Predictive Modeling for Social Needs in Emergency Department Settings
NCT ID: NCT06655974
Last Updated: 2025-04-01
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
RECRUITING
NA
48000 participants
INTERVENTIONAL
2025-03-10
2025-12-31
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
* Does providing emergency department clinicians with risk scores on health-related social needs increase screening and referral activities?
* Does providing emergency department clinicians with risk scores on health-related social needs change patients' use of healthcare services?
The decision support system with health-related social needs risk scores will be introduced for all adult patients at one emergency department. Screening rates, referrals, and subsequent healthcare encounters will be compared with emergency departments that did not have access to the decision support system.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Resuscitation Decisions in the Emergency Department (ED)
NCT02575573
Development and Validation of a Regional Multi-scale System for the Prediction of the Patient Flow in the Emergencies and the Need for Hospitalization
NCT03051737
Predictive Tracking of Patient Flow in the Emergency Services During the Virus Winter Epidemics
NCT02858531
Emergency Department Utilization in Germany
NCT03224078
Forecasting ED Overcrowding With Statistical Methods: A Prospective Validation Study
NCT05174481
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
NON_RANDOMIZED
PARALLEL
HEALTH_SERVICES_RESEARCH
NONE
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
Decision support intervention group
Adult ED patients seeking care the ED site with the health-related social needs decision support system live.
Health-related social needs decision support system
The clinical decision support intervention will present emergency department clinicians at an Indianapolis, IN ED with a likelihood score for an adult patient screening positive for the following health-related social needs (HRSNs): housing instability, food insecurity, transportation barriers, financial strain, and history of legal involvement. For each HRSN, the likelihood of screening positive is reported as "high", "medium", or "low". These categorizations are the product of logistic regression models. The clinical decision support intervention will be delivered through an existing FHIR (Fast Healthcare Interoperability Resources) standards-based clinical decision support platform.
Comparison group
Adult ED patients created using statistical matching from ED sites in the same metropolitan area.
No interventions assigned to this group
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
Health-related social needs decision support system
The clinical decision support intervention will present emergency department clinicians at an Indianapolis, IN ED with a likelihood score for an adult patient screening positive for the following health-related social needs (HRSNs): housing instability, food insecurity, transportation barriers, financial strain, and history of legal involvement. For each HRSN, the likelihood of screening positive is reported as "high", "medium", or "low". These categorizations are the product of logistic regression models. The clinical decision support intervention will be delivered through an existing FHIR (Fast Healthcare Interoperability Resources) standards-based clinical decision support platform.
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* Seeking care at Indianapolis, Indiana area emergency departments (EDs).
Exclusion Criteria
* Encounters by patients that present with a critical illness/injury (e.g. severe trauma patients or those with Emergency Severity Index (ESI) classification level 1)
* Encounters by patients who have been transferred from another inpatient facility
* Patients that die during the ED encounter
* Encounters among patients who were ultimately admitted during their ED visits from our analysis
18 Years
ALL
Yes
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
Indiana University
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Joshua R. Vest, PhD
Professor
Principal Investigators
Learn about the lead researchers overseeing the trial and their institutional affiliations.
Joshua R Vest, PhD,MPH
Role: PRINCIPAL_INVESTIGATOR
Indiana University
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Indiana University Health
Indianapolis, Indiana, United States
Countries
Review the countries where the study has at least one active or historical site.
Central Contacts
Reach out to these primary contacts for questions about participation or study logistics.
Facility Contacts
Find local site contact details for specific facilities participating in the trial.
References
Explore related publications, articles, or registry entries linked to this study.
Mazurenko O, Hirsh AT, Harle CA, McNamee C, Vest JR. Acceptance of Automated Social Risk Scoring in the Emergency Department: Clinician, Staff, and Patient Perspectives. West J Emerg Med. 2024 Jul;25(4):614-623. doi: 10.5811/westjem.18577.
Mazurenko O, Harle CA, Blackburn J, Menachemi N, Hirsh A, Grannis S, Boustani M, Musey PI Jr, Schleyer TK, Sanner LM, Vest JR. Effectiveness of a clinical decision support system with prediction modeling to identify patients with health-related social needs in the emergency department: Study protocol. PLoS One. 2025 May 12;20(5):e0323094. doi: 10.1371/journal.pone.0323094. eCollection 2025.
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
2011558232
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.