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Billing and Software Case Studies

Medical case study categories.

Browse a sample of over 512 case studies we have performed in the last 36 months. Our internal team, along with third party independent analysts, utilizes in-process metrics and science-driven case studies to evaluate the processes, systems, workflows, and products we produce. They establish data sets and averages as well as ensure the application of the data across practice sizes and specialties.

Building great software is just a starting point when continual refinement can generate measurable improvements over time.

The data ultimately speaks for itself so here at Claimocity we have a team that is dedicated to gathering and presenting the data in case studies, intake surveys, product comparisons, bench-marking, scientific analyses, measurements, time studies, A/R evaluations, and deep dives into impacts on productivity, efficiency, and accuracy.

Our goal comes down to constant improvement around three core key areas: revenue efficiency, time efficiency, and effective decision making. Everything we do from product development to data gathering is to give practices and providers more time, money, and relevant information.

Revenue Studies

Hospitalist Charge Capture Process

Coding Efficiency

Lost and Missing Claims

Performance per Encounter

Impact Studies

New Client A/R Audit

Paper Billing Transition

Satisfaction Studies

Physician Happiness Ratings

Time Studies

Code Assist Time Study

Mobile Charge Capture by Encounter Type

Administrative Billing Time Costs

Value Studies

Stand Alone Charge Capture Options

ICD-10 Smart Directory

New Client Perceived Value

“In 2017, we were generating 2.3 million in total revenue for our practice. By 2018, we were above 3 million and by 2019 we were a hair over 3.9 million. By the end of 2020 we are now projected to hit over 5 million.”

-Dr. Farzin Farhan, Physical Medicine & Rehabilitation, Founder

“Within the first year we saw increases in every notable metric and by the end of the first twelve months, we were setting the best marks for our practice over the last ten years.”

-Uzma Zafar M.D., Psychiatry

“Our executive team approved participation in a voluntary two year time and revenue study in order to evaluate the quantitative and qualitative value compared to the numbers we were generating with the prior software in order to justify the switch. Within 24 months we had a 41.6% net revenue rate increase as well as an average of 38.8 hour savings per month per physician across our practice, which was a remarkable result.”

Integrity Driven Results

Our medical billing case studies and internal evaluative tools are designed to eliminate bias because our internal resource allocations are dependent upon the results of these studies. We use physician performance evaluations and segmented revenue cycle benchmarks to determine the priority and allocation percentages of the budget as well as where to spend time, energy, and effort. Any bias skews the results and generates faulty allocations of internal resources, causing us to focus on the wrong areas, waste time and money, and poorly prioritize our plans of action.

We do everything we can to hinder the results of every study to ensure that they are not a result of aberrations, poor sample sizes, or for some reason not truly representative of the larger population. Any gray areas are attacked by opposition advocate groups within the organization whose purpose is to argue the contrary position, pick apart the science, find inaccuracies, and point out flaws in the logic.

We do all this because we have developed a product that can truly help Hospitalists save time and maximize revenue and our 99.6% customer retention rate is the most important indicator of our success thus far.

Case Study: Physician Happiness and Work-Life Balance

Core Question: Why are the hospital and facility-based physicians using Claimocity software and/or RCM services reporting 30%+ higher satisfaction and work life balance scores than the national average?

Summary of Findings: The 2023 Medscape Report on Physician Happiness shows a remarkable drop in provider satisfaction and work life balance. Yet here at Claimocity, our internal random sample surveys of both practice owners and providers shows a consistent increase in physician satisfaction and work life balance. So we decided to take a closer look and see if we can determine why our metrics are significantly higher than the national average. Read more about our conclusions in the full study.

Written 2/7/23

Revenue study: hospitalist charge capture process.

Core Question: Does accelerated charge capture save time and make doctors more money?

Summary of Findings: Switching to just the Claimocity accelerated charge capture, a native component of the full-service end-to-end mobile practice management and billing app, resulted in a 9% increase in time efficiency and 11% increase in revenue efficiency for an Internal Medicine hospitalist group in Florida. This equated to an extra half hour per day per provider and an extra 41K for the practice over 2 months.

Updated 12/14/2022

Impact study: new client a/r audit and revenue changes.

Core Question: Is there an initial dip in revenue when switching to Claimocity?

Summary of Findings: A common concern among new clients is what level of initial revenue dip to expect when switching from their current billing, A/R, and/or charge capture software to the all-in-one Claimocity solution. This study follows a single practitioner through the process and finds that instead of seeing a dip, the move from a stand alone legacy software to a full-service option generated a 28% increase in monthly revenue. As a powerful secondary benefit, this case study exposes that moving from a one dimensional billing system to an intelligent end-to-end process uncovers and triages any live claims stuck in bottlenecks within the former A/R process, converting them to additional total revenue. In this case study an extra 52k was uncovered and converted.

