Mobile Clinical Decision Tools Among Emergency Department Clinicians: Web-Based Survey and Analytic Data for Evaluation of The Ottawa Rules App
Bibliographic record
Abstract
BACKGROUND: The Canadian CT Head Rule (CCHR), the Canadian Transient Ischemic Attack (TIA) Score, and the Subarachnoid Hemorrhage (SAH) Rule have all previously demonstrated the potential to significantly standardize care and improve the management of patients in emergency departments (EDs). On the basis of user feedback, we believe that the addition of these rules to the Ottawa Rules App has the potential to increase the app's usability and user acceptability. OBJECTIVE: This study aimed to evaluate the perceived usefulness, acceptability, and uptake of the enhanced Ottawa Rules App (which now includes CCHR, TIA, and SAH Rules) among ED clinicians (medical students, residents, nurses, and physicians). METHODS: The enhanced Ottawa Rules App was publicly released for free on iOS and Android operating systems in November 2018. This study was conducted across 2 tertiary EDs in Ottawa, Canada. Posters, direct enrollment, snowball sampling, and emails were used for study recruitment. A 24-question Web-based survey was administered to participants via email, and this was used to determine user acceptability of the app and Technology Readiness Index (TRI) scores. In-app user analytics were collected to track user behavior, such as the number of app sessions, length of app sessions, frequency of rule use, and the date app was first opened. RESULTS: A total of 77 ED clinicians completed the study, including 34 nurses, 12 residents, 14 physicians, and 17 medical students completing ED rotations. The median TRI score for this group was 3.38, indicating a higher than average propensity to embrace and adopt new technologies to accomplish goals in their work or daily lives. The majority of respondents agreed or strongly agreed that the app helped participants accurately carry out the clinical rules (56/77, 73%) and that they would recommend this app to their colleagues (64/77, 83%). Feedback from study participants suggested further expansion of the app-more clinical decision rules (CDRs) and different versions of the app tailored to the clinician role. Analysis and comparison of Google Analytics data and in-app data revealed similar usage behavior among study-enrolled users and all app users globally. CONCLUSIONS: This study provides evidence that using the Ottawa Rules App (version 3.0.2) to improve and guide patient care would be feasible and widely accepted. The ability to verify self-reported user data (via a Web-based survey) against server analytics data is a notable strength of this study. Participants' continued app use and request for the addition of more CDRs warrant the further development of this app and call for additional studies to evaluate its feasibility and usability in different settings as well as assessment of clinical impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".