# Accounting for total variation and robustness in profiling health care providers

## TL;DR

This paper introduces a smoothed empirical null approach to improve fairness in evaluating healthcare providers by accounting for total variation and provider size, applicable to various outcome models.

## Contribution

It proposes a novel empirical null method that better accounts for provider variation and size, enhancing fairness in healthcare provider profiling.

## Key findings

- Method effectively adjusts for provider size and variation.
- Application to dialysis data demonstrates improved provider assessment.
- Numerical simulations validate the approach's robustness.

## Abstract

Monitoring outcomes of health care providers, such as patient deaths, hospitalizations and hospital readmissions, helps in assessing the quality of health care. We consider a large database on patients being treated at dialysis facilities in the United States, and the problem of identifying facilities with outcomes that are better than or worse than expected. Analyses of such data have been commonly based on random or fixed facility effects, which have shortcomings that can lead to unfair assessments. A primary issue is that they do not appropriately account for variation between providers that is outside the providers' control due, for example, to unobserved patient characteristics that vary between providers. In this article, we propose a smoothed empirical null approach that accounts for the total variation and adapts to different provider sizes. The linear model provides an illustration that extends easily to other nonlinear models for survival or binary outcomes, for example. The empirical null method is generalized to allow for some variation being due to quality of care. These methods are examined with numerical simulations and applied to the monitoring of survival in the dialysis facility data.