Updated 1/11/23

Impact study: transitioning from paper billing.

Core Question: Is there an initial dip in productivity when switching from paper billing?

Summary of Findings: A common misconception about transitioning from a paper-billing system to the Claimocity full service software with mobile charge capture is an expected initial loss of productivity and time during the implementation and learning process. A pulmonology practice in California was expecting and preparing for between a quarter and a third loss of encounters to account for the transition but the actual end result was an increase in workflow productivity as measured by total number of encounters on a practice level. In spite of the change over, total encounters rose by 7% within the first 14 days and by 13% within the first 90 days. The time efficiency of the app and charge capture more than offset the learning curve, additional support needs, and transition issues.

Updated 10/8/22

Value study: stand alone charge captures.

Core Question: Do stand alone charge capture options create the same value as end-to-end billing?

Summary of Findings: Stand-alone charge capture software options such as pMD, MDCoder, Ingenious Med, PatientKeeper, SwiftPay MD, NueMD, Medaptus, DrRounds, and others saturate the mobile medical app market. Often referred to as one dimensional or flat software options because of their singular and specialized focus on the charge capture segment of the RCM process, these software options perform very specific services efficiently (measured by time efficiency) but the associated cost comes in the form of lower revenue efficiency. As a stand-alone service they are missing the end to end revenue cycle intelligence necessary to drive higher efficiency models and larger bottom line revenue totals. The piecemeal approach to billing generates gray area sticky points and A/R bottlenecks that slip under the radar for busy doctors and practices who do not even realize there is an issue. Stand-alone charge capture software users lose an average of 10-16% of revenue to expired claims that needed a higher level of end to end billing expertise to finesse or push these charges through to the payout.

Updated 9/6/22

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Revenue Study: Coding Efficiency on Total Revenue

Core Question: What is the level of impact of coding efficiency on total revenue?

Summary of Findings: The stakes are high when it comes to coding efficiency. Even a 1-2% improvement can have up to a 20% impact on revenue. Unfortunately, this works both ways and a negative efficiency shift can leave hundreds of thousands of dollars on the table. The exact amount of variance and impact depends on the specialty, geographic region, patient demographics, insurance types, practice size, and other variables, but in every case study we found two results. Once that higher coding efficiency created higher total revenue and lower risk levels, and two, that the majority of coding inefficiencies stemmed from patterns created by a central set of encounters with unclear coding guideposts that cause physicians to either play it safe and under code (losing earned revenue) or over estimate the value of that encounter and over code (setting up audit risks and repercussions).

Updated 2/6/23

Time study: code assist.

Core Question: What is the time savings value of using Code Assist?

Summary of Findings: Using code assist during complex coding encounters reduces the time cost of coding to 2-3 seconds (from an average of 1-3 minutes) per encounter used, reducing workload stress levels and enabling a seamless workflow transition into the next encounter. Additionally, since the majority of under and over coding efficiency issues occur during complicated or unclear encounters, by reducing the risk of under or over coding in the keystone gray area encounters (with unclear coding situations) a physician’s overall coding efficiency and benchmarks significantly increase over time, helping to eliminate the revenue losses associated with consistent under coding and legal/financial audit risks associated with consistently over coding.

Updated 7/26/22

Time study: mobile charge capture by encounter type.

Core Question: Do mobile charge capture claims hold up under a closer look?

Summary of Findings: Though the aggregate data oversimplifies the time stamps of the accelerated charge capture feature to an average of 9 seconds per encounter, there is an important distinction between initial and follow up encounters. The results show that charge capture on initial encounters averages 15 seconds while that of follow up encounters averages less than 2 seconds. Meanwhile, stand alone charge capture alternatives advertise charge capture times as low as 2-7 seconds but a breakdown analysis shows that as a group they average 32 seconds per initial encounter and 18 seconds for follow up encounters, significantly higher than stated. In fact, the 2-7 second average was only applicable in 12% of encounters, with the other 88% requiring a much heftier time commitment than expected.

Updated 5/22/22

Value study: icd-10 smart directory.

Core Question: Does a streamlined ICD-10 directory help streamline the coding process?

Summary of Findings: Using a set of intake and exit surveys, new clients were asked their comfort levels, familiarity, and time expenditures when it comes to coding. At the end of the survey, having used the ICD-10 directory the entire time, they were asked to rate the value and the results show that it was positively received and the providers felt that it streamlined the coding phase of billing by 5-10%.

