of bosses (Tseng & Kang, 2015 ) and gGuānxi (Tseng, 2016 ) encourage the increase in HIF.

We have proceeded to an analysis of the 47 identified factors, their corresponding influences supported by theoretical or applied contributions to increasing or decreasing the HIF (Tables  3 and 4 ). We also considered evaluating the quality of the 53 studies (Additional file 4 ) that support our RQ3 question (Fig. 1 ). For a better presentation, we categorized the studies by their quality score: high (11 to 13), medium (7 to 10) and low (4 to 6); the results are presented in Table  5 .

We confirmed that the factors are affected by other factors and depend on their studied and developed context. In the macroenvironment, in the category of high quality, concerning the factors, no study showed the influence on the HIF, while the geography presented theoretical influence in favour of the HIF, supported by a theoretical study. On the other hand, if we analyzed the category of medium quality, culture contributes to an increase in the fraud supported by an applied study (Zourrig et al., 2018 ), even when a theoretical study shows that it decreases the HIF.

In the mesoenvironment, in the high-quality category, the factors related to the audit, supervision, sanction, control (Hillerman et al., 2017 ; Maroun & Solomon, 2014 ; Myckowiak, 2009 ; Smith et al., 2013 ; Vian et al., 2012 ), and the type of health professional, particularly the nurses (Goel, 2020 ), shows influence in reducing HIF. Additionally, the general characteristics of the provider contribute to an increase in the HIF supported by two applied studies (Herland et al., 2018 ; Kang et al., 2010 ); even though a theoretical study shows that the HIF decreases, this study also confirms that the lack of policies and management of complaints increases fraud (Wan & Shasky, 2012 ). In this category, other factors (medical record, provider responsibility, provider internal mechanisms, internal staff evaluations, patient identification mechanisms, among others) have been shown to contribute to reducing HIF; several are theoretical or of medium or low quality.

In the microenvironment, in the high-quality category, no applied studies have demonstrated an influence that reduces HIF. While the factors related to having an older age to be deceived (> 65), place of residence (Goel, 2020 ), patient diagnoses, medical and surgical treatments (Liou et al., 2008 ), medications (Aral et al., 2012 ; Herland et al., 2018 ; Lin et al., 2008 ; Liou et al., 2008 ; Weiss et al., 2015 ), chronic health condition (Liou et al., 2008 ), and deductibles and coinsurance (Lammers & Schiller, 2010 ) showed influence in increasing the HIF. However, other theoretical studies showed that the diagnoses (Sun et al., 2020 ) and medications reduced HIF (Haddad Soleymani et al., 2018 ; Sun et al., 2020 ; Victorri-Vigneau et al., 2009 ). It is important to mention the factor related to ethics and morals, which has shown a theoretical and applied contribution to reducing the HIF.

In the high-quality category of collaborative environment, the provider-insurer relationship (Lin et al., 2008 ), the consumer-provider relationship (Lin et al., 2008 ), and bosses influence (Tseng & Kang, 2015 ); increased HIF. Also, in the middle-quality category, the consumer-insurer relationship (Manocchia et al., 2012 ), the Guānx i (Tseng, 2016 ), showed influence in increasing the HIF.

We analyzed 67 primary studies from January 2006 to July 2020, including definitions, manifestations, and HIF factors. Our findings identified 6 theoretical definitions, 22 manifestations (13 of the provider, 7 of the insurer and 2 of the insurer) and 47 factors categorized into macroenvironment (6), mesoenvironment (15), microenvironment (20), and collaborative (6).

Definition fraud

The HIF definition that we have found is related to the general definition of insurance fraud given by the International Association of Insurance Supervisors (IAIS), which defines fraud as an act intended to obtain a dishonest advantage (IAIS, 2011 ). Similarly, the nongovernmental organization Transparency International defines fraud as a criminal or civil crime that consists of intentionally deceiving someone to obtain an unfair or illegal advantage (financial, political or otherwise) (Transparency International, 2017 ). In this same sense, other authors determine fraud as a criminal deception to generate illegal financial gains (Steinmeier, 2016 ). Likewise, Onwubiko specifies the concept of fraud as a criminal offence, illegal, intentional, deliberate act, characterized by deception, concealment, violation of trust or the use of dishonest means, which can cause injury or material damage (Onwubiko, 2020 ). In addition, our definition of HIF includes the one given by Yusuf et al.: «... is the one that covers the payments of benefits as a result of an illness or injury, includes insurance for accidental losses, medical expenses, disability or accidental death and dismemberment» (Yusuf & Ajemunigbohun, 2015 ). The HIS also identifies an institution or programme that helps pay for medical expenses, either through privately acquired health insurance, social insurance or a social welfare programme funded by the government (Brooks et al., 2017 ).

With the HIF definition, we seek to dispel the omission of a standard definition and highlight their element variability, which will potentially contribute to better identifying the characteristics of fraud, its causes (factors), manifestations, consequences and delimiting the differences with abuse, and the error. In general, the term abuse is used to define the practices of providers that result in unnecessary costs; these activities do not constitute fraud because they are legal (Gasquoine & Jordan, 2009 ). Likewise, the error is not intentional, so it does not constitute fraud (Brooks et al., 2017 ). These differences make the identification of the HIF more complex.

However, legal frameworks refer to fraud as a “conceptual swamp” (Green, 2007 ). The definition of HIF depends on the legal framework and regulations of the country or each state, so the same practices could be legal or illegal depending on their regulations. In the USA, the HIF is highly regulated with laws such as the Federal False Claims Act statute (31 USC 3279) - FCA and the Anti-Kickback Statute (42 USC 1320 a - 7) AKS., the Physician Self-referral Law (42 USC 1395nn)-StarK, the Health Insurance Portability and Accountability ACT of 1996-HIPAA (Myckowiak, 2009 ), while in other countries they lack regulation; so it could not be constituted in HIF.

The HIF definition is made tangible through its manifestations, which have been grouped into three categories: provider, underwriter, and insurer. In any category, the demonstration must meet the HIF definition and show intentionality on the perpetrator’s part to obtain some benefit. The act of defrauding is conscious and is evidenced day by day by self-referring patients, providing unnecessary care or misrepresenting clinical and administrative documents. However, when the evidence, they often argue deficiencies in supplies, medical materials, stock, in the number of professionals or sustain that the equipment is damaged or under maintenance, thereby circumventing the true intentions that they have as perpetrators.

Manifestation of fraud

The most frequently studied manifestations are related to the provider, like ghost claims, the manipulation of documents and unnecessary care, where deception and intentional misrepresentation are the most evident. While in other manifestations such as coding, billing for services provided by unskilled personnel, and duplication of billing. They are more complex to detect and separate the error intentionality. At this point, experts must make strenuous efforts to develop cost-effective tools or strategies to separate and recognise the provider’s intent.

On the other hand, we appreciate that some manifestations lack studies, such as self-reference, a widespread manifestation and public knowledge, becoming even a common practice in HS. This phenomenon is explained by having a culture permissive to fraud, where ethics and morals are often bypassed by economic interests, the lack of patients to report and lax legislation. From the HIF manifestations of the subscriber, we found identity fraud more frequently, the one that takes advantage of the absence or inefficiency of the controls, and the one that clearly shows intentionality and deception. However, there is little evidence of the HIF manifestation defined as “Doctor Shopping” since it is confused with abuse, which will depend on regulation. A characteristic of the manifestations of the insurers' HIF is that they do not make the fraud visible since the organisations prefer to separate the fraudster silently protecting the reputational risk.

Factors fraud

Regarding the factors, we have identified that they can increase or decrease the HIF. Moreover, these factors are interrelated in various ways, constituting a complex network whose behaviour over time could, to some extent, be unpredictable, contradictory and ambiguous. According to Brugé et al., «... the main problem lies in contrast between the simplicity of our administrations and the complexity of the problems to be solved; the classical administrative modus operandi consists of simplifying problems by reducing themselves to a specialized field...»; This explains why our results show the same factor with ambivalent influence, which confirms the sensitivity of the factors (Brugué et al., 2015 ).

The results in Table 5  compared Tables 3 and 4 , considering with the quality assessment of the studies 5 ; in order to verify whether the initially found results maintain an influence on the HIF after considering only high and medium quality studies and excluding ambivalent results; and we find within the macroenvironment factors that norms and regulations confirm an increase in fraud (Ribeiro et al., 2020 ) as well as geography (Musal, 2010 ). However, the economy, politics and social conditions confirm a decrease in fraud (Ribeiro et al., 2020 ). On the other hand, the infrastructure (Brooks et al., 2012 ), the complexity of the health systems (Vian et al., 2012 ) (Faux et al., 2019 ) lose influence in favour of increasing the HIF. All this, supported by theoretical studies. Furthermore, with mixed results, we found that culture maintains the results concerning HIF (Ribeiro et al., 2020 ; Zourrig et al., 2018 ).

Likewise, by continuing with the verification of the results of the mesoenvironmental factors, considering only high and medium quality studies, we analyzed that the general characteristics of the provider, supported by applied studies, confirm the ambivalent results (Herland et al., 2018 ; Kang et al., 2010 ; Wan & Shasky, 2012 ). The Payment method and contracts also show ambivalent results and lose their influence on the HIF. The responsibility of the provider (Kerschbamer & Sutter, 2017 ), the measures of the administrative authority (Jator & Hughley, 2014 ), and the internal mechanisms of discipline (Myckowiak, 2009 ), the performance and quality evaluation system (Kerschbamer & Sutter, 2017 ), the commercial implication (Konijn et al., 2015 ), the employability and satisfaction with his work (Brooks et al., 2012 ), patient identification mechanisms (Jator & Hughley, 2014 ); they lose their influence in favour of reducing fraud, all supported by theoretical studies. Reputation only shows ambivalent results and loses its influence. Additionally, reimbursement processes and billing characteristics (Hillerman et al., 2017 ) confirm an increase in the HIF. Also, the Lacks of Complaints management and policy confirms the increase to HIF (Wan & Shasky, 2012 ) even when another theoretical study opposes this statement (Lee et al., 2016 ). An important finding is the audit, supervision, sanction and control, supported by an applied study (Kang et al., 2010 ) and two theoretical studies (Hillerman et al., 2017 ; Smith et al., 2013 ). The role of nurses, supported by an applied study (Goel, 2020 ), confirms their contribution to reducing fraud. Finally, the medical record supported by a theoretical study (Smith et al., 2013 ) confirms a decrease in HIF.

