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is quite powerful in the clinical space where the rules are more complex and the decisions more risky.

Decision support is not new to the

medical fi eld, but it is certainly not per- vasive either. Legacy decision-support tools have serious limitations. They are generally infl exible (taking every user down the same, single path), they focus primarily on historical trending and it’s hard to keep them current with chang- ing clinical protocols. User adoption and decision outcomes are subsequently suboptimal.

The convergence of increased EMR usage, advanced BPM technology and analytics heralds a new era for clinical decision-support tools. More data is be- ing codifi ed in EMR systems and can be made actionable by BPM systems. With its inherent integration capabilities, BPM systems easily collect data from multiple disparate systems and then apply analytics and rules to the data to inform next steps, automating processes wherever possible. This is particularly useful in informal and extended delivery systems such as ACOs which do not typically share common technology platforms.

What’s more, new information and content is easily incorporated, particu- larly through the use of adaptive analyt- ics which facilitates automated learning in the system so that content presenta- tion and consumer choices are continu- ously fi ne-tuned for each individual user. Several healthcare organizations are now authoring their own clinical rules in BPM systems, allowing physicians and other business users to personalize the protocols for their own use. Codifying the rules in BPM systems makes them more visible and manageable, making them easier to change and keep current with new medical knowledge. Despite advancements (current and potential) in clinical decision-support software, the area of most immediate opportunity is with the non-provider population, where technology adoption is generally less challenging. Optimiza- tion of ICD-10 coding for claims and provider contracting is ripe for BPM-

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driven decision support, where users will be guided to choose from numerous relevant ICD codes within their specifi c process. The BPM system would then track the outcome of each user’s deci- sions, apply adaptive analytics to assess impact (lost revenue, cost, etc.) and improve guidance and suggestions to be provided to future users facing the same situation.

Another place where advanced decision support has huge potential is in engaging consumers, particularly in the areas of disease management and health and wellness programs. In fact, whether you are a health plan, provider, pharmacy benefi t manager (PBM) or another healthcare company, the best approach to staying, or becoming, a trusted healthcare advisor today and safeguarding customer loyalty is to help individuals develop their own knowledge to make their own health-

care decisions. By providing essential data, sorting and processing complex information and guiding users through the process of decision making, today’s decision-support tools are improving patient knowledge regarding treatment options, plan choices and provider selec- tions, creating realistic expectations, re- ducing frustration with decision making and stimulating people to take a more active role in their own healthcare. Technological advancement has brought intelligence to decision sup- port, which promises improvement across numerous areas – clinical, ad- ministrative and personal – that can lead to improved health and reduced costs. By providing process orchestra- tion, advanced analytics, real-time decision making, system integration and optimized channel delivery, BPM systems are the best decision for decision-support software.

HMT

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Visit www.rsleads.com/111ht-003 HEALTH MANAGEMENT TECHNOLOGY November 2011 15

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