HIM 215 Module 7 Project Two Example

Project Two: Catching Errors Before the Payer Does, a Coding Quality Audit Plan for Glenwood Regional Hospital

[Student Name]

Southern New Hampshire University

HIM 215: Coding & Classification Systems

Project Two

[Instructor Name]

[Date]

The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.

Project Two: Catching Errors Before the Payer Does, a Coding Quality Audit Plan for Glenwood Regional Hospital

Purpose and Scope

This plan establishes a coding quality audit program for Glenwood Regional Hospital covering inpatient ICD-10-CM, ICD-10-PCS and MS-DRG assignment and outpatient ICD-10-CM, CPT and HCPCS coding, including office visit levels and modifiers. Its purpose is to measure accuracy, identify patterns, correct claims in both directions and target education. It applies to all fifteen staff coders and to the two clinical documentation specialists whose queries shape coded data.

Where Glenwood Stands

The last external audit agreed with Glenwood's MS-DRG assignment on 91% of 200 sampled inpatient cases. About 23% of sepsis claims submitted last year were denied on clinical validation grounds, and fewer than half of those denials were overturned on appeal. Outpatient visit levels have never been audited, although physicians' distribution of established patient visits has shifted toward 99214 and 99215 over three years. Discharged charts wait an average of 5.8 days for final coding against a four-day target.

Objectives

Within twelve months, the program aims to raise MS-DRG agreement to at least 95%, reach code-level accuracy of at least 95% for inpatient diagnoses and procedures, reach at least 90% agreement on outpatient visit levels, reduce sepsis denials by a third through pre-bill review and achieve full compliance with query practice standards in a quarterly query audit. Accuracy targets apply to errors in both directions.

Why Audits Matter

Research supports routine auditing. Burns et al. (2012) found wide variation in discharge coding accuracy across studies, which suggests that local processes strongly affect results. O'Malley et al. (2005) listed a hospital's own checking routines among the forces that raise or lower accuracy. Cheng et al. (2009) used a blind re-coding audit of 752 surgical discharges to reveal discrepancies that changed payment groups, many traceable to documentation, and showed how audit findings can direct improvement.

Types of Review

The program combines two timings and two selection methods. Pre-bill reviews examine charts before claims are sent, which prevents errors from reaching payers and avoids rebilling. Retrospective reviews examine paid claims to measure accuracy over time. Random samples give an unbiased estimate of overall accuracy, while focused samples target case types known to carry high risk. Using both avoids the trap of measuring only problem areas or only easy cases.

Inpatient Sampling

Each month, the audit team will randomly select 30 inpatient discharges, about 3% of volume, stratified so every coder contributes at least two charts. A focused pre-bill sample of 20 more charts will target sepsis, respiratory failure, heart failure with a documented acute exacerbation, malnutrition reported as a complication and cases where a single secondary diagnosis moved the MS-DRG to a higher level. Findings from the focused sample are reported separately so they do not distort the overall accuracy rate.

Outpatient Sampling

For outpatient coding, five charts per coder will be selected at random each month from emergency, clinic and outpatient surgery encounters. A separate provider review will examine ten established patient visits per physician each year, with more frequent review for physicians whose share of the two highest levels is well above peers. Modifier 25 and 59 claims will be sampled quarterly, since these modifiers bypass edits.

How Accuracy Is Scored

Reviewers re-code each chart independently, without seeing the original codes, following the blind approach Cheng and colleagues used, and then compare. Code-level accuracy is the share of codes that match, counting added and missing codes as errors. MS-DRG accuracy is the share of cases whose group did not change. Each discrepancy is assigned to a category and marked for financial direction: overpayment, underpayment or no payment effect.

Table 1. Error Categories

Note. Categories adapted by the author for Glenwood's audit database.

Reviewer Qualifications and Independence

Internal reviewers must hold a coding credential appropriate to the setting and have at least three years of experience. No one reviews their own work. The coding quality lead reports to the HIM director, not to revenue cycle leadership, to keep audits focused on accuracy. An outside firm will audit a sample of 100 inpatient and 100 outpatient cases each year, including a check on the internal reviewers' own accuracy.

Reconciliation and Correction

Each discrepancy goes back to the original coder, who may agree or submit a written rebuttal with guideline references within five business days. The coding quality lead resolves disagreements, consulting official guidance when needed. For retrospective findings, corrected claims are resubmitted whether the correction raises or lowers payment. When an overpayment is identified, federal law gives the hospital 60 days from identification to report the money and pay it back, so confirmed overpayments are sent to the compliance office within two business days of final resolution.

Education and Feedback

Audit results drive education rather than punishment. Each coder receives a monthly report showing accuracy and error categories. Patterns across coders prompt short group sessions, for example on sepsis sequencing or PCS approaches. Patterns tied to documentation, such as unspecified heart failure, go to the clinical documentation specialists and physician advisors for provider education. Coders whose accuracy stays below 90% for two quarters receive a focused improvement plan with additional review.

Reporting

The coding quality lead will produce a monthly dashboard with overall and coder-level accuracy, MS-DRG agreement, error categories, financial effects in both directions and sepsis denial rates. A quarterly summary will go to the compliance committee. Rhee et al. (2017) showed how documentation and coding changes can alter what data appear to show, so the dashboard will also track the share of discharges coded with sepsis to watch for unexplained shifts.

Timeline

The program will launch in phases over six months so tools and staff are ready before full sampling begins.

Table 2. Implementation Timeline

Note. Timeline approved by the HIM director.

Evaluating the Program

After one year, the program will be judged on whether objectives were met, whether external audit results agree with internal findings and whether errors in targeted categories declined. If internal and external accuracy rates differ by more than three percentage points, the sampling or scoring method will be reviewed.

Conclusion

A structured audit program gives Glenwood a reliable measure of coding accuracy, catches high-risk errors before billing and turns findings into targeted education. By combining random and focused samples, blind re-coding, reviewer independence and prompt correction in both directions, the plan protects revenue and compliance while strengthening the data the hospital and its patients depend on.

References

Burns, E. M., Rigby, E., Mamidanna, R., Bottle, A., Aylin, P., Ziprin, P., & Faiz, O. D. (2012). Systematic review of discharge coding accuracy. Journal of Public Health, 34(1), 138-148. https://doi.org/10.1093/pubmed/fdr054

Cheng, P., Gilchrist, A., Robinson, K. M., & Paul, L. (2009). The risk and consequences of clinical miscoding due to inadequate medical documentation: A case study of the impact on health services funding. Health Information Management Journal, 38(1), 35-46. https://doi.org/10.1177/183335830903800105

O'Malley, K. J., Cook, K. F., Price, M. D., Wildes, K. R., Hurdle, J. F., & Ashton, C. M. (2005). Measuring diagnoses: ICD code accuracy. Health Services Research, 40(5, Pt. 2), 1620-1639. https://doi.org/10.1111/j.1475-6773.2005.00444.x

Rhee, C., Dantes, R., Epstein, L., Murphy, D. J., Seymour, C. W., Iwashyna, T. J., Kadri, S. S., Angus, D. C., Danner, R. L., Fiore, A. E., Jernigan, J. A., Martin, G. S., Septimus, E., Warren, D. K., Karcz, A., Chan, C., Menchaca, J. T., Wang, R., Gruber, S., & Klompas, M. (2017). Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014. JAMA, 318(13), 1241-1249. https://doi.org/10.1001/jama.2017.13836