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Incidence, Persistence, and Steady-State Prevalence in Coding Intensity for Health Plan Payment.

Thomas G McGuire¹, Oana M Enache², Michael Chernew¹

¹Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA.

Health Services Research

|November 8, 2025

Summary

This summary is machine-generated.

New measures define Medicare diagnosis coding intensity, revealing how coding practices evolve. These metrics show how diagnosis prevalence can grow over time due to coding dynamics, not just behavioral shifts.

Keywords:

Accountable Care Organizations Medicare Medicare Advantage clinical coding risk adjustment

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Area of Science:

Health Services Research
Medical Informatics
Public Health

Background:

Medicare diagnosis coding is crucial for risk adjustment.
Understanding changes in coding practices is essential for accurate data interpretation.
Existing methods may not fully capture the dynamics of coding behavior over time.

Purpose of the Study:

To develop novel measures of Medicare diagnosis coding intensity.
To capture the temporal dynamics of changes in healthcare provider coding practices.
To provide tools for monitoring coding behavior and its impact on prevalence data.

Main Methods:

Retrospective analysis of Medicare beneficiary claims data.
Utilized a 20% random sample of Medicare beneficiaries assigned to Accountable Care Organizations in 2018.
Decomposed diagnosis code prevalence into incidence and persistence rates.

Main Results:

Introduced 'steady-state prevalence' to model long-run diagnosis prevalence without behavioral change.
Demonstrated that coding dynamics alone can explain continued growth in observed diagnosis prevalence.
Projected a rise in Specified Heart Arrhythmias prevalence from 18.7% to 28.0% solely due to coding persistence.

Conclusions:

Proposed measures offer insights into the lag between coding practice changes and their reflection in data.
Researchers and policymakers can utilize these measures to better understand and monitor coding behavior.
Enhanced understanding of coding intensity dynamics can improve the accuracy of health data analysis and risk adjustment.