Searching for underlying social determinants of health for thirty-day hospital readmissions
Bibliographic record
Abstract
Objective: An evaluation of social factors associated with 30-day readmission was undertaken at our institution to determine which factors would be significantly associated with time to hospital readmission.Methods: Prospective observational study at an academic tertiary care hospital in the mid-Atlantic region of patients who were readmitted within 30 days of their last inpatient discharge. The electronic health record in conjunction with the regional hospital information system was used to generate a daily report to identify a convenience sample of readmitted patients. Using a standardized interview, data on 117 patients were collected for an exploratory analysis of social factors associated with readmission.Results: Regression modeling demonstrated poor correlation with prediction of time to readmission (R-squared = 0.2189). No individual social variables were found to be significant for influencing time to readmission (all p-values > .05). Common social factors were seen within the population affecting their utilization and access of healthcare. Poly-pharmacy was found in the majority of patients. Self-reported medication adherence was good, except with regards to mental health medication compliance. 97% of patients reported filling their prescriptions. 36% of the patients went to their follow-up appointment within 7 days although the vast majority of patients (92%) reported having a primary care doctor. 23% of patients expressed difficulty getting to their follow up appointments.Conclusions: At one single-center tertiary care hospital, there were some common underlying social determinants of health that may be related to readmission; however, no factors in isolation were predictive of hospital readmission. While there are common themes among readmitted populations, particularly in regard to factors driven by poverty, it is likely that the complex interaction of social factors with health continues to limit attempted administrative modeling of these data.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".