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Finally, those that lead healthcare innovation will not only master descriptive, comparative and prescriptive analyt- ics, they will also develop prescriptive approaches that turn data into directives and ultimately into action that prevents unnecessary utilization, improves outcomes and drives high value to patients, payers and employers.


Todd Fisher, Founder, Chairman and Principal, Intraprise Solutions As the amount of data available for analysis and the power and sophistica- tion to conduct such analysis continues to increase at an exponential rate, we sit on the precipice of great advancement in healthcare analytics designed to glean pre-


viously inaccessible intelligence that will lead to improved population health management and community-based care, health and wellness. We must, however, remain vigilant and think deeply about the motivation and impact of the inferences that are inherent in all analytical exercises. It is not only natural but also an integral part of the exercise to draw conclusions from the analysis of data. As relatively new consumers of and participants in such analysis, we are obligated to understand the diff erence, for example, between correlation and causation. As we all have biases, we also have a responsibility to avoid drawing specifi c conclusions that may align with our world-view but are not yet clearly identifi ed as truly useful signals amidst all the noise. Acting on intelligence gleaned from analysis rings a bell that can’t be un-rung. As our level of excitement and designs on future innovation increase alongside the expanding mountains of data, we must never forget that the true source and usefulness of analysis always rests in human hands bound by moral, ethical and legal constraints that govern the quality of care those that place trust in the healthcare industry expect and deserve. In short, advance- ment in healthcare analytics is not just about technology. It’s about real, powerful thinking.


Keith Blankenship, Vice President, Technical Solutions, Lumeris Inc. As healthcare institutions across the country are trying to utilize Big Data to improve patient outcomes, many of their efforts are encountering considerable roadblocks. Health systems, hospitals and provider groups are recognizing the


value of collecting vast amounts of data and analyzing the information to improve the coordination of patient care. However, they are fi nding that clinical data integration can often be a complex, costly and time-consuming un- dertaking, and many health systems, hospitals and provider groups have been reluctant to pull the trigger on this fi rst step toward value-based care. Fortunately, there are new ap-


20 March 2014


proaches available that cut both costs and time constraints. T rough a process called interfacing, health systems, hospitals and provider groups can now extract data from hundreds of diff erent EMR and practice management sys- tems without disrupting practice operations or patient care. While many organizations are focused on pushing insights back into EMRs and other legacy applications (what we call workfl ow integration), the higher payback comes when or- ganizations extract the data through interfacing, consolidate it and present information to all users (physicians all the way to hospital and health system CEOs), via a patient- centric view. From the doctor’s perspective, they gain the ability to see a comprehensive patient profi le, helping them close gaps in care and true-up patient information. If data is consolidated and presented in a timely and meaningful way to a user (i.e., physician), workfl ows will work them- selves out. If the data is not consolidated and presented in a timely and meaningful way, the most seamless workfl ows will have little impact on achieving the goals of the Triple Aim Plus One: better health outcomes, lower costs and improved patient plus physician satisfaction.


Joell Keim, President, Outcomes Health Connections Interoperability of systems and integra- tion of claims and encounter data from across the care continuum is a logical fi rst step in the shift toward accountable care. However, even as organizations gain ad- ditional access to volumes of patient data,


the challenge lies in bringing context to this information with the goal of guiding care interventions to improve outcomes. Sophisticated analytic processes are being developed to


address this issue. T ese processes have the ability to drill down vast amounts of data in order to stratify patients by risk, identify gaps in care and uncover opportunities to improve outcomes. For example, advanced analytics can identify trends in patient behavior. If a chronically ill patient normally sees their physician every six months, but hasn’t been in the offi ce for nearly a year, an ACO might be noti- fi ed to contact the patient to discuss with their PCP or go so far as to schedule an in-home visit by a nurse practitioner. By applying predictive modeling to this data, organiza- tions can also prioritize opportunities according to their potential to impact outcomes and quality metrics. T is allows for strategic allocation of costly resources, such as care coordination eff orts and disease management programs, where they will be most eff ective. If healthcare organizations aim to achieve cost-saving goals while improving the overall health of their member- ship, they need much more than data alone. T ese organi- zations need actionable insight. Armed with insight as it relates to gaps in care and opportunities to improve quality,


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