{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T13:28:39Z","timestamp":1777555719667,"version":"3.51.4"},"reference-count":34,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2014,12,18]],"date-time":"2014-12-18T00:00:00Z","timestamp":1418860800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"published-print":{"date-parts":[[2015,5,1]]},"abstract":"<jats:p>\n                    Background: Current treatment guidelines for type 2 diabetes (T2D) recommend individualized intensification of therapy for glycated hemoglobin (A1C) \u2265 7% in most patients. The purpose of this investigation was to explore the ability of an electronic medical record (EMR) to identify glycemic intensification strategies among T2D patients receiving pharmacologic therapy. Methods: Patient records between 2005 and 2011 with documentation of A1C and active prescriptions for any diabetes medications were queried to identify potential candidates for intensification based on A1C \u2265 7% while on 1-2 oral diabetes medications (ODM). Patients with follow-up A1C values within 1 year of index A1C were grouped according to intensification with insulin, GLP-1 receptor agonists (GLP-1RA), a new class of ODM, or no intensification. Changes in A1C and continuation of intensification therapy were determined. Results: A total of 4921 patients meeting inclusion criteria were intensified with insulin (n = 416), GLP-1RA (n = 68), ODM (n = 1408), or no additional therapy (n = 3029). Patients receiving insulin had higher baseline (9.3 \u00b1 2.0 vs 8.3 \u00b1 1.2 vs 8.3 \u00b1 1.3 vs 7.6 \u00b1 1.0%, P &lt; .0001) and follow-up A1C (8.1 \u00b1 1.6 vs 7.5 \u00b1 1.2 vs 7.6 \u00b1 1.3 vs 7.2 \u00b1 1.1%, P &lt; .0001) despite experiencing larger absolute A1C reductions (\u22121.2 \u00b1 2.1 vs \u22120.8 \u00b1 1.4 vs \u22120.7 \u00b1 1.4 vs \u22120.3 \u00b1 1.1%, P &lt; .0001). Patients receiving GLP-1RA were more obese at baseline (BMI: 33.6 \u00b1 7.1 vs 37.7 \u00b1 6.1 vs 33.7 \u00b1 6.8 vs 32.9 \u00b1 7.1 kg\/m\n                    <jats:sup>2<\/jats:sup>\n                    , P &lt; .0001) and follow-up (BMI: 33.9 \u00b1 7.3 vs 36.6 \u00b1 6.1 vs 33.8 \u00b1 7.0 vs 32.4 \u00b1 7.0 kg\/m\n                    <jats:sup>2<\/jats:sup>\n                    , P &lt; .0001) despite experiencing more absolute weight reduction. Insulin was the most and GLP-1RA the least likely therapy to be continued. Conclusions: An EMR allows identification of prescribing practices and compliance with T2D treatment guidelines. Patients receiving intensification of glycemic medications had baseline A1C &gt;8% suggesting that treatment recommendations are not being followed.\n                  <\/jats:p>","DOI":"10.1177\/1932296814564183","type":"journal-article","created":{"date-parts":[[2014,12,19]],"date-time":"2014-12-19T22:04:02Z","timestamp":1419026642000},"page":"593-601","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Use of an Electronic Medical Record (EMR) to Identify Glycemic Intensification Strategies in Type 2 Diabetes"],"prefix":"10.1177","volume":"9","author":[{"given":"Mary T.","family":"Korytkowski","sequence":"first","affiliation":[{"name":"Division of Endocrinology, University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Brooks","sequence":"additional","affiliation":[{"name":"Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manuel","family":"Lombardero","sequence":"additional","affiliation":[{"name":"Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dilhari","family":"DeAlmeida","sequence":"additional","affiliation":[{"name":"Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Justin","family":"Kanter","sequence":"additional","affiliation":[{"name":"UPMC, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasudev","family":"Magaji","sequence":"additional","affiliation":[{"name":"Lehigh Valley Health Network, Diabetes and Endocrinology, Allentown, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trevor","family":"Orchard","sequence":"additional","affiliation":[{"name":"Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linda","family":"Siminerio","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, University of Pittsburgh, Pittsburgh, PA, 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