Quality Improvement Intervention Using Social Prescribing at Discharge in a University Hospital in France: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Social prescription is seen as a public health intervention tool with the potential to mitigate social determinants of health. On one side, social prescription is not yet well developed in France, where social workers usually attend to social needs, and historically, there is a deep divide between the health and social sectors. On the other side, discharge coordination is gaining attention in France as a critical tool to improve the quality of care, assessed indirectly using unplanned rehospitalization rates. OBJECTIVE: This study aims to combine social prescription and discharge coordination to assess the need for social prescription and its effect on unplanned rehospitalization rates. METHODS: We conducted a quasi-experimental study in two departments of medicine in a French university hospital in a disadvantaged suburb of Paris over 2 years (October 2019-October 2021). A discharge coordinator screened patients for social prescribing needs and provided services on the spot or referred the patient to the appropriate service when needed. The primary outcome was the description of the services delivered by the discharge coordinator and of its process, as well as the characteristics of the patients in terms of social needs. The secondary outcome was the comparison of unplanned rehospitalization rates after data chaining. RESULTS: A total of 223 patients were included in the intervention arm, with recruitment being disrupted by the COVID-19 pandemic. More than two-thirds of patients (n=154, 69.1%) needed help understanding discharge information. Slightly less than half of the patients (n=98, 43.9%) seen by the discharge coordinator needed social prescribing, encompassing language, housing, health literacy, and financial issues. The social prescribing covered a large range of services, categorized into finding a general practitioner or private sector nurse, including language-matching; referral to a social worker; referral to nongovernmental organization or group activities; support for transportation issues; support for health-related administrative procedures; and support for additional appointments with nonmedical clinicians. All supports were delivered in a highly personalized way. Ethnic data collection was not legally permitted, but for 81% (n=182) of the patients, French was not the mother tongue. After data chaining, rehospitalization rates were compared between 203 patients who received the intervention (n=5, 3.1%) versus 2095 patients who did not (n=51, 2.6%), and there was no statistical difference. CONCLUSIONS: First, our study revealed the breadth of patient's unmet social needs in our university hospital, which caters to an area where the immigrant population is high. The study also revealed the complexity of the discharge coordinator's work, who provided highly personalized support and managed to gain trust. Hospital discharge could be used in France as an opportunity in disadvantaged settings. Eventually, indicators other than the rehospitalization rate should be devised to evaluate the effect of social prescribing and discharge coordination.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".