Associations Between Successful Home Discharge and Posthospitalization Care Planning: Cross-Sectional Ecological Study
Bibliographic record
Abstract
Background Effective discharge planning is crucial for successful care transitions, reducing hospital length of stay and readmission rates. Japan offers a financial incentive to enhance the coordination of posthospitalization care planning for patients with complex needs. However, the national impact of this incentive remains unclear. Objective This study aimed to (1) assess the association between the number of claims submitted for discharge planning, as an indicator of the provision of posthospitalization care planning, and key health care outcomes, including discharges to home, 30-day readmissions, length of stay, and medical expenditures at the prefectural level in Japan, and (2) to describe regional differences in the provision of posthospitalization care planning and explore associated factors. Methods This ecological study used prefectural-level data from fiscal year 2020. Claims submitted for discharge planning were used as indicators that posthospitalization care planning was provided. Supply-adjusted standardized claim ratios (SCRs) were calculated using data from the Seventh National Database of Health Insurance Claims, to evaluate and compare the number of claims across 47 prefectures in Japan, accounting for differences in population structure. Key outcomes included discharges to home, 30-day readmissions, length of stay, and medical expenditures. Multivariate negative binomial regression models assessed associations between SCRs and outcomes, adjusting for socioeconomic covariates. In addition, regional differences in the provision of posthospitalization care planning and associated factors were analyzed using the Mann-Whitney U test. Prefectures were divided into 3 groups (low, medium, and high) based on tertiles of each factor, and supply-adjusted SCRs were compared across these groups. Results The ratio of the minimum to maximum supply-adjusted SCR was 10.63, highlighting significant regional variation. Higher supply-adjusted SCRs, indicating more frequent provision of posthospitalization care planning, were associated with an increase of 9.68 (95% CI 0.98-18.47) discharges to home per 1000 patients for each SD increase in supply-adjusted SCR. Several factors contributed to regional differences in the supply-adjusted SCR for posthospitalization care planning. A higher supply-adjusted SCR was significantly associated with a greater number of nurses per 100 hospital beds (median SCR in low, medium, and high groups: 0.055, 0.101, and 0.103, respectively); greater number of care manager offices per 100 km2 of habitable area (0.088, 0.082, and 0.116); higher proportion of hospitals providing electronic medical information to patients (0.083, 0.095, and 0.11); lower proportion of older adults living alone (0.116; 0.092; 0.071); and higher average per capita income (0.078, 0.102, and 0.102). Conclusions The provision of posthospitalization care planning is associated with an increased likelihood of discharge to home, underscoring its importance in care transitions. However, significant regional disparities in care coordination exist. Addressing these disparities is crucial for equitable health care outcomes. Further research is needed to clarify causal mechanisms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".