Implementation of a Modified Early Screening for Discharge Tool to Optimize Case Manager Efficiency and Impact Length of Stay
Bibliographic record
Abstract
PURPOSE OF STUDY: The postacute landscape has been challenged since the onset of the COVID-19 pandemic by staffing shortages and a decline in postacute bed availability. As a result, patients in acute care hospitals are experiencing longer lengths of stay (LOS) and case managers (CMs) are managing increasingly complex discharge plans. This project involved the design and implementation of a modified Early Screen for Discharge Planning (ESDP) tool to support prioritizing patients with complex discharge needs, with the primary outcome of decreasing LOS. PRIMARY PRACTICE SETTING: The project took place in a community teaching hospital, part of a large academic health system in the Northeast, United States. METHODOLOGY AND PARTICIPANTS: The project was designed as a prospective controlled study (between September 1 and November 30, 2021) with defined intervention and control cohorts, involving a modified ESDP electronic health record-based score including self-rated walking limitation, age, prior living status, and mobility level of assist. A modified ESDP score of 10 and greater indicated that patients would benefit from ongoing CM support, whereas those with an ESDP score of less than 10 were unlikely to have discharge planning needs. Participants were adult patients on medical and surgical inpatient units. RESULTS: The project included 718 patients, 376 and 342 in the intervention and control cohorts, respectively. The modified ESDP performed comparably with the standard ESDP (14% discrepancy, with all patients appropriately identified for CM services). Implementation of the modified ESDP led to 53.5% of patients screening out of CM services, thereby increasing the time CMs were able to spend on complex discharge planning and was associated with a trend in LOS reduction (0.55 days). IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: The findings of this project demonstrate that implementation of a modified ESDP can improve CM efficiency and improve hospital throughput. Given the unprecedented capacity challenges in both the acute and postacute settings, there is a need to implement CM workflow strategies that will optimize the effectiveness of critical resources, while ensuring that patients' complex discharge needs are met.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".