Uptake and Utilization of the Management of Anticoagulation in the Periprocedural Period App: Longitudinal Analysis
Bibliographic record
Abstract
BACKGROUND: Anticoagulants are major contributors to preventable adverse drug events, and their optimal management in the periprocedural period is particularly challenging. Traditional methods of disseminating clinical guidelines and tools cannot keep pace with the rapid expansion of available therapeutic agents, approved indications for use, and published medical evidence, so a mobile app, Management of Anticoagulation in the Periprocedural Period (MAPPP), was developed and disseminated to provide clinicians with guidance that reflects the most current medical evidence. OBJECTIVE: The objective of this study was to assess the global, national, and state-level acquisition of a mobile app since its initial release and characterize individual episodes of use based on drug selection, procedural bleeding risk, and patient thromboembolic risk. METHODS: Data were extracted from a mobile app usage tracker (Google Analytics) to characterize new users and completed episodes temporally (by calendar quarter) and geographically (globally, nationally, and in the targeted US state of New York) for the period between April 1, 2016 and September 30, 2017. RESULTS: The app was acquired by 2866 new users in the measurement period, and the users completed nearly 10,000 individual episodes of use. Acquisition and utilization spanned 51 countries globally, predominantly in the United States and particularly in New York State. Warfarin and rivaroxaban were the most frequently selected drugs, and completed episodes most frequently included the selection of high bleeding risk (4888/9963, 49.06%) and high thromboembolic risk categories (4500/9963, 45.17%). CONCLUSIONS: The MAPPP app is a successful means of disseminating current guidance on periprocedural anticoagulant use, as indicated by broad global uptake and upward trends in utilization. Limitations in access to provider and patient-specific data preclude objective evaluation of the clinical impact of the app. An ongoing study incorporating app logic into electronic health record systems at participant health systems will provide a more definitive evaluation of the clinical impact of the app logic.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".