When interpreting the results of the microenvironment factors, we verify that an applied study confirms that being a women, and the risk of becoming ill, increase the probability of fraud (Manocchia et al., 2012 ); it is worth mentioning that the proportion of women who seek medical attention is higher than men. Likewise, an applied study confirms that the involvement of adults over 65 years of age increases the probability of fraud (Goel, 2020 ), and other theoretical studies confirm this (Timofeyev & Busalaeva, 2019 ; Zhou et al., 2016 ). As well as, being married maintains mixed results (Zhou et al., 2016 ) concerning HIF. In addition, the predominant white race (Manocchia et al., 2012 ), and the place of residence in more urbanized areas (Goel, 2020 ), confirm the increase in HIF based on applied studies; other theoretical studies confirm this finding (Musal, 2010 ; Ribeiro et al., 2020 ). Likewise, having health insurance, speaking an official language of the country based on an applied study increases the HIF (Manocchia et al., 2012 ). Also, the influence of diagnoses confirms an applied result that encourages HIF (Liou et al., 2008 ).

Two applied studies, and one theoretical study confirm that medical and surgical treatments, and chronic health condition increase the probability of fraud,  (Liou et al., 2008 ; Manocchia et al., 2012 ; Hillerman et al., 2017 ). In addition, the medical specialities and the bad economic situation confirms an increase in fraud, supported by an applied study (Shin et al., 2012 ). As well as, the influence of drugs confirms applied results that incentivize HIF (Aral et al., 2012 ; Herland et al., 2018 ; Lin et al., 2008 ; Liou et al., 2008 ; Weiss et al., 2015 ), Two applied studies confirm that medical and surgical treatments increase the probability of fraud (Liou et al., 2008 ; Manocchia et al., 2012 ). Another theoretical study confirms this claim (Hillerman et al., 2017 ). Additionally, the perception of inequity and injustice supported by a theoretical study (Ribeiro et al., 2020 ) and the asymmetry of information supported by three applied studies (Kumar et al., 2011 ; Ribeiro et al., 2020 ; Zhou et al., 2016 ) confirm a decrease in fraud. Also, for the decisions of the adjusters, the reputation only shows ambivalent results. Further, the capacity building shows results in favour of reducing HIF and loses influence. Likewise, the high deductibles and coinsurance, supported by an applied study, confirm the increase to the HIF (Lammers & Schiller, 2010 ).

Regarding the collaborative environment, we have been able to verify that the relationship between the health professional and the patient confirms an increase in HIF (Shin et al., 2012 ). An applied study confirms a strong relationship between the provider and the insurer and that they increase the probability of fraud (Lin et al., 2008 ), and a theoretical study confirms this statement (Bayerstadler et al., 2016 ). An applied study confirms that a strong relationship between the consumer and the provider increases the probability of HIF (Lin et al., 2008 ). Two other theoretical studies confirm this statement (Musal, 2010 ; Shin et al., 2012 ). The consumer-insurer relationship, the influence of the bosses (Tseng & Kang, 2015 ), and Guānxi (Tseng, 2016 ) are support by applied studies, confirms the increase in HIF.

Limitations

Regarding the limitations of our study, some deserve special attention. Regarding the design, we can mention the selection biases that did not include conferences, posters, editorials, letters, misprints and grey literature; however, we included the most relevant evidence from indexed journals. Likewise, another bias can be attributed to the limited search, that the range from January 1, 2006, to July 31, 2020, which it is justified by fraud penalty rules were given starting 2006. Despite the limitations, the study results reveal an effort to dispel the concept of HIF, recognize its manifestations and mainly identify its underlying factors, which can positively or negatively influence fraud. The results of this study may have important implications, as we seek to implement effective interventions that to date have eluded us.

Regarding the definition of HIF that we propose is “an act based on deception or intentional misrepresentation to obtain illegal benefits concerning the coverage provided by health insurance”, which provides us with theoretical support that emphasizes its essential elements. We believe that this will distinguish it from abuse, corruption or error. The multifactorial nature of HIF is evident, as well as the particular characteristics of its manifestations, which are subject to its definition and may differ from one country to another according to its regulatory framework and the scope of health coverage provided. Identifying the factors and their influence will allow any subsequent attempt to propose practices that mitigate HIF.

The factors that have shown strength concerning reducing fraud are auditing, monitoring, sanction and control, nurses’ role (supported by applied studies), the economy, politics and social conditions, the medical record, and the commercial implication (supported by theoretical studies). On the other hand, the factors that have shown strength concerning increasing fraud are sex, age, predominant race, have health insurance, place of residence, medical and surgical treatments, chronic health conditions, risk of illness, deductibles and coinsurance, the complicity between the provider and the insurer, the relationship between the provider and the consumer, the relationship between the consumer and the insurer, the influence of the bosses and the Guanxi, (supported by applied studies), the geography, reimbursement processes and billing characteristics, information asymmetry, and poor economic situation of the patient (supported by theoretical studies).

Based on our results, we recommend that future investigations that explore HIF look for the relationships between the factors and their manifestations. Likewise, we suggest evaluating the relationship between the factors and the fraud theories themselves, developing computational methods that identify factors, identifying the costs generated and their impact on the HS, and proposing and implementing practices that mitigate the positive factors and enhance the negative ones. All with the purpose of help its detection and prevention with a comprehensive approach.

Availability of data and materials

The data supporting analysis of this work and additional files can be found in the main document.

Abbreviations

Association for Computing Machinery

Current procedural terminology

Codes for Evaluation and Management

Economics working papers

The Employee Retirement Income Security Act of 1974

Health insurance fraud

Health insurance coverage

Health systems

International Association of Insurance Supervisors

Internal Revenue Service, the federal government of the United States

Medical Subject Headings

National Health Care Anti-Fraud Association

SCImago Journal Rank

A third-party administrator

United States of America

Web of Science

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JVO conceptualised the study and designed the scoping review, planned the search strategies, carried out the screening, extraction of the studies, examined and evaluated the quality of the articles, the data analysis, wrote the first draft of the review article, wrote the manuscript, approved the final version of the manuscript and accepts responsibility for the entire content of the article. LBB performed the formal analysis, assessed the quality of the articles and the writing of the first original draft, accepted the final version and accepted responsibility for the entire content of the article. DM conceptualised the study and designed the scoping review, planned the search strategies, performed the screening, extraction of the studies, examined and assessed the quality of the articles, analysed the data, wrote the first draft of the review article, revised the manuscript, approved the final version of the manuscript and accepts responsibility for the entire content of the article. The author(s) read and approved the final manuscript.

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Additional file 1..

Scoping Reviews (PRISMA-ScR) Checklist. This file contains details of the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist.

Additional file 2.

Complete search strategy and characteristics of the included studies. This file contains details of the complete search strategy in the seven sources of information consulted.

Additional file 3.

Excel matrix. This file contains details of all processed studies.

Additional file 4.

Quality scoring of included studies. This file contains details of the four quality assessment factors for each of the 67 studies included in the analysis.

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Villegas-Ortega, J., Bellido-Boza, L. & Mauricio, D. Fourteen years of manifestations and factors of health insurance fraud, 2006–2020: a scoping review. Health Justice 9 , 26 (2021). https://doi.org/10.1186/s40352-021-00149-3

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literature review on health insurance

Health insurance sector in India: an analysis of its performance

Vilakshan - XIMB Journal of Management

ISSN : 0973-1954

Article publication date: 30 November 2020

Issue publication date: 16 December 2020

Health insurance is one of the major contributors of growth of general insurance industry in India. It alone accounts for around 29% of total general insurance premium income earned in India. The growth of this sector is important from the perspective of overall growth of general insurance Industry. At the same time, problems in this sector are also many which are affecting its performance.

Design/methodology/approach

The paper provides an understanding on performance of health insurance sector in India. This study attempts to find out how much claims and commission and management expenses it has to incur to earn certain amount of premium. Methodology used for the study is regression analysis to establish relationship between dependent variable (Profit/Loss) and independent variable (Health Insurance Premium earned).

Findings of the study indicate that there is significant relationship between earned premium and underwriting loss. There has been increase of premium earnings which instead of increasing profit for the sector in fact has increased underwriting loss over the years. The earnings of the sector is growing at compounded annual growth rate of 27% still it is unable to earn underwriting profit.

Originality/value

This study is self-driven based on secondary data obtained from insurance regulatory and development authority site.

  • Health insurance premium
  • Management expenses
  • Insurance regulatory and development authority
  • Underwriting loss
  • Compound annual growth rate

Dutta, M.M. (2020), "Health insurance sector in India: an analysis of its performance", Vilakshan - XIMB Journal of Management , Vol. 17 No. 1/2, pp. 97-109. https://doi.org/10.1108/XJM-07-2020-0021

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Copyright © 2020, Madan Mohan Dutta.

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1. Introduction

1.1 meaning of insurance.

Insurance is a contract between two parties where by one party agrees to undertake the risk of the other in exchange for consideration known as premium and promises to indemnify the party on happening of an uncertain event. The great advantage of insurance is that it spreads the risk of a few people over a large group of people exposed to risk of similar type.

Insurance has been identified as a sunrise sector by the financial planners of India. The insurance industry has lot of potential to grow, penetrate and service the masses of India. Insurance is all about protection. An insured needs two types of protection life and non-life. General insurance industry deals with non-life protection of the insured of which health insurance is a part.

1.2 Meaning of health insurance

Health insurance is a part of general insurance which contributes about 29% of premium amongst all other sectors of general insurance. But problems in this sector are many which is the driving force behind this study. This study will help the insurance companies to understand their performance and the quantum of losses that this sector is making over the years.

A plan that covers or shares the expenses associated with health care can be described as health insurance. These plans fall into commercial health insurance, which is provided by government, private and stand-alone health insurance companies.

Health insurance in India typically pays for only inpatient hospitalization and for treatment at hospitals in India. Outpatient services are not payable under health policies in India. The first health policy in India was Mediclaim Policy. In 2000, the Government of India liberalized insurance and allowed private players into the insurance sector. The advent of private insurers in India saw the introduction of many innovative products like family floater plans, critical illness plans, hospital cash and top-up policies.

Health insurance in India is an emerging insurance sector after life and automobile insurance sector. Rise in middle class, higher hospitalization cost, expensive health care, digitization and increase in awareness level are some important drivers for the growth of health insurance market in India.

Lifestyle diseases are on the rise. A sedentary lifestyle has pervaded our being. There is lower physical labour today than earlier and there is no reason why this would not be the trend going forward. The implication is the advent of lifestyle chronic diseases such as cardiac problems and diabetes.

In the context of the Indian health insurance industry, one could look at it both ways. Mired by low penetration and negative consumer perception about its utility are affecting the prospect of this industry. The flipside though is that we have hardly scratched the surface of the opportunity that lies in the future. It is as if the glass is half full. Much remains to be conquered and even more remains to be accomplished.

Health insurance companies needs to be optimistic and have courage to bring in innovation in the areas of product, services and distribution system. Bring it to the fold as the safety net that smartly covers and craft a health insurance plan befitting the need of the customers.

