Faced with ongoing U.S healthcare reform that forces hospitals to bear financial risk for patient outcomes, Partners Healthcare (Partners) should embrace the power of machine learning to cut costs and improve quality of care.\u00a0 In particular, better analytical tools that integrate multiple health data sources, from electronic health record (EHR) data to fitness tracker output, to accurately predict heart disease complication risk could save Partners tens of millions of dollars per year.<\/p>\n
Across the U.S., machine learning techniques hold the potential to significantly reduce the $10+ billion of preventable annual hospitalization costs attributed to heart-disease.[i]<\/a> The current standard for evaluating risk in heart-disease patients, the Framingham 10-year risk score, is inaccurate in certain populations, especially in women and minority groups.[ii]<\/a>\u00a0 A more accurate and less biased machine learning-based prediction tool would better identify high-risk patients for heart attack, stroke, and hospitalization, allowing better preventative measures.\u00a0 For example, patients determined high-risk by the prediction tool could be targeted with additional doctor\u2019s appointments, screening tests, and medication adjustments to prevent costly complications requiring hospitalization.<\/p>\n Such a tool has already been built and validated by researchers in Boston using using EHR data.[i]<\/a>\u00a0These researchers found that their tool predicted heart-related hospitalizations with an accuracy of 82% compared with 56% for the Framingham 10-year risk score.[iii]<\/a>\u00a0 However, it is safe to assume that a machine learning algorithm that integrated data sources in addition to EHR data, such as retinal fundus images and fitness tracker data would be even more accurate at predicting hospitalizations. A Google research group recently applied deep learning techniques to retinal fundus images alone and was able to predict 5-year risk for Major Adverse Cardiac Events as accurately as the current risk prediction standard.[iv]<\/a>\u00a0 The predictive capacity of machine learning to evaluate cardiac risk based on multiple data inputs is immense.<\/p>\n With the ongoing transition from fee-for-service to value-based reimbursement, Partners is under pressure to find novel ways to both control costs and improve outcomes. Leveraging the predictive capacity of machine learning offers a potential solution. \u00a0The Affordable Care Act encouraged the creation of Accountable Care Organizations (ACOs), networks of doctors and hospitals that share medical and financial responsibility for providing coordinated patient care.\u00a0 The ultimate goal of ACOs is to increase efficiency and quality of care through financial incentives for not duplicating services and preventing medical errors.[v]<\/a> Since 2012, Partners, the largest healthcare provider network in New England, has entered ACO contracts with all major insurers, including Medicare, commercial payers, and MassHealth.\u00a0 Ultimately, Partner\u2019s ACO contracts rewards it for avoiding unnecessary hospitalizations and preventing progression of chronic diseases, like heart disease. With preventable heart-related hospitalizations accounting for over 30% of total preventable hospitalizations, leveraging a machine learning prediction tool to avert these unnecessary heart-related hospitalizations would generate for Partners significant financial rewards in its ACO contracts.[i]<\/a><\/p>\n To adjust to the the new ACO reimbursement framework, Partners has expanded its population health management programs, namely care coordination, behavioral health, and substance use disorder programs.[vi]<\/a> These initiatives allow Partners to better address the social determinants of health, such as housing and education, that play a large role in health outcomes. In the first five years of its Medicare ACO, Partners saved $38 million and achieved a quality score of 94.5%, among the highest for ACOs in the country.[vii]<\/a> \u00a0However, despite these early successes, Partners has increasing incentives to further improve given the rising risk burden of its ACO contracts, especially its MassHealth ACO contract, and the uncertain future of federal healthcare legislation.<\/p>\n So how would machine-learning prediction tools be integrated with Partners\u2019 population health management programs for heart-disease?\u00a0 First, every patient in the Partners system would be screened for heart-disease progression and hospitalization risk using an EHR-data derived machine learning algorithm.\u00a0 Second, those screened to be high-risk would be monitored with regular retina fundus images and a fitness tracker.\u00a0 Finally these additional data sources along with the original EHR data would be used as inputs for an even more accurate machine learning-based prediction tool. With better predictions of disease progression risk, Partners\u2019 population health management initiatives could be better targeted and streamlined.\u00a0 The end result would be higher financial rewards for Partners and better care for patients.<\/p>\n Providing the best quality healthcare to patients at the lowest cost to society must be the mission of large healthcare systems like Partners, especially as financial incentives are aligning to drive this mission. By leveraging machine learning techniques to prevent and reverse the progression of heart-disease, Partners has the potential to not only to save considerable money, but also radically improve the trajectory of its patients\u2019 heart disease.<\/p>\n (763)<\/p>\n [i]<\/a>Dai, Wuyang, et al. \u201cPrediction of Hospitalization Due to Heart Diseases by Supervised Learning Methods.\u201d\u00a0International Journal of Medical Informatics<\/em>, vol. 84, no. 3, 2015.<\/p>\n [ii]<\/a>Ioannis et al. \u201cMachine Learning Outperforms ACC\/AHA CVD Risk Calculator in MESA.\u201d\u00a0 Journal of the American Heart Association. 2018.<\/p>\n [iii]<\/a> Paschalidis, Yannis. \u201cHow Machine Learning Is Helping Us Predict Heart Disease and Diabetes.\u201d\u00a0性视界 Business Review<\/em>, 30 May 2017, hbr.org\/2017\/05\/how-machine-learning-is-helping-us-predict-heart-disease-and-diabetes.<\/p>\n [iv]<\/a> Poplin et al. \u201cPrediction of cardiovascular risk factors from retinal fundus photographs via deep learning.\u201d Nature Biomedical Engineering <\/em>volume\u00a02,\u00a0pages158\u2013164. 2018.<\/p>\n [v]<\/a> Gold, Jenny. \u201cAccountable Care Organizations, Explained.\u201d Kaiser Health News.<\/em> 2015<\/p>\n [vi]<\/a> \u201cPartners to participate in innovative Medicaid program to improve patient care, reduce costs\u201d Partners Patient Care.<\/em> August 2017. https:\/\/www.partners.org\/Newsroom\/Press-Releases\/Partners-Participates-MassHealth-ACO-Medicaid-Program.aspx<\/a>.<\/p>\n [vii]<\/a>\u201cAccountable Care Organization Overview\u201d Partners. 2018. <\/em>\u00a0https:\/\/www.partners.org\/for-patients\/ACO\/ACO-Overview.aspx<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" Faced with ongoing U.S healthcare reform that forces hospitals to bear financial risk for patient outcomes, Partners Healthcare should embrace the power of machine learning to cut costs and improve quality of care<\/p>\n","protected":false},"author":11532,"featured_media":36952,"comment_status":"open","ping_status":"closed","template":"","categories":[2560,887,5103,5104],"class_list":["post-35964","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-electronic-health-record","category-health-care","category-heart-disease","category-prevention","hck-taxonomy-organization-partners-healthcare","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":"\n