Embedded Analytics

Reducing Healthcare Inequality with Better Access to Patient Data [Guest Post]

By Charles Boicey
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This post was written by Charles Boicey, Chief Innovation Officer at Clearsense.

There is no shortage of disparity in healthcare. In my 30 years of nursing, I’ve witnessed countless situations where having better informationbetter datacould have made the difference between life and death. The vital importance of getting the right information, to the right clinician, at the right time to empower data-driven decisions is what drove me to pursue clinical informatics.

Emerging technologiesand insight gained through advanced data scienceare available at top-tier healthcare systems and academic health science centers. However, research teams at academic institutions providing care for underserved populations often have to comb through data manually, slowing data-driven decisions and creating further disparities in patient care.

As co-founder of Clearsense, my vision was to build a healthcare data platform which could quickly ingest, clean, organize, govern, and analyze large data ecosystems (including structured and unstructured data), in real-time. More importantly, I wanted to make this technology available to any academic health science center or safety-net health system. After 10 years of building and iterating what is now the Clearsense Platform, the first program we put on our application layer deployed predictive algorithms to alert clinicians of two life-threatening inpatient conditions: sepsis and cardiopulmonary arrest.

In addition to supporting clinical applications, harnessing unlimited types of data on the platform allows us to transform our view of patient populations and discover previously unknown insights.

In 2016, we began a partnership with Meharry Medical College in Nashville, Tennessee. Together, we launched a Data Science Institute, built on the Clearsense Platform, to expand research capabilities and drive down costs while improving the level of care for Meharry’s underserved and minority patient populations. When the Data Science Institute launched in September 2017, it had a data ecosystem of 4 million de-identified medical records from 209,000 patients collected over 10 years.   

In addition to clinical data, the Meharry data lake incorporates publicly available health and environmental data at the neighborhood level. This data includes air quality metrics; crime statistics; information on access to affordable housing; violence; poverty; and availability of grocery stores, liquor stores, or other retail outlets.

Pinpointing Risk Factors with Real-Time Data

Our most recent success in our partnership with Meharry is the development of six chronic disease predictive models allowing Meharry to pinpoint the risk factors in patients’ lives and provide proactive, preventative support and care. By applying the algorithm to specific data ecosystems at other care facilities, any provider can begin to understand how specific social determinants of health may predict chronic health conditions for their unique patient population.

One of the advantages of a platform with a robust application layer is we can develop and train predictive algorithms, but also visualize the data to tell its story. Being able to clearly communicate what the data is telling us is key to using information for making critical decisions that can make the difference between life and death. Bringing complex data science models into our data visualization tool, translating complex messages into crisp, pleasing imagery to tell a story, and using that story to make data-driven decisions is part of ending disparity in healthcare.

Our data visualization tool is powered by Zoomdata from Logi Analytics.

Originally published February 3, 2020

About the Author

Charles Boicey, MS, RN-BC, PMP, CPHIMS, is the Chief Innovation Officer at Clearsense, and an Assistant Clinical Professor of Health Sciences at Stony Brook Medicine. He is the architect of the Clearsense Platform as well as numerous healthcare data warehouses, clinical and business intelligence tools, big data ecosystems, and a health information exchange. He is a futurist and evangelist for the use of clinical technology to drive daily operations, analysis, and decisioning.