{"id":31382,"date":"2018-11-13T13:49:09","date_gmt":"2018-11-13T18:49:09","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/an-epic-use-of-machine-learning\/"},"modified":"2018-11-13T13:49:09","modified_gmt":"2018-11-13T18:49:09","slug":"an-epic-use-of-machine-learning","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/an-epic-use-of-machine-learning\/","title":{"rendered":"An Epic use of Machine Learning"},"content":{"rendered":"

Epic Systems is a leader in the US healthcare software field and its core product is an electronic health record (EHR) system.\u00a0\u00a0 Electronic health records are files that are used to store patient information relating to their past, current, and future treatments.\u00a0 Currently Epic Systems stores electronic medical records of over 200 million Americans and is the leader in market share relative to other EHR companies [1].<\/p>\n

Medical misdiagnosis is a major cause of mistreatment in the United States. Misdiagnosis and medical errors are both a financial strain on the healthcare system and leads to pain and suffering among patients [2].\u00a0 Machine learning paired with medical records has already shown value at predicting suicide risk [3] and at predicting a range of clinical problems and outcomes [4].\u00a0 With the sum total of the data they currently possess, Epic can improve health outcomes using artificial intelligence.\u00a0 Rather than simply storing patient data, Epic is trying to position themselves to enable physicians and nurses to more directly benefit from the data.\u00a0 Considering the scale at which they currently operate, even marginal improvements in diagnosis or treatment plans caused by machine learning could benefit thousands of people.<\/p>\n

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[5]<\/figcaption><\/figure>Additionally, I believe there are compelling business reasons why Epic would benefit from an increased incorporation of machine learning in their core product.\u00a0 Switching EHR providers is a costly and timely investment. \u00a0In 2015 when New England based Partners Healthcare implemented Epic the overall project cost more than a billion dollars [6].\u00a0 Moving forward, it will become more difficult for Epic to generate incremental income through user growth.\u00a0 The EHR landscape has become more consolidated and most health systems have already adopted an EHR [1].\u00a0 Compared to this, artificial intelligence in healthcare is anticipated to grow at an annual growth rate of 53.9% for the next five years [7].\u00a0 The addition of robust artificial intelligence services allows the business to continue to grow and expand while delivering real value to patients.<\/p>\n

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[7]<\/figcaption><\/figure>Epic has taken steps to build out its predictive artificial intelligence capabilities.\u00a0 \u00a0In 2017, the American Medical Association launched a collaborative effort between healthcare analytics firms, EHR companies, and groups focused on using artificial intelligence to improve health outcomes.\u00a0 Initially Epic did not join this consortium [8] but has since joined.\u00a0\u00a0 The idea with this community is to set standards to allow data interoperability between EHR systems and to then pool that data into an environment where continuous learning is possible [9].<\/p>\n

Their most prominent efforts to date have revolved around partnerships with other organizations.\u00a0 In conjunction with Microsoft, Epic rolled out a system-wide solution for Oscher Health System in Louisiana [10].\u00a0 The design of this program is to allow physicians and staff to use the predicative power of the data to identify gaps in treatment and recognize root-causes.\u00a0 \u00a0Epic has also taken steps to allow for interoperability between its proprietary software and that of firms developing dedicate healthcare artificial intelligence software.\u00a0 Last year it created an integration with Nuance, a machine learning driven voice documentation tool for physicians that also incorporates machine learning performance monitoring analytics [11].<\/p>\n

Moving forward, I think Epic needs to be more aggressive in their approach to investigating artificial intelligence. \u00a0\u00a0The growth of the artificial intelligence market and the need for improved diagnostic tools leaves white space for Epic to expand in this area.\u00a0 In order to fulfill their mission as an organization in the healthcare field and to continue to create value as a business, they need to hasten their efforts to develop and implement solutions that incorporate artificial intelligence.<\/p>\n

To accomplish this Epic should consider expanding through acquisition and an aggressive recruitment strategy to build out their own internal capabilities.\u00a0 With their current integration with patient health records I believe they are uniquely situated to motivate teams to develop this capability and therefore succeed.\u00a0 \u00a0Expanding via acquisition would be an efficient way to move more strongly into this field.\u00a0 Epic, as a privately held company, also has some flexibility in determining which companies to acquire.\u00a0 Beyond acquisitions, Epic should also build out an internal department focused on developing these machine learning capabilities.\u00a0 Making this somewhat easier to accomplish, they are headquartered in Madison, Wisconsin and are near a top-15 computer science university [12].<\/p>\n

Thinking about the implications of the incorporating artificial intelligence into Epic\u2019s health records, how can they ensure they have the trust of consumers with using their data to feed the artificial intelligence algorithms? The second side of the equation is healthcare providers, how can Epic encourage physicians to use the new recommendations as one additional tool in their diagnosis process?<\/p>\n

Word Count: 766<\/p>\n

Sources<\/p>\n

[1] Healthcare Analytics: Technologies and Global Markets, accessed via BCC Research.\u00a0 Neha Maliwal.\u00a0 November 2017. Accessed November 9, 2018.<\/p>\n

[2] Khullar, D., Jha, A., & Jena, A. (2015). Reducing Diagnostic Errors \u2014 Why Now? The New England Journal of Medicine, 373(26), 2491-2493.\u00a0Accessed November 8, 2018.<\/p>\n

[3] Simon, G. E., Johnson, E., Lawrence, et. al. Predicting suicide attempts and suicide deaths following outpatient visits using electronic health records. The American Journal of Psychiatry. May 24, 2018.\u00a0 Accessed November 10, 2018.<\/p>\n

[4] Rajkomar, Alvin, et al. \u201cScalable and Accurate Deep Learning with Electronic Health Records.\u201d Nature News, Nature Publishing Group, 8 May 2018, www.nature.com\/articles\/s41746-018-0029-1. Accessed November 10, 2018.<\/p>\n

[5] O’Donnell, Jayne. \u201cSecond Study Says Medical Errors Third-Leading Cause of Death in U.S.\u201d USA Today, Gannett Satellite Information Network, 4 May 2016, www.usatoday.com\/story\/news\/politics\/2016\/05\/03\/second-study-says-medical-errors-third-leading-cause-death-us\/83874022\/. \u00a0\u00a0Accessed November 7, 2018.<\/p>\n

[6]\u00a0 Wiggs, Jonathan. \u201cPartners Launches $1.2 Billion Electronic Health Records System – The Boston Globe.\u201d BostonGlobe.com, The Boston Globe, 1 June 2015, www.bostonglobe.com\/business\/2015\/05\/31\/partners-launches-billion-electronic-health-records-system\/oo4nJJW2rQyfWUWQlvydkK\/story.html. Accessed November 9, 2018.<\/p>\n

[7] Artificial Intelligence: Applications and Global Markets, accessed via BCC Research.\u00a0 BCC Research Staff.\u00a0 November 2018. Accessed November 9, 2018.<\/p>\n

[8] AMA launches collaborative data project. By: Arndt, Rachel Z., Modern Healthcare, 01607480, 10\/23\/2017, Vol. 47, Issue 43.\u00a0 Accessed November 9, 2018.<\/p>\n

[9] \u201cAMA Integrated Health Model Initiative (IHMI) Collaboration Ecosystem.\u201d AMA Integrated Health Model Initiative (IHMI) Collaboration Ecosystem, 2018, ama-ihmi.org\/. Accessed November 9, 2018.<\/p>\n

[10] Ochsner Online Newsroom. \u201cOchsner Health System Adopts New AI Technology to Save Lives in Real-Time | Ochsner Online Newsroom.\u201d Online Newsroom, Ochsner Health System, 28 Feb. 2018, news.ochsner.org\/news-releases\/ochsner-health-system-adopts-new-ai-technology-to-save-lives-in-real-time. Accessed November 8, 2018.<\/p>\n

[11] \u201cNuance and Epic Join Forces on Artificial Intelligence to Revolutionize Disabled Veterans Accessibility to Health IT.\u201d Business Wire, 21 Feb. 2017, www.businesswire.com\/news\/home\/20170221005625\/en\/Nuance-Epic-Join-Forces-Artificial-Intelligence-Revolutionize. Accessed November 8, 2018.<\/p>\n

[12] \u201cThe Best Computer Science Programs in America, Ranked.\u201d U.S. News & World Report, U.S. News & World Report, www.usnews.com\/best-graduate-schools\/top-science-schools\/computer-science-rankings. Accessed November 9, 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

Moving into the next phase of digitized healthcare, Epic Systems needs to continue to develop, test, and implement its machine learning capabilities.<\/p>\n","protected":false},"author":11363,"featured_media":31383,"comment_status":"open","ping_status":"closed","template":"","categories":[4365,4055,2754,981,346,4496,344,663],"class_list":["post-31382","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-artifical-intelligence","category-digital-healthcare","category-ehealth","category-electronic-medical-records","category-machine-learning","category-medical-ai","category-product-development","category-product-innovation","hck-taxonomy-organization-epic-systems","hck-taxonomy-industry-health","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nAn Epic use of Machine Learning - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/an-epic-use-of-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"An Epic use of Machine Learning - 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