This book includes a plain text version that is designed for high accessibility. To use this version please follow this link.
need to implement focused processes and guide clinical decision making. Analytics will be invaluable as

HCOs tackle the surge in Medicaid benefi ciaries generated by the Patient Protection and Aff ordable Care Act. Medicaid is already expensive, with costs reaching $466 billion in 2011 for approximately 60 million benefi ciaries, and just 5 percent (2.4 million) of benefi ciaries accounting for half of total Medicaid spending. HCOs face a unique set of chal- lenges in caring for newly eligible patients, such as minimizing or avoid- ing costs, working within the confi nes of bundled payment or shared sav- ings models and tackling problems prevalent within low-income Medicaid populations (such as chronic disease, high emergency department usage, dual eligibility, dual medical and behavioral diagnoses and prevention of admissions and readmissions). HCOs will increase investments

and rely upon predictive analytics. HCO adoption of health data analytics will increase over the next fi ve years, according to a 2012 Frost & Sullivan report, with half of hospitals relying on analytics by 2016. A 2012 Black Book Rankings survey provided a similar insight: 84 percent of HCOs without a clinical decision-support (CDS) system still expect to implement at least one new or added clinical analytics tool in 2013. Predictive analytics is far from

reaching widespread acceptance. A 2012 survey by the eHealth Ini- tiative (EHI) and the College for Health Information Management Executives (CHIME) revealed that while more than half of respondents reported using ad-hoc queries, data mining and data warehousing, less than half reported use of exploratory data analysis and online analytical processing. Only 23.6 percent of respondents reported use of predic- tive modeling, while 58.3 percent

said that they focused resources on retrospective analysis. Still, there is reason for optimism.

According to a 2012 eHealth Initiative report, HCOs will be on the lookout for solutions that deliver standardized data across systems, provide infrastruc- ture to support analytics, safeguard data privacy and security, and deliver practical, usable results. Again, vendors must deliver on the clinical, fi nancial and operational expectations of clini- cians and executives. Health systems and academic medi- cal centers will likely be the fi rst to step up to the plate in using predictive analytics. For example, the University of Pittsburgh Health System has already announced its intention to invest more than $100 million in data analytics, including creation of a data warehouse with clinical, fi nancial, operational and genomic data from more than 200 af- fi liated facilities. Among the goals: per- sonalized medicine, population health management, increased operational effi ciency and more accurate predic- tions of patient risk and treatment eff ectiveness. In the future, predictive analytics

innovation will likely be driven by cre- ative partnerships, such as the fi ve-year collaboration between the Regenstrief Institute and Merck that uses clinical data to design interventions for chronic conditions, such as diabetes, heart dis- ease and osteoporosis. Predictive analytics will emerge as

a core strategy for detecting fraud. T e Centers for Medicare and Medicaid Services (CMS) has already committed to a $90 million system that relies on predictive analytics to battle Medicare and Medicaid fraud. Focused on iden- tifi cation of high-risk claims, providers and analysis of fee-for-service data, the fraud prevention system probably will not meet its initial January 2013 implementation goal. However, rec- ommendations from the Government Accountability Offi ce (GAO) related


to integration of fraud reporting and payment processing systems will likely keep the project on track. T e Depart- ment of Health and Human Services’ Offi ce of the Inspector General (OIG) also plans to use predictive analytics to

Predictive analytics will play an indispensible role in healthcare transformation.

detect Medicare billing abuse within electronic health records, according to the Center for Public Integrity. Predictive analytics will facilitate patient engagement. Companies have developed systems that leverage na- tional, local and patient encounter data to drive communication, education and behavior change. By identifying patients who are likely to develop a condition or need a medical procedure or service, or those who might develop a chronic disease, predictive analytics offers a solid foundation for com- munication and education campaigns and disease management programs. HCOs can choose all available means of persuasion – from mobile alerts and patient portals to telephone coaches and real-world classes – to deliver the right message to the right patient at the right time using the right medium or technology. Backed by the insights of predictive analytics, these engagement campaigns will help direct patients to appropriate providers or service loca- tions, deliver ongoing education or dis- seminate targeted messages on disease and condition prevention, testing and monitoring. Looking forward, predictive analyt- ics will play an indispensable role in healthcare transformation and reform, generating positive results in patient outcomes and cost and resource utilization effi ciency, prevention of early hospital readmissions, patient engagement and fraud reporting and detection.

HMT January 2013 17

Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28