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A New GIS-driven Approach to Optimize Service Area Boundaries for ACOs

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While many organizations use patient registries from EMRs to determine their patient population, there is a better way. Using GIS location technology, a health system can identify its care population based on geography and drive times. Health Catalyst uses Dartmouth Atlas hospital referral regions, a hierarchy of facility levels with appropriate drive time isochrones, and medical specialties-based central place theory to develop a more comprehensive view of a health system’s minimum bounding geometry. Using this method, ACOs derive a better understanding of their enrolled patients and eligible payer groups resulting a better basis for strategy and decision making.

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A New GIS-driven Approach to Optimize Service Area Boundaries for ACOs

  1. 1. GIS Location Technology for Healthcare A patient population is usually defined by who has already been seen for care. Care providers will sometime use the patient registries in their EMRs to define patient populations. There is a better way using GIS location technology and supporting data sources that may be especially useful for ACOs and other organizations concerned with managing population health—optimized service area boundaries. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
  2. 2. GIS Location Technology for Healthcare A more precise approach would be to first objectively define the geography within which the health system operates, and second to define the subsequent population belonging to that overall service area. This is known as network coverage optimization, and it provides a more robust way to define boundaries and identify populations. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
  3. 3. GIS Location Technology for Healthcare Once the geography-based care population is well defined, value-based strategies become more feasible. Within a rapidly changing healthcare market, it makes sense to leverage location analytics for more robust strategic assessments for healthcare systems, physician groups, health exchanges, and other payer groups. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
  4. 4. GIS Location Technology for Healthcare Determining Adequate Service Coverage One simple approach to gauge adequate service coverage across population density is to visualize access times for various healthcare facilities as shown in Figure 1. © 2014 Health Catalyst www.healthcatalyst.com Figure 1: An example showing drive time access to healthcare facilities within a given service area. Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
  5. 5. GIS Location Technology for Healthcare Determining Adequate Service Coverage Health Catalyst developed a more comprehensive approach using objective and independent methods for defining geography. This system was used to generate the minimum bounding geometry of the overall reach of a given healthcare system as shown in Figure 2. Figure 2: An example of using minimum bounding geometry to define the reach of a health system. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
  6. 6. GIS Location Technology for Healthcare Determining Adequate Service Coverage To better measure the overall viability of this health system, a network coverage score was calculated using zip code and population-based statistics. This score represents a system’s enrolled patients as compared to the total population. © 2014 Health Catalyst www.healthcatalyst.com Figure 1: An example showing drive time access to healthcare facilities within a given service area. Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
  7. 7. GIS Location Technology for Healthcare Determining Adequate Service Coverage Lastly, consumer data from sources such as Census.gov, Esri Tapestry market segmentation, and Medicare.gov were used to characterize the population defined by the final resulting boundary as shown in Figure 3. Figure 3: Leveraging socioeconomic and demographic data will enhance the understanding of health system boundaries and covered population. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
  8. 8. Optimized Service Area Boundaries Result in a Better Understanding for Population Health Management Strategy © 2014 Health Catalyst www.healthcatalyst.com So, how does this really work? A healthcare system can define its service area, by zip code, to determine the total population of that geography. Then it’s easy math to determine the percentage of the population already under care and the available new patient opportunity. Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
  9. 9. ACOs Benefit from GIS Technology © 2014 Health Catalyst www.healthcatalyst.com Using GIS-powered analytics to define patient populations, ACOs derive a better under-standing of their enrolled patients and payer groups. This process can yield better decisions in population health management, leading to improving quality and lower costs. Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
  10. 10. © 2014 Health Catalyst www.healthcatalyst.com Conclusion GIS location technology can be used to readily identify objective service area boundaries relevant to a specific healthcare system. This automated method of defining the service area also identified the exact population for which health coverage is currently provided. This health network optimization tool was built in collaboration with GISi (a platinum Esri partner) and is available by invitation at http://cloud.gisinc.com/hc/. Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 11
  11. 11. Link to original article for a more in-depth discussion. A New GIS-driven Approach to Optimize Service Area Boundaries for ACOs A Definitive Guide to Accountable Care Organizations Dr. David A. Burton, Senior Vice President and Dr. John Haughom, Senior Advisor Predictive Analytics: It’s the Intervention That Matters (Webinar, Slides, or Transcript) Dale Sanders, Senior Vice President and David K. Crockett, Ph.D., Senior Director of Research and Predictive Analytics © 2014 Health Catalyst www.healthcatalyst.com More about this topic Accountable Care Transformation Framework (an executive report) Dr. David A. Burton, Senior Vice President ACO Success Requires Precise Patient Population Definitions Luke Skelley, MSN, Vice President The Power of Geo-Analytics (and Maps) to Improve Predictive Analytics David Crockett, Senior Director of Research and Predictive Analytics Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
  12. 12. – John Haughom, MD, Senior Advisor, Health Catalyst © 2014 Health Catalyst www.healthcatalyst.com For more information: Download Healthcare: A Better Way. The New Era of Opportunity “This is a knowledge source for clinical and operational leaders, as well as front-line caregivers, who are involved in improving processes, reducing harm, designing and implementing new care delivery models, and undertaking the difficult task of leading meaningful change on behalf of the patients they serve.” Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
  13. 13. Other Clinical Quality Improvement Resources David K. Crockett, Ph.D. is the Senior Director of Research and Predictive Analytics. He brings nearly 20 years of translational research experience in pathology, laboratory and clinical diagnostics. His recent work includes patents in computer prediction models for phenotype effect of uncertain gene variants. Dr. Crockett has published more than 50 peer-reviewed journal articles in areas such as bioinformatics, biomarker discovery, immunology, molecular oncology, genomics and proteomics. He holds a BA in molecular biology from Brigham Young University, and a Ph.D. in biomedical informatics from the University of Utah, recognized as one of the top training programs for informatics in the world. Dr. Crockett builds on Health Catalyst’s ability to predict patient health outcomes and enable the next level of prescriptive analytics – the science of determining the most effective interventions to maintain health. © 2014 Health Catalyst www.healthcatalyst.com Click to read additional information at www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.

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