Effectiveness of a clinical decision support system with prediction modeling to identify patients with health-related social needs in the emergency department: Study protocol.
<h4>Introduction</h4>Health-related social needs (HRSNs) encompass various non-medical risks from a patient's life circumstances. The emergency department (ED) is a crucial yet challenging setting for addressing patient HRSNs, a clinical decision support (CDS) intervention could ass...
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Public Library of Science (PLoS)
2025-01-01
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| Online Access: | https://doi.org/10.1371/journal.pone.0323094 |
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| author | Olena Mazurenko Christopher A Harle Justin Blackburn Nir Menachemi Adam Hirsh Shaun Grannis Malaz Boustani Paul I Musey Titus K Schleyer Lindsey M Sanner Joshua R Vest |
| author_facet | Olena Mazurenko Christopher A Harle Justin Blackburn Nir Menachemi Adam Hirsh Shaun Grannis Malaz Boustani Paul I Musey Titus K Schleyer Lindsey M Sanner Joshua R Vest |
| author_sort | Olena Mazurenko |
| collection | DOAJ |
| description | <h4>Introduction</h4>Health-related social needs (HRSNs) encompass various non-medical risks from a patient's life circumstances. The emergency department (ED) is a crucial yet challenging setting for addressing patient HRSNs, a clinical decision support (CDS) intervention could assist in identifying patients at high risk of having HRSNs. This project aims to implement and evaluate a CDS intervention that offers ED clinicians risk prediction scores to determine which patients will likely screen positive for one or more HRSNs.<h4>Materials & methods</h4>The FHIR-based CDS intervention, implemented in the ED setting of a health system in Indianapolis, Indiana, will use health information exchange data to generate logit-derived probability scores that estimate an adult patient's likelihood of screening positive for each of the following HRSNs: housing instability, food insecurity, transportation barriers, financial strain, and history of legal involvement. For each HRSN, ED clinicians will have access to the patient's likelihood of screening positive categorized as "high," "medium," or "low" based on tertiles in the distribution of each likelihood score. Clinician participation in the CDS will be voluntary. The intervention's effects will be assessed using a difference-in-difference approach with a pre-post design and a propensity-matched comparison group of ED patients from the same metropolitan area. Outcomes of interest include whether a formal HRSN screening was conducted, whether a referral was made to an HRSN service provider (e.g., social worker), and whether a repeat ED revisit (at 3, 7, and 30 days) or primary care follow-up (within 7 days) occurred.<h4>Discussion</h4>Efficiently and accurately identifying patients with HRSNs could help link them to needed services, improving outcomes and reducing healthcare costs. This protocol will contribute to a growing body of research on the role of CDS interventions in facilitating improved screenings and referrals for HRSNs.<h4>Trial registration</h4>Clincialtrials.gov NCT06655974. |
| format | Article |
| id | doaj-art-a6af9ea4e1cf4e6096c4c126eb656202 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-a6af9ea4e1cf4e6096c4c126eb6562022025-08-20T03:22:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e032309410.1371/journal.pone.0323094Effectiveness of a clinical decision support system with prediction modeling to identify patients with health-related social needs in the emergency department: Study protocol.Olena MazurenkoChristopher A HarleJustin BlackburnNir MenachemiAdam HirshShaun GrannisMalaz BoustaniPaul I MuseyTitus K SchleyerLindsey M SannerJoshua R Vest<h4>Introduction</h4>Health-related social needs (HRSNs) encompass various non-medical risks from a patient's life circumstances. The emergency department (ED) is a crucial yet challenging setting for addressing patient HRSNs, a clinical decision support (CDS) intervention could assist in identifying patients at high risk of having HRSNs. This project aims to implement and evaluate a CDS intervention that offers ED clinicians risk prediction scores to determine which patients will likely screen positive for one or more HRSNs.<h4>Materials & methods</h4>The FHIR-based CDS intervention, implemented in the ED setting of a health system in Indianapolis, Indiana, will use health information exchange data to generate logit-derived probability scores that estimate an adult patient's likelihood of screening positive for each of the following HRSNs: housing instability, food insecurity, transportation barriers, financial strain, and history of legal involvement. For each HRSN, ED clinicians will have access to the patient's likelihood of screening positive categorized as "high," "medium," or "low" based on tertiles in the distribution of each likelihood score. Clinician participation in the CDS will be voluntary. The intervention's effects will be assessed using a difference-in-difference approach with a pre-post design and a propensity-matched comparison group of ED patients from the same metropolitan area. Outcomes of interest include whether a formal HRSN screening was conducted, whether a referral was made to an HRSN service provider (e.g., social worker), and whether a repeat ED revisit (at 3, 7, and 30 days) or primary care follow-up (within 7 days) occurred.<h4>Discussion</h4>Efficiently and accurately identifying patients with HRSNs could help link them to needed services, improving outcomes and reducing healthcare costs. This protocol will contribute to a growing body of research on the role of CDS interventions in facilitating improved screenings and referrals for HRSNs.<h4>Trial registration</h4>Clincialtrials.gov NCT06655974.https://doi.org/10.1371/journal.pone.0323094 |
| spellingShingle | Olena Mazurenko Christopher A Harle Justin Blackburn Nir Menachemi Adam Hirsh Shaun Grannis Malaz Boustani Paul I Musey Titus K Schleyer Lindsey M Sanner Joshua R Vest 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 |
| title | Effectiveness of a clinical decision support system with prediction modeling to identify patients with health-related social needs in the emergency department: Study protocol. |
| title_full | Effectiveness of a clinical decision support system with prediction modeling to identify patients with health-related social needs in the emergency department: Study protocol. |
| title_fullStr | Effectiveness of a clinical decision support system with prediction modeling to identify patients with health-related social needs in the emergency department: Study protocol. |
| title_full_unstemmed | Effectiveness of a clinical decision support system with prediction modeling to identify patients with health-related social needs in the emergency department: Study protocol. |
| title_short | Effectiveness of a clinical decision support system with prediction modeling to identify patients with health-related social needs in the emergency department: Study protocol. |
| title_sort | effectiveness of a clinical decision support system with prediction modeling to identify patients with health related social needs in the emergency department study protocol |
| url | https://doi.org/10.1371/journal.pone.0323094 |
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