Published 3/18/20

Revenue study: performance per encounter on total revenue.

Core Question: What is the impact of revenue per encounter on total revenue for an efficiently run practice?

Summary of Findings: With no other changes to the practice except the software used, the performance per encounter average value rose 22.07% from the start date to the end date, resulting in a significant increase in total net revenue. Switching to Claimocity generated just under a one-fifth increase in total revenue as a function of performance per encounter over 3 years for a practice that felt they were running at optimal revenue efficiency. Focusing on revenue per encounter was highly effective at raising overall bottom line financial growth even on practices operating efficiently.

Updated 1/11/22

Revenue study: claimocity impact on lost/missing claims.

Core Question: Does Claimocity reduce lost/missing claims for practices already using mobile charge capture?

Summary of Findings: In year one, with no other changes to the practice except the accelerated mobile charge capture aspect of the revenue cycle management portion of the software used, the number of total payable claims rose by 2.4% from found claims, correlating with a 3.76% increase in total net revenue. Switching to Claimocity for revenue cycle management (excluding PM functionalities) generated a 3.76% increase in total revenue as a function of a 2.4% increase in found claims.

Updated 11/25/22

Medical billing case study results.

Not only does Claimocity increase efficiency by reducing the time cost of administrative billing burdens but it generates higher performance per encounter metrics and creates significant jumps to the bottom line net revenue for the group.

We are speaking about significant improvements, and the time cost reductions alone mean Claimocity is freeing up an average of 165.71 minutes per week, generating a measurable opportunity value for that new time. If your billing time costs are typically above average than the savings become even more significant. And all of this efficiency happens while you are generating higher financial value per visit and feeling more comfortable and confident in the process.

Summary of Findings:

  • +99% increase in understanding of claim payment values.
  • +22.07% increase in performance per encounter revenue.
  • -89.5% reduction in concern about missing charges
  • +89% level of comfort with the charge capture process.
  • +62% better understanding of the revenue cycle nuances.
  • -92.06% average billing admin time costs per patient.
  • -2.4% reduction in lost/missing claims (+3.6% net revenue).
  • +87.5% average increase in reported TPVI ratings.
  • +82% increase in access to physician performance insights.
  • +22% increases to annual net revenue over 3 years.

Value Study: Total Perceived Value Index Survey (TPVI)

Core Question: What do new clients think of Claimocity vs their previous software?

Summary of Findings: Using Claimocity for 90 days increased the average TPVI (total perceived value index) survey scores of Clinicians who work in acute care or sub-acute step down facilities by 87.5% – including self-reported feelings of greater confidence, higher efficiency, and significantly stronger comfort levels. Areas of improvement included understanding of claim values, access to physician performance insights, the charge capture process, the nuances of proper coding, billing accuracy. Physicians also reported reductions in charge lag, fewer concerns about missing charges, and less confusion about the billing process as a whole.

Published 2/1/20

Time study: administrative billing time costs.

Core Question: How does Claimocity impact the daily administrative burden of billing for physicians?

Summary of Findings: The time study revealed that the Claimocity software reduced the administrative burden of the billing work process time cost to 3 minutes per 20 patients on average, which is an average of nine seconds per patient, or a 92.06% decrease from the national average of 1.89 minutes.

Published 12/12/19

Time to Seeing Tangible RCM Billing Increase

Annual Revenue Increase Per Provider

47-58 Hours

Monthly Time Saved Per Provider

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Improved efficiency of coding systems with health information technology

Jinhyung lee.

1 Department of Economics, and Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University College of Economics, Seoul, Republic of Korea

Jae-Young Choi

2 Department of Business Administration, Hallym University College of Business, 1 Hallymdaehak-gil, Chuncheon, Gangwon-do 200-702 Republic of Korea

Associated Data

The datasets analyzed in the current study are available from the California government’s Office of Statewide Health Planning and Development (OSHPD).

Code is available from the authors upon reasonable request.

This study aimed to investigate the impact of health information technology (IT) on the Case Mix Index (CMI). This study was a retrospective cohort study using hospital financial data from the Office of Statewide Health Planning and Development (OSHPD) in California. A total of 309 unique hospitals were included in the study for 7 years, from 2009 to 2015, resulting in 2,135 hospital observations. The effects of health information technology (IT) on the Case Mix Index (CMI) was evaluated using dynamic panel data analysis to control endogeneity issues. This study found that more health IT adoption could lead to a lower CMI by improving coding systems. Policy makers, researchers, and healthcare providers must be cautious when interpreting the effect of health IT on the CMI. To encourage the adoption of health IT, the cost savings and reimbursement reductions resulting from health IT adoption should be compared. If any profit loss occurs (i.e., the cost savings is less than reimbursement reduction), more incentives should be provided to healthcare providers.