1.3 Background of health insurance sector in India

India’s tryst with health insurance programme goes back to the late 1940s and early 1950s when the civil servants (Central Government Health Scheme) and formal sector workers (Employees’ State Insurance Scheme) were enrolled into a contributory but heavily subsidized health insurance programmes. As a consequence of liberalization of the economy since the early 1990s, the government opened up private sector (including health insurance) in 1999. This development threw open the possibility for higher income groups to access quality care from private tertiary care facilities. However, India in the past five years (since 2007) has witnessed a plethora of new initiatives, both by the central government and a host of state governments also entering the bandwagon of health insurance. One of the reasons for initiating such programs may be traced to the commitment of the governments in India to scale up public spending in health care.

1.4 The need for health insurance in India

1.4.1 lifestyles have changed..

Indians today suffer from high levels of stress. Long hours at work, little exercise, disregard for a healthy balanced diet and a consequent dependence on junk food have weakened our immune systems and put us at an increased risk of contracting illnesses.

1.4.2 Rare non-communicable diseases are now common.

Obesity, high blood pressure, strokes and heart attacks, which were earlier considered rare, now affect an increasing number of urban Indians.

1.4.3 Medical care is unbelievably expensive.

Medical breakthroughs have resulted in cures for dreaded diseases. These cures however are available only to a select few. This is because of high operating and treatment expenses.

1.4.4 Indirect costs add to the financial burden.

Indirect sources of expense like travel, boarding and lodging, and even temporary loss of income account for as much as 35% of the overall cost of treatment. These facts are overlooked when planning for medical expenses.

1.4.5 Incomplete financial planning.

Most of us have insured our home, vehicle, child’s education and even our retirement years. Ironically however we have not insured our health. We ignore the fact that illnesses strike without warning and seriously impact our finances and eat into our savings in the absence of a good health insurance or medical insurance plan.

1.5 Classification of health insurance plans in India

Health insurance plans in India today can be broadly classified into the following categories:

1.5.1 Hospitalization.

Hospitalization plans are indemnity plans that pay cost of hospitalization and medical costs of the insured subject to the sum insured. There is another type of hospitalization policy called a top-up policy . Top-up policies have a high deductible typically set a level of existing cover.

1.5.2 Family floater health insurance.

Family health insurance plan covers entire family in one health insurance plan. It works under assumption that not all member of a family will suffer from illness in one time.

1.5.3 Pre-existing disease cover plans.

It offers covers against disease that policyholder had before buying health policy. Pre-existing disease cover plans offers cover against pre-existing disease, e.g. diabetes, kidney failure and many more. After waiting for two to four years, it gives covers to the insured.

1.5.4 Senior citizen health insurance.

This type of health insurance plan is for older people in the family. It provides covers and protection from health issues during old age.

1.5.5 Maternity Health insurance.

Maternity health insurance ensures coverage for maternity and other additional expenses.

1.5.6 Hospital daily cash benefit plans.

Daily cash benefits are a defined benefit policy that pays a defined sum of money for every day of hospitalization.

1.5.7 Critical illness plans.

These are benefit-based policies which pay a lump sum amount on certain critical illnesses, e.g. heart attack, cancer and stroke.

1.5.8 Disease-specific special plans.

Some companies offer specially designed disease-specific plans such as Dengue Care and Corona Kavach policy.

1.6 Strength, weakness, opportunity and threat analysis of health insurance sector (SWOT analysis)

The strengths, weaknesses, opportunities and threats (SWOT) is a study undertaken to identify internal strengths and weaknesses as well as external opportunities and threats of the health insurance sector.

1.6.1 Strengths.

The growth trend of the health insurance sector is likely to be high due to rise in per capita income and emerging middle-income group in India. New products are being launched in this sector by different insurance companies which will help to satisfy customers need. Customers will be hugely benefited when cash less facility will be provided to all across the country by all the insurance companies.

1.6.2 Weaknesses.

The financial condition of this sector is weak due to low investment in this sector. The public sector insurance companies are still dominating this industry due to their greater infrastructure facilities. This sector is prone to high claim ratio and many false claims are also made.

1.6.3 Opportunities.

The possibility of future growth of this sector is high, as penetration in the rural sector is low. The improvement of technology and the use of internet facility are helping this sector to grow in magnitude and move towards environment-friendly paperless regime.

1.6.4 Threats.

The biggest threat of this sector lies in the change in the government regulations. The profitability of this sector is affected due to increasing expenses and claims. The economic slowdown and recession in the economy can affect growth of this sector adversely. The increasing losses and need for insurance might reach a point of no return where insurance companies may be compelled to decline an insurance policy.

1.7 Political economic socio cultural and technological analysis of health insurance sector (PEST analysis)

This analysis describes a framework of macro-environmental factors used as strategic tool for understanding business position, growth potential and direction for operations.

1.7.1 Political factors.

Service tax on premium on insurance policies is being increased by the government for past few years during budget. Government monopoly in this sector came to an end after insurance companies were opened up for private participation in the year 2000. Foreign players were allowed to enter into joint venture with their Indian counterpart with 26% holding and which was further increased to 49% in the year 2015.

1.7.2 Economic factors.

The gross savings of people in India have increased significantly thereby encouraging people to buy insurance policy to cover their risks. Insurance companies are fast becoming prominent players in the security market. As these companies have huge disposable income which they are investing in the security market.

1.7.3 Socio-cultural factors.

Increase in insurance knowledge is helping people to increase their awareness about the risk to be covered through insurance. Change in lifestyle is leading to increase in risk thereby giving an opportunity to insurance companies to innovate newer products. Societal benefit is derived by transfer of risk through insurance due to improved socio-cultural environment.

1.7.4 Technological factors.

Insurance companies deals in large database and maintaining it by the application of latest technology is huge gain for this sector. Technological advancement has helped insurance companies to sale their products through their electronic portals. This has made their task of providing service to the customers easier and faster.

2. Review of literature

After opening up of the insurance industry health insurance sector has become significant both from economic and social point of view and researchers have explored and probed these aspects.

Ellis et al. (2000) reviewed a variety of health insurance systems in India. It was revealed that there is a need for a competitive environment which can only happen with the opening up of the insurance sector. Aubu (2014) conducted a comparative study on public and private companies towards marketing of health insurance policies. Study revealed that private sector services evoked better response than that of public sector because of new strategies and technologies adopted by them. Nair (2019) has made a comparative study of the satisfaction level of health insurance claimants of public and private sector general insurance companies. It was revealed that majority of the respondents had claim of reimbursement nature through third party administrator. Satisfaction with respect to settlement of claim was found relatively higher for public sector than private sector. Devadasan et al. (2004) studied community health insurance to be an important intermediate step in the evolution of an equitable health financing mechanism in Europe and Japan. It was concluded that community health insurance programmes in India offer valuable lessons for its policy makers. Kumar (2009) examined the role of insurance in financing health care in India. It was found that insurance can be an important means of mobilizing resources, providing risk protection and health insurance facilities. But for this to happen, it will require systemic reforms of this sector from the end of the Government of India. Dror et al. (2006) studied about willingness among rural and poor persons in India to pay for their health insurance. Study revealed that insured persons were more willing to pay for their insurance than the uninsured persons. Jayaprakash (2007) examined to understand the hurdles preventing the people to purchase health insurance policies in the country and methods to reduce claims ratio in this sector. Yadav and Sudhakar (2017) studied personal factors influencing purchase decision of health insurance policies in India. It was found that factors such as awareness, tax benefit, financial security and risk coverage has significant influence on purchase decision of health insurance policy holders. Thomas (2017) examined health insurance in India from the perspective of consumer insights. It was found that consumers consider various aspects before choosing a health insurer like presence of a good hospital network, policy coverage and firm with wide product choice and responsive employees. Savita (2014) studied the reason for the decline of membership of micro health insurance in Karnataka. Major reason for this decline was lack of money, lack of clarity on the scheme and intra house-hold factors. However designing the scheme according to the need of the customer is the main challenge of the micro insurance sector. Shah (2017) analysed health insurance sector post liberalization in India. It was found that significant relationship exists between premiums collected and claims paid and demographic variables impacted policy holding status of the respondents. Binny and Gupta (2017) examined opportunities and challenges of health insurance in India. These opportunities are facilitating market players to expand their business and competitiveness in the market. But there are some structural problems faced by the companies such as high claim ratio and changing need of the customers which entails companies to innovate products for the satisfaction of the customers. Chatterjee et al. (2018) have studied health insurance sector in India. The premise of this paper was to study the current situation of the health-care insurance industry in India. It was observed that India is focusing more on short-term care of its citizens and must move from short-term to long-term care. Gambhir et al. (2019) studied out-patient coverage of private sector insurance in India. It was revealed that the share of the private health insurance companies has increased considerably, despite of the fact that health insurance is not a good deal. Chauhan (2019) examined medical underwriting and rating modalities in health insurance sector. It was revealed that while underwriting a health policy one has to keep in mind the various aspects of insured including lifestyle, occupation, health condition and habits. There have been substantial studies on health insurance done in India and abroad. But there has not been any work on performance of health insurance sector based on underwriting profit or loss.

3. Research gap

After extensive review of literature it is understood that there has not been substantial study on the performance of health insurance sector taking underwriting profit or loss into consideration. In spite of high rate of growth of earned premium, this sector is unable to make underwriting profit. This is mainly because growth of premium is more than compensated by claims incurred and commission and other expenses paid. Thereby leading to growth of underwriting loss over the years across the different insurance companies covered under both public and private sector. This unique feature of negative performance of this sector has not been studied so far in India.

4. Objectives

review health insurance scenario in India; and

study the performance of health insurance sector in India with respect to underwriting profit or loss by the application of regression analysis.

5. Research methodology

The study is based on secondary data sourced from the annual reports of Insurance Regulatory Development Authority (IRDA), various journals, research articles and websites. An attempt has been made to evaluate the performance of the health insurance sector in India. Appropriate research tools have been used as per the need and type of the study. The information so collected has been classified, tabulated and analysed as per the objectives of the study.

The data is based on a time period of 12 years ranging from 2006–2007 to 2018–2019.

Secondary data analysis has been done using regression of the form: Y =   a   +   b X

The research has used SPSS statistics software package for carrying out regression and for the various graphs Microsoft Excel software has been used.

5.1 The problem statement

It is taken to be a general assumption that whenever the premium increases the profit also increases. This determines that profits are actually dependent on the premium income. Hence, whenever the premium tends to increase, the profit made also supposed to increase.

The aim of the study is to find out whether the underwriting profit of the health insurance sector is increasing or there is an underwriting loss.

The problem statement is resolved by applying regression analysis between the premium earned and underwriting profit or loss incurred. It is assumed that if the underwriting profit increases along with the premium received, then the pattern forms a normal distribution and alternate hypothesis can be accepted and if this pattern of dependability is not found then the null hypothesis will be accepted stating that there is no relation between the premium and the underwriting loss or the underwriting profit by the sector. But what is happening in this sector is the increase in premium is leading to increase in underwriting loss. So premium is negatively impacting underwriting profit which is astonishing thing to happen and is the crux of the problem of this sector.

