The Development, Implementation, and Evaluation of a Social Engagement Support System

NCT06913049 | Not Yet Recruiting

• 2 other identifiers: Kuali #15691R01MD019814-01
• Study Type: interventional
• Target: 249,660
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this clinical trial is to determine if artificial intelligence and machine learning (AI/ML) models can help address social needs in Medicaid enrollees. The main questions it aims to answer are: Can AI/ML models accurately identify social needs from administrative healthcare data? Can AI/ML models accurately predict which people will engage with social supports? Researchers will compare individuals who live in different regions to see if AI/ML models perform better than the status quo.

Conditions

Social Determinants of Health (SDOH)

Outcome Measures

Primary Outcomes (1)

• Change in the prevalence of Health Related Social Needs (HRSNs) as assessed by the Maryland Department of Health HRSN Screening Tool

  We will analyze social screening results from individuals in the treatment arm to determine if our system leads to the assessment of individuals with a higher social need profile.

  From enrollment to 12 months.

Secondary Outcomes (1)

• Change in the proportion of participants who receive social services who have a health-related social need

  From enrollment to 12 months.

Study Arms (2)

SESS - Treatment

EXPERIMENTAL

This arm will receive care coordination resources supported by our Social Engagement Support System, including the triage of screening outreach based on predicted risk of an unmet social need and engagement support to decrease like likelihood of dropout from the social services workflow.

Other: Social Engagement Support System

SESS - Control

NO INTERVENTION

This arm will receive no intervention.

Interventions

Social Engagement Support System OTHER

In this protocol, we will develop and deploy a set of machine learning models that use multiple individual- and community-level data sources to predict which members use the emergency department to fulfill social or non-urgent needs as opposed to treatment for urgent medical conditions. These models will identify individuals whose social needs are driving inappropriate utilization so that high-risk individuals will be given enhanced outreach services to facilitate completion of a comprehensive social needs assessment. We will also develop and deploy an engagement support system that identifies and displays the characteristics of members that prevent them from engaging with a Community Based Organization (CBO). This system will use artificial intelligence techniques to identify characteristics of individuals who have historically disengaged from the social service pipeline before receiving social services and suggest potential strategies for increasing engagement.

SESS - Treatment

Eligibility Criteria

• Age: 18 Years - 64 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)

You may qualify if:

• Members of partner health plan aged 18-64

Sponsors & Collaborators

• University of Maryland, Baltimore County: lead
• National Institute on Minority Health and Health Disparities (NIMHD): collaborator

Study Sites (1)

University of Maryland, Baltimore County

Baltimore, Maryland, 21250, United States

Location

Central Study Contacts

Ian Stockwell, PhD

CONTACT

410-455-8424; istock1@umbc.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: SUPPORTIVE CARE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 25, 2025
• First Posted: April 6, 2025
• Study Start: March 1, 2026
• Primary Completion (Estimated): April 1, 2028
• Study Completion (Estimated): April 1, 2029
• Last Updated: April 6, 2025

Record last verified: 2025-02

Locations

US (1)