Introduction

The Federal government of the United States (U.S.) has aggressively imposed use of the electronic health record (EHR) upon healthcare organizations by passing the American Recovery and Reinvestment Act of 2009. This act introduced both economic incentives and punitive actions. The use of EHRs can enhance coordinated care, reduce medical errors, and improve quality and patient safety 1 – 4 . Moreover, EHRs can enhance the accuracy of coding and the efficiency of reimbursement mechanisms 5 . EHRs may better document comorbid conditions to justify higher reimbursement rates from insurers to providers. Many insurers pay higher reimbursement rates for insured patients with a higher severity of illness (SOI) or with multiple comorbidities; thus, electronic documentation may improve charge capture 6 . Improved accuracy and efficiency of coding and billing might be correlated with the Case Mix Index (CMI), which is a measure to assess the clinical complexity, diversity, and the use of resources necessary to treat hospitalized patients.

It has been speculated that EHR systems might be misused either by manipulating data input or by processing data inappropriately to upcode claims 7 . Upcoding results from intentionally inflating the overall cost of care born by the insurer and society. Previous research on the relationship between the EHR adoption and coding behavior has shown mixed results 8 – 12 . Li 8 and Ganju, et al. 9 used the hospital-level CMI as the payment measure and found that health IT system adoption led to inflated reimbursement. Singh et al. 10 showed EHRs facilitated the upcoding of evaluation and management codes in a large ophthalmologic practice. However, Adler-Milstein and Jha 11 found no significant difference in hospital payments per discharge or in the change of a hospital’s CMI between IT adopters and non-adopters. They concluded that hospitals were not systematically using EHR systems to improve coding, thereby driving up costs. Park et al. 12 examined the effect of health IT on the CMI and found that health IT was positively associated with the CMI, indicating that increased IT adoption could lead to a higher CMI or billing though diagnosis related group (DRG) up-coding. However, previous studies either specified limited functions such as EMR and computerized physician order entry (CPOE) among various health IT systems or used outdated datasets collected before the HITECH Act. The aim of this study is to examine the effect of health IT investment on the CMI by utilizing longitudinal data from the California Office of Statewide Health Planning and Development (OSHPD) from 2009 to 2015. We applied dynamic panel data analysis to control endogeneity issues.

We used hospital financial data from the California government’s Office of Statewide Health Planning and Development (OSHPD) from 2009 to 2015. The data used was collected after the HITECH Act passed in 2009. The California hospital financial data provide characteristics about the organization in addition to financial information. These data have been used in some healthcare and economic studies 12 , 13 . The study sample was an unbalanced panel of hospitals with a total of 309 unique hospitals participated, for a total of 2,135 hospital year observations. Thus, the unit of analysis is hospital year.

Dependent variable

For purposes of the study, the CMI was used as a dependent variable, which is the relative value assigned to the DRG of patients in a medical care environment. The CMI was applied to determine the allocation of resources to care for the patient groups 14 . To calculate the CMI, each patient treatment record was assigned to a Medicare Severity-DRG, based on patient characteristics. The Medicare Severity-DRG represents the consumption of national average hospital resources by patient group, relative to that of all patients 15 .

Independent variable

Assets (non-IT) include Current Assets, Property, Plant and Equipment, Intangible Assets, Assets whose use is limited, and Other Assets. Labor (non-IT) is defined as the total conventional Salaries, Wages, Employee Benefits, and Professional Fees excluding any cost related to IT labor.

As a key explanatory variable, health IT expenditures are measured as a dollar amount and extracted from hospitals’ trial balance worksheets and supplemental information sheets. IT expenditures include IT capital-related costs (i.e., physical capital, purchased services, and lease/rental and other direct expenditures) as well as IT labor-related costs such as salaries and wages, employee benefits, and professional fees 14 .

In contrast with data sets used in previous studies, the OSHPD data did not provide the adoption status of each IT system. In order to examine the validity of continuous health IT measures, we examined the relationship between discrete measures of each health IT system (EMR, CPOE, PACS, patient billing, order entry, radiology information systems, clinical documentation, etc.) and health IT measures and found that the measure of IT system adoption was associated with IT cost. Thus, it showed IT investment could be the proxy of IT system adoption.

