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Incorporating Present-on-Admission Indicators in Medicare Claims to Inform Hospital Quality Measure Risk Adjustment
Elizabeth W Triche1, Xin Xin1,2, Sydnie Stackland1
1Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut.
Insights
Present-on-admission (POA) indicators improve hospital quality measures by better identifying patient risk factors. This enhances risk adjustment models without additional hospital effort, aiding accurate public reporting.
Area of Science:
- Health Services Research
- Health Informatics
- Quality Improvement
Background:
- Present-on-admission (POA) indicators distinguish preexisting conditions from hospital-acquired ones in administrative claims.
- Their impact on claims-based hospital quality measures and risk factor understanding in ICD-10 settings is not well-established.
Purpose of the Study:
- To evaluate the use of POA indicators on Medicare claims.
- To assess their impact on hospital and patient outcomes for risk factor identification in CMS quality measures.
Main Methods:
- Comparative effectiveness study using national Medicare claims data (July 2015-June 2018).
- Six hospital quality measures (readmission, mortality) were modified with POA indicators for risk adjustment.
- Compared POA-inclusive models with existing complications-of-care algorithms.
- Included Medicare and VA beneficiaries aged 65+ with specific inpatient conditions.
Main Results:
- POA indicators improved risk adjustment model performance, especially for mortality measures.
- Example: C statistic for acute myocardial infarction mortality increased from 0.728 to 0.774.
- Demonstrated improvements in patient-level and hospital-level performance metrics.
Conclusions:
- Leveraging POA indicators enhances risk adjustment, better capturing patient risk factors and improving model performance.
- Incorporating POA indicators is easily implementable for public quality reporting without extra hospital burden.
Importance:
Present-on-admission (POA) indicators in administrative claims data allow researchers to distinguish between preexisting conditions and those acquired during a hospital stay. The impact of adding POA information to claims-based measures of hospital quality has not yet been investigated to better understand patient underlying risk factors in the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision setting.
Objective:
To assess POA indicator use on Medicare claims and to assess the hospital- and patient-level outcomes associated with incorporating POA indicators in identifying risk factors for publicly reported outcome measures used by the Centers for Medicare & Medicaid Services (CMS).
Design, Setting, And Participants:
This comparative effectiveness study used national CMS claims data between July 1, 2015, and June 30, 2018. Six hospital quality measures assessing readmission and mortality outcomes were modified to include POA indicators in risk adjustment models. The models using POA were then compared with models using the existing complications-of-care algorithm to evaluate changes in risk model performance. Patient claims data were included for all Medicare fee-for-service and Veterans Administration beneficiaries aged 65 years or older with inpatient hospitalizations for acute myocardial infarction, heart failure, or pneumonia within the measurement period. Data were analyzed between September 2019 and March 2020.
Main Outcomes And Measures:
Changes in patient-level (C statistics) and hospital-level (quintile shifts in risk-standardized outcome rates) model performance after including POA indicators in risk adjustment.
Results:
Data from a total of 6 027 988 index admissions were included for analysis, ranging from 491 366 admissions (269 209 [54.8%] men; mean [SD] age, 78.2 [8.3] years) for the acute myocardial infarction mortality outcome measure to 1 395 870 admissions (677 158 [48.5%] men; mean [SD] age, 80.3 [8.7] years) for the pneumonia readmission measure. Use of POA indicators was associated with improvements in risk adjustment model performance, particularly for mortality measures (eg, the C statistic increased from 0.728 [95% CI, 0.726-0.730] to 0.774 [95% CI, 0.773-0.776] when incorporating POA indicators into the acute myocardial infarction mortality measure).
Conclusions And Relevance:
The findings of this quality improvement study suggest that leveraging POA indicators in the risk adjustment methodology for hospital quality outcome measures may help to more fully capture patients' risk factors and improve overall model performance. Incorporating POA indicators does not require extra effort on the part of hospitals and would be easy to implement in publicly reported quality outcome measures.
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