Root causes for delayed hospital discharge in patients with ST-segment Myocardial Infarction (STEMI): a qualitative analysis
Bibliographic record
Abstract
BACKGROUND: The majority of patients who suffer a ST-segment myocardial infarction (STEMI) are hospitalized for longer than 48 h. With the advent of reperfusion therapy, the benefits of such extended hospitalization has been questioned. The goal of this qualitative study was to identify the root causes for prolonged hospitalization in STEMI patients in order to refine future interventions to optimize the length of hospitalization. METHODS: Practitioners involved in the discharge process for STEMI patients at a single tertiary care STEMI center underwent semi-structured interviews focused on three fictional patient cases. Data were transcribed and analyzed for key themes by thematic analysis. RESULTS: Interviews were conducted with 17 practitioners (5 Attending Physicians, 4 Internal Medicine Residents, 4 Cardiology Residents, 4 Nursing Staff). The key themes were patient factors, provider factors, and transitions to outpatient care. Patient factors included concerns that early discharge would limit dose titration of medications, the educational experience of the patient, and prevent monitoring for complications. Provider factors included past clinical experience with STEMI complications, in turn impacting discharging behaviour. Transitions of care factors were difficulty in establishing reliable follow-up plans and home care services. CONCLUSIONS: Several themes were identified that influence the timing of discharge post STEMI. The majority of these issues are not incorporated into currently available post STEMI risk stratification tools. Future quality improvement interventions to reduce STEMI length of stay should focus on in-patient and out-patient strategies to address these unique clinical situations.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".