Development of an Evidence-Based Antimicrobial Stewardship Smartphone App in a Tertiary Academic Pediatric and Women’s Health Centre in Canada
Bibliographic record
Abstract
Smart phone use by medical professionals is ubiquitous. In a recent survey, > 90% of health care providers were interested in locally developed antimicrobial stewardship (AMS) and infectious diseases applications (“apps”). We describe the process by which our antimicrobial stewardship program (ASP) developed an app to provide guidance regarding empiric antimicrobial choice, and education about antimicrobials and pathogens, integrating local laboratory data. We also describe early app uptake. The IWK Health Centre is a 271-bed tertiary care Pediatric and Women’s health centre serving the Maritime Provinces in eastern Canada. Using the Spectrum Mobile Health platform, our ASP developed an app in consultation with pediatric and women’s health clinical divisions. Through collaboration with the microbiology laboratory, the app was integrated with our laboratory information system (LIS) allowing real-time access to local antibiogram results. The iPhone- and Android- compatible app was introduced to health care providers through presentations, hospital intranet, email, and word of mouth. Following the official launch, uptake was monitored both in number of app downloads and number of hits. Adherence to empiric treatment guidelines included in the app will be assessed utilizing our existing ASP prospective audit and feedback service. From December 2015 to March 2017, the ASP created content for the IWK AMS App. Three sections were developed. (1) Syndromes: evidence-based empiric treatment guidelines for common syndromes. (2) Antimicrobials: spectrum of activity, dosing regimens, drug monitoring, common usage, adverse effects, drug interactions and pharmacology. (3) Pathogens: information on precautions, local susceptibilities through linkage with our recently developed virtual antibiogram, associated syndromes, and epidemiology. In May 2017, the app was launched. Within the first 24 hours, it was downloaded 157 times and accessed 1,193 times. We describe the process and early uptake of a locally developed AMS app to complement our ASP, which includes a virtual antibiogram through interfacing with our LIS. This is the first AMS app available in a Pediatric and Women’s Health Care Centre in Canada. Further analysis of the app’s impact on antimicrobial usage is planned. K. A. Top, Pfizer: Investigator, Research support. GSK: Investigator, Research grant. J. M. Langley, GSK: Investigator, Research grant. Canadian Institutes of Health Research: Investigator, Research grant.S. A. Halperin, GSK: Scientific Advisor, Consulting fee. GSK: Grant Investigator, Research grant
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 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".