Statistical analysis

We employed dynamic panel data (DPD) specifications to consistently estimate parameters under less restrictive assumptions than ordinary least square (OLS) and fixed effect (FE) panel data models. When serial correlation is detected, information exists in the error term instead of the estimated part of the model. In this case, the problem cannot be solved through estimation with robust standard errors, but must be investigated further by specifying and estimating a dynamic model. The DPD approach can simultaneously estimate the equation of interest using both levels and differences specifications where appropriate lags of the levels and differenced variables can be used as instruments. This simultaneous estimation strategy results in lower finite sample bias and increased precision. Thus, dynamic panel data analysis was adopted to examine the effect of IT on the CMI 15 . First we examined the model 1.

Here, i is hospital, t year. y it is the log of CMI, y i t - 1 is the lagged term of the log of CMI, l it is the log of total labor, k it is the log of total capital, I T it is the log of information technology investment, t is the year effect, and α i is the hospital fixed effect. In the equation above, θ l , θ k , and γ are the input elasticities for each respective input.

Then, we examined the interaction effect of IT investment and Meaningful Use (MU) in model 2.

MU is defined as the stage 1, 2 and 3. MU stage 1 (MU1) was coded 1 before 2010 and 0 otherwise. Meaningful use stage 2 (MU2) was coded 1 between 2011 and 2012 and 0 otherwise. Meaningful use stage 3 (MU3) was coded 1 after 2012 and 0 otherwise. All analyses were conducted using Stata version 14 (Stata Corp College Station, Texas, USA) ( https://www.stata.com/ ).

Tables ​ Tables1 1 and ​ and2 2 shows the descriptive statistics for the hospital financial variables and characteristics used. The average CMI was 1.26 and it increased by 1.7% annually over 7 years. The average labor cost was $196 million and it increased by 2.9% annually, and average assets were $301 million and increased by 6.7% annually in the same timeframe. Significantly, the IT investment almost doubled from $11.07 million to $20.7 million over the seven-year period. For hospital characteristics, average licensed bed was 246. The hospitals were more likely to be not-for-profit hospitals (61.1%) and less likely to be teaching hospitals (10.3%). Parameters of the DPD model are presented in Table ​ Table3. 3 . Serial correlation specification tests indicated that second differences removed the serial correlation and were used in the estimation. As the model is over-identified, the Hansen test for instrument validity was employed. The Hanson test p‐value was 0.41 indicating that the over-identification restrictions were not rejected.

Descriptive statistics for hospital financial variables and characteristics (unit: hospital year).

Descriptive Statistics for financial variables across year (unit: hospital year).

DPD regression results: a sample of 2,135 pooled observations representing 309 unique acute care hospitals in California operating between 2009 and 2015.

*p < 0.1, **p < 0.05; ***p < 0.01.

The DPD estimates indicate that IT was negatively associated with the CMI to a marginal extent ( p  < 0.1). For example, in Model 1, the CMI decreased by 0.09% when IT increased by 10%. While total assets were positively associated with the CMI, total labor was not significant. Additionally, The HITECH Act authorized up to $27 billion for an EMR incentive program over 10 years. The HITECH Act set meaningful use of interoperable EHR adoption in the health care system. In our sample, we examined the effect of meaningful use and the interaction of meaningful use and IT cost on the CMI in Model 2. However, we could not find the significant effect of meaningful use stage and interaction of meaningful use stage and IT cost. Nevertheless, the coefficients for IT costs and assets are similar to those of Model 1.

Recently, increasing concerns have emerged that the adoption of IT systems is likely to make it easier for providers to change patients' billing codes, and this could contribute to rising health expenditures and have an extensive impact on the healthcare industry. Further, it could negatively impact the data integrity and the quality of care if the coding system does not represent actual risk-adjusted quality measures. Overcoming methodological limitations in previous research, we found that health IT was inversely associated with patient severity of illness measured by the CMI, although the magnitude of the effect was relatively minor. Findings from this study are consistent with prior studies reporting lack of evidence of upcoding behavior arising from EHR adoption.

