Tame Your Health Data and Build Meaningful Analytics

Virtually all healthcare experts agree that analytics plays a huge role in both profitability and improved health outcomes, both as a catalyst for reform and as a foundation for the next evolution of healthcare. What is less understood is the vital role of health data management to achieving this goal. While analytics continues to provide ever more sophisticated insight into data, the data itself is plagued by inaccuracies, fragmentation and duplication across both systems and sources.

Health reform is focusing on moving from reactive to proactive healthcare, while at the same time making sure that previously known health issues get the appropriate treatment. Based on these known personal characteristics, the demand and desire for analytics-driven healthcare is growing rapidly. The technology limitations of the past are not the barriers that they once were. With the government focused on aligning incentives for adopting and leveraging health information technology for delivering meaningful care, the environment is ripe for the integration of data from both the Life Sciences and Healthcare sectors.

By examining the hurdles and opportunities presented in this current health data environment, both the challenges and benefits from health data convergence will be explored. As healthcare moves closer to the consumer, new business opportunities and improved quality of care are both achievable for Providers, Insurers and the Life Sciences.

Historically challenged with all the difficulties of getting healthcare data, numerous waves of analytic tools appeared within healthcare. These tools usually confined themselves to specific phases of the healthcare data spectrum and were usually justified by the promise of cost-savings rather than improved quality of care for the consumer. Health insurers, including commercial health plans and government plans alike, looked to analytics to reduce the expenses of their most costly users. These costly healthcare consumers were usually individuals with one or more chronic conditions (such as heart disease, diabetes and asthma). The costliest individuals were those that were either unaware of their condition or unaware of how to properly manage their conditions. Payers eventually realized that they could save significant amounts of money if they could get these individuals into a care management plan that helped them better manage their conditions, as well as to be more proactive in identifying warning signs. Seeking care when appropriate, rather than waiting for an event and the subsequent hospital costs associated with the event, are understood to be part of the quest. These initiatives highlighted the importance of analytics – but the main barrier to achieving this insight was the data itself.

Integrated informatics often focuses on obtaining and implementing an analytical software tool, ahead of a thorough examination of the underlying data structures, processes and inadequacies. In order to support internal customers, requiring the integration of large and disparate data sets for analytics and reporting, the goal is to improve operations and at the same time to reduce overall costs and inefficiencies. As these efforts can often take many years to achieve, smaller organizations in particular often lack both the time and resources to undertake this daunting endeavor.

The majority of health information today that is in an analysis-ready state is based on claims submitted to either health plans or to the government. This data focuses on the information necessary to process financial payment and usually does not include the clinical values needed to accurately assess the problem utilizing clinical metrics (e.g. lab values). While it has become possible to assess (with some level of confidence) what occurred and why, it is nearly impossible to assess how a treatment or program can potentially impact the patient or consumer.

By integrating the data requirements with planning for future analytical reporting applications, health data analytics can be a cornerstone of the overall health data strategy for the organization.

Advanced analytics can now be applied to better understand individuals, conditions, and treatments – both in specific comparisons and long-term perspectives. The availability of advanced analytics for modeling, simulation, and forecasting allows both the Life Sciences and Healthcare industries to focus their efforts in researching and delivering more effective strategies for preventing, managing, and curing individuals’ health issues. Individual consumers, with a comprehensive electronic health record at their disposal, will be able to leverage a variety of analytic tools to better understand their current and potential health future in a way that allows them to be truly informed and active participants in the healthcare system rather than passive bystanders with minimal access to health professionals.

With the practical application of advanced data management capabilities and processes, meaningful analytics are now possible in the new world of converged health and life sciences. Data management has evolved from a backroom, I.T. focused effort to the key for enabling business insights with analytics.

Speakers

Dan Soceanu, Senior Data Management Solution Architect, SAS

Dan Soceanu has extensive experience with data integration, data quality and data management, working closely with SAS customers to build solutions that result in competitive advantage. Prior to joining SAS, Dan has held marketing, alliance and research positions with DataFlux, Teradata and General Electric, as well as being a longtime data practitioner in the private sector. In addition to various industry, methodology and technology certifications, Dan received his Bachelor of Science in Business Administration from Kutztown University of Pennsylvania, as well as earning his Master of Business Administration from Bloomsburg University of Pennsylvania.

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Bill Shimp, Healthcare Data Management Account Executive, SAS

Bill Shimp has 15+ years of experience in business intelligence & reporting, data integration, data quality and data management, working closely with customers in a variety of industries to help them turn data into a strategic asset. Prior to joining SAS, Bill worked as a Sales Consultant with Crystal Decisions, Business Objects, SAP and Informatica. Bill received his Bachelor of Science in Engineering from Rutgers University.

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