Aspirin decision support using data-driven treatment algorithms [poster]
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INTRODUCTION: The role of aspirin therapy for reducing risk of cardiovascular events among those with pre-existing cardiovascular disease is well-established. However, a more individualized approach is recommended for primary prevention based on estimated risks for cardiovascular disease and gastrointestinal bleeding. METHODS: The United States Preventive Service task force has published methods and tables to estimate the number of MIs and strokes prevented and estimated harms of using aspirin based on age categories in hypothetical cohorts of men and women. Translation of the guideline requires data and formulas to calculate risk which are not readily available to practicing clinicians. We took advantage of the opportunity to enhance the efficiency of provider and patient decision making regarding aspirin through the use of electronic health record data and computer program assistance to assess the risks and benefits. RESULTS: The decision support program for aspirin in HPMG was integrated with the electronic health record through a web-service called Cardiovascular (CV) Wizard. At the point of care, de-identified data including pertinent demographics, diagnosis codes, lab results, medications, and allergies are transmitted to the web service and run through a set of sophisticated algorithms to assess whether aspirin is indicated and to provide individualized treatment suggestions and safety alerts based on known allergies and intolerance, contraindications, and identification of previous bleeding risks. Conclusion: Using electronic decision support algorithms, it is possible to provide patients and providers with printable information to engage them in more evidence-based decisions about aspirin use for primary prevention.