Rapid Prototyping and Implementation of Electronic Order Sets for Critically Ill Adults with COVID-19 Admitted to a Children's Hospital
Bibliographic record
Abstract
Abstract Objectives An eight-bed adult coronavirus (COVID-19) critical care (CC) unit was established within our pediatric CC unit (PCCU) when SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) variants increased the CC bed demand. Our objective was to rapidly implement electronic order sets (OSs) to facilitate computerized provider order entry (CPOE) for adult patients admitted within a children's hospital. Methods OS development began from the assessment of OSs from seven adult CC units. Using a pre-existing PCCU admission template, we created two OSs: adult COVID-19 admission and ongoing care. We tested the prototypes in a multidisciplinary onsite–virtual hybrid tabletop simulation to evaluate usability within established workflows. Participants utilized role-specific profiles within the electronic health record (EHR) training environment which paralleled their computer interface, permitting charting and documentation. EHR analysts were present to gather change requests. Following implementation, we performed twice-daily huddles with end users to identify issues. Results A total of 13 multidisciplinary bedside providers participated in simulation testing of the prototypes. Two safety issues were addressed before implementation. The electronic OSs were developed, tested, and implemented within 8 days. The postimplementation huddles identified one medication addition, and no deletions were necessary. Conclusion Caring for adult COVID-19 patients within a freestanding children's hospital presents challenges and has the potential to introduce latent safety threats. Rapid development and implementation of electronic OSs within 8 days to facilitate CPOE and reduce health care provider cognitive burden relied on leveraging functionality within the EMR system, performing iterative testing with a tabletop simulation, integration into previously established workflows, and gathering post-implementation feedback for continuous improvement.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".