Effectiveness of a Mobile App (PIMPmyHospital) in Reducing Therapeutic Turnaround Times in an Emergency Department: Protocol for a Pre- and Posttest Study
Bibliographic record
Abstract
BACKGROUND: Delays in reviewing issued laboratory results in emergency departments (EDs) can adversely affect efficiency and quality of care. One opportunity to improve therapeutic turnaround time could be to provide real-time access to laboratory results on mobile devices available to every caregiver. We developed a mobile app named "Patients In My Pocket in my Hospital" (PIMPmyHospital) to help ED caregivers automatically obtain and share relevant information about the patients they care for including laboratory results. OBJECTIVE: This pre- and posttest study aims to explore whether the implementation of the PIMPmyHospital app impacts the timeliness with which ED physicians and nurses remotely access laboratory results while actively working in their real-world environment, including ED length of stay, technology acceptance and usability among users, and how specific in-app alerts impact on its effectiveness. METHODS: This single-center study of nonequivalent pre- and posttest comparison group design will be conducted before and after the implementation of the app in a tertiary pediatric ED in Switzerland. The retrospective period will cover the previous 12 months, and the prospective period will cover the following 6 months. Participants will be postgraduate residents pursuing a ≤6-year residency in pediatrics, pediatric emergency medicine fellows, and registered nurses from the pediatric ED. The primary outcome will be the mean elapsed time in minutes from delivery of laboratory results to caregivers' consideration by accessing them either through the hospital's electronic medical records or through the app before and after the implementation of the app, respectively. As secondary outcomes, participants will be queried about the acceptance and usability of the app using the Unified Theory of Acceptance and Use of Technology model and the System Usability Scale. ED length of stay will be compared before and after the implementation of the app for patients with laboratory results. The impact of specific alerts on the app, such as a flashing icon or sound for reported pathological values, will be reported. RESULTS: Retrospective data collection gathered from the institutional data set will span a 12-month period from October 2021 to October 2022, while the 6-month prospective collection will begin with the implementation of the app in November 2022 and is expected to cease at the end of April 2023. We expect the results of the study to be published in a peer-reviewed journal in late 2023. CONCLUSIONS: This study will show the potential reach, effectiveness, acceptance, and use of the PIMPmyHospital app among ED caregivers. The findings of this study will serve as the basis for future research on the app and any further development to improve its effectiveness. Trial Registration: ClinicalTrials.gov NCT05557331; https://clinicaltrials.gov/ct2/show/NCT05557331. TRIAL REGISTRATION: ClinicalTrials.gov NCT05557331; https://clinicaltrials.gov/ct2/show/NCT05557331. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/43695.
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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 | 0.010 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".