What Causes Delays in Admission to Rehabilitation Care? A Structural Estimation Approach
Bibliographic record
Abstract
Problem definition: Delays in admission to rehabilitation care can adversely impact patient outcomes. In addition, delayed patients keep occupying their acute care beds, making them unavailable for incoming patients. Admission delays are mainly caused by a lack of rehabilitation bed capacity and the time required to plan for rehabilitation activities, which we refer to as processing times. Because of non-standard bed allocation decisions and data limitations in practice, quantifying the magnitude of the two sources of delays can be technically challenging yet critical to the design of evidence-based interventions to reduce delays. We propose an empirical approach to understanding the contributions of the two sources of delays when only a single (combined) measure of admission delay is available. Methodology/results: We propose a hidden Markov model (HMM) to estimate the unobserved processing times and the status-quo bed allocation policy. Our estimation results quantify the magnitude of processing times versus capacity-driven delays and provide insights into factors impacting the bed allocation decision. We validate our estimated policy using a queueing model of patient flow and find that ignoring processing times or using simple bed allocation policies can lead to highly inaccurate delay estimates. In contrast, our estimated policy allows for accurate evaluation of different operational interventions. We find that reducing processing times can be highly effective in reducing admission delays and bed-blocking costs. In addition, allowing early transfer—whereby patients can complete some of their processing requirements in the rehabilitation unit—can significantly reduce admission delays, with only a small increase in rehab LOS. Managerial implications: Our study demonstrates the importance of quantifying different sources of delays in the design of effective operational interventions for reducing delays in admission to rehabilitation care. The proposed estimation framework can be applied in other transition-of-care settings with personalized capacity allocation decisions and hidden processing delays. History: This paper was selected for Fast Track in the M&SOM journal from the 2022 MSOM Healthcare SIG Conference. Funding: J. Dong was supported in part by the National Science Foundation [Grant CMMI-1762544]. V. Sarhangian was supported in part by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2018-04518] and the Connaught Fund. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.0377 .
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".