Cover: Methodologies to Measure Upcoding in Provider Settings

Methodologies to Measure Upcoding in Provider Settings

A Scoping Review

Published Mar 27, 2024

by Jonathan S. Levin, Daniel J. Crespin, Jin Kim, Rachel O. Reid, Christopher M. Whaley, Michael Dworsky

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Research Question

  1. In the literature, which methodologies are used to measure upcoding in provider settings?

Increases in payment for more-complicated patients can incentivize upcoding, a practice in which hospitals code more secondary diagnoses or complications to classify admissions or visits at higher complexity levels. However, the lack of standardized methodologies to measure upcoding hinders researchers and policymakers from fully understanding its prevalence, the medical conditions especially likely to be upcoded, the types of hospitals and geographic areas with high upcoding rates, and the overall impact of upcoding on health care spending. To address this gap, the authors of this report provide a review of methodologies to measure upcoding using literature from 2000 to 2023. Using seven electronic databases, the authors identified 39 articles that measure upcoding, which they classified into the following categories: variations in payments (n = 14 studies), variations in provider and patient attributes (n = 6), prediction algorithms (n = 6), self-reports (n = 3), and validations (n = 10). In most of these studies (n = 26), researchers examine upcoding at aggregated levels (e.g., frequency of upcoding at hospitals or in geographic areas) rather than identifying individually upcoded admissions (n = 13). Only three studies measure upcoding by comparing coding intensity with a gold standard, while the rest examine upcoding indirectly via measures of coding intensity. In their conclusion, the authors recommend that researchers and policymakers designing studies on provider-based upcoding consider outcome measures based on severity levels, payment changes that incentivize upcoding, provider and patient characteristics associated with upcoding, and indicators in administrative claims or electronic health record data.

Key Findings

  • The methodologies of the 39 studies reviewed could be classified using the following categories: variations in payments, variations in provider and patient attributes, prediction algorithms, self-reports, and validations.
  • The majority of studies detected upcoding indirectly via measures of coding intensity rather than by comparing coding intensity with a gold standard.
  • Most studies examined upcoding at aggregated levels and based on changes in upcoding resulting from changes in payments rather than the full extent of upcoding within a given locality.

Research conducted by

This research was sponsored by the National Institute for Health Care Reform and conducted within the Payment, Cost, and Coverage Program of RAND Health Care.

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