5.1.1 Underwriting profit/loss = net premium earned – (claim settled + commission and management expenses incurred).

Underwriting profit is a term used in the insurance industry to indicate earned premium remaining after claims have been settled and commission and administrative expenses have been paid. It excludes income from investment earned on premium held by the company. It is the profit generated by the insurance company in the normal course of its business.

5.2 Data analysis

Table 1 shows that health insurance premium increased from Rs.1910 crores in 2006–2007 to Rs. 33011 crores in 2018–2019. But claims incurred together with commission and management expenses have grown from Rs. 3349 crores to Rs. 40076 crores during the same period. So the claims and management expenses incurred together is more than the health insurance premium earned in all the years of our study thereby leading to underwriting loss.

Claim incurred shown above is the outcome of the risk covered against which premium is received and commission and management expenses are incurred to obtain contract of insurance. Both these expenses are important for insurance companies to generate new business as stiff competition exists in this sector since it was opened up in the year 2000.

Figure 1 depicts the relationship between health insurance premium earned and claims and management expenses incurred by the insurance companies of the health insurance sector for the period 2006–2007 to 2018–2019.

Bar chart between premiums earned and claims and management expenses incurred show that claims and management expenses together is higher than premium earned in all the years of the study thereby leading to losses. Claims, commission and management expenses are important factors leading to the sale of insurance policies thereby earning revenue for the insurance companies in the form of premium. But proper management of claims and commission and management expenses will help this sector to improve its performance.

Table 2 provides insight into the performance of health insurance sector in India. The growth of health insurance in India has been from Rs.1909 crores for the financial year 2006–2007 to Rs. 33011crores for the financial year 2018–2019. The growth percentage is 1629% i.e. growing at an average rate of 135% per annum. Compounded Annual Growth Rate (CAGR) is working out to be 27%.

From the same table, it can be inferred that health insurance sector is making underwriting loss in all the financial years. There is no specific trend can be seen, it has increased in some years and decreased in some other years. Here underwriting loss is calculated by deducting claims and commission and management expenses incurred from health insurance premium earned during these periods.

With every unit of increase in premium income the claims incurred together with commission and management expenses paid increased more than a unit. Thereby up setting the bottom line. So instead of earning profit due to better business through higher premium income, it has incurred losses.

Underwriting principles needs to be streamlined so that proper scrutiny of each policy is carried out so that performance of this sector improves.

It is seen from Figure 2 that there is stiff rise in premium earned over the years but claims and commission and management expenses incurred have also grown equally and together surpassed earned premium. So the net impact resulted in loss to this sector which can also be seen in the figure. It is also seen that loss is increasing over the years. So, increase in earnings of revenue in the form of premium is leading to increase in losses in this sector which is normally not seen in any other sectors.

But a time will come when commission and management expenses will stabilize through market forces to minimize underwriting losses. On the other hand, it will also require proper management of claims so that health insurance sector can come of this unprofitable period.

5.3 Interpretation of regression analysis

5.3.1 regression model..

Where Y = Dependent variable

X = Independent variablea = Intercept of the lineb = Slope of the line

5.3.2 Regression fit.

Here, Y is dependent variable (Underwriting Profit or Loss) which is to be predicted, X is the known independent variable (Health Insurance Premium earned) on which predictions are to be based and a and b are parameters, the value of which are to be determined ( Table 3 ). Y =   − 1028.737 − 0.226   X

5.3.3 Predictive ability of the model.

The value of R 2 = 0.866 which explains 86.6% relationship between health insurance premium earned and loss made by this sector ( Table 4 ). In other words, 13.4% of the total variation of the relationship has remained unexplained.

4.1 Regression coefficients ( Table 5 ).

H1.1 : β = 0 (No influence of Health Insurance Premium earned on Underwriting Profit or Loss made)

5.4.1.2 Alternative hypothesis.

H1.2 : β ≠ 0 (Health Insurance Premium earned influences underwriting Profit or Loss made by this sector)

The computed p -value at 95% confidence level is 0.000 which is less than 0.05. This is the confidence with which the alternative hypothesis is accepted and the null hypothesis is rejected. Thus regression equation shows that there is influence of health insurance premium earned on loss incurred by this sector.

The outcome obtained in this analysis is not what happens normally in the industry. With the increase of revenue income in the form of premium, it may lead to either profit or loss. But what is happening surprisingly here is that increase of revenue income is leading to increase of losses. So growth of premium income instead of influencing profit is actually influencing growth of losses.

6.1 Findings

The finding from the analysis is listed below:

The average growth of net premium for the health insurance has been around 135% per annum even then this sector is unable to earn underwriting profit.

The CAGR works out to around 27%. CAGR of 27% for insurance sector is considered to be very good rate of growth by any standard.

Along with high growth of premium, claims and commission and management expenses incurred in this sector have also grown substantially and together it surpassed in all the years of the study.

Thus, growth of claims and commission and management expenses incurred has more than compensated high rate of growth of health insurance premium earned. This resulted into underwriting loss that this sector is consistently making.

Astonishing findings has been higher rate of increase of premium earnings leading to higher rate of underwriting loss incurred over the years. Even though the sector is showing promise in terms of its revenue collection, but it is not enough to earn underwriting profit.

6.2 Recommendations

COVID 19 outbreak in India has led to a spike in health-care costs in the country. So, upward revision of premium charges must be considered to see bottom line improvement in this sector.

Immediate investigation of the claim is required. This will enable the insurers to curb unfair practice and dishonest means of making a claim which is rampant in this sector.

Health insurance market is not able to attract younger generation of the society. So entry age-based pricing might attract this group of customers. An individual insured at the age 30 and after 10 years of continuous coverage the premium will be less than the other individual buying a policy at the age of 40 for the first time.

6.3 Limitations and scope of future studies

The analysis of performance of health insurance sector in India taking underwriting profit into consideration is the only study of its kind in this sector. As a result, adequate literature on the subject was not available.

Health insurance and health care are part of medical care industry and are inter dependent with each other. So performance of health insurance sector can be better understood by taking health-care industry into consideration which is beyond the scope of the study.

This sector is consistently incurring losses. So, new ideas need to be incorporated to reduce losses if not making profits.

Opportunity of the insurance companies in this sector lies in establishing innovative product, services and distribution channels. So, continuous modification by the application of research is required to be undertaken.

Health insurance sector will take a massive hit, as tax benefit is going to be optional from this financial year. This can be a subject of study for the future.

6.4 Conclusion

This sector is prone to claims and its bottom line is always under tremendous pressure. In recent times, IRDA has taken bold step by increasing the premium rate of health insurance products. This will help in the growth of this sector.

With better technological expertise coming in from the foreign partners and involvement by the IRDA the health insurance sector in India must turn around and start to earn profit.

The COVID-19 pandemic is a challenge for the health insurance industry on various fronts at the same time it provides an opportunity to the insurers to fetch in new customers.

The main reason for high commission and management expense being cut-throat competition brought in after opening up of the insurance sector in the year 2000. So, new companies are offering higher incentives to the agents and brokers to penetrate into the market. This trend needs to be arrested as indirectly it is affecting profitability of this sector.

The study will richly contribute to the existing literature and help insurance companies to know about their performance and take necessary measures to rectify the situation.

literature review on health insurance

Chart on health insurance premium earned and claims and management expenses paid

literature review on health insurance

Chart on performance of health insurance sector in India

Data showing health insurance premium earned and claims and management expenses paid

. Dependent variable: Underwriting profit or loss;

. Predictors: (Constant), Health insurance premium earned

Aubu , R. ( 2014 ), “ Marketing of health insurance policies: a comparative study on public and private insurance companies in Chennai city ”, UGC Thesis, Shodgganga.inflibnet.ac.in .

Chatterjee , S. , Giri , A. and Bandyopadhyay , S.N. ( 2018 ), “ Health insurance sector in India: a study ”, Tech Vistas , Vol. 1 , pp. 105 - 115 .

Chauhan , V. ( 2019 ), “ Medical underwriting and rating modalities in health insurance ”, The Journal of Inssurance Institute of India , Vol. VI , pp. 14 - 18 .

Devadasan , N. , Ranson , K. , Damme , W.V. and Criel , B. ( 2004 ), “ Community health insurance in India: an overview ”, Health Policy , Vol. 29 No. 2 , pp. 133 - 172 .

Dror , D.M. , Radermacher , R. and Koren , R. ( 2006 ), “ Willingness to pay for health insurance among rural and poor persons: Field evidence form seven micro health insurance units in India ”, Health Policy , pp. 1 - 16 .

Ellis , R.P. , Alam , M. and Gupta , I. ( 2000 ), “ Health insurance in India: Prognosis and prospectus ”, Economic and Political Weekly , Vol. 35 No. 4 , pp. 207 - 217 .

Gambhir , R.S. , Malhi , R. , Khosla , S. , Singh , R. , Bhardwaj , A. and Kumar , M. ( 2019 ), “ Out-patient coverage: Private sector insurance in India ”, Journal of Family Medicine and Primary Care , Vol. 8 No. 3 , pp. 788 - 792 .

Gupta , D. and Gupta , M.B. ( 2017 ), “ Health insurance in India-Opportunities and challenges ”, International Journal of Latest Technology in Engineering, Management and Applied Science , Vol. 6 , pp. 36 - 43 .

Hand book on India Insurance Statistics revisited ( 2020 ), “ Insurance regulatory and development authority website ”, available at: www.irda.gov.in ( accessed 2 July 2020 ).

Jayaprakash , S. ( 2007 ), “ An explorative study on health insurance industry in India ”, UGC Thesis, Shodgganga.inflibnet.ac.in .

Kumar , A. ( 2009 ), “ Health insurance in India: is it the way forward? ”, World Health Statistics (WHO) , pp. 1 - 25 .

Nair , S. ( 2019 ), “ A comparative study of the satisfaction level of health insurance claimants of public and private sector general insurance companies ”, The Journal of Insurance Institute of India) , Vol. VI , pp. 33 - 42 .

Savita ( 2014 ), “ A qualitative analysis of declining membership in micro health insurance in Karmataka ”, SIES Journal of Management , Vol. 10 , pp. 12 - 21 .

Shah , A.Y.C. ( 2017 ), “ Analysis of health insurance sector post liberalisation in India ”, UGC Thesis, Shodgganga.inflibnet.ac.in .

Thomas , K.T. ( 2017 ), “ Health insurance in India: a study on consumer insight ”, IRDAI Journal , Vol. XV , pp. 25 - 31 .

Yadav , S.C. and Sudhakar , A. ( 2017 ), “ Personal factors influencing purchase decision making: a study of health insurance sector in India ”, BIMAQUEST , Vol. 17 , pp. 48 - 59 .

Further reading

Beri , G.C. ( 2010 ), Marketing Research , TATA McGraw Hill Education Private , New Delhi, ND .

Dutta , M.M. and Mitra , G. ( 2017 ), “ Performance of Indian automobile insurance sector ”, KINDLER , Vol. 17 , pp. 160 - 168 .

