<p>Development and Implementation of a Clinical Pathway to Reduce Inappropriate Admissions Among Patients with Community-Acquired Pneumonia in a Private Health System in Brazil: An Observational Cohort Study and a Promising Tool for Efficiency Improvement</p>
Bibliographic record
Abstract
PURPOSE: Patients with community-acquired pneumonia (CAP) at low risk of death by CURB-65 scoring system are usually unnecessarily treated as inpatients generating additional economic and clinical burden. We aimed to implement an evidence-based clinical pathway to reduce hospital admissions of low-risk CAP and investigate factors related to mortality and readmissions within 30 days. PATIENTS AND METHODS: From November 2015 to August 2017, a clinical pathway was implemented at 20 hospitals. We included patients aged >18 years, with a diagnosis of CAP by the attendant physician. The main outcome was the monthly proportion of low-risk CURB-65 admission after the implementation of the clinical pathway. Logistic regression models were performed to assess variables associated with mortality and readmission in the admitted population within 30 days. RESULTS: We included 10,909 participants with suspected CAP. The proportion of low-risk CAP admitted decreased from 22.1% to 12.8% in the period. Among participants with low risk, there has been no perceptible increase in deaths (0.80%) or readmissions (6.92%). Regression analysis identified that CURB-65 variables, presence of pleural effusion (OR= 1.74; 95%CI=1.08-2.8; p=0.02) and leucopenia (OR= 2.47; 95%CI=1.11-5.48; p=0.02) were independently associated with 30-day mortality, whereas a prolonged hospital stay (OR= 2.09; 95%CI=1.14-3.83; p=0.01) was associated with 30-day readmission in the low-risk population. CONCLUSION: The implementations of a clinical pathway diminished the proportion of low-risk CAP admissions with no apparent increase in clinical outcomes within 30 days. Nonetheless, additional factors influence the clinical decision about the site of care management in low-risk CAP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".