Case Study – Leveraging advanced data analytics in Healthcare to ameliorate significant rural health care challenges

Cerner Corporation, one of the nations largest electronic medical record companies in the world, provided a dataset with clinical information for 63 million patients collected over 18 years. Oklahoma State University is leading the nation in developing and, more importantly, deploying these advanced analytic solutions in the rural health care environment. This presentation will provide real world examples of these tools being developed and deployed to address and ameliorate significant rural health care challenges.

Approximately 60 million US citizens, representing over 20% of the population, live in rural America including millions of Medicare and Medicaid beneficiaries. Compared to their urban counterparts, rural Americans are poorer, older, sicker, uninsured or underinsured, and medically underserved. Additionally, rural America has a fragmented health care delivery system, stretched and diminishing rural health workforce, insurance affordability issues, and lack of access to specialty and primary care providers. Solutions to address these problems include head count growth, licensure adjustments, workflow improvements, and advanced predictive analytic tools. This makes the rural market one of America’s largest health disparity zone.

Oklahoma State University’s Center for Health Systems Innovation (http://chsi.okstate.edu) mission is to “transform rural and Native American health” through the “implementation of innovative care delivery and predictive analytic solutions”. The Center is focused on not only innovating solutions but, more importantly, implementing those innovations.

The Center is endowed by OSU alum and the founding Chairman and CEO of Cerner Corporation, Neal Patterson. In addition to providing a financial commitment to create the Center, Cerner Corporation donated the largest clinical dataset known to exist. The Center is applying advanced predictive analytics and Artificial Intelligence to this data which contains clinical information from over 63 million patients, collected from across the US, and covering the past 18 years.

The Center has 28 full time employees and 6 graduate students, making us one of the largest innovation groups in the US focused on rural and Native American health and predictive health analytics. Approximately half of the team operates within the “Institute for Predictive Medicine” and the other half operates within the “Institute for Rural Care Delivery Innovation”. In addition to a strong team and differentiated assets, the Center has a strong collection of corporate partners including Google, Verily, Aetna, Wellcare, Center for Medicare and Medicaid Services (CMS), Centers for Disease Control (CDC), etc.

    • Institute for Predictive Medicine is applying advanced predictive analytics and Artificial Intelligence to the largest health care database known, which contains clinical information from over 63 million patients, collected from across the US, and covering the past 18 years. The database includes clinical event information, laboratory, pharmacy, admission and discharge, and financial data.

    • Institute for Rural Care Delivery Innovation is focused on the identification of the major workflow and care delivery challenges facing rural and Native American health, innovating solutions to address these challenges, and implementing those solutions in the field.

Case Study

Diabetes and the management of diabetic complications are major challenges across the country and in Oklahoma. In particular, there is an absence of subspecialists available in rural markets to manage diabetic complications. To address this issue and utilizing Artificial Intelligence and Machine Learning, the Center has built a clinical decision support algorithm to predict which patients have diabetic complications based on data collected in a normal primary care visit, potentially eliminating the need to visit a subspecialist. This algorithm was built utilizing clinical data from over 2 million diabetic patients and 1.5 billion data elements extracted from the Center’s in-house data repository. The initial tool addresses diabetic retinopathy with greater than 95% sensitivity and specificity. The goal is to have a single tool to allow physicians located in a health disparity zone to manage diabetic complications with data collected from a normal primary care visit. This case study serves as an example of the power of the Center’s integrated innovation, predictive health analytics, and implementation capabilities.