The modest inverse association of health IT and the CMI may imply that hospitals implementing EHR systems seem to selectively focus on certain complex and important conditions utilizing advanced technologies such as computer-assisted coding (CAC) tools combined with advanced natural language processing (NLP) technology to accurately document the severity of illness. The American Health Information Management Association (AHIMA) defines CAC as “the use of computer software that automatically generates a set of medical codes for review, validation, and use based upon clinical documentation” 16 . By automatically analyzing electronic documentation, CAC and NLP technology more accurately and completely identify major complications and comorbid conditions that impact the severity of illness than relying on manual coding. Previous studies reported that CAC tools with fully implemented EHR systems improve clinical coding accuracy due to greater consistency and improved capture in patient complexity level 17 , 18 . Meanwhile, hospitals seem to respond to Medicare and other federal and state policies. A recent study revealed that HITECH incentives were associated with a modest increase in the measured severity of illness determined by the number of condition categories from secondary discharge diagnosis codes 19 . Interestingly, the increase in the measured severity of illness associated incentives for health IT were concentrated among diagnoses targeted under the Hospital Readmissions Reduction Program (that is, acute myocardial infarction, heart failure, and pneumonia) 19 . This study reported an opposite pattern, decrease in the measured severity of illness, for untargeted conditions (all other conditions). While many insurers pay higher reimbursement rates for insured patients with higher severities or with multiple comorbidities, a higher CMI generally has a negative effect on hospital profitability 20 , implying that not all healthcare providers have incentive to maximize their CMI by intentionally and systematically upcoding claims. Consequently, the modest inverse association of health IT with the CMI we observed in the current study is less likely to support the evidence of fraudulent up-coding and more likely reflects better documentation.

It is worth mentioning that other factors were found to be associated with the CMI. Assets were found to have a positive effect on the CMI, implying that hospitals with larger assets might induce more severely ill patients. However, labor did not make any impact on the CMI. Additionally, MU 2 itself had no effect on the CMI. The Federal government set aside $27 billion for an incentive program that encourages hospitals and providers to adopt EMR. To receive these funds, providers must do more than simply purchase an EHR system. That is, they are required to show that they have achieved "meaningful use" of that system in terms of improving quality to receive the incentive. Thus, healthcare organizations needed to prepare for or begin IT investment before implementation of the HITECH Act. It could thus be interpreted that the HITECH Act itself could be a major factor in stimulating IT investment, although it is not directly associated with the CMI.

There is a limitation of the current study. The OSHPD database analyzed in the current work contains data only from California hospitals, so external validity to hospitals in other states in the United States and to hospitals in other countries is limited. Confirmation of these findings in other large administrative datasets in other geographic areas both within and beyond the United States is warranted. This is particularly important as our finding on association between CMI and health IT expenditure was marginally significant, and thus further studies with large dataset need to validate our findings.

With the enactment of the HITECH Act, health IT investments have increased significantly. However, the impact of health IT on the CMI has not been well examined. We investigated the effect of IT on the CMI using hospital data from 2009 to 2015. The DPD regression results showed that health IT investment significantly and negatively affected the CMI.

This study has important policy implications. Healthcare providers should remember that reimbursement payments from insurers could be reduced by adopting health IT systems. However, they could save cost from health IT adoption through better coordination of care, reduction of medical errors, and adverse drug events (ADEs). On the other hand, the cost savings and reimbursement payment reduction from healthcare providers could reduce overall healthcare expenditures. Thus, health care policy makers may push healthcare organizations to adopt more health IT. This could result in a conflict of interest between healthcare providers and the healthcare policy makers regarding the adoption of health IT. Thus, to encourage the adoption of health IT, the cost savings and reimbursement reductions resulting from health IT adoption should be compared. If any profit loss occurs (i.e. the cost savings is less than reimbursement reduction), more incentives should be provided to healthcare providers.

Author contributions

J.L. and J.C. designed the study; J.L. performed analyses and J.L. and J.C. wrote the main manuscript text paper; J.C. supervised the research. J.L. and J.C. reviewed the manuscript.

This research was supported by the Hallym University Research Fund (H20200621).

Data availability

Code availability, competing interests.

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Medical Coding Tutorial

  • What is Medical Coding?
  • What are Medical Coding Systems?
  • Importance of Medical Coding
  • What are Medical Coding Guidelines?
  • What is Medical Decision-Making Level?
  • What is ICD-10 coding?
  • What is CPT Coding?
  • What is HCPCS coding?
  • What are HIPAA Regulations?
  • What is Protected Health Information?
  • Anatomy and Physiology Fundamentals
  • Human Anatomy: An Overview of Major Body Systems, Organs, and Tissues
  • Physiology Basics: Understanding Body Functions for Accurate Medical Coding
  • ICD-10-CM Coding Overview
  • Code Structure of ICD-10-CM Coding
  • Coding Conventions of Medical Coding
  • Coding Guidelines for ICD-10-CM
  • How to navigate the Code Sets?

