Translating medical evidence into clinical decision-support software: Requirements for an interactive mobile tool for oral-contraceptive selection

Background: Combination oral contraceptives are prescribed more than 10 million times each year in America alone. Prescribers have more than 70 oral-contraceptive choices, but individual women's medical conditions may make some contraceptives inappropriate or even deadly. Traditional sources of prescribing information are impractical at the point of care. Mobile apps are revolutionizing clinical workflow, but none have been deployed for oral-contraceptive selection. Objective/Purpose: Our objective was to model an evidence-based interactive mobile tool for selecting oral contraceptives. Methods: We indexed the practice guidelines for hormonal contraception from the World Health Organization and the American Congress of Obstetricians and Gynecologists, and expert recommendations from two contraceptive textbooks. We gathered the rules into IF-THEN use cases, annotated with evidence quality and prioritized by likelihood and importance, and classified contraceptive choices into specific recommendation groups. Results: We identified 32 guidelines and 25 expert recommendations using a total of 73 data elements, mostly TRUE/FALSE choices or numeric values. We organized these into 22 use cases evaluating one to eight data elements each, few enough to fit on a smartphone screen. Discussion/Conclusions: Handheld clinical applications can facilitate evidence-based medicine, and reduce medical errors, by moving the best available medical knowledge from labor-intensive traditional resources to prescribers' fingertips. They may create privacy and data-security risks, but limiting apps to anonymous single-use queries obviates these. Potential enhancements include additional object- oriented treatment domains, automatically updating rules engines using the Guideline Elements Model, and allowing user communities to add rules.