Majumdar , P.I. and Diwan , M.G. ( 2001 ), Principals of Insurance , Insurance Institute of India , Mumbai, MM .

Pai , V.A. and Diwan , M.G. ( 2001 ), “ Practice of general insurance ”, Insurance Institute of India , Mumbai, MM .

Shahi , A.K. and Gill , H.S. ( 2013 ), “ Origin, growth, pattern and trends: a study of Indian health insurance sector ”, IOSR Journal of Humanities and Social Science , Vol. 12 , pp. 1 - 9 .

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Health insurance policy renewal: an exploration of reputation, performance, and affect to understand customer inertia

  • Original Article
  • Published: 04 September 2021
  • Volume 10 , pages 261–278, ( 2022 )

Cite this article

  • Pradeep Kautish   ORCID: orcid.org/0000-0002-2908-6720 1 ,
  • Arpita Khare 2 &
  • Rajesh Sharma 3 , 4  

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The aim of the study is to understand the role of insurance company reputation, performance, and positive/negative affect on health insurance policy customer retention and the moderating influence of customer inertia. A structured questionnaire was used for data collection. Covariance-based structural equation modeling was employed to assess the hypothesized relationships between the variables. The findings revealed that reputation, performance, and affect influenced customer retention in insurance sector. Positive affect had greater impact on customer retention in comparison to other constructs. Further, customer inertia was an important moderating influence on the negative affect for health insurance policy customer retention. To the best of our knowledge, the present study is the first of its kind that attempts to investigate customer inertia in the health insurance sector in an emerging market context, i.e., India. Customer inertia has not been much studied in light of company reputation, performance, and positive and negative affect in the health insurance milieu. The research findings may help health insurance companies understand the importance of reputation, performance, customer retention, and inertia while marketing insurance services.

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Introduction

The high-quality service delivery is the key to success in service sector. Notwithstanding the ubiquitous accessibility of a number of retail insurance avenues (i.e., life and non-life policies), customers are not fully aware of the varied options available (Barwitz 2020 ; Hu and Tracogna 2020 ). The fierce competition and continuously evolving customer expectations has compelled service companies to take steps to offer services that are focused toward enhancing service experiences, e.g., health insurance (Abu-Salim et al. 2017 ; Yun and Hanson 2020 ). Harrison ( 2000 ) recognized that the globalization of financial services has increased pressure on firms to add value to their service offerings, i.e., service quality, customer satisfaction, productivity, and costs rationalization in ever changing regulatory and technological landscape (Heckl et al. 2010 ; Marshall 1985 ).

According to PwC’s ( 2020 ) Health Insurance Consumer Pulse Survey, health insurance policy holders held favorable views toward their health insurance. However, the intense competition in the health insurance segment is of great concern for any insurance player as it leads to customer’s switching service providers (Christiansen et al. 2016 ; Dominique-Ferreira 2017 ). The retention of existing customers is crucial for any health insurance policy renewal especially in Indian market (Meesala and Paul 2018 ; Panda et al. 2016 ). Research claims that competitive services industries are less differentiated and have more intermediated levels of client satisfaction (Abu-Salim et al. 2017 ; Fornell and Johnson 1993 ). The lack of product/service differentiation culminates into competitive challenges for the insurance firms. Given the multitude of options, the insurance policy customers are demanding and choice rich, and ‘policy lapses’ increasingly become a norm. The financial service customers often pursue comparable and/or better alternatives to their existing service providers (Kannadhasan 2015 ; Sharma and Patterson 2000 ).

The theory of Status Quo Bias emphasized the possible explanations why health plan consumers choose to sustain the status quo—the perpetuation of an incumbent company’s service offerings (Samuelson and Zeckhauser 1988 ; Seth et al. 2020 ). According to Samuelson and Zeckhauser ( 1988 ), upholding the status quo is a normal action because consumers reflect the decisional impacts in terms of switching costs, price for status quo, and perceived risks associated with alternatives. Consumers often make a risk aversion-oriented decision regarding changing service provider, or switch (Masatlioglu and Ok 2005 ). Thus, consumers with resilient status quo bias have positive value, information, and emotional attachment, irrespective of the lucrative proposals of rival players or common pressures for change (Kahneman and Tversky 1979 ; Kahneman et al. 1991 ; Taylor 2012 ).

The extant literature argued that the switching costs tend to be more imperious in services, but it was difficult for customers to evaluate and differentiate similar services offered by firms (Fernandes and Pinto 2019 ; Gremler and Brown 1996 ). Keaveney ( 1995 ) provided a typology of customer reasons for service switching and measure the frequency of incidents with reasons (i.e., a service defect or failure) and conditions (i.e., change in needs). The customer reasons were classified into eight broad categories: inconvenience, pricing, core service failures, response to failed service, failed service encounters, competition, ethical concerns, and involuntary switching. East et al ( 2012 ) extended the Keaveney’s ( 1995 ) work by using the critical incident technique and suggested that in comparison to non-located service, a located service is much less conceivable to be abandoned owing to price as location may increase switching costs and/or decrease price rationales. Research suggested that the insurance service possesses high credence, complex nature, abstract evaluation, and is largely focused on futuristic benefits that are difficult to verify (e.g., life, financial or non-financial protection) (Crosby and Stephens 1987 ). Klemperer ( 1995 ) posits that losing customers on account of transactions switching costs not just leads to loss of available opportunities in the marketplace, but also builds pressure on the service organizations to squeeze resources to acquire new clients. Customer switching has negative consequences on companies’ market share and profitability (Ganesh et al. 2000 ). Thus, it becomes critical for insurance service companies to retain the existing customers and due the perilous nature of customer inertia in services.

Few researchers have attempted to understand switching costs paradigm with respect to customer retention practice, primarily for the service sector (Lam et al. 2004 ; Lee et al. 2001 ; Li 2015 ). The reluctance to change service provider has been attributed to customer inertia (Bawa 1990 ; Meidan 1996 ; Panther and Farquhar 2004 ; White and Yanamandram 2004 ). In the state of inertia, the customer avoids spending time to reassess the service dimensions of another company brand (Assael 1998 ). Disparate to other research constructs directly or indirectly influencing customer retention, consumer inertia embraces a moderating distinction that impacts the customer retention and potential behavioral antecedents such as satisfaction, regret, or disappointment (Ghose and Lowengart 2013 ; Oliver 1997 ). Despite its proven relevance and significance in service domain, the role of customer inertia in the context of financial services has not been much researched with respect to customer switching behavior (Thaichon et al. 2017 ).

The current study assumed that corporate reputation reduces service switching; the performance is an antecedent of customer satisfaction which also reduces switching behavior. It is anticipated that switching costs play a decisive role in determining whether the health insurance policy holder decides to stay (retained the existing policy) or exit (switched the insurance provider) from their respective service provider. According to the Insurance Regulatory and Development Authority of India (IRDAI) norms, the customer may easily switch or port their existing health insurance policy to another insurance company in the case of perceived poor customer service, claim settlement issues, additional cover facility offered by any other company, and availability of better option or transparency concerns due to the health insurance company reputation (ET 2019 ; IRDAI 2020 ). In mature markets like financial services, customer retention provides a number of intrinsic benefits to the firms (Harrison and Ansell 2002 ; Pickett et al. 2017 ). The current study is an initial attempt to examine the role of insurance company reputation, insurance service performance, and positive and negative affect in facilitating customer retention in the health insurance sector and the moderating influence of customer inertia. Furthermore, we intend to test the mediating role of positive and negative affect as explanatory variables in ensuring customer retention. The subsequent sections comprise of detailed literature review, theoretical underpinning, followed by research methodology and results. At the end, we deliberate on the study findings and limitations with future research imperatives.

Conceptual framework

In the last two decades, domains like marketing, economics, sociology, and strategy observed a rising research trend related to corporate reputation and its impact on customer behavior (Chun 2005 ; Fombrun and Shaney 1990 ; Walker 2010 ). Corporations with higher reputation were linked with superior customer loyalty (Bartikowski et al. 2011 ), higher customer satisfaction (Walsh and Beatty 2007 ; Walsh et al. 2009 ), and retention (Bartikowski and Walsh 2011 ). Several studies have stressed the relevance of financial service company’s reputation/image (Boyle 1996 ; Worcester 1997 ; Devlin 1997 ; Nguyen and LeBlanc 2002 ; Wang et al. 2003 ), service quality performance (Siddiqui and Sharma 2010 ; Tsoukatos and Rand 2006 ), fairness (Page and Fearn 2005 ) and affective experiences (Hellier et al. 2003 ; Taylor 2001 ) as strategic factors in explaining customer retention, repurchase intentions and loyalty (Mittal and Lassar 1998 ; Rundle-Thiele 2005 ). Most studies investigated these variables separately and did not investigate their impact on loyalty. Danaher and David ( 2012 ) contended that merely improving customer satisfaction parameters may not always ensure loyalty. Factors like customer engagement, service involvement, switching costs and societal benefits also influenced satisfaction–loyalty relationship.

Literature review

Health insurance in india.

The rapid growth of the health insurance services both in public and private sector has considerably influenced the healthcare scenario in the last two decades (Michielsen et al. 2011 ; Sen et al. 2014 ). Within a short span of time, private insurance players have acquired substantial market share in the Indian insurance sector (Gambhir et al. 2019 ). The insurance services growth potential has augmented the range of opportunities for insurance marketers in India (Dror et al. 2007 ; Gupta 2007 ). The growth has triggered intense competition in the Indian health insurance market, especially for private sector players. Thus, for long-term success in the competitive insurance market, firms are compelled to innovate their service offerings, strengthen existing delivery processes and cost rationales (Chakrabarti and Shankar 2015 ; Thomas and Vel 2011 ). Owing to rising income levels in emerging markets like India, the demand for health insurance is projected to increase which may impact the affordability for insurance products (Dragos 2014 ). Treerattanapun ( 2011 ) asserted that as gross domestic product (GDP) /capita increase, non-life insurance affordability also increases, especially in the context of emerging economies. The Indian health insurance sector is either state funded or publicly funded, thus insurance schemes do not totally mitigate inequitable access to health services in a private health care delivery market (Sodhi and Rabbani 2014 ). Indian insurance market is different from western countries where people are more aware about health-related hazards especially the adverse effects of not having medical insurance. Customers in western countries are conscientious about regular medical checkups and aware of government health and medical initiatives/facilities available for common citizens. On the contrary, in India, the accessibility to good health insurance services is limited to higher income groups due to lack of awareness, income disparity, individualistic slackness, paucity of government support etc. (Chakrabarti and Shankar 2015 ). Most people consider regular medical checkups and investing on health insurance a waste of resources. When people avail health insurance policy for evading taxes or getting income tax rebate or as employers’ compensation, they continue with their existing policy without much serious efforts to compare the alternatives. Therefore, the common negligence toward primary health services, lack of awareness about health issues, poor accessibility to medical facilities in smaller cities, and cost of medical checkups creates an inertia, disengagement and disinterest toward health programs or medical insurance (Sodhi and Rabbani 2014 ). This behavior has an impact on consumers’ perception toward insurance services that provide financial support during illness. Most Indians feel it an unnecessary expense because health-related problems are not given a serious thought barring situation like COVID-19 pandemic (ET 2020 ; FE 2020 ).

