# A Framework for the Social Impact of Algorithms in Health Care

> **NIH NIH DP1** · STANFORD UNIVERSITY · 2023 · $1,081,325

## Abstract

PROJECT ABSTRACT: Health care algorithms make decisions that impact the lives of millions of
individuals, yet few are rigorously evaluated for systemic harms after they are implemented. Unfortunately,
these oversights have led to the perpetuation of health care disparities. In an effort to leap ahead of the
current state of the field and protect marginalized groups, our research would build a framework for
assessing the social impact of health care algorithms before they are deployed, which is a novel line of
investigation. This proposal is inherently interdisciplinary, spanning machine learning, ethical AI, health
economics, public health, decision sciences, statistics, health policy, and qualitative research. Mathematical
decision science microsimulation models will be developed, positing an underlying complex causal network
of the health care system. In order to initialize these microsimulation models, a diverse collection of data
sources will be used, including health care billing claims, policy intervention information, and community-
based qualitative feedback. A main priority is to prevent harm to racial and ethnic minorities, individuals with
disabilities, and older adults, among other marginalized groups. Primary outcomes under consideration
center health care access, quality, and costs. Robustness and rigor are central to our work. Despite no
comparator framework existing, we will additionally develop simpler causal network models along with
Markov cohort models to provide a basis for comparison. Outputs produced will also be freely shared with
the research and stakeholder communities in the form of open-source code and open-access preprints
featuring transparent descriptions of all models and assumptions. This work would involve a substantial shift
in focus for the Principal Investigator; pivoting to research that leverages mathematical decision science
modeling and qualitative approaches while merging them with her expertise in machine learning for health
care. Importantly, this framework has the potential to influence a new set of standards and guidelines for AI
algorithms, establishing a blueprint where tools are routinely evaluated for social impact before deployment.
Thus, creating this first-of-its-kind social impact framework could transform the development and application
of algorithms in health care, representing a substantial paradigm shift with broad impact.

## Key facts

- **NIH application ID:** 10695095
- **Project number:** 5DP1LM014278-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Sherri Rose
- **Activity code:** DP1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $1,081,325
- **Award type:** 5
- **Project period:** 2022-09-30 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10695095

## Citation

> US National Institutes of Health, RePORTER application 10695095, A Framework for the Social Impact of Algorithms in Health Care (5DP1LM014278-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10695095. Licensed CC0.

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