Risk Scoring is the subset of healthcare analytics in which organizations attempt to quantify the most complex but important measure for running their business – the health of each member under care. Although the healthcare industry’s central concern is improving member health, payers and providers alike have been slow to adopt Risk Scoring capabilities that provide a holistic depiction of member health. The most popular Risk Scoring models compute only one Risk Score for each member. These models measure members’ relative health but fail to provide critical insights relating to health improvement and illness prevention.
The Risk Scoring landscape is changing, however, with the emergence of big data technologies and advanced analytics. These innovations are challenging the status quo, rapidly fueling evolution of Risk Scoring capabilities towards providing specific and actionable insights. Unlike the calculation of one Risk Score for each member, the calculation of Risk Scores by disease state allows organizations to better understand and serve the medical needs of each member. Healthcare payers and providers are using machine learning predictive analytics to calculate disease-specific Risk Scores that identify members most likely to be admitted to the hospital. Early adopters of this methodology have already seen results, decreasing hospital admissions and readmissions while improving member health and reducing medical expenses.
Gaining deeper insights into each member’s health to prevent avoidable hospitalizations is one of the top priorities of every healthcare organization. Risk Scoring by disease state is becoming a key competitive differentiator leading to healthier members and an improved bottom-line.
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