Although, health insurance sector in India is in nascent stage, still health insurance companies are expected to offer medical benefits during hospitalization, insurance policy performance (e.g., claim settlement, cashless benefits), and healthcare service quality (i.e., comfortable hospitalization, insurance coverage). As compared to other insurance products, health insurance firms need to ensure adequate level of affective service experience, customer-centric interface (i.e., easy documentation, service efficacy) and easy claim settlement (Ahlin et al. 2015 ; Chakrabarti and Shankar 2015 ). In addition, financial services performance is influenced by trust, image and reputation of the service provider (Macintosh 2009 ; Nienaber et al. 2014 ). The following sections discusses the variables taken up for the study.

Switching costs

The switching costs for insurance services include–search costs (the costs of time invested in searching for the information about claims settlement), habit (investment oriented behavior), learning (financial strength of the insurance company), inertia, costs of transaction in terms of contractual and continuity (the costs of time and effort desired for price bargain and administrative charges) as discussed by several researchers (e.g., Berger et al. 1989 ; Posey and Tennyson 1998 ; Posey and Yavas 1995 ; Schlesinger and Schulenburg 1991 , 1993 ). Bell et al ( 2005 ) explored the effects of customers’ investment know-how and perceived switching costs on technical and functional service quality and customer loyalty. Increased level of perceived switching costs makes altering service providers costly and ensures a dependency of the customer on the service provider (Ruyter et al. 1998 ; Weerahandi and Moitra 1995 ). As the perceived switching costs escalate, at least in the short period of time customers are unlikely to alter the service providers (Ranaweera and Prabhu 2003 ).

The switching costs are acknowledged as a means for holding customers in business relationships, irrespective of their level of satisfaction with the service provider (Bansal et al. 2004 ; 2005 ; Burnham et al. 2003 ; Jones et al. 2002 ). Gremler and Brown ( 1996 ) employed in-depth interviews to understand the relevance of switching costs as an antecedent to customer loyalty. Switching costs was demarcated as investment of time, money and search efforts that in customer perception made it challenging to switch. The switching costs are formally manifested as the costs of changing the service providers owing to varied reasons (Carter et al. 2016 ; De Matos et al. 2013 ; Dick and Basu 1994 ). Recent studies have revealed that switching costs are diverse and multidimensional in nature (Barroso and Picón 2012 ; Gremler et al. 2020 ; Kuo 2020 ). Burnham et al ( 2003 ) recognized three dimensions of switching costs, each with a few subcategories, i.e., procedural, relational and financial.

  • Customer inertia

Inertia is described as a non-conscious form of emotion, uni-dimensional in nature entailing “passive service patronage without true loyalty” (Huang and Yu 1999 ). Deliberate inertia entails an intentional persistence to maintain status quo, when there are better alternatives and incentives available in market (Schwarz 2012 ). Schwarz ( 2012 ) categorized inertia as spontaneous, forced, unobtrusive, and deliberate inertia. The grouping is based on motivation for change (high vs low value) and the influencing condition (external vs internal). The deliberate inertia is customers’ conscious and rational decision which takes into account the value, benefits and switching costs where the role of insurance executive becomes crucial to offer better service (Terpstra and Verbeeten 2014 ; Yu and Tseng 2016 ). The inert customers are more likely to respond to various marketing activities like promotions and discounts (Huang and Yu 1999 ). Few scholars suggest that inertia is an outcome to avoid change or barriers to switch due to the dearth of attractive alternatives or higher switching costs. Thus, the customers prefer to stay with the existing service provider instead of exploring new options (Bodet 2008 ; Bozzo 2002 ; Picón et al. 2014 ). Customer inertia is the continued use and purchase of the same brand passively over a period of time without much thought (White and Yanamandram 2004 ). While Yanamandram and White ( 2006 ) defined inertia with respect to passivity, we propose that inertia is a much broader and complex concept (Zeelenberg and Pieters 2004 ).

The customer inertia originated from the Status Quo Bias (SQB) theoretical framework (Masatlioglu and Ok 2005 ; Samuelson and Zeckhauser 1988 ). Inertia is defined as the tendency to stick to the prevailing habits or course of actions even when a better decision substitute is offered or available to someone (Samuelson and Zeckhauser 1988 ). Other customer inertia metaphors are: status quo preservation (De Guinea and Markus 2009 ), consumer resistance (Mani and Chouk 2018 ), drive for repeat purchases (Ranaweera and Neely 2003 ), systematic bias (Wu et al. 2018 ) and loyalty or spurious loyalty (Wu and Lo 2012 ) which is demarcated as the condition when a customer purchases the same offering every time without conscious thinking or commitment toward company (Huang and Yu 1999 ). Numerous reasons can explain inertia as a phenomenon, e.g., convenience orientation, uncertainty avoidance, habitual orientation toward decision making and the risk avoidance (Lee and Joshi 2017 ). Moreover, inertia can be divided into two distinct measures: first, cognitive inertia and second, affective inertia (Greenfield 2005 ; Polites and Karahanna 2012 ). The cognitive inertia denotes as conscious sticking to the status quo, despite being aware of that it might not be the best option to choose, while affective inertia characterizes as sticking to the status quo because other options are observed as being difficult to acquire (Greenfield 2005 ; Handel 2013 ). A few studies on health insurance have employed inertia to describe the stickiness to the alternatives with certain risk levels (Handel 2013 ; Handel and Kolstad 2015 ). At the same time, strong empirical evidence exists to explain the persistence of inertia in investment decision making (Auger et al. 2016 ).

Company reputation and service performance

Company reputation as a theoretical construct echoes the broad assessment of a corporation. Though the concept is quite broad in nature, there have always been a lack of precise definitions and research scholars attempted to define it differently. The perceptual exemplification of a corporation in the minds of key stakeholders is described as the reputation of the organization (Fombrun 1996 ). Company reputation is understood as a set of connotations that the target customer ascribes about the company and then uses it to designate, recall and relate to the same as a consequence of positive or negative experience, favorable or unfavorable impressions, beliefs, value dispositions, feelings, information and knowledge (Dowling 2004 ). A good company reputation encourages product purchases in the form of simplifying customer decision making process. The service literature emphasizes the importance of firm reputation, service quality and delivery in improving customer satisfaction and performance. Nguyen and LeBlanc ( 1998 ) investigated the role of service quality, customer satisfaction with services offered and value perceptions on firm’s image and loyalty toward the banks. Customers’ perceptions about bank image and reputation influenced loyalty, while satisfaction affected loyalty more than perception of bank’s image. Andreassen and Lindestad ( 1998a ) found that company reputation is positively correlated with customer’s gratification and loyalty toward the service. Yoon et al ( 1993 ) in their research on business insurance services posited that customer expectations, reputation and availability of information influence buying decision. Further, customers’ response to the service is consistent with their attitude toward the company reputation. Abdelfattah et al ( 2015 ) and Rahman et al ( 2018 ) analyzed the relationship between service quality, customer’s satisfaction and religiosity on the customer’s purchase and patronage behavior toward health insurance. Nguyen ( 2010 ) highlighted the importance of competence and benevolence attitudes of service staff on customers’ perception of reputation of financial service firms. In case of services, the perceptions of corporate reputation and image influence customer commitment and loyalty (Nguyen and LeBlanc 2001 ).

Positive and negative affect

Affect is “the psychological attachment of an exchange partner to the other and is based on feelings of identification, loyalty and affiliation” (Verhoef et al. 2002 , p. 204). The affect denotes individuals’ valence response to service or products, their attributes, service staff and company for the positive or negative emotion it evokes (Ortony et al. 1988 ). Research examined the importance of positive affect and attitude in stressful situations; however, not enough attention has been paid to the appraisal of stressful situations, which can lead to gains for the firm by inducing positive emotions such as excitement, eagerness and confidence (Folkman 1997 ). Negative affect induces stress, but there are several studies that emphasized the relevance of positive affect during stressful situations (Folkman and Moskowitz 2000 ). Positive and negative affect varies across individuals, considerations, and situations (i.e., intensity and frequency) thus, making it difficult to understand (Diener et al. 1985 ). Bougie et al ( 2003 ) investigated the relevance of feeling of anger and dissatisfaction on customers’ behavior to failed service encounters across firms. Favorable or positive emotion/affect or behavioral intentions influence customer retention, and customers are less likely a switch to another service provider (Burnham et al. 2003 ; Colgate and Hedge 2001 ; Jones et al. 2007 ). The ‘affect’ conceptualization implies a pleasant or unpleasant state (Plutchik 2003 ). Steiner and Maas ( 2018 ) described that value with respect to insurance services comprises of firms, staff or agent and the insurance policy. These elements affect customer’s perception of satisfaction and trust. It is widely acknowledged that customers’ product or service consumption situations evoke positive and negative emotion (Oliver 1997 ). The positive and negative affect and the relationship congruence of these affects with other cognitive variables (i.e., firm reputation, service performance, and customer value) influence customer retention and satisfaction (Mano and Oliver 1993 ; Oliver 1993 ; Westbrook and Oliver 1991 ). On the basis of arguments presented above, the following hypotheses are proposed:

A good reputation of insurance company would have a positive influence on positive affect.

A good reputation of insurance company would have a negative influence on negative affect.

A good performance of insurance service would have a positive influence on positive affect.

A good performance of insurance service would have a negative influence on negative affect.

Positive affect would positively influence customer retention.

Negative affect would negatively influence customer retention.

Customer inertia, switching costs and affects

In the service marketing literature, two different perspectives emerge in explaining customer inertia as classification of switching barriers (Patterson and Smith 2003 ; White and Yanamandram 2004 ; Yanamandram and White 2006 ). The first perspective on customer inertia has been associated with lack of attractive alternatives (Bozzo 2002 ; Colgate and Lang 2001 ) and the second described inertia as a function of customers’ passivity, inactivity, and non-conspicuousness (White and Yanamandram 2007 ). Whenever insurance customers wish to change service providers, they have to incur higher search costs as well as transaction costs (Kautish and Rastogi 2008 ). Customers who are lethargic and find searching for new alternatives cumbersome experience inertia. The approach to avoid change and choosing to remain with current service provider suggests a behavioral lock-in effect (Barnes et al. 2004 ). Nevertheless, the broad consensus among researchers is that inertia explains customer’s resistance or barriers to switch and is a loyalty facilitator in service marketing (Chen and Wang 2009 ; Oliver 1999 ). Bansal and Taylor ( 1999 ) defined perceived switching barriers as consumers’ assessment of the efforts needed to explore other options to switch from an existing provider. Gray et al ( 2017 ) confirmed that inertia has a negative impact on the intention to change service providers but does not provide any evidence on its effect on the actual behavior to switch service providers. Thus, inertia has an impact on customer’s evaluations of alternatives and the customers prone to higher levels of inertia do not consider choosing alternative service providers even when they are dissatisfied (Lee 2019 ).

