Toward Integrating Machine Learning-powered Polysocial Risk Scores into Electronic Health Record Workflows.

Xing He, Yu Huang, Yu Hu, Michael Pappa, Natacha Miller, Megan E Gregory, Jingchuan Serena Guo, Jiang Bian

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Co-designing an Outpatient Clinical Decision Support Prototype for Managing Social Risks in Patients Living with Dementia.Applied clinical informatics · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xing HeDepartment of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA.

Yu HuangDepartment of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA.

Yu HuDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.

Michael PappaIntegrated Data Repository Research Services, University of Florida, Gainesville, Florida, USA.

Natacha MillerDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.

Megan E GregoryDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.

Jingchuan Serena GuoDepartment of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, Florida, USA.

Jiang BianDepartment of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social determinants of health (SDoH) account for 80% of modifiable factors driving health disparities. Health systems play a critical role in addressing patients' unmet social needs essential to health outcomes. To integrate social risk management into patient health care, we developed an electronic health record (EHR)-based machine learning-powered pipeline to identify and address unmet social needs associated with hospitalization risk. By quantifying social risk via a polysocial risk score, this tool enables healthcare providers to identify patients at high social risk and prioritize targeted SDoH interventions. However, gaps exist regarding integrating our polysocial risk score tool into clinical flow. Therefore, in this study, through participatory design sessions with healthcare providers and social workers following user-centered design (UCD) principles, we initiated the integration of this predictive model into EHR workflows. This preliminary work lays the foundation for a comprehensive formal user-centered design process to enhance social risk assessment and intervention implementation.

Indexed as

Electronic Health RecordsMachine LearningSocial Determinants of HealthWorkflowDigital HealthHospitalizationHumansPredictive Learning ModelsRisk AssessmentUser-Centered Design

Identifiers

PMID41726481

PMCPMC12919456

What Socratic holds

Textmetadata

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.