Collecting and using social needs data in health settings: a systematic review of the literature on health service utilization and costs
Bibliographic record
Abstract
BACKGROUND: Social determinants of health significantly influence health outcomes and contribute to health inequities across populations. Systematic and routine collection of social needs data and its use to inform interventions within healthcare settings are proposed to reduce health services utilization and healthcare costs. This systematic review examines the impact of social needs data collection and use on health service utilization and healthcare costs. METHODS: Following PRISMA guidelines, we conducted a systematic review of studies published between January 2015 and February 2024. We included studies that reported on the collection and use of social needs data within healthcare settings in high-income countries. The review included randomized controlled trials, observational studies, quasi-experimental studies, qualitative studies, quality improvement studies, and mixed methods designs. Databases searched included Ovid MEDLINE, EMBASE, and Cochrane CENTRAL. The primary outcomes assessed were changes in health service utilization and healthcare costs. RESULTS: The review identified 35 relevant studies, predominantly from the United States. Interventions utilizing social needs data were implemented across various healthcare settings, including emergency departments, primary care, and inpatient facilities. Most studies reported reductions in emergency department visits (13/35) and hospitalizations (14/35) associated with collecting and using social needs data. Several studies demonstrated associated cost reductions, particularly in emergency department and hospitalization costs. However, the findings were mixed, with some studies reporting no significant changes or increased costs in certain areas, such as diagnostic testing and ambulatory care. CONCLUSIONS: Collecting and using social needs data within healthcare settings shows potential for reducing health service utilization and associated costs, particularly in targeted populations. The variability in outcomes suggests the need for context-specific approaches and further research to standardize reporting and understand the long-term impacts of these interventions. Standardized reporting and more robust study designs are needed to better understand these interventions' long-term impact, best implementation strategies, and scalability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".