{"id":31723,"date":"2018-11-13T13:58:09","date_gmt":"2018-11-13T18:58:09","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/partnering-with-ai-optum-labs-efforts-to-improve-u-s-health-care\/"},"modified":"2018-11-13T13:58:09","modified_gmt":"2018-11-13T18:58:09","slug":"partnering-with-ai-optum-labs-efforts-to-improve-u-s-health-care","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/partnering-with-ai-optum-labs-efforts-to-improve-u-s-health-care\/","title":{"rendered":"Partnering with AI: Optum Labs\u2019 Efforts to Improve U.S. Health Care"},"content":{"rendered":"

The cost and quality challenges facing the U.S. health care system have been well documented over the years. The U.S. currently spends over $10,000 per capita, totaling over $3.3 trillion dollars in annual health care spend[1]<\/a>. Although U.S. spend exceeds all other developed nations, the U.S. fares worse than the Organization for Economic Cooperation and Development (OECD) average on most measures of health quality, including life expectancy[2]<\/a>. It is within this context that pressure has been mounting for organizations to identify methods to improve health care quality while lowering cost.<\/p>\n

Optum Labs was established in 2013 with the purpose of using data to tackle health care\u2019s biggest problems. Through partnerships with over 25 organizations spanning the industry, including hospitals, payers, policy makers, academic institutions, and medical device manufacturers, Optum Labs has created a database of over 200 million de-identified lives containing linked data from insurance claims, electronic medical records (EMRs), and self-reported health information[3]<\/a>. Using this immense data set and new machine learning technologies, Optum Labs\u2019 goal is to \u201cturn artificial intelligence into health care intelligence\u201d[4]<\/a>.<\/p>\n

The use of machine learning is critical to achieving Optum Labs\u2019 goal due to the unstructured and disjointed nature of health care data. According to Optum Labs, roughly 80% of data in EMRs are physician\u2019s unstructured notes[4]. Without the use of AI, utilizing this enormous amount of unstructured data in conjunction with other fragmented claims and administrative data sets would be nearly impossible. The concurrent use of these data sets, through AI, allows Optum Labs\u2019 to not only better understand the health of individuals, but also their impact on industry costs.<\/p>\n

Big data and the use of machine learning has allowed Optum Labs\u2019 to compliment a clinician\u2019s judgement with access to a massive amount of pertinent experiences from other clinicians and patients. This supplementary data provides additional support to clinicians when creating treatment plans for complex cases, hopefully leading to better patient outcomes. In addition to care delivery improvements, Optum Labs has focused its use of AI on solving practical business problems and improving disease prediction. Machine learning techniques have been used to develop predictive models that can diagnose conditions such as diabetes, heart failure, Alzheimer\u2019s, and dementia years in advance of traditional methods[5]<\/a>.<\/p>\n

Predictive models are also being used for cost saving efficiencies. In 2017, Optum Labs began using AI to automate pre-screening medical charts and forecasting out-of-network and emergency department utilization, two large causes of high consumer spend[5]. Utilizing AI, Optum Labs was also able to create a prototype network design tool that identifies smaller networks of providers that have naturally collaborated in patient care. In one test market, the tool has shown that these smaller, natural networks can achieve better outcomes at ~20% lower cost than some defined payer networks[6]<\/a>.<\/p>\n

Optum Labs has already made great progress in identifying ways to address the U.S.\u2019 quality and cost challenges; however, there is still many years\u2019 worth of work to be done to solve these inefficiencies. Moving forward, Optum Labs has begun focusing the application of AI to the analysis of EMRs. The goal over the coming years is to use natural language processing to extract meaning from physician notes and feed this data to machine learning models[4]. Optum Labs must be careful in this venture as physicians may have different observations on the same patient, especially in complex cases. Varying physician perceptions makes this EMR data difficult to utilize appropriately without increasing the susceptibility to AI error.<\/p>\n

Another important consideration for Optum Labs to consider, as it continues to obtain and analyze data, is the minute amount of time most patients interact with the health care system. Patients who currently do not suffer from chronic conditions only interact with the health care system a few times a year; yet, it is these patients that Optum Labs hopes to benefit with early diagnosis. To optimize this process, Optum Labs should focus on obtaining data on consumers outside of their interactions with providers and payers. Optum Labs could benefit greatly by partnering with organizations such as Fitbit, Apple, and Samsung that produce wearable technologies. Additional partners, such as Amazon and Google, that understand the gamut of consumer behavior could help complete patient personas and further link behavior to health outcomes.<\/p>\n

It is clear that, when used appropriately, AI can improve clinical outcomes and enhance operational efficiencies. However, compared to other industries, health care has greater inherent risks. It is one thing to use AI for operational efficiency and waste reduction, but when used in clinical scenarios controls must be put in place to protect patients and clinicians. The question remains: What types of control systems can Optum Labs implement against inaccurate AI insights? How can Optum Labs protect clinician partners from the threat of malpractice resulting from AI research errors?<\/p>\n

 <\/p>\n

 <\/p>\n

(796 Words)<\/p>\n

[1]<\/a> Centers for Medicare and Medicaid Services, \u201cHistorical National Health Expenditure Data,\u201d https:\/\/www.cms.gov\/Research-Statistics-Data-and-Systems\/Statistics-Trends-and-Reports\/NationalHealthExpendData\/NationalHealthAccountsHistorical.html, accessed November 2018.<\/p>\n

[2]<\/a> Joseph Walker, \u201cWhy Americans Spend So Much on Health Care\u2014In 12 Charts,\u201c The Wall Street Journal, July 31, 2018, https:\/\/www.wsj.com\/articles\/why-americans-spend-so-much-on-health-carein-12-charts-1533047243, accessed November 2018.<\/p>\n

[3]<\/a> Optum Labs, \u201cOur story,\u201d https:\/\/www.optumlabs.com\/about\/story.html, accessed November 2018.<\/p>\n

[4]<\/a> Optum Labs, \u201cHarnessing artificial intelligence (AI),\u201d https:\/\/www.optumlabs.com\/work\/artificial-intelligence.html, accessed November 2018.<\/p>\n

[5]<\/a> Optum Labs, \u201c2017 Year in Review\u201d (PDF File), downloaded from Optum Labs website, https:\/\/www.optumlabs.com\/about\/year-in-review.html, accessed November 3, 2018.<\/p>\n

[6]<\/a> Optum Labs, \u201cDesigning better provider networks,\u201d https:\/\/www.optumlabs.com\/work\/designing-networks.html, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

Through industry partnerships, a 200 million life database, and the use of artificial intelligence (AI), Optum Labs is confronting the daunting cost and quality challenges facing the U.S. health care system<\/p>\n","protected":false},"author":11268,"featured_media":31724,"comment_status":"open","ping_status":"closed","template":"","categories":[4365,298,703,208,887,4242,346,4653,4654,442,4651,2483,2373,4652,4655],"class_list":["post-31723","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-artifical-intelligence","category-big-data","category-cost-savings","category-health","category-health-care","category-ibm-watson","category-machine-learning","category-optum","category-optumlabs","category-partnership","category-payer","category-policy","category-process-improvement","category-provider","category-us-healthcare","hck-taxonomy-organization-optum-labs","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":"\nPartnering with AI: Optum Labs\u2019 Efforts to Improve U.S. Health Care - 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