Implementation of a Mobile Clinical Decision Support Application to Augment Local Antimicrobial Stewardship
Bibliographic record
Abstract
BACKGROUND: Medical applications for mobile devices allow clinicians to leverage microbiological data and standardized guidelines to treat patients with infectious diseases. We report the implementation of a mobile clinical decision support (CDS) application to augment local antimicrobial stewardship. METHODS: We detail the implementation of our mobile CDS application over 20 months. Application utilization data were collected and evaluated using descriptive statistics to quantify the impact of our implementation. RESULTS: Project initiation focused on engaging key stakeholders, developing a business case, and selecting a mobile platform. The preimplementation phase included content development, creation of a pathway for content approval within the hospital committee structure, engaging clinical leaders, and formatting the first version of the guide. Implementation involved a media campaign, staff education, and integration within the electronic medical record and hospital mobile devices. The postimplementation phase required ongoing quality improvement, revision of outdated content, and repeated staff education. The evaluation phase included a guide utilization analysis, reporting to hospital leadership, and sustainability and innovation planning. The mobile application was downloaded 3056 times and accessed 9259 times during the study period. The companion web viewer was accessed 8214 times. CONCLUSIONS: Successful implementation of a customizable mobile CDS tool enabled our team to expand beyond microbiological data to clinical diagnosis, treatment, and antimicrobial stewardship, broadening our influence on antimicrobial prescribing and incorporating utilization data to inspire new quality and safety initiatives. Further studies are needed to assess the impact on antimicrobial utilization, infection control measures, and patient care outcomes.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".