SAN FRANCISCO, June 20, 2019 /PRNewswire/ -- Innovaccer Inc., a leading healthcare data activation company, has taken a major step towards revolutionizing the way we identify the high-risk patients and perform population health management with their in-house research on estimating the future cost of care based on past medical history, clinical and socio-economic data, and many other factors.
The research paper, titled "Bringing Artificial Intelligence to Healthcare: Enhancing Risk Models to Predict the Future Cost of Care," explores basics of risk scoring and stratification, historical models of risk determination, and how cutting-edge ML techniques such as AI and advanced regression algorithms are instrumental parts in the transformation to value-based care, from eliminating variations in care quality to ensuring accurate reimbursements.
Traditionally, providers and health systems have relied on claims-based risk models, such as the CMS-HCC, ACG, and DxCG, which were built to forecast the risk of populations, but not at an individual level. While these models give a fairly good estimation of the risk of the population, they exhibit unsatisfactory estimation if used to predict the risk at an individual level.
Innovaccer's model leverages advanced machine learning algorithms to adapt itself to the data and the goals of the user. The model analyzes data from multiple data sources including EHRs, social determinants of health, and claims data.
"The transformation from volume to value requires innovative strategies for assessing risk and predicting outcomes. This innovation must be based on a solid data foundation, and it's encouraging to see Innovaccer's data-driven approach being applied to an AI-based risk scoring model - something that can address one of the most pressing needs in healthcare today," says Glenn Steele Jr. MD, Ph.D., Vice Chair, Health Transformation Alliance, former Geisinger Health System President and CEO.
The algorithm uses an ensemble of 6 different regression models including Lasso and Elastic regression models and 62 independent features to predict the future cost of a patient. Due to the use of varied regression models, Innovaccer's risk model is able to account for the outliers present in the data.
The AI-assisted machine learning algorithm has a Coefficient of Determination (R2) of 61.2%, which is highest among the leading models such as:
- ACG system (17.8%),
- DxCG system (24.7%), and
- CRG system (16.8%), among many.
In the concurrent model, the algorithm shows an accuracy of over 52% against other models such as the CMS-HCC model with the accuracy of just 12%.
"Every patient is different and just because the technology in healthcare is still stuck in the pre-internet era, healthcare needs an information superhighway that can open up the space for innovation," says Abhinav Shashank, CEO and Co-founder at Innovacccer. "With this new research, we take a step ahead and move one step closer to preventive care."
To get a more detailed understanding of Innovaccer's approach to predicting the future cost of care, download the research paper here.
Innovaccer Inc. is a leading healthcare data activation company making a powerful and enduring difference in the way care is delivered. Innovaccer's aim is to make full use of all the data our industry has worked so hard to collect by righting the wrongs, doing away with long-standing problems and replacing them with ideal solutions. The Gartner and KLAS-recognized products have been deployed all over the US across more than 500 locations, letting over 10,000 providers transform care delivery and work as one. The data activation platform has been delivering value to several institutions, governmental organizations, and several corporate enterprises such as Mercy ACO, StratiFi Health, UniNet Healthcare Network, Catalyst Health Network, Hartford Healthcare, and Osler Health Network. Innovaccer is based in San Francisco and has offices all over the United States and Asia.
For more information, please visit innovaccer.com.