{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T03:54:27Z","timestamp":1761710067790,"version":"3.37.3"},"reference-count":58,"publisher":"Georg Thieme Verlag KG","issue":"03","license":[{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"content-version":"vor","delay-in-days":72,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health NIDDK","doi-asserted-by":"crossref","award":["R01DK116898"],"award-info":[{"award-number":["R01DK116898"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Background\u2003Chronic kidney disease (CKD) is common and associated with adverse clinical outcomes. Most care for early CKD is provided in primary care, including hypertension (HTN) management. Computerized clinical decision support (CDS) can improve the quality of care for CKD but can also cause alert fatigue for primary care physicians (PCPs). Computable phenotypes (CPs) are algorithms to identify disease populations using, for example, specific laboratory data criteria.<\/jats:p><jats:p>\n          Objectives\u2003Our objective was to determine the feasibility of implementation of CDS alerts by developing CPs and estimating potential alert burden.<\/jats:p><jats:p>\n          Methods\u2003We utilized clinical guidelines to develop a set of five CPs for patients with stage 3 to 4 CKD, uncontrolled HTN, and indications for initiation or titration of guideline-recommended antihypertensive agents. We then conducted an iterative data analytic process consisting of database queries, data validation, and subject matter expert discussion, to make iterative changes to the CPs. We estimated the potential alert burden to make final decisions about the scope of the CDS alerts. Specifically, the number of times that each alert could fire was limited to once per patient.<\/jats:p><jats:p>\n          Results\u2003In our primary care network, there were 239,339 encounters for 105,992 primary care patients between April 1, 2018 and April 1, 2019. Of these patients, 9,081 (8.6%) had stage 3 and 4 CKD. Almost half of the CKD patients, 4,191 patients, also had uncontrolled HTN. The majority of CKD patients were female, elderly, white, and English-speaking. We estimated that 5,369 alerts would fire if alerts were triggered multiple times per patient, with a mean number of alerts shown to each PCP ranging from 0.07\u2013to 0.17 alerts per week.<\/jats:p><jats:p>\n          Conclusion\u2003Development of CPs and estimation of alert burden allows researchers to iteratively fine-tune CDS prior to implementation. This method of assessment can help organizations balance the tradeoff between standardization of care and alert fatigue.<\/jats:p>","DOI":"10.1055\/s-0043-1768994","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T22:52:49Z","timestamp":1689202369000},"page":"528-537","source":"Crossref","is-referenced-by-count":7,"title":["Refining Clinical Phenotypes to Improve Clinical Decision Support and Reduce Alert Fatigue: A Feasibility Study"],"prefix":"10.1055","volume":"14","author":[{"given":"Lipika","family":"Samal","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States"},{"name":"Harvard Medical School, Boston, Massachusetts, United States"}]},{"given":"Edward","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States"},{"name":"Alabama College of Osteopathic Medicine, Dothan, Alabama, United States"}]},{"given":"Skye","family":"Aaron","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States"}]},{"given":"John L.","family":"Kilgallon","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States"}]},{"given":"Michael","family":"Gannon","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States"},{"name":"Eastern Virginia Medical School, Norfolk, Virginia, United States"}]},{"given":"Allison","family":"McCoy","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, Tennessee, United States"}]},{"given":"Saul","family":"Blecker","sequence":"additional","affiliation":[{"name":"NYU School of Medicine, New York, New York, United States"}]},{"given":"Patricia C.","family":"Dykes","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States"},{"name":"Harvard Medical School, Boston, Massachusetts, United States"}]},{"given":"David W.","family":"Bates","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States"},{"name":"Harvard Medical School, Boston, Massachusetts, United States"}]},{"given":"Stuart","family":"Lipsitz","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States"},{"name":"Harvard Medical School, Boston, Massachusetts, United States"},{"name":"Department of Biostatistics, Harvard T. 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Chan School of Public Health, Boston, Massachusetts, United States"}]},{"given":"Adam","family":"Wright","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, Tennessee, United States"}]}],"member":"194","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"issue":"04","key":"ref1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1136\/jamia.1999.00660313","article-title":"The impact of computerized physician order entry on medication error prevention","volume":"6","author":"D W Bates","year":"1999","journal-title":"J Am Med Inform Assoc"},{"issue":"15","key":"ref2","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1001\/jama.280.15.1339","article-title":"Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review","volume":"280","author":"D L Hunt","year":"1998","journal-title":"JAMA"},{"issue":"06","key":"ref3","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1136\/jamia.1996.97084513","article-title":"A meta-analysis of 16 randomized controlled trials to evaluate computer-based clinical reminder systems for preventive care in the ambulatory setting","volume":"3","author":"S Shea","year":"1996","journal-title":"J Am Med Inform Assoc"},{"issue":"02","key":"ref4","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1136\/jamia.1999.0060104","article-title":"Computer-based guideline implementation systems: a systematic review of functionality and effectiveness","volume":"6","author":"R N Shiffman","year":"1999","journal-title":"J Am Med Inform Assoc"},{"issue":"02","key":"ref5","first-page":"187","article-title":"Clinical decision support: effectiveness in improving quality processes and clinical outcomes and factors that may influence success","volume":"87","author":"E V Murphy","year":"2014","journal-title":"Yale J Biol Med"},{"key":"ref6","first-page":"w14073","article-title":"Clinical decision support systems","volume":"144","author":"P E Beeler","year":"2014","journal-title":"Swiss Med Wkly"},{"issue":"09","key":"ref7","first-page":"626","article-title":"Electronic health records, clinical decision support, and blood pressure control","volume":"17","author":"L Samal","year":"2011","journal-title":"Am J Manag Care"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"e145","DOI":"10.1136\/amiajnl-2011-000743","article-title":"Evaluating alert fatigue over time to EHR-based clinical trial alerts: findings from a randomized controlled study","volume":"19","author":"P J Embi","year":"2012","journal-title":"J Am Med Inform Assoc"},{"issue":"03","key":"ref9","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1001\/archinternmed.2008.551","article-title":"Overrides of medication alerts in ambulatory care","volume":"169","author":"T Isaac","year":"2009","journal-title":"Arch Intern Med"},{"issue":"12","key":"ref10","doi-asserted-by":"crossref","first-page":"2310","DOI":"10.1377\/hlthaff.2010.1111","article-title":"Clinical decision support systems could be modified to reduce \u2018alert fatigue\u2019 while still minimizing the risk of litigation","volume":"30","author":"A S Kesselheim","year":"2011","journal-title":"Health Aff (Millwood)"},{"issue":"03","key":"ref11","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1136\/amiajnl-2012-001089","article-title":"Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records","volume":"20","author":"S Phansalkar","year":"2013","journal-title":"J Am Med Inform Assoc"},{"issue":"01","key":"ref12","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s12911-017-0430-8","article-title":"Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system","volume":"17","author":"J S Ancker","year":"2017","journal-title":"BMC Med Inform Decis Mak"},{"issue":"03","key":"ref13","doi-asserted-by":"crossref","first-page":"686","DOI":"10.4338\/ACI-2017-01-RA-0003","article-title":"Electronic health record alert-related workload as a predictor of burnout in primary care providers","volume":"8","author":"M E Gregory","year":"2017","journal-title":"Appl Clin Inform"},{"issue":"04","key":"ref14","first-page":"18","article-title":"Clinical alerts that cried wolf. 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