Medical Coding Case Studies: Practice Coding Real-World Scenarios

  • Tough Medical Coding Case Studies
  • Current Procedural Terminology Coding System
  • CPT Coding Categories
  • What is Chief Complaint?
  • What is History of the Present Illness?
  • What are Surgical Modifiers?
  • What are Bundled Procedures?
  • Evaluation and Management (E/M) Codes in CPT coding system
  • CPT Coding: Case Studies
  • EM Coding: Case Studies
  • Inpatient E/M coding Case Studies
  • Outpatient E/M Coding Case Studies
  • Difference between inpatient and outpatient E/M coding
  • What is the HCPCS Coding?
  • What is DMPEOS Coding?
  • HCPCS Overview
  • What is Medical Coding Compliance?
  • Ethical Considerations in Medical Coding
  • Medical Coding Compliance and Fraud
  • Health Insurance Portability and Accountability Act (HIPAA)
  • Medical Coding Auditing and Documentation
  • What Medical Billing?
  • Revenue Cycle Management in Medical Billing
  • Medical Coding and Reimbursement Relationship
  • Medical Coding Claim Submission
  • What are Medical Terminologies?
  • Medical Language Basics for Improved Coding Accuracy
  • Medical Terminologies: Prefixes, Suffixes, and Root Words

Module 1: Introduction to Medical Coding

Module 2: anatomy and physiology fundamentals, module 3: icd-10-cm coding, module 4: cpt coding, module 5: hcpcs level ii coding, module 6: medical coding compliance and ethics, module 7: medical billing and reimbursement, module 8: medical terminology, module 10: medical coding interview questions & answers, join our community on telegram, join the biggest community of pharma students and professionals..

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Below are practice medical coding study cases meant to provide you with some real-world case studies. Keep in mind that medical coding requires knowledge of the current coding systems, such as ICD-10-CM (International Classification of Diseases, 10th Revision, Clinical Modification) for diagnosis coding and CPT (Current Procedural Terminology) for procedure coding. 

Here are a few case studies for you to practice medical coding:

Case Study 1: Diagnosis Coding

Patient: John Smith

Age: 45 years

Chief Complaint: Severe abdominal pain and vomiting

Medical History: Hypertension, Diabetes Type 2

Assessment and Diagnosis: Acute pancreatitis due to gallstones

Case Study 2: Procedure Coding

Patient: Jane Doe

Age: 62 years

Procedure: Total knee replacement surgery (right knee)

Medical History: Osteoarthritis

Case Study 3: Inpatient Coding

Patient: Robert Johnson

Age: 70 years

Admission Diagnosis: Myocardial Infarction (Heart Attack)

Procedures: Percutaneous Coronary Intervention (PCI) with stent placement

Medical History: Hypertension, Hyperlipidemia, Diabetes Type 2

Case Study 4: Ambulatory Surgery Coding

Patient: Emily Adams

Age: 32 years

Procedure: Laparoscopic cholecystectomy (gallbladder removal)

Chief Complaint: Recurrent upper abdominal pain

Medical History: No significant medical history

Please code each case study using the appropriate coding system (ICD-10-CM for diagnoses and CPT for procedures). If you are unsure about any specific codes or guidelines, feel free to ask for clarification.

Remember, medical coding accuracy is crucial for proper billing, reimbursement, and healthcare data analysis. Double-check your codes and make sure they accurately reflect the information provided in each case study.

case studies for medical billing and coding

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Coding case study: Type 2 Diabetes follow-up

What you need to know about coding a follow-up appointment for Type 2 diabetes.

Coding case study: Type 2 Diabetes follow-up

In the medical billing and coding field, getting paid requires accurate documentation and selecting the correct codes. In our Coding Case Studies, we explore the correct coding for a specific condition based on a hypothetical clinical scenario. This scenario involves a patient presenting for a follow-up visit with symptoms of Type 2 Diabetes. See if you can choose the correct codes.

Clinical Scenario

Chief Complaint

Patient, a 52-year-old male, came to the office for follow up of Type 2 diabetes mellitus (T2DM), hyperlipidemia, hypertension, urine micro albumin. Patient reports he was diagnosed with T2DM at age 45. Patient has been on insulin since 2010. Since last visit, patient reports blood sugars are stable.

Current Treatment:

Current diabetic regimen includes Tresiba 36 units daily and Xigduo (5/1000mg) 2 tab daily.

Reviewed medication list – medication compliance is good.

Glucose records reviewed

Blood glucose monitoring is done 0-1 times daily.

The patient denies hypoglycemia or hypoglycemic symptoms, i.e. no dizziness, sweating, confusion or headaches.

Diet / Exercise / Weight:

Patient is overweight but generally follows a healthy diet. Goes to gym two to three times per week.

Diabetic Related Complications:

Neuropathy symptoms : Positive stocking/glove numbness or tingling. No mononeuropathy. No postprandial bloating.

Retinopathy: Up-to-date on routine surveillance. First diagnosed 1/22/2018.

Nephropathy: Positive. Up-to-date on routine surveillance.