Lee and Neale ( 2012 ) related switching costs to customer inertia. They found that while inertia led to customer retention, it influenced positive or negative word of mouth. Favorable or unfavorable word of mouth depends on whether customer inertia stems from the satisfaction or lack of interest. Financial services customers experiencing higher degree of inertia are likely to purchase a specific service repeatedly (Colgate and Lang 2001 ). They believe that searching for new alternatives is time consuming, bothering, and labor intensive (Yanamandram and White 2006 ). Inertia helps in retaining customers for the company. Carter et al’s ( 2016 ) qualitative study on switching behavior and inertia revealed that the customer may avoid switching even when circumstances are conducive to changing service provider in terms of fairness and trust. The research suggested that inertia had direct and moderating effects on service provider switching intentions, though not necessarily the behavior of changing service providers (Gray et al. 2017 ; Lai et al. 2011 ). Thus, the following hypotheses are proposed:

H 7a : Customer inertia would moderate the relationship between positive affect and customer retention.

H 7b : Customer inertia would moderate the relationship between negative affect and customer retention.

Therefore, the current research attempted to examine the relationship of insurance company reputation (ICR), insurance service performance (ISP), positive affect (PA), negative affect (NA), customer inertia on customer retention (see Fig.  1 ). The hypothesized model comprised of six hypotheses describing the associations among research variables. It included two hypotheses specifying the moderating influence of customer inertia.

figure 1

Hypothesized model. Annotations: ICR Insurance Company Reputation, ISP Insurance Service Performance, PA Positive Affect, NA Negative Affect

Methodology

Measurement instrument.

The survey instrument provided a description of the study objectives, and the questionnaire items were related to health insurance policy only. The questionnaire items were conceptualized from extensive literature review and adapted from previous studies (Bansal and Taylor 1999 ; Colgate and Lang 2001 ; Fombrun et al. 2000 ; Jones et al. 2000 ; Oliver and Swan 1989 ; Vázquez‐Casielles et al. 2010 ) and some items were modified according to the research context. Specifically, three-three items each were utilized to assess insurance company reputation and insurance service performance, the five items for positive affect, four items for negative affect, two items for consumer inertia and three items for understanding customer retention. All items used 7-point Likert-type scale where 7 = “strongly agree” to 1 = “strongly disagree”. The measurement instrument was pre-tested and on the basis of feedback received about language and content from two service marketing professors and four health insurance company managers. The final scale items and descriptive statistics are presented in Table 1 .

Data collection and sampling

The data were collected through an online survey. A structured and open-ended questionnaire was developed about health insurance for the existing policy holders. The samples were randomly chosen with the help of two private health insurance companies’ database. The procedure began with an explanation about the underlined objectives of the study through an email invitation. The detail explanation about health insurance policy, service quality, performance, and feedback were given in the email. In addition, to ensure the information credibility insurance company executives were also involved in the initial phase of the research (Robson and Sekhon 2011 ). Based on the policy descriptions, respondents whose policy renewals were due in the next three months and had availed the minor or major medical claim from the company in last two years were only eligible. The respondents were emailed an URL and requested to fill the questionnaire after answering the screening question of policy renewal. Total 400 e-mails requests sent to the policy holders to fill the questionnaire, a total of 285 received out of which 228 were utilized in the data analysis. Though the response rate was acceptable, the non-response bias test suggested by Armstrong and Overton ( 1977 ) was administered. The means for the constructs of early versus late responses were compared under the assumption that those who responded later were likely to be similar to non-respondents. No significant differences between early and late groups were reported (0.05 level), confirming the absence of significant non-respondent bias. Of these 228 respondents, 123 were males and 105 were females. The average age of the respondents was 43.2 years (range from 28 to 54 years) which clearly show the age in which usually people purchase health insurance policy in order to get health protection at late age. The respondents were well educated and more than 80% of them were graduates and post-graduates.

Though all the constructs and scale items were adapted from standardized, reliable and validated scales from previous studies, still the need for dimensionality check was attained by conducting factor analytics, the measure of sampling adequacy or Kaiser–Meyer–Olkin (KMO) test, and Bartlett’s test of sphericity was employed. The KMO value was 0.812, and Bartlett’s test of sphericity value was also found to be significant. Table 1 provides the details about acceptable factor loadings for each scale item of all constructs and more than the recommended threshold of 0.60 (Hair et al. 2014 ).

Measurement model

A confirmatory factor analysis was executed to generate the measurement model estimates. The results from the study indicated that the models’ goodness-of-fit statistics was satisfactory ( χ 2  = 265.069; d f  = 136; χ 2 /d f  = 1.949; p  = 0.001; CFI = 0.974; IFI = 0.973; TLI = 0.968; RMSEA = 0.066). To evaluate the internal consistency among scale items for each latent factor, composite reliability (CR) was considered and the results revealed that the CR values ranged from 0.787 to 0.985 (Hair et al. 2014 ). Table 1 shows that all the CR values exceeded the threshold value of 0.70; hence it confirms the internal consistency among the scale items of each variable (Hair et al. 2014 ). Later, to assess the convergent validity, we assessed the average variance extracted (AVE) values. The AVE values ranged from 0.599 to 0.947 and all values were more than 0.50, thus the convergent validity was confirmed. Lastly (see Table 2 ), as all AVE values were larger than the correlation (squared) between variables, maximum shared variance (MSV) and average shared variance (ASV) which ensure discriminant validity (Fornell and Larcker 1981 ).

Structural model

With the maximum likelihood estimation approach, a structural equation modeling was conducted to generate the structural model. The structural model revealed a satisfactory level of goodness-of-fit statistics ( χ 2  = 272.219; d f  = 113; χ 2 /d f  = 2.409; p  = 0.001; CFI = 0.967; IFI = 0.968, TLI = 0.958; RMSEA = 0.080). The structural model χ 2 /d f value (2.409) fell within an acceptable range from 2.00 to 5.00 (Marsh and Hocevar 1988 ). Furthermore, the suggested model adequately accounted for the total variance in customer retention ( R 2  = 0.498) and positive and negative affect as well-accounted ( R 2  = 0.653) and ( R 2  = 0.336), respectively, for by good/bad insurance company reputation (ICR) and high/low insurance service performance (ISP) (see Fig.  2 and Table 3 ).

figure 2

Structural model. Annotation 1: ICR = Insurance company reputation; ISP = Insurance service performance; PA = Positive Affect; NA = Negative Affect. Annotation 2: Goodness-of-fit statistics: χ 2  = 272.219; d f  = 113; χ 2 /d f  = 2.409; p  = 0.00; CFI = 0.967; IFI = 0.968, TLI = 0.958; RMSEA = 0.080. * p  < 0.05; ** p  < 0.01. Annotation 3: Dotted lines indicate an insignificant impact

The hypothesized relationships were assessed. As expected, the insurance company reputation has a significant positive influence on positive affect ( β  = 0.337; p  < 0.01) and a significant negative infleunce on negative affect ( β  = − 0.223; p  < 0.01), thus hypothesis 1 as well as hypothesis 2 were accepted. Correspondingly, the results specified that health insurance service performance revealed a significantly positive infleunce on positive affect ( β  = 0.535; p  < 0.01) and a significant negative infleunce on negative affect ( β  = − 0.396; p  < 0.01) so the hypotheses 3 and 4 were accepted as the insurance claim settlement experience lead to either positive or negative affect the policy renewal behavior in the Indian context (Khare et al. 2012 ). The hypothesized impact of positive and negative affects on customer retention was also evaluated. As estimated, positive affect significantly influenced the growing trend for customer retention ( β  = 0.638; p  < 0.01) which ultimately leads to health insurance policy renewal without a fail and negative affect significantly influenced the reducing trend for customer retention ( β  = − 0.125; p  < 0.01) and the health insurance policy renewal get hold by the customer owing to either a unfavorable response from the company or due to negative perception about the company, the study result is in tandom with previous research on customer retention and insurance claim settlement (Smith et al. 2000 ). Hence, hypothesis 5 as well as hypothesis 6 were accepted.

We tested the indirect influence of construct variables. As displayed in the Table 2 , good/bad insurance company reputation revealed a significant influence on customer retention through positive and negative affects ( β ICR-PAandNA-Customer Retention  = 0.242; p  < 0.01). High/low insurance service performance also puts forth a significant indirect influence on customer retention via positive and negative affects ( β ISP-PAandNA-Customer Retention  = 0.388; p  < 0.01). It means the high service performance leads to better company reputation hence the service performance indirectly has positive affect on customer retention, whereas, if the service performance is low it indirectly affects customer retention via negatively affecting the company reputation. Subsequently, the total effect of study variables was also tested. It was acknowledged that the positive affect ( β  = 0.638) comprised the highest total impact on customer retention, followed by high/low insurance service performance ( β  = 0.388), good/bad insurance company reputation ( β  = 0.242), and negative affect ( β  = − 0.127). Thus, we can ascertain the continous improvement and stable insurance service delivery (i.e., claim settlement) is the cornerstone for health insurance policy renewal in the Indian market (Kautish and Rastogi 2008 ; Khare et al. 2012 ).

Invariance model analyses

Prior to structural invariance, measurement invariance was carried out invariance analyses for high customer inertia ( n  = 82) and low customer inertia ( n  = 146) groups. To test customer inertia, we first compared the samples, and then merged these samples to test high and low inertia groups. Initially, a grouping was confirmed based on the findings of K-means cluster exploration. This cluster analysis is considered to be suitable when grouping survey contributors’ responses into a certain quantity of clusters ( K ) having comparable characteristics (Gough and Sozou 2005 ; Milner and Rosenstreich 2013 ). We fit the configural (Model 1), and metric (Model 2) models, and compared the relative fit of each successively constrained model (Satorra and Bentler 2001 ). The baseline or configural model (Model 1) fit statistics for high customer inertia was as follows: RMSEA = 0.06 (0.03, 0.08), CFI = 0.95, and TLI = 0.96 and for low customer inertia was as follows: RMSEA = 0.05 (0.04, 0.08), CFI = 0.96, and TLI = 0.97. These indicate adequate fit compared to predictable values for a decent fitting model (RMSEA < 0.08; TLI > 0.95; CFI > 0.95). The metric model (Model 2) constrained loadings to be the same across samples and it did not have a significantly inferior fit ( χ 2 diff (7) = 5.07, p  < 0.65). The global fit statistics for Model 2 showed a slight improvement and remained satisfactory.

