Successful Implementation of Workflow-Embedded Clinical Pathways During the COVID 19 Pandemic
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Clinical pathways have been found effective for improving adherence to evidence-based guidelines, thus providing better patient outcomes. As coronavirus disease-2019 (COVID-19) clinical guidance changed rapidly and evolved, a large hospital system in Colorado established clinical pathways within the electronic health record to guide clinical practice and provide the most up-to-date information to frontline providers. METHODS: On March 12, 2020, a system-wide multidisciplinary committee of specialists in emergency medicine, hospital medicine, surgery, intensive care, infectious disease, pharmacy, care management, virtual health, informatics, and primary care was recruited to develop clinical guidelines for COVID-19 patient care based on the limited available evidence and consensus. These guidelines were organized into novel noninterruptive digitally embedded pathways in the electronic health record (Epic Systems, Verona, Wisconsin) and made available to nurses and providers at all sites of care. Pathway utilization data were analyzed from March 14 to December 31, 2020. Retrospective pathway utilization was stratified by each care setting and compared with Colorado hospitalization rates. This project was designated as a quality improvement initiative. RESULTS: Nine unique pathways were developed, including emergency medicine, ambulatory, inpatient, and surgical care guidelines. Pathway data were analyzed from March 14 to December 31, 2020, and showed that COVID-19 clinical pathways were used 21 099 times. Eighty-one percent of pathway utilization occurred in the emergency department setting, and 92.4% applied embedded testing recommendations. A total of 3474 distinct providers employed these pathways for patient care. CONCLUSIONS: Noninterruptive digitally embedded clinical care pathways were broadly utilized during the early part of the COVID-19 pandemic in Colorado and influenced care across many care settings. This clinical guidance was most highly utilized in the emergency department setting. This shows an opportunity to leverage noninterruptive technology at the point of care to guide clinical decision-making and practice.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".