{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:23:42Z","timestamp":1786116222495,"version":"build-2736575974"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Boehringer-Ingelheim Pharmaceuticals, Inc"},{"name":"Lilly USA LLC"},{"DOI":"10.13039\/100001003","name":"BIPI","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001003","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Treatment guidelines can improve population health; however, their implementation within electronic health records (EHRs) can be challenging. We aimed to create an implementable framework using the American Diabetes Association (ADA) Standards of Care (SOC) for people with type 2 diabetes and cardiovascular or renal disease as an example.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>A multidisciplinary team used agile methods to translate the text-based ADA SOC into structured elements within the EHR, including logic-driven algorithms and ontology groupers for diagnoses, laboratory values, and medications, leveraging standard terminologies such as SNOMED CT, LOINC, and RxNorm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The structured elements were used to implement 3 tools in the EHR: a real-time patient registry and 2 clinical decision support (CDS) instruments. The real-time registry enables dynamic, ongoing identification of patients eligible for guideline-directed medical therapy, supports more advanced analytics, and can be filtered to evaluate treatment gaps at the population and individual provider levels. The CDS tools allow clinicians to address these gaps directly within their EHR workflows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Transforming clinical guidelines into executable constructs within the EHR is feasible but remains complex and labor-intensive. Broader and more consistent implementation could be achieved if guideline organizations provided technical frameworks, regular updates (through addenda or shared interfaces), and collaborated with EHR vendors to support the distribution and maintenance of implementable algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The integration of executable logic into clinical guidelines, using deterministic frameworks such as Unified Modeling Language and standardized ontologies, would simplify guideline implementation across EHR platforms.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooag141","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T11:57:58Z","timestamp":1783598278000},"source":"Crossref","is-referenced-by-count":0,"title":["Challenges implementing treatment guidelines in electronic health records: the American Diabetes Association Standards of Care as a case example"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7022-9961","authenticated-orcid":false,"given":"Irene","family":"Demarie","sequence":"first","affiliation":[{"name":"The University of Texas Southwestern Medical Center Clinical Informatics Center, , Dallas, TX 75390,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael E","family":"Bowen","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine, Department of Internal Medicine and Peter O\u2019Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center , Dallas, TX 75390,","place":["United States"]},{"name":"Division of Endocrinology, Department of Internal Medicine, The University of Texas Southwestern Medical Center , Dallas, TX 75390,","place":["United 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Pharmaceuticals, Inc. , Ridgefield, CT 06877,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel F","family":"Heitjan","sequence":"additional","affiliation":[{"name":"Peter O\u2019Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center Department of Health Data Science and Biostatistics, , Dallas, TX 75390,","place":["United States"]},{"name":"Department of Statistics and Data Science, Southern Methodist University , Dallas, TX 75275,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zichang","family":"Xiang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, Southern Methodist University , Dallas, TX 75275,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph U","family":"Lehmann","sequence":"additional","affiliation":[{"name":"Department of Pediatrics and Clinical Informatics 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