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5 Reasons Why Healthcare Data is Unique and Difficult to Measure

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Healthcare data is not linear. It is a complex, diverse beast unlike the data of any other industry. There are five ways in particular that make healthcare data unique:
1. Much of the data is in multiple places.
2. The data is structured and unstructured.
3. It has inconsistent and variable definitions; evidence-based practice and new research is coming out every day. 4. The data is complex.
5. Changing regulatory requirements.

The answer for this unpredictability and complexity is the agility of a late-binding Data Warehouse.

Published in: Health & Medicine
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5 Reasons Why Healthcare Data is Unique and Difficult to Measure

  1. 1. © 2014 Health Catalyst www.healthcatalyst.com Proprietary and Confidential © 2014 Health Catalyst www.healthcatalyst.comProprietary and Confidential 5 Reasons Healthcare Data is Difficult to Measure By Dan LeSueur, Vice President Technology
  2. 2. © 2014 Health Catalyst www.healthcatalyst.com Proprietary and Confidential Characteristics of Healthcare Data Those of us who work with data tend to think in very structured, linear terms. We like B to follow A and C to follow B, not just some of the time, but all the time. Healthcare data isn’t that way. It’s both diverse and complex making linear analysis useless. What follows are five characteristics that makes healthcare data unique
  3. 3. © 2014 Health Catalyst www.healthcatalyst.com Proprietary and Confidential Multiple Data Locations Healthcare data tends to reside in multiple places. From different source systems, like EMRs or HR software, to different departments, like radiology or pharmacy. TOP FIVE PROPERTIES OF HEALTHCARE DATA 1 Healthcare data also occurs in different formats (e.g., text, numeric, paper, digital, pictures, videos, multimedia, etc.
  4. 4. © 2014 Health Catalyst www.healthcatalyst.com Proprietary and Confidential Data Structured/Unstructured Electronic medical record software has provided a platform for consistent data capture, but the reality is data capture is anything but consistent. TOP FIVE PROPERTIES OF HEALTHCARE DATA 2 As EMR products improve and healthcare providers become more accustomed to entering data in structured fields, the result will be better data for analytics.
  5. 5. © 2014 Health Catalyst www.healthcatalyst.com Proprietary and Confidential Inconsistent/variable Definitions Multiple clinicians from different groups may have inconsistent views of treatment for the same condition. Additionally, as we learn more about how the body works, our understanding continues to change of what is important, what to measure, how and when to measure it, and the goals to target. We’re trying to create order out of chaos. TOP FIVE PROPERTIES OF HEALTHCARE DATA 3
  6. 6. © 2014 Health Catalyst www.healthcatalyst.com Proprietary and Confidential The Data is Complex Clinical data from sources like EMRs give a more complete picture of the patient’s story. Healthcare data is an amalgam of complex individual systems. Managing the data related to each of those systems (which is often being captured in disparate applications), and turning it into something usable across a population, requires a far more sophisticated set of tools than is needed for other industries. TOP FIVE PROPERTIES OF HEALTHCARE DATA 4
  7. 7. © 2014 Health Catalyst www.healthcatalyst.com Proprietary and Confidential Changing Regulatory Requirements Regulatory and reporting requirements also continue to increase and evolve. CMS needs quality reports around measures like readmissions, and healthcare reform means more transparent quality and public pricing information. TOP FIVE PROPERTIES OF HEALTHCARE DATA 5
  8. 8. © 2014 Health Catalyst www.healthcatalyst.com Proprietary and Confidential Healthcare Data Will Get More Complex Healthcare data will not get simpler in the future. Healthcare faces unique challenges and with that comes unique data challenges. The solution for this challenge is an agile approach, tuned for healthcare.
  9. 9. © 2014 Health Catalyst www.healthcatalyst.com Proprietary and Confidential Agility Compensates for Complexity and Uncertainty The generally accepted method of aggregating data from disparate source systems so it can be analyzed is to create an enterprise data warehouse (EDW). Health Catalyst uses a Late- Binding™ Data Warehouse approach. With this schema, data is brought into the EDW from the source applications as-is, and when needed, transformed into exactly what the analysis requires.
  10. 10. © 2014 Health Catalyst www.healthcatalyst.com Proprietary and Confidential Summary The Health Catalyst Late-Binding™ Data Warehouse system allows you to aggregate complex data quickly and develop business rules on the fly so users can develop hypotheses, use the data to prove them right or wrong, and continue the discovery process until they are able to make scientific, evidence- based decisions.
  11. 11. © 2013 Health Catalyst www.healthcatalyst.com Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Dan LeSueur , Vice President Technology has been developing and implementing the core products and services of Health Catalyst since February of 2011. He started as a data architect, moved into a technical director role and is now a Vice President of Client and Technical Operations. Prior to joining Health Catalyst, Dan owned and operated a management consultancy for five years that assisted ambulatory practices in the implementation of electronic health records and data-driven management methodologies. In this venture he served as data architect, business-intelligence developer, and strategic advisor to physicians and practice owners in the strategic management and growth of their practices. Dan holds Master’s degrees in Business Administration and Health-Sector Management from Arizona State University and a Bachelor of Arts degree in Economics from Brigham Young University.

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