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Background <p>Population health programs rely on healthcare predictive models to allocate resources, yet models can perpetuate biases that exacerbate health disparities among marginalized communities.</p> Objective <p>We developed the&#xa0;Bias-reduction and Equity Framework for Assessing, Implementing, and Redesigning (BE-FAIR) healthcare predictive models, an applied framework tested within a large health system using a population health predictive model, aiming to minimize bias and enhance equity.</p> Design <p>Retrospective cohort study conducted at an academic medical center. Data collected from September 30, 2020, to October 1, 2022, were analyzed to assess bias resulting from model use.</p> Participants <p>Primary care or payer-attributed patients at the medical center identified through electronic health records and claims data. Participants were stratified by race-ethnicity, gender, and social vulnerability defined by the Healthy Places Index (HPI).</p> Intervention <p>BE-FAIR implementation involved steps such as an anti-racism lens application, de-siloed team structure, historical intervention review, disaggregated data analysis, and calibration evaluation.</p> Main Measures <p>The primary outcome was the calibration and discrimination of the model across different demographic groups, measured by logistic regression and area under the receiver operating characteristic curve (AUROC).</p> Results <p>The study population consisted of 114,311 individuals with a mean age of 43.4 years (SD 24.0 years), 55.4% female, and 59.5% white/Caucasian. Calibration differed by race-ethnicity and HPI with significantly lower predicted probabilities of hospitalization for African Americans (0.129±0.051, <i>p</i>=0.016), Hispanics (0.133±0.047, <i>p</i>=0.004), AAPI (0.120±0.051,<i> p</i>=0.018), and multi-race (0.245±0.087, <i>p</i>=0.005) relative to white/Caucasians and for individuals in low HPI areas (0 – 25%, 0.178±0.042, <i>p</i>&lt;0.001; 25 – 50%, 0.129±0.044, <i>p</i>=0.003). AUROC values varied among demographic groups.</p> Conclusions <p>The BE-FAIR framework offers a practical approach to address bias in healthcare predictive models, guiding model development, and implementation. By identifying and mitigating biases, BE-FAIR enhances the fairness and equity of healthcare delivery, particularly for minoritized groups, paving the way for more inclusive and effective population health strategies.</p>

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Developing and Applying the BE-FAIR Equity Framework to a Population Health Predictive Model: A Retrospective Observational Cohort Study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月14日

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  • Reshma Gupta,
  • Mayu Sasaki,
  • Sandra L. Taylor,
  • Sili Fan,
  • Jeffrey S. Hoch,
  • Yi Zhang,
  • Matthew Crase,
  • Dan Tancredi,
  • Jason Y. Adams,
  • Hendry Ton

摘要

Background

Population health programs rely on healthcare predictive models to allocate resources, yet models can perpetuate biases that exacerbate health disparities among marginalized communities.

Objective

We developed the Bias-reduction and Equity Framework for Assessing, Implementing, and Redesigning (BE-FAIR) healthcare predictive models, an applied framework tested within a large health system using a population health predictive model, aiming to minimize bias and enhance equity.

Design

Retrospective cohort study conducted at an academic medical center. Data collected from September 30, 2020, to October 1, 2022, were analyzed to assess bias resulting from model use.

Participants

Primary care or payer-attributed patients at the medical center identified through electronic health records and claims data. Participants were stratified by race-ethnicity, gender, and social vulnerability defined by the Healthy Places Index (HPI).

Intervention

BE-FAIR implementation involved steps such as an anti-racism lens application, de-siloed team structure, historical intervention review, disaggregated data analysis, and calibration evaluation.

Main Measures

The primary outcome was the calibration and discrimination of the model across different demographic groups, measured by logistic regression and area under the receiver operating characteristic curve (AUROC).

Results

The study population consisted of 114,311 individuals with a mean age of 43.4 years (SD 24.0 years), 55.4% female, and 59.5% white/Caucasian. Calibration differed by race-ethnicity and HPI with significantly lower predicted probabilities of hospitalization for African Americans (0.129±0.051, p=0.016), Hispanics (0.133±0.047, p=0.004), AAPI (0.120±0.051, p=0.018), and multi-race (0.245±0.087, p=0.005) relative to white/Caucasians and for individuals in low HPI areas (0 – 25%, 0.178±0.042, p<0.001; 25 – 50%, 0.129±0.044, p=0.003). AUROC values varied among demographic groups.

Conclusions

The BE-FAIR framework offers a practical approach to address bias in healthcare predictive models, guiding model development, and implementation. By identifying and mitigating biases, BE-FAIR enhances the fairness and equity of healthcare delivery, particularly for minoritized groups, paving the way for more inclusive and effective population health strategies.