Review of Systems

Constitutional/Endocrine/Musculoskeletal: Negative.

Social History

Smoking status: Does not smoke.

Physical Exam

Weight: 271 lb 12.8 oz (123.3 kg)

BMI: Body mass index is 36.86 kg/m²

General: Alert; NAD with normal affect.

Eyes: EOMI; no icterus.

HENT: Atraumatic; oropharynx clear with moist mucous membranes.

Neck: supple, normal size thyroid, no palpable nodules.

Respiratory: Normal respiratory effort.

Cardiovascular: Regular rate & rhythm; no edema.

Musculoskeletal: FROM; no synovitis.

Neurological: reflexes 2+ at biceps, relaxation phase normal; no tremor.

Skin: No rash; no ulcerations.

Diabetic Foot Exam : No Lesions; good pulses.

Type 2 diabetes mellitus with hyperglycemia, with long-term current use of insulin

Type 2 diabetes mellitus with polyneuropathy

Type 2 diabetes mellitus with microalbuminuria, with long-term current use of insulin

Type 2 diabetes, uncontrolled, with retinopathy

Class 2 severe obesity due to excess calories with serious comorbidity and body mass index (BMI) of 36.0 to 36.9 in adult

Hyperlipidemia associated with type 2 diabetes mellitus

Hypertension associated with diabetes

Vitamin D deficiency

Documentation Coding Requirements

When documenting diabetes, include the following:

Due to underlying condition

Drug or chemical induced diabetes mellitus

Diabetes mellitus in pregnancy, childbirth, and the puerperium

With or without complication

With or without coma

Left Right Bilateral

Diagnosis Codes

E11.65 Type 2 diabetes mellitus with hyperglycemia

E11.42 Type 2 diabetes mellitus with diabetic polyneuropathy

E11.29 Type 2 diabetes mellitus with other diabetic kidney complication

E11.319 Type 2 diabetes with unspecified diabetic retinopathy with macular edema

E11.69 Type 2 diabetes mellitus with other specified complication

E11.59 Type 2 diabetes mellitus with other circulatory complications

E66.01 Morbid (severe) obesity due to excess calories

Z68.36 Body mass index (BMI) 36.0-36.9, adult

Z79.4 Long term (current) use of insulin

E55.9 Vitamin D deficiency, unspecified

R80.9 Proteinuria, unspecified

E78.5 Hyperlipidemia, unspecified

Renee Dowling is a compliance auditor at Sansum Clinic, LLC, in Santa Barbara, California.

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Healthcare Administration: Medical Billing and Coding Resources

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Introduction

Medical coding involves extracting billable information from the medical record and clinical documentation, while medical billing uses those codes to create insurance claims and bills for patients. (Xtelligent, 2018). 

The following resources are meant to help you introduce yourself to or continue your studies in medical billing and coding. 

The Medical Billing and Collection Cycle

case studies for medical billing and coding

https://samci.org/wp-content/uploads/2018/05/training-img_03.jpg

Introduction to Medical Coding

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  • Case Studies

Expert Witness Case Study: Finding a Medical Billing and Coding Expert

Madison Dunn

Medical billing and coding experts are individuals who review medical billing and coding to ensure claims submitted for payment by the provider are usual, customary, and reasonable (UCR). Oftentimes, these specialized experts are hired by the client to offer an opposing viewpoint to the original billing affidavit by signing a counter affidavit. Juris Medicus was tasked with identifying and vetting a medical billing and coding expert who could provide a counter-affidavit in a personal injury case.  

Primary Case Issue  

Our client represented a defendant in a personal injury lawsuit involving a slip and fall incident. The client hired Juris Medicus to provide a medical billing and coding expert to controvert medical bills from two providers.  

Expert Witness Requirements  

  • Certified by the American Academy of Professional Coders (AAPC)  
  • Comprehensive knowledge in determining UCR fees  
  • Prior testimony experience preferred  

Expert Witness Search  

A Juris Medicus case manager identified two in-house billing experts who fit the client’s requirements. The case manager sent the experts’ credentials, certifications, testifying experience, and background to the client.   

Expert Witness Placement  

The selected expert had 20 years of experience in medical billing, inpatient coding, and outpatient coding as well as   comprehensive knowledge of and familiarity with determining usual, customary, and reasonable (UCR) fees for medical services. Once selected, the expert provided a powerful counter affidavit to oppose the original affidavit.  

Case Results  

This case was settled.  

Learn More  

Let Juris Medicus help you find the best medical expert for your case.  

https://www.jurismedicus.net/expert-panel         

Madison Dunn

Madison Dunn

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