{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T19:53:40Z","timestamp":1791316420472,"version":"4.3.1"},"reference-count":26,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Assessing medication adherence is central to quality care, yet linking electronic health record (EHR) medication orders to outpatient pharmacy dispense data remains technically complex.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to present a generalized, reproducible tutorial for linking EHR medication orders to pharmacy dispense data that can be used to assess medication dispense proportions.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We developed and validated a structured query approach to link EHR medication orders to external pharmacy dispense data using patient identifiers, medication-level identifiers, pharmacy identifiers, and temporal constraints. The tutorial emphasizes key design decisions, including handling multiple triggering events, deduplication across vendors, and managing formulation changes. A retrospective cohort of pediatric acute otitis media encounters (January 1, 2021, to January 1, 2024) was used as an illustrative example.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Overall, 98.3% (302\/307) of pharmacies in the cohort returned at least 1 dispense record during the study period and were therefore classified as reporting pharmacies. Among 3404 orders, 2616 (76.9%) had a recorded dispense.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n EHR-integrated pharmacy data provide a feasible, timely proxy for assessing medication adherence. This tutorial provides a scalable framework for linking EHR and pharmacy data for medication adherence studies, while highlighting key methodological considerations for SQL coding.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/90954","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T19:21:00Z","timestamp":1791314460000},"page":"e90954-e90954","source":"Crossref","is-referenced-by-count":0,"title":["Linking Dispense Data to Electronic Health Orders: Tutorial for Querying Commercial Pharmacy Databases to Support Systemwide Quality Improvement"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6787-0448","authenticated-orcid":false,"given":"Benjamin","family":"Michaels","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8438-6163","authenticated-orcid":false,"given":"Jessica","family":"Pourian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,10,6]]},"reference":[{"issue":"4","key":"R1","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1007\/s13142-015-0315-2","article-title":"Self-report measures of medication adherence behavior: recommendations on optimal use","volume":"5","author":"Stirratt","journal-title":"Transl Behav Med"},{"issue":"1","key":"R2","doi-asserted-by":"publisher","DOI":"10.1186\/s40545-021-00385-w","article-title":"Estimating proportion of days covered (PDC) using real-world online medicine suppliers\u2019 datasets","volume":"14","author":"Prieto-Merino","journal-title":"J Pharm Policy Pract"},{"key":"R3","unstructured":"Measures & resources. 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