The Healthy Nevada Project - An Approach to Improving Health and Healthcare of an Entire Region

Anthony D. Slonim, MD, DrPH and Kirtan P. Patel, MBA, Renown Health and Ron J. Fuschillo, MBA, and Joseph Grzymski, PhD, Renown Health
Anthony D. Slonim, MD, DrPH and Kirtan P. Patel, MBA, Renown Health

Anthony D. Slonim, MD, DrPH and Kirtan P. Patel, MBA, Renown Health

Systems at the forefront of population health are creating opportunities to transform the current state of health and healthcare through the use of data and data analytics. This was the origin of the Healthy Nevada Project, which capitalized on the strengths of two organizations, an integrated health network, Renown Health, and a freestanding research enterprise, the Desert Research Institute.

Healthcare organizations need to advance beyond the services that are provided to the sick and injured into a genuine effort to improve population health. Population health allows all community members in a service area to benefit from an improved health status. In order to achieve this effort, organizations must pay attention to the major domains of an individual’s health status including clinical care, the environment of care, the social determinants of care, and genetics. Three important challenges impede efforts to improve population health. First, these domains do not exist in a space that allows for important analytics to be applied. Second, there are limitations in terms of bandwidth, capability, and access to non-hospital data for a hospital-based informatics team. Finally, the use of sophisticated analytic techniques—like predictive analytics and machine learning that can identify patterns and trends in the data— are outside the realm of many hospital-based informatics departments.

In 2016, the Healthy Nevada Project was implemented as an effort at population health by improving health and healthcare, both at the individual and community level. This project integrated 10 years of clinical data from electronic medical records, environmental data, social data, and genetic data into a singular health determinants data platform. Initially designed as a research study, the project began to offer complimentary genetic testing to community participants. The results have been incredible. In Phase 1, 10,000 participants registered in 48 hours and had their genetic tests performed within 90 days. In Phase 2, another 10,000 participants registered in 72 hours and had their genetic tests performed within 84 days. Now, there are nearly 35,000 participants and the project is a model for improving the health and healthcare of an entire region through participant engagement, education, and return of results.Ron J. Fuschillo, MBA, and Joseph Grzymski, PhD, Renown Health

The co-location of different domains of data in a singular data warehouse and the ability to apply machine based learning and predictive analytics to identify patterns of health, disease and care have provided an important mechanism to improve population health, specifically the health and healthcare of community members living in this region. At the community level, this region experiences higher mortality rates from cardiac disease, cancer, and lower respiratory disease. Through the use of machine learning and data science techniques, previously unknown relationships between risk factors and disease are being identified. This allows a healthcare system to communicate to participants their specific risks (can be regional in nature, for example, forest fires), and manage behaviors to change their risks for illness and disease. At the individual level, the project has just recently begun to provide genetic results to participants with familial hypercholesterolemia, a genetic tie to high cholesterol; BRCA 1/BRCA 2, genetic markers for hereditary breast and ovarian syndrome; and Lynch Syndrome, with a genetic tie to endometrial and colon cancer. Participants are provided with information about their condition, the diagnostic approach, and follow-up needed to assure that they receive proper care. In turn, the health system learns. For example, our doctors and clinics are now better informed about inherited genetic conditions and their prevalence in the community.

When viewed as an industry, healthcare lags behind others in the use of predictive analytics to identify problems, find solutions, and maintain progress. Healthcare needs to overcome the resistance to these kinds of investments for population health improvement at the patient, community, and national levels. For healthcare systems looking to impact the health and healthcare of individuals and their communities, it is essential to aggregate the different data elements into a singular data warehouse, to take advantage of sophisticated analytic techniques to identify patterns in the data, and partner with experts in ancillary fields to complement the skill set of their internal informatics resources. With these approaches, the Healthy Nevada Project has been able to make a genuine effort to improve population health and healthcare improvements.

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