Furthermore, in order to calculate the moderating influence of customer inertia, a test for metric invariance was executed (Hair et al. 2014 , p. 847). Afterward, a baseline model was generated. As displayed in the Table 4 , the baseline model was found to have a satisfactory model fit ( χ 2  = 452.459; d f  = 235, χ 2 /d f  = 2.048; p  < 0.001, CFI = 0.953; IFI = 0.954; TLI = 0.948; RMSEA = 0.065). Then, this baseline model was matched with nested models where one specific path of interest is constrained to be equal. The findings showed that there was no statistical difference in the hypothesized relationship between positive affect and customer retention (∆ χ 2 [1] = 3.540; p  > 0.05) which substantiate the previous results (Al-Weshah 2017 ). Hence, the hypothesis 7a was not accepted. Moreover, it revealed a significant difference in the relationship between negative affect and customer retention (∆ χ 2 [1] = 4.457; p  < 0.05) so the hypothesis 7b was accepted.

As per the recommendations of Hayes (2013), the study employed PROCESS Macro Model IV to perform the mediation analysis to understand the indirect effects of negative affect (NA) and positive affect (PA) on the association between insurance service performance (ISP) and insurance company reputation (ICR) with customer retention (CR) for health insurance policy renewal. The data analysis shows that NA and PA partially mediated the link between ICR with CR: PE Direct  = 0.19, 95% confidence interval (CI) (CI 0.0868–0.2174); PE Indirect Effect (NA)  = 0.17, BCa 95% (CI 0.1041–0.2215); PE Indirect Effect (PA)  = 0.10, BCa 95% (CI 0.2157–0.3315). Correspondingly, both NA and PA also reasonably mediated the relationship between ISP and CR: PE Direct  = 0.29, 95% (CI 0.1814–0.3238); PE Indirect Effect (NA)  = 0.16, BCa 95% (CI 0.1031–0.1924); PE Indirect Effect (PA)  = 0.11, BCa 95% (CI 0.1120–0.1427). The nonappearance of zeros in the bootstrapped CIs substantiates the existence of partial mediation for all the aforementioned relationships.

Theoretical contributions

The current study adds to the existing service literature by examining the influence of customer inertia, company reputation, affect, insurance service quality on customer retention in the context of insurance services. Research suggests that “desire to get satisfaction” may influence “behavioral intentions”. Nevertheless, there was a dearth of studies on customer inertia in the health insurance sector especially in emerging market context. The proposed theoretical model established the contribution of insurance company reputation, its service performance, positive and negative affect and customer inertia on customer retention in the health insurance sector. The convoluted associations of these factors have never been explored in the insurance segment. The study indicated the relevance of firm reputation, service performance on positive and negative emotion/affect and customer retention. In addition, positive and negative affect were key mediators and customer inertia acted as a moderator in the study. The findings are in tandem with the research of White and Yanamandram ( 2004 ) where they posited that awareness of customer inertia may help financial service institutions to prevent customer defections. It helps in identifying past behavior patterns which predict customers’ switching and complaining behavior. Thus, customer retention rate can improve by overcoming avoidable circumstances during the moment of truth for service performances.

The study considered the effect of customer inertia as moderator in case of positive affect as well as negative affect. Additionally, the study contributes to the existing knowledge by providing limited support for other theories attempt to explain health insurance behavior in the non-western context. Particularly, it demonstrates the support for expectation disconfirmation theory (EDT), economic cost models of consumer behavior and exploratory buyer behavior in explaining the marketing relationships that took place among Indian consumers and health insurance service providers in the market. It asserts the usability of these theories that had been developed in western culture to be used in developing country context.

Therefore, it presents a meaningful understanding of the role of inertia in influencing health insurance service switching behavior. The test for metric invariance exhibited that both high as well as low customer inertia significantly moderated the relationship between negative affect and customer retention. Specifically, the impact of negative affect on customer retention in the low customer inertia group ( β NA-customer retention  = − 0.186, p  < 0.05) was significantly higher than in the high customer inertia group ( β NA-customer retention  = 0.054, p  > 0.05) (∆ χ 2 [1] = 4.457, p  < 0.05). That is, when policy holders perceive that, compared with the existing health plan, there are many other health plans that fulfill their specific needs and that they are too indifferent to change. The indifference to find a better financial alternative determines policy holders desire to continue with service provider, even they have negative affective service experiences. Insurance companies should improve service quality elements (i.e., number of hospital empanelment, policy coverage, across the country accessibility of the plan, cashless options, serviceability) so that switching to other service provider does not appear relevant. Such endeavors would ultimately support existing policy holders believe that seeking a better insurance plan would be cumbersome in terms of time and effort consuming, thereby encouraging them to stay with the current insurance company.

The descriptive statistical analysis presented that respondent within high customer inertia group positively unveiled a higher willingness to remain with the current insurance company (mean = 6.488) than those within the low customer inertia group (mean = 5.627). Furthermore, it is interesting to note that more male respondents (51.8%) than female respondents (48.2%) were within the high customer inertia group. Respondents within the low customer inertia group were more highly educated (77.4%) with university/graduate degrees than those within high customer inertia group (70.6%) with university/graduate degrees. So in order to confirm that whether the male respondents (high customer inertia) and highly educated respondents (low customer inertia) express inertia differently, we conducted the independent sample t tests. The results revealed that the t values were significant at the 0.01 level. The findings related to difference between two customer inertia groups, could help health insurance companies to make distinctive marketing strategies for the retention of policyholders with high and low customer inertia, respectively. Any form of customer inertia is not good for the policy holders as well as health insurance companies thus consumer awareness campaigns should be launched in order to facilitate better informed decisions regarding policy renewal.

Managerial implications

In a firmly competitive environment like insurance, gaining repeat business by retaining existing customers seems to be a requirement for any financial services company survival (Chen and Wang 2009 ; Williams and Williams 2015 ). In this research, we tried to develop a better understanding of the customer retention process. The theoretical framework developed in an insurance setting adequately and successfully explained a total variance in customer retention. Customer retention has long been considered as a powerful force influencing insurance service business success (Ansell et al. 2007 ). The findings would facilitate insurance companies in building retention strategies by trying to identify inertia or disinterest factors. It is also important to distinguish inertia from satisfaction. Insurance firms should try to interact with their customers in order to understand their reasons for continuing the firm. The behavioral factors need to differentiated from service-related factors. This would help in focusing on improving the service quality elements.

A close investigation was conducted to comprehend the comparative total impact of insurance company reputation, insurance service performance, and positive and negative affect on customer retention. As described earlier, the magnitude of standardized coefficients, positive affect emerged as the most critical contributor to customer retention in the insurance sector, followed by the insurance service performance, company reputation, and negative affect at the last. Distinguishing this prominent role of positive affect, insurance marketing practitioners must employ potential monetary and non-monetary means to elicit customers’ positive affective experiences. Ramamoorthy et al.’s ( 2018 ) indicated that service quality (e.g., reliability and responsiveness) are key factors that effectively evoke insurance customers’ affective/satisfaction evaluation. The role of such factors on positive behavioral intentions becomes more vital for repeat customers compared to first-time insurance purchasers. It would be important for insurance companies to focus on the service quality elements to improve customers’ positive affect, which would eventually lead to the lift in customer retention.

The loss aversion put together into the prospect theory postulates that the outcome of one’s perceived losses tends to be more eminent than the impact of his/her perceived gains (Einhorn and Hogarth 1981 ; Slovic et al. 1977 ). In other words, insurance customers’ negative affect derived from unpleasant or unfavorable service experiences would have a greater impact on retention than positive affect. Thus, in the highly subject to solicited business like health insurance, the companies cannot afford to take risk as far as the service performance is concerned. In contrast, our empirical results from the structural analysis specified the greater impact of positive affect on customer retention than negative affect, which may be ascribed to the multidimensional nature of insurance service (Jayasimha and Murugaiah 2008 ). Although not in line with Einhorn and Hogarth ( 1981 ), the findings are in tandem with research assertion that positive experiences under certain situations/conditions generate a stronger customer response than negative experiences (Kautish and Rastogi 2008 ; Oliver 1992 ; Westbrook and Oliver 1991 ). The recent developments in building relationships and delivering quality service has focused on the beneficial effects of customer retention. It is often argued that customer attraction costs are higher than retention costs in services (Eisingerich and Bell 2006 ; Ennew and Binks 1996 ). Thus, marketing strategies focused on customer inertia is unlikely to be sustainable in the long term which can be explored by incorporating the marketing analytics framework in the model. However, firms having customers who feel succumbed to other providers are likely to lead to negative word of mouth, less ability to cross-sell, lower acceptance of new products, and other negative outcomes associated with customer defection (Colgate and Lang 2001 , p. 343).

Limitations and future research imperatives

Alike any other service research, the present study also holds some limitations that propose future research avenues. First, the research has not examined the impact of demographic factors, e.g., age, income, occupation, and marital status in the conceptual model which may have shown significant differences in the marketing analytics-oriented results. The price comparison has been highlighted as significant factor for service organizations in view of ‘de-locating’ their offerings by utilizing the Internet facility (East et al. 2012 ). Thus, testing the role of these factors in the marketing model statistics and analytics would be a critical extension of the present study. Second, the data were collected from health policy holders from one insurance company through questionnaire-based survey only. While it has the advantage to get the information from the real customers at the same time if we have included policy holders from different companies it would have provided a better comparative assessment of their insurance service experiences. Future research may collect consumer choice-based data from a few more insurance companies and then compare the marketing relationships of the research variables. Indian life and medical insurance is governed by inertia not only because of difficulty in evaluating service dimensions, but also by lack of awareness and commitment toward health issues. This behavior is further augmented by poor focus of government toward health services, inability to provide medical facilities and medical staff in the small cities and villages, and regular medical checkups being considered as futile practice. The perception toward investment on regular health checkups and monitoring systems are unheard of in Indian context. Thus, inertia maybe seen as a resultant of social, cultural, and economic conditions that play a critical role in conditioning people’s behavior toward health insurance. Future study may be directed to understand the role of these factors along with the psychological factors on consumers’ attitude toward health insurance. This study is restricted to the exploration of the influence of insurance company reputation, insurance service performance, positive and negative affects, and their impact on customer retention with moderation of customer inertia. In the future, in light of the differentiated insurance services offered by the companies, the reconsideration of the relationships between the aforesaid constructs is a topic worthy to be researched. Lastly, it is observable that this research employs cross-sectional data to test the hypotheses; thus, longitudinal data may provide better understanding of the entire health insurance market. In addition, in the future studies, the health insurance policy implications with respect to COVID-19 pandemic situation can also be explored keeping in mind the customer inertia and customer retention aspects.

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Acknowledgements

In addition, the authors would like to express their gratitude toward Institute of Management, Nirma University, Ahmedabad, Gujarat for administrative support and special thanks to Prof. M. Mallikarjun for providing motivation for the study.

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Pradeep Kautish

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Arpita Khare

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Kautish, P., Khare, A. & Sharma, R. Health insurance policy renewal: an exploration of reputation, performance, and affect to understand customer inertia. J Market Anal 10 , 261–278 (2022). https://doi.org/10.1057/s41270-021-00134-7

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