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. 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.
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] 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 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. For example, patients determined high-risk by the prediction tool could be targeted with additional doctor’s appointments, screening tests, and medication adjustments to prevent costly complications requiring hospitalization.
Such a tool has already been built and validated by researchers in Boston using using EHR data.[i] These 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] 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] The predictive capacity of machine learning to evaluate cardiac risk based on multiple data inputs is immense.
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. The 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. 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] 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. Ultimately, Partner’s 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]
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] 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] However, 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.
So how would machine-learning prediction tools be integrated with Partners’ population health management programs for heart-disease? 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. Second, those screened to be high-risk would be monitored with regular retina fundus images and a fitness tracker. 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’ population health management initiatives could be better targeted and streamlined. The end result would be higher financial rewards for Partners and better care for patients.
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’ heart disease.
[i]Dai, Wuyang, et al. “Prediction of Hospitalization Due to Heart Diseases by Supervised Learning Methods.” International Journal of Medical Informatics, vol. 84, no. 3, 2015.
[ii]Ioannis et al. “Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA.” Journal of the American Heart Association. 2018.
[iii] Paschalidis, Yannis. “How Machine Learning Is Helping Us Predict Heart Disease and Diabetes.” Harvard Business Review, 30 May 2017, hbr.org/2017/05/how-machine-learning-is-helping-us-predict-heart-disease-and-diabetes.
[iv] Poplin et al. “Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.” Nature Biomedical Engineering volume 2, pages158–164. 2018.
[v] Gold, Jenny. “Accountable Care Organizations, Explained.” Kaiser Health News. 2015
[vi] “Partners to participate in innovative Medicaid program to improve patient care, reduce costs” Partners Patient Care. August 2017. https://www.partners.org/Newsroom/Press-Releases/Partners-Participates-MassHealth-ACO-Medicaid-Program.aspx.
[vii]“Accountable Care Organization Overview” Partners. 2018. https://www.partners.org/for-patients/ACO/ACO-Overview.aspx.