Opportunity to inform social needs within a hospital setting using data-driven patient engagement
Bibliographic record
Abstract
BACKGROUND: High-risk patients account for a disproportionate amount of healthcare use, necessitating the development of care delivery solutions aimed specifically at reducing this use. These interventions have largely been unsuccessful, perhaps due to a lack of attention to patients' social needs and engagement of patients in developing solutions. METHODS: The project team used a combination of administrative data, information culled from charts and interviews with high-risk patients to understand social needs, the current experience of addressing social needs in the hospital, and patient preferences and identified opportunities for improvement. Interviews were conducted in March and April 2020, and patients were asked to reflect on their experiences both before and during the COVID-19 pandemic. RESULTS: A total of 4579 patients with 26 168 visits to the emergency department and 2904 inpatient admissions in the previous year were identified. Qualitative analysis resulted in three themes: (1) the interaction between social needs, demographics, and health; (2) the hospital's role in addressing social needs; and (3) the impact of social needs on experiences of care. Themes related to experiences before and during COVID-19 did not differ. Three opportunities were identified: (1) training for staff related to stigma and trauma, (2) improved documentation of social needs and (3) creation of navigation programmes. DISCUSSION: Certain demographic factors were clearly associated with an increased need for social support. Unfortunately, many factors identified by patients as mediating their need for such support were not consistently captured. Going forward, high-risk patients should be included in the development of quality improvement initiatives and programmes to address social needs.
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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 | 0.051 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".