in analytics Predictive analytics will play an indispensable role in healthcare transformation and reform. By Peter Edelstein, M.D.
he era of predictive analytics has arrived. Stage II and III meaningful use and the ongoing surge of big data demand predictive models and analytics that enable analysis and interpretation of large
volumes of increasingly complex data. Healthcare organiza- tions (HCOs) must tap data-mining technologies with the capacity to analyze data from varied perspectives and translate outcomes into meaningful information. By doing so, HCOs will be better positioned to make timely, evidence-based deci- sions, improve outcomes, streamline operations, boost cost effi ciency, minimize risks and increase revenues. T is year promises to be an exciting one for predictive analytics, with major achievements and milestones in the prevention of early post-discharge readmissions, fraud de- tection, patient engagement and provider investment and partnership. Among the emerging trends for predictive analytics in 2013 are: Predictive analytics will target identifi cation of early
readmissions risk. T anks to the Hospital Readmissions Reduction Program, which reduces reimbursement to hospi- tals with excess readmissions, more than 2,000 hospitals that demonstrated above-average 30-day post-discharge patient readmission rates were recently penalized up to 1 percent of their Medicare reimbursement in fi scal year 2012, resulting in total penalties of about $280 million. HCOs are spending signifi cant money and volumes of
resources in a desperate attempt to reduce early readmissions, much of which is due to factors out of the control of the hospitals (homelessness, transportation challenges, unem- ployment, drug or alcohol dependency, etc.). While predictive analytics cannot solve these core patient challenges, by incor-
16 January 2013
Peter Edelstein, M.D., is chief medical officer, Elsevier/MEDai. For more on Elsevier/ MEDai: www.rsleads. com/301ht-201
porating clinical, demographic and socioeconomic data, and through the use of regression analyses of large-scale databases to generate weighted drivers of risk, predictive modeling can much more clearly identify which inpatients are at the high- est (or growing) risk of post-discharge readmission in real or near-real time. Clinicians can take action while a patient is still hospitalized, and simultaneously target the patient for post-discharge processes aimed at preventing early readmission. Such predictive analyt- ics allow HCOs to not only reduce readmission rates (and the associated reim- bursement penalties), but also to reduce costs and resources allocated for prevention programs. T e results of predictive analytics are impressive. HCOs,
CMS has committed to a $90 million system that relies on predictive analytics to battle fraud.
such as Intermountain Medical Center (Murray, Utah), have reduced readmissions by relying on predictive analytics. But HCOs will soon come to expect more from vendors, including additional rigorous predictive models on the risk of morality, sepsis, transfers to an intensive care unit and excessive length of stay. Forward-looking vendors must fulfi ll these expectations. Predictive analytics will facilitate population health management and value-based accountable care. Risk analy- sis is impossible unless HCOs derive meaningful analytics from data. T e algorithms of predictive modeling can analyze hundreds of data points – retrospectively or prospectively – to predict risk for populations and individuals. Such “intelligent analytics” off er HCOs the information and predictions they
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