How to Avoid the Three Most Common Healthcare Analytics Pitfalls and Related Inefficiencies

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Russ Staheli Technical Director How to Avoid the Three Most Common Healthcare Analytics Pitfalls and Related Inefficiencies

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Data-driven Solutions By investing in analytics you are signing up for data-driven solutions. You want to identify and eliminate those problems and inefficiencies that keep you from improving quality and lowering cost. Finding a sustainable approach to using analytics without problems and further waste can be a challenge, it is possible to avoid these concerns if you better understand how to choose a good analytics solution.

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Three Common Pitfalls in Analytics When developing an analytics platform, some health systems deploy one or more best-of-breed or point solutions. These applications focus on a single goal and a single slicing of the data. They offer little insight outside the specific area of focus.

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Three Common Pitfalls in Analytics While implementing an EHR is a crucial step towards data-driven care, an EHR system alone is insufficient to enable an enterprise-wide, consistent data view from multiple sources. Clinical, financial, patient satisfaction, and administrative data must become a single source of truth to truly harness the analytic power of the data.

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Three Common Pitfalls in Analytics Independent data marts from different databases provide limited analytics capabilities because they can only deliver little sources of truth from the different system siloes. For example, when the ADT (admission, discharge, transfer) data lives in the EMR, analyzing associated costs and quality impacts are extremely inefficient.

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Three Common Pitfalls in Analytics Avoid These Analytics Pitfalls with an EDW For actionable clinical, financial, and operational insights that meet your needs across the enterprise, an enterprise data warehouse (EDW) is the best solution we’ve seen to date. An EDW captures, aggregates, and analyzes data in near real-time from the EHR and other internal and external systems that reside in silos.

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Two Common Sources of Inefficiency The report factory approach uses an analytics platform alone and assumes that if you build it, people will come. Reports can begin to back up in the request queue. If IT can’t keep up or report delivery is too slow, the situation can render your chosen solution redundant.

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Two Common Sources of Inefficiency To avoid becoming a report factory, sometimes a different, more measured approach is used to develop an analytics platform. This, however, can result in a project-by-project or flavor-of-the-month approach to analytics. It’s difficult to keep up with multiple projects and quality and cost gains made in the initial projects are quickly lost.

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Two Common Sources of Inefficiency A good deployment system addresses both the flavor of the month and report factory inefficiencies. Avoid Inefficiencies with a Robust Deployment System A good analytics deployment system provides a methodology for effectively getting clinicians and other stakeholders throughout the organization to embrace your analytics solution and to use data themselves to drive decisions. A robust deployment system leverages existing personnel to form permanent, cross-functional teams within each area where the healthcare data analytics will be used. Each team takes ownership for their own projects in their own space.

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More about this topic 4 Ways to Enable Healthcare Data Analysts to Provide Their Full Value Russ Staheli, Technical Director Why Point Solutions Strike Out Ken Trowbridge, Vice President Problems with EHRs and Built-In Analytics David Burton, MD, Senior VP and Former Executive Chairman Why a Partial Data Warehouse Can’t Solve Your Transformation Challenges Dan Burton, CEO 3 Essential Systems to Overcome Population Health Analytics Challenges Jared Crapo, Vice President Link to original article for a more in-depth discussion. How to Avoid the 3 Most Common Healthcare Analytics Pitfalls and Related Inefficiencies

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For more information:

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Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Russell Staheli joined Catalyst as a data architect in October 2011. He started his career as an Intern and later Outcomes Analyst at Intermountain Healthcare in the Institute for Health Care Delivery Research supporting the Advanced Training Program for Executives & QI Leaders (ATP) and the Primary Care Clinical Program. Before coming to Catalyst he worked as a Management Engineer Programmer Analyst for the Duke University Health System in their Performance Services department supporting their Infection Control and Epidemiology efforts. While there, he also worked as an external consultant to advance the analytical work of the Duke Infection Control Outreach Network (DICON), a collaborative of over 30 community hospitals. Russ holds an Master of Public Health in Health Policy and Administration from University of North Carolina Chapel Hill and a Bachelor’s degree in Health Services Research from the University of Utah.