Utilizing information technology to improve transition of care from hospital to home
Bibliographic record
Abstract
Background and objective: Failure to appropriately plan for a safe and effective transition to the next level of care leads to a greater use of hospital and emergency services, often measured by rates of readmission. A large academic medical center located in Houston, Texas, USA consistently achieves an overall University Health System Consortium (UHC) ranking for most benchmarks in the top decile (90th percentile) except for 30-day all-cause readmission rate, which ranks in the bottom decile. The objective of the study was to implement changes in Midas+, the system used by Houston Methodist Hospital for quality and case management activities, select a formal transition of care plan and implement the process on a pilot unit to reduce the 30-day readmission rate and improve the discharge planning process. Methods: Setting: Cardiovascular Intermediate Care Unit (CVIMU), a 30-bed cardiovascular surgery unit within an academic medical center in Houston Texas. The project intervention included the addition of a readmission risk screen in the Hospital Case Management (HCM) and intervention screens based on the Coleman Model in the Community Case Management (CCM) module of Midas+. The clinical improvement involved three components spanning from hospital to home: (1) Screening patients for readmission risk upon admission and assigning those identified as high-risk for readmission (with a planned discharge to home) to a Transition Coach, (2) A visit by the Transition Coach during the patient’s hospital stay to assess the patient and provide coaching, and (3) Providing five follow-up phone calls from the Transition Coach post-discharge. Results: The system changes in Midas+ were implemented and were effective in tracking the interventions. Of the 258 patients admitted from July through August 2014, 226 or 87.5% of the patients were screened using the readmission risk assessment tool. Of the patients’ screened, 49 were considered high risk with 26 discharged home, 22 or 45.8% discharged to another level of care and one patient expired. During the pilot, of 19 patients were followed by a transition coach only one patient readmitted to the hospital. Conclusions: The project demonstrated that utilization of a computer system to record the readmission risk screen, track the assessment of the pillars (medication management, continued care, red flags to report, and personal health record) over the six time intervals of the pilot transition program was effective in tracking the intervention. The data collected through information technology was easily retrieved for tracking progress and evaluation. The outcome of this pilot has shown that a well-defined transition of care program may decrease the 30-day readmission rate.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".