DRIVING INTELLIGENT DECISIONS IN HEALTHCARE WITH RECOVERY-AWARE SYSTEM REDESIGN
Bibliographic record
Abstract
Delayed hospital discharge, often recorded as Alternate Level of Care, remains a persistent barrier to safe and timely transitions for older adults. This thesis integrates three studies that move from system diagnosis to temporal analysis of functional change to interpretable prediction that can inform planning. Article 1 is an in-depth scoping review of 23 systematic reviews and more than 700 studies. It shows that discharge delays arise from structural and operational gaps in information flow, coordination, and decision rights. It proposes a continuous process improvement model that treats discharge as an iterative cycle of planning, measurement, learning, and adaptation, which sets requirements for later analyses. Article 2 analyzes 878,000 longitudinal observations from Veterans Affairs long term care. Using survival and count models, it quantifies the timing and recurrence of recovery and decline across functional domains. Results show that change is heterogeneous and domain specific, with clear differences by sex and limited added value for chronological age. These findings justify measuring early and late improvement and motivate simple, transparent profiles of recovery. Article 3 applies these ideas to Ontario data from the Institute for Clinical Evaluative Sciences, more than 1.8 million episodes from 2004 to 2023. It engineers early, late, and total gains and constructs rule based Recovery Archetypes, then trains a gradient boosted model to explain ALC duration. Explanations highlight locomotion rate of gain, bathing and shower transfer improvements, and late mobility gains as leading drivers. The framework supports episode-level risk stratification, earlier intervention, capacity planning, and evaluation of policy periods. Together, the studies contribute a system map and process model, a temporal measurement strategy for functional data, and a transparent predictive tool that turns routine records into decision-ready insight for safer and more responsive discharge planning
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".