Improving the management of Acute Myocardial Infarctions: Exploring the barriers and facilitators to implementing a Smartphone Application for physicians (Preprint)
Bibliographic record
Abstract
BACKGROUND The delivery of timely and appropriate care is crucial for survival in patients experiencing ST-elevation myocardial infarction (STEMI). Efficient communication and exchange of test results between the referring emergency medicine (EM) physician or Emergency Medical Service (EMS) paramedic and the interventional cardiologist (IC) is essential to achieving timely care. In many communities, sharing of some information crucial to decision making, such as electrocardiograms (ECG), relies on using fax or text message. The SMART AMI App was developed to streamline communication and ensure that information is shared in a secure manner. The application is simple to use, privacy-compliant, and allows for rapid ECG sharing between healthcare providers, enabling timely decision-making. OBJECTIVE This paper details the results of targeted pre-implementation surveys to establish the barriers and enablers of using a smartphone application to transmit ECG images among ICs, EM physicians and EMS paramedics to help tailor implementation interventions. METHODS To assess the proposed acceptability and uptake of the application, pre-implementation surveys were disseminated to ICs, EM physicians and EMS paramedics in one region of Ontario, Canada. Questions were generated based on selected components of the Consolidated Framework for Implementation Research, results from a pilot study carried out at a regional hospital where the SMART-AMI App was previously implemented, and predicted barriers based on expert guidance. The pre-implementation surveys consisted of both 7-point Likert scale questions (1=strongly disagree and 7=strongly agree) and open-ended questions. Open-ended data was extracted verbatim and analyzed using an inductive qualitative approach, with transcripts coded into descriptive qualitative codes and then collapsed into themes. RESULTS Uptake of the survey was acceptable, with 9 of the invited 10 ICs, 51 of the invited 223 EM physicians, and 93 of the invited 1138 EMS paramedics responding. Survey findings demonstrated a need for an App, as all groups recognized that current practices for sharing ECGs allowed room for improvement, accepting that fax can be inconvenient and text messages may not be secure. When asked whether there was a need for a smartphone application to transmit ECGs, ICs (M=6.67, SD=0.50), EM physicians (M=5.57, SD=1.30) and EMS paramedics (M=5.79, SD=1.45) consistently agreed. Commonly reported barriers were concerns over technological challenges, privacy issues, and cell phone reception strength. Through identification of the barriers in each stakeholder group, implementation strategies were developed that facilitated the scale-up of this system-change intervention. CONCLUSIONS Results from the three online pre-implementation surveys to identify key barriers and enablers to implementation of the App helped inform the selection of tailored implementation strategies to support roll-out of the App across the health region. The surveys identified key barriers around technology, privacy concerns, and access to required WiFi that needed to be addressed during App implementation to facilitate uptake and use. Results from the surveys, and ongoing evaluation of effectiveness, are informing expansion of the App intervention to local ambulance services and other health regions. CLINICALTRIAL https://clinicaltrials.gov/study/NCT05290389
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".