Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies
  1. Home
  3. Identification Of Patients For A Community Health Worker Program Using An Artificial Intelligence Algorithm.
  1. Home
  3. Identification Of Patients For A Community Health Worker Program Using An Artificial Intelligence Algorithm.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Implementing an Evidence-Based Guideline to Improve Perioperative Antimicrobial Prophylaxis Quality Among Patients With Penicillin Allergy Labels.

Mayo Clinic proceedings. Innovations, quality & outcomes·2026
Same author

Toward Developing a Patient Reported Outcomes Measure for Children with Heart Failure: A Qualitative Study Design.

American heart journal·2026
Same author

Champions and early adopters in an implementation-effectiveness study of shared decision making: implications for interpersonal strategies.

Implementation science communications·2026
Same author

Exploring Opioid Prescriber Decision-Making: A Qualitative Study.

Pharmacoepidemiology and drug safety·2026
Same author

Hepatocellular Carcinoma in Southeast Asian Americans: Epidemiologic Trends, Screening Challenges, and Policy Implications.

Healthcare (Basel, Switzerland)·2026
Same author

Interdisciplinary Insights on Intubation Checklist Use in the ICU: A Qualitative Study.

ATS scholar·2026
Same journal

The HALO Model: A Learning Health System Framework for Artificial Intelligence.

Learning health systems·2026
Same journal

Surveillance of Pharmaceutical Risk-Mitigation Behavior: Applying and Comparing Statistical Process Control Methods Using Real World Data.

Learning health systems·2026
Same journal

The Value and Challenges of Stakeholder Engagement in Rehabilitation Learning Health Systems: A Qualitative Pilot Study of Rehabilitation Directors.

Learning health systems·2026
Same journal

Shared Experiences and Care Improvement Priorities for Multimorbidity Management: An Experience-Based Co-Design Study.

Learning health systems·2026
Same journal

AI-powered nursing handoffs: Introducing and evaluating the patient report template.

Learning health systems·2026
Same journal

Large Language Models for Summarizing Advance Care Planning Information From Goals of Care Notes in the EHR.

Learning health systems·2026
See all related articles

Related Experiment Video

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Identification of Patients for a Community Health Worker Program Using an Artificial Intelligence Algorithm.

Samuel T Savitz1, Brendan Broderick1, Margaret M Paul1

  • 1Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic Rochester Minnesota USA.

Learning Health Systems
|June 8, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

An electronic health record (EHR) algorithm effectively identifies patients needing community health worker (CHW) support, including those missed by traditional screening. This tool improves access to vital resources for underserved populations.

Keywords:
artificial intelligencecommunity health workerselectronic health recordssocial determinants of health

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Health Informatics
  • Public Health
  • Clinical Decision Support

Background:

  • Community health workers (CHWs) are crucial for connecting patients with community resources, improving health outcomes and reducing healthcare use.
  • Identifying eligible patients for CHW programs presents a significant challenge, often relying on incomplete screening methods.

Purpose of the Study:

  • To develop and evaluate an electronic health record (EHR)-based algorithm for predicting referrals to a CHW program.
  • To determine if the algorithm identifies a distinct patient subgroup compared to traditional health-related social needs (HRSN) screening questionnaires.

Main Methods:

  • A gradient boosted time-to-event model was developed using EHR data from primary care patients.
  • The model incorporated demographics, diagnoses, HRSN questionnaire responses, and HRSNs extracted from clinical notes via natural language processing.
  • Algorithm-identified patients' characteristics were compared to those identified by questionnaires.

Main Results:

  • The algorithm achieved high performance (AUC 0.87 training, 0.92 hold-out).
  • Algorithm-identified patients were more likely to use interpreter services, prefer non-English languages, and have documented health literacy issues.
  • A significant portion (53%) of algorithm-identified patients had not completed an HRSN questionnaire within two years; health literacy and financial strain were key predictors.

Conclusions:

  • The EHR-based algorithm serves as an effective chart-review prioritization tool for CHW program referrals.
  • It addresses challenges in identifying patients with HRSNs, particularly when questionnaire data is incomplete.
  • This approach enhances the identification of patients who may benefit from CHW services.