A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models.

ABSTRACT:

Objective

Health care providers increasingly rely upon predictive algorithms when making important treatment decisions, however, evidence indicates that these tools can lead to inequitable outcomes across racial and socio-economic groups. In this study, we introduce a bias evaluation checklist that allows model developers and health care providers a means to systematically appraise a model's potential to introduce bias.

Materials and methods

Our methods include developing a bias evaluation checklist, a scoping literature review to identify 30-day hospital readmission prediction models, and assessing the selected models using the checklist.

Results

We selected 4 models for evaluation: LACE, HOSPITAL, Johns Hopkins ACG, and HATRIX. Our assessment identified critical way

SUBMITTER: Wang HE 

PROVIDER: S-EPMC9277650 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature