{"id":35502,"date":"2018-11-13T19:07:12","date_gmt":"2018-11-14T00:07:12","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/coding-the-way-to-better-patient-outcomes-machine-learning-at-cigna\/"},"modified":"2018-11-13T19:24:51","modified_gmt":"2018-11-14T00:24:51","slug":"coding-the-way-to-better-patient-outcomes-machine-learning-at-cigna","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/coding-the-way-to-better-patient-outcomes-machine-learning-at-cigna\/","title":{"rendered":"Coding the Way to Better Patient Outcomes: Machine Learning at Cigna"},"content":{"rendered":"<p><strong>A Changing Landscape<\/strong><\/p>\n<p>As the healthcare industry in the United States shifts toward a pay-for-performance model, many insurers, like Cigna, are embracing patient-centric approaches to care compensation.<a href=\"#_ftn1\" name=\"_ftnref1\">[1]<\/a> For Cigna, the fourth largest health insurer in the United States, data has become a key competitive asset in responding to this new healthcare landscape.<a href=\"#_ftn2\" name=\"_ftnref2\">[2]<\/a> To actually tap into the value of this data, Cigna has strategically made investments in machine learning.<a href=\"#_ftn3\" name=\"_ftnref3\">[3]<\/a><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Machine Learning brings both Opportunities and Challenges<\/strong><\/p>\n<p>As part of this investment, Cigna faces the challenge of reevaluating its management of product development within the company. The incorporation of machine learning places new requirements on Cigna to not only increase investment in data infrastructure and talent, but also to rethink the way in which the company engages and interacts with patients<a href=\"#_ftn4\" name=\"_ftnref4\">[4]<\/a>. Specifically, Cigna faces three key questions as it works to leverage the benefits of machine learning:<\/p>\n<p>&nbsp;<\/p>\n<ul>\n<li>Can Cigna actually deliver on predicting patient outcomes? Machine learning is inherently complex, and implementation is a huge factor in overall success.<a href=\"#_ftn5\" name=\"_ftnref5\">[5]<\/a><\/li>\n<li>Is Cigna engaged enough with its patient and provider populations to deliver machine learning insights? Regardless of how great the insights are, patients and providers need to be aware of, and have easy access to, these insights.<\/li>\n<li>Will a new patient-centric strategy be difficult for Cigna to implement? Traditionally, the company has focused on forging B2B relationships.<a href=\"#_ftn6\" name=\"_ftnref6\">[6]<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>To address these key questions, Cigna\u2019s short-term implementation of Machine Learning has largely focused on their existing historical data set.<a href=\"#_ftn7\" name=\"_ftnref7\">[7]<\/a> Based on this historical information, Cigna\u2019s machine learning algorithms have had success in predicting when patient issues will arise and in helping providers assess the most appropriate treatments.<a href=\"#_ftn8\" name=\"_ftnref8\">[8]<\/a> Cigna has also rolled out a patient facing application call OneGuide, which engages with patients and surfaces insights during the important moments of care.<a href=\"#_ftn9\" name=\"_ftnref9\">[9]<\/a> Through their research, data scientists at Cigna have found that increased patient engagement results in better health outcomes for patients, and that the timing of these engagements is critically important.<a href=\"#_ftn10\" name=\"_ftnref10\">[10]<\/a> With over 10% of Cigna insured patients using the app as of 2017, Cigna is seeing some significant traction.<a href=\"#_ftn11\" name=\"_ftnref11\">[11]<\/a><\/p>\n<figure id=\"attachment_35793\" aria-describedby=\"caption-attachment-35793\" style=\"width: 640px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-7.21.48-PM.png\"><img loading=\"lazy\" decoding=\"async\" class=\"size-large wp-image-35793\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-7.21.48-PM-1024x576.png\" alt=\"\" width=\"640\" height=\"360\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-7.21.48-PM-1024x576.png 1024w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-7.21.48-PM-300x169.png 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-7.21.48-PM-768x432.png 768w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-7.21.48-PM-600x337.png 600w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Screen-Shot-2018-11-13-at-7.21.48-PM.png 1924w\" sizes=\"auto, (max-width: 640px) 100vw, 640px\" \/><\/a><figcaption id=\"caption-attachment-35793\" class=\"wp-caption-text\">Christer Johnson and Doug Melton, \u201cMachine learning and Big Data to Drive Better Health Outcomes,\u201d PowerPoint Presentation, March 5, 2018, HIMSS18, Las Vegas, NV, http:\/\/365.himss.org\/sites\/himss365\/files\/365\/handouts\/550235514\/handout-20180308143157-275.pdf, accessed November 2018. (Slide 7)<\/figcaption><\/figure>\n<p>Longer term, Cigna wants to create more complex machine learning techniques in the areas of chronic disease prevention, claims fraud, and wearables.<a href=\"#_ftn12\" name=\"_ftnref12\">[12]<\/a> To do this, Cigna will need to leverage more data, likely from both internal and external sources. This poses additional risks, as third-party data is often more difficult integrate into existing models, challenging to acquire, and hard to verify in terms of accuracy.<a href=\"#_ftn13\" name=\"_ftnref13\">[13]<\/a> Cigna has also been making strategic investments in startups who are also focused on using machine learning data to drive better patient outcomes.<a href=\"#_ftn14\" name=\"_ftnref14\">[14]<\/a><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Next Steps for Cigna<\/strong><\/p>\n<p>While Cigna has been successful in addressing the aforementioned challenges, the company will need to focus on deeper customer engagement in order to ensure continued success in the near term. The initial traction of OneGuide is very promising, and Cigna should double down on their investment in patient facing applications and work to drive more patients to the platform. Gaining a stronger foothold here will make it easier for Cigna to surface future machine learning insights to customers, while also creating inroads for Cigna to become more involved in the overall care of patients.<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>It is also important for Cigna to consider the ethical challenges that can arise through the use of machine learning. According to a study that appeared in the New England Journal of Medicine, \u201cAlgorithms introduced in non-medical fields have already been shown to make problematic decisions that reflect biases inherent in the data used to train them.\u201d<a href=\"#_ftn15\" name=\"_ftnref15\">[15]<\/a> Especially for an insurer like Cigna, who has access to a plethora of patient data and is actively working to help influence patient decisions, awareness of ethical biases and constructing safeguards to prevent them will be extremely important.<\/p>\n<p>&nbsp;<\/p>\n<p>Longer term, Cigna should consider growing their machine learning investment via acquisition. With a wide range of promising startups specifically focusing on machine learning and patient outcomes, Cigna could attain a fast mover advantage by acquiring some of these companies.<a href=\"#_ftn16\" name=\"_ftnref16\">[16]<\/a> Additionally, Cigna should focus on not only expanding its data set to incorporate more outside sources, but in structuring and cleaning-up this data. Healthcare today is plagued with data issues that can largely be blamed on the lack of interoperability between platforms.<a href=\"#_ftn17\" name=\"_ftnref17\">[17]<\/a> Cigna has an opportunity here to not only improve input data for its own machine learning models, but for the healthcare industry as a whole.<a href=\"#_ftn18\" name=\"_ftnref18\">[18]<\/a><\/p>\n<p>&nbsp;<\/p>\n<p><strong>The Big Question<\/strong><\/p>\n<p>Interestingly, Cigna\u2019s focus on machine learning and its initial success in implementing it has surfaced unique ethical issues. What responsibility does Cigna have in helping the entire U.S healthcare industry improve patient outcomes, and how should they balance this responsibility with the need to maintain a competitive edge?<\/p>\n<p>&nbsp;<\/p>\n<p>(Word Count &#8211; 789)<\/p>\n<p><a href=\"#_ftnref1\" name=\"_ftn1\">[1]<\/a> \u201cWhat is Pay for Performance in Healthcare?,\u201d <em>NEJM Catalyst<\/em>, March 1, 2018, <a href=\"https:\/\/catalyst.nejm.org\/pay-for-performance-in-healthcare\/\">https:\/\/catalyst.nejm.org\/pay-for-performance-in-healthcare\/<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref2\" name=\"_ftn2\">[2]<\/a> \u201cAmerica\u2019s Biggest Health Insurance Providers,\u201d <a href=\"https:\/\/www.forbes.com\/pictures\/fefi45ejdih\/4-cigna\/#20e83885779b\">https:\/\/www.forbes.com\/pictures\/fefi45ejdih\/4-cigna\/#20e83885779b<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref3\" name=\"_ftn3\">[3]<\/a> Cigna, 2017 Annual Report, p.45, <a href=\"https:\/\/www.cigna.com\/assets\/docs\/annual-reports-and-proxy-statements\/cigna-2017-annual-report.pdf?WT.z_nav=about-us%2Finvestors%2Fannual-reports-and-proxy-statements%3Blink-List%3BAnnual%20Reports%3B2017%20Annual%20Report\">https:\/\/www.cigna.com\/assets\/docs\/annual-reports-and-proxy-statements\/cigna-2017-annual-report.pdf?WT.z_nav=about-us%2Finvestors%2Fannual-reports-and-proxy-statements%3Blink-List%3BAnnual%20Reports%3B2017%20Annual%20Report<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref4\" name=\"_ftn4\">[4]<\/a> Cigna, 2017 Annual Report, p.45.<\/p>\n<p><a href=\"#_ftnref5\" name=\"_ftn5\">[5]<\/a> \u201cThe Challenges and Opportunities of Implementing AI in Healthcare\u201d, <em>PokitDok<\/em>, July 31, 2018, <a href=\"https:\/\/blog.pokitdok.com\/ai-in-healthcare\/\">https:\/\/blog.pokitdok.com\/ai-in-healthcare\/<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref6\" name=\"_ftn6\">[6]<\/a> Jonah Comstock, \u201cFor Cigna, United Healthcare, digital innovation is all about the customer,\u201d <em>mobi health news<\/em>, October 6, 2017, <a href=\"https:\/\/www.mobihealthnews.com\/content\/cigna-unitedhealthcare-digital-innovation-all-about-customer\">https:\/\/www.mobihealthnews.com\/content\/cigna-unitedhealthcare-digital-innovation-all-about-customer<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref7\" name=\"_ftn7\">[7]<\/a> Mark Boxer, \u201cCigna uses artificial intelligence to sift through big data \u2018noise\u2019,\u201d <em>Managed Healthcare Executive<\/em>, February, 8, 2018, <a href=\"http:\/\/www.managedhealthcareexecutive.com\/mhe-articles\/cigna-uses-artificial-intelligence-sift-through-big-data-noise\">http:\/\/www.managedhealthcareexecutive.com\/mhe-articles\/cigna-uses-artificial-intelligence-sift-through-big-data-noise<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref8\" name=\"_ftn8\">[8]<\/a> Mark Boxer, \u201cCigna uses artificial intelligence to sift through big data \u2018noise\u2019\u201d.<\/p>\n<p><a href=\"#_ftnref9\" name=\"_ftn9\">[9]<\/a> Jonah Comstock, \u201cFor Cigna, United Healthcare, digital innovation is all about the customer\u201d.<\/p>\n<p><a href=\"#_ftnref10\" name=\"_ftn10\">[10]<\/a> Christer Johnson and Doug Melton, \u201cMachine learning and Big Data to Drive Better Health Outcomes,\u201d PowerPoint Presentation, March 5, 2018, HIMSS18, Las Vegas, NV, <a href=\"http:\/\/365.himss.org\/sites\/himss365\/files\/365\/handouts\/550235514\/handout-20180308143157-275.pdf\">http:\/\/365.himss.org\/sites\/himss365\/files\/365\/handouts\/550235514\/handout-20180308143157-275.pdf<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref11\" name=\"_ftn11\">[11]<\/a> Jonah Comstock, \u201cFor Cigna, United Healthcare, digital innovation is all about the customer\u201d.<\/p>\n<p><a href=\"#_ftnref12\" name=\"_ftn12\">[12]<\/a> Mark Boxer, \u201cCigna uses artificial intelligence to sift through big data \u2018noise\u2019\u201d.<\/p>\n<p><a href=\"#_ftnref13\" name=\"_ftn13\">[13]<\/a> Donna Marbury, \u201cSix Healthcare Interoperability Barriers to Watch\u201d, <em>Managed Healthcare Executive<\/em>, November 1, 2017, <a href=\"http:\/\/www.managedhealthcareexecutive.com\/business-strategy\/six-healthcare-interoperability-barriers-watch\">http:\/\/www.managedhealthcareexecutive.com\/business-strategy\/six-healthcare-interoperability-barriers-watch<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref14\" name=\"_ftn14\">[14]<\/a> Erin Dietsche, \u201cPrognos raises $20,5 million from investors including Cigna, Merck Global, Health Innovation Fund,\u201d <em>MedCity News,<\/em> December 4, 2017, <a href=\"https:\/\/medcitynews.com\/2017\/12\/prognos-investors\/\">https:\/\/medcitynews.com\/2017\/12\/prognos-investors\/<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref15\" name=\"_ftn15\">[15]<\/a> Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care &#8211; Addressing Ethical Challenges.\u00a0<em>N Engl J Med<\/em>. 2018;378(11):981-983.<\/p>\n<p><a href=\"#_ftnref16\" name=\"_ftn16\">[16]<\/a> Louis Columbus, \u201c25 Machine Learning Startups to Watch in 2018,\u201d <em>Forbes, August 26, 2018, <\/em><a href=\"https:\/\/www.forbes.com\/sites\/louiscolumbus\/2018\/08\/26\/25-machine-learning-startups-to-watch-in-2018\/#3a6d2b776f99\">https:\/\/www.forbes.com\/sites\/louiscolumbus\/2018\/08\/26\/25-machine-learning-startups-to-watch-in-2018\/#3a6d2b776f99<\/a>, accessed November 2018.<\/p>\n<p><a href=\"#_ftnref17\" name=\"_ftn17\">[17]<\/a> Amelia Coleman, \u201cSolving the Interoperability Crisis in Healthcare with Consumer Tech, <em>Becker\u2019s Health IT &amp; CIO Report, <\/em>June 22, 2017, <a href=\"https:\/\/www.beckershospitalreview.com\/healthcare-information-technology\/solving-the-interoperability-crisis-in-healthcare-with-consumer-tech.html\">https:\/\/www.beckershospitalreview.com\/healthcare-information-technology\/solving-the-interoperability-crisis-in-healthcare-with-consumer-tech.html<\/a>, accessed November 2018<\/p>\n<p><a href=\"#_ftnref18\" name=\"_ftn18\">[18]<\/a> Matt McElheny,\u201cFacing Data Integration Demands\u201d, <em>Healthcare IT News, <\/em>April 1, 2015, <a href=\"https:\/\/www.healthcareitnews.com\/blog\/facing-data-integration-demands\">https:\/\/www.healthcareitnews.com\/blog\/facing-data-integration-demands<\/a>, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>American health insurer Cigna must balance the benefits of machine learning with the internal challenges that arise during implementation and the external ethical implications inherent to the healthcare industry.<br \/>\n(Image Citation &#8211; https:\/\/www.cigna.com\/guideme)<\/p>\n","protected":false},"author":11222,"featured_media":35503,"comment_status":"open","ping_status":"closed","template":"","categories":[4365,4055,3947,41,346],"class_list":["post-35502","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-artifical-intelligence","category-digital-healthcare","category-health-insurance","category-healthcare","category-machine-learning","hck-taxonomy-organization-cigna","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":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Coding the Way to Better Patient Outcomes: Machine Learning at Cigna - 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\/coding-the-way-to-better-patient-outcomes-machine-learning-at-cigna\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Coding the Way to Better Patient Outcomes: Machine Learning at Cigna - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"American health insurer Cigna must balance the benefits of machine learning with the internal challenges that arise during implementation and the external ethical implications inherent to the healthcare industry. 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