Exact Science and the Art of Approximating Quality in Hospital Performance Metrics

Mary S Vaughan Sarrazin,Saket Girotra

doi:10.1001/jamanetworkopen.2019.7321

Abstract

Exact Science and the Art of Approximating Quality in Hospital Performance Metrics

Highlights

To account for differences in patient mix among hospitals, risk models are used to statistically adjust for patient factors that are clinically relevant and related to patient outcomes
Krumholz et al[5] demonstrated a 3-pronged approach to further improve risk-adjustment models for riskstandardized mortality rates (RSMRs) estimation, including: (1) using the present on admission indicator mandated for Medicare claims since 2014, (2) distinguishing diagnoses incurred during the 12 months prior to admission from those recorded as present at the time of the admission, and (3) using individual International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes for risk adjustment rather than codes aggregated to clinical condition categories
The first modification addresses the threat of misclassifying a complication as a preexisting condition, the second modification reflects the recency with which patients have sought treatment for specific diagnoses, and the third provides greater detail about specific patient diagnoses than previous models

Summary

Introduction

To account for differences in patient mix among hospitals, risk models are used to statistically adjust for patient factors that are clinically relevant and related to patient outcomes (eg, age, sex, comorbid diseases). The first modification addresses the threat of misclassifying a complication as a preexisting condition (ie, only comorbidities that represent the patient’s health status prior to or at the time of admission and not complications that arise during the course of the hospitalization should be included in risk adjustment), the second modification reflects the recency with which patients have sought treatment for specific diagnoses, and the third provides greater detail about specific patient diagnoses than previous models.

Results

Conclusion