{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T09:53:26Z","timestamp":1786182806831,"version":"3.56.0"},"reference-count":15,"publisher":"Georg Thieme Verlag KG","issue":"03","funder":[{"DOI":"10.13039\/100006513","name":"Duke Clinical Research Institute","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006513","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2017,7]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Signed in 2009, the Health Information Technology for Economic and Clinical Health Act infused $28 billion of federal funds to accelerate adoption of electronic health records (EHRs). Yet, EHRs have produced mixed results and have even raised concern that the current technology ecosystem stifles innovation. We describe the development process and report initial outcomes of a chronic kidney disease analytics application that identifies high-risk patients for nephrology referral. The cost to validate and integrate the analytics application into clinical workflow was $217,138. Despite the success of the program, redundant development and validation efforts will require $38.8 million to scale the application across all multihospital systems in the nation. We address the shortcomings of current technology investments and distill insights from the technology industry. To yield a return on technology investments, we propose policy changes that address the underlying issues now being imposed on the system by an ineffective technology business model.<\/jats:p><jats:p>Citation: Sendak MP, Balu S, Schulman KH. Barriers to Achieving Economies of Scale in Analysis of EHR Data. Appl Clin Inform 2017; 8: 826\u2013831 https:\/\/doi.org\/10.4338\/ACI-2017-03-CR-0046<\/jats:p>","DOI":"10.4338\/aci-2017-03-cr-0046","type":"journal-article","created":{"date-parts":[[2017,8,9]],"date-time":"2017-08-09T08:12:10Z","timestamp":1502266330000},"page":"826-831","source":"Crossref","is-referenced-by-count":45,"title":["Barriers to Achieving Economies of Scale in Analysis of EHR Data"],"prefix":"10.4338","volume":"08","author":[{"given":"Mark P.","family":"Sendak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suresh","family":"Balu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin A.","family":"Schulman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2017,12,20]]},"reference":[{"issue":"24","key":"ref2","doi-asserted-by":"crossref","first-page":"2527","DOI":"10.1056\/NEJMhpr066212","article-title":"Information technology comes to medicine","volume":"356","author":"D Blumenthal","year":"2007","journal-title":"N Engl J Med"},{"issue":"07","key":"ref3","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1377\/hlthaff.2014.0041","article-title":"Big Data In health care: using analytics to identify and manage high-risk and high-cost patients","volume":"33","author":"DW Bates","year":"2014","journal-title":"Health Aff (Millwood)"},{"issue":"02","key":"ref4","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1136\/amiajnl-2014-003023","article-title":"Implications of an emerging EHR monoculture for hospitals and healthcare systems","volume":"22","author":"R Koppel","year":"2015","journal-title":"J Am Med Inform Assoc"},{"issue":"24","key":"ref5","doi-asserted-by":"crossref","first-page":"2240","DOI":"10.1056\/NEJMp1203102","article-title":"Escaping the EHR trap - the future of health IT","volume":"366","author":"KD Mandl","year":"2012","journal-title":"N Engl J Med"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"b2395.","DOI":"10.1136\/bmj.b2395","article-title":"The role of specialists in managing the health of populations with chronic illness: the example of chronic kidney disease","volume":"339","author":"BJ Lee","year":"2009","journal-title":"BMJ"},{"issue":"01","key":"ref7","first-page":"1.","article-title":"Effects of proactive population-based nephrologist oversight on progression of chronic kidney disease: a retrospective control analysis","volume":"12","author":"B Lee","year":"2012","journal-title":"BMC Health Serv Res"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"S30","DOI":"10.1097\/MLR.0b013e31829b1dbd","article-title":"Caveats for the use of operational electronic health record data in comparative effectiveness research","volume":"51","author":"WR Hersh","year":"2013","journal-title":"Med Care"},{"issue":"07","key":"ref10","first-page":"488","article-title":"Using Medicare data for comparative effectiveness research: opportunities and challenges","volume":"17","author":"V Fung","year":"2011","journal-title":"Am J Manag Care"},{"issue":"01","key":"ref11","doi-asserted-by":"crossref","first-page":"e5.","DOI":"10.2196\/medinform.3172","article-title":"Next generation phenotyping using the unified medical language system","volume":"2","author":"T Adamusiak","year":"2014","journal-title":"JMIR Med Inform"},{"issue":"15","key":"ref12","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1001\/jama.2011.451","article-title":"A predictive model for progression of chronic kidney disease to kidney failure","volume":"305","author":"N Tangri","year":"2011","journal-title":"JAMA"},{"issue":"24","key":"ref13","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1001\/jama.2014.6634","article-title":"CKD Prognosis Consortium. Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality","volume":"311","author":"J Coresh","year":"2014","journal-title":"JAMA"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"S22","DOI":"10.1097\/MLR.0b013e31829b1e2c","article-title":"Data quality assessment for comparative effectiveness research in distributed data networks","volume":"51","author":"JS Brown","year":"2013","journal-title":"Med Care"},{"issue":"08","key":"ref16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2215\/CJN.00940115","article-title":"CKD as a model for improving chronic disease care through electronic health records","volume":"10","author":"PE Drawz","year":"2015","journal-title":"Clin J Am Soc Nephrol"},{"issue":"02","key":"ref19","first-page":"99","article-title":"Improve data quality for competitive advantage","volume":"36","author":"TC Redman","year":"1995","journal-title":"Sloan Manag Rev"},{"key":"ref21","first-page":"574","article-title":"Observational health data sciences and informatics (OHDSI): opportunities for observational researchers","volume":"216","author":"G Hripcsak","year":"2015","journal-title":"Stud Health Technol Inform"}],"container-title":["Applied Clinical Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.4338\/ACI-2017-03-CR-0046.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T07:45:48Z","timestamp":1685519148000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.4338\/ACI-2017-03-CR-0046"}},"subtitle":["A Cautionary Tale"],"short-title":[],"issued":{"date-parts":[[2017,7]]},"references-count":15,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2017,12,20]]},"published-print":{"date-parts":[[2017,7]]}},"URL":"https:\/\/doi.org\/10.4338\/aci-2017-03-cr-0046","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["1869-0327"],"issn-type":[{"value":"1869-0327","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,7]]}}}