Supporting Patient-Centered Medical Homes through Healthcare Analytics

By Lili Hay

19 March 2015

Although the concept of Patient-Centered Medical Homes (PCMH) has been around for more than 50 years 1, the last decade has seen a revitalization of the PCMH model and an increase in its presence across the nation. The model’s popularity hinges on an approach to providing comprehensive primary care and redesigning healthcare delivery processes. This is accomplished through an emphasis on team-based care delivery, a whole-person approach to patient care, collaborative relationships between individuals and their physicians, and the use of evidence-based medicine and clinical decision support tools. In 2007, four nationally recognized physician organizations identified seven principles considered foundational to the PCMH2 model:

  1. Personal physicians
  2. Physician directed medical practices
  3. Whole person orientation
  4. Coordinated/integrated care
  5. Quality and safety
  6. Enhanced access
  7. Payment reform

Although the foundational principles of the PCMH have been largely agreed upon, there is no clear mode for how to create a successful PCMH. One of the most widely recognized models in place today is sponsored by the National Committee for Quality Assurance (NCQA), though there are numerous different demonstration and pilot projects in process across the country. As stated by Stange et al. in their Journal of General Internal Medicine article, “…the context for operationalizing the PCMH is still evolving based on what is being learned in many ongoing demonstrations,”underscoring the importance of evaluating and incorporating unique geographic, demographic, and economic considerations into the design of any new care model. Successful care delivery transformation projects, especially PCMH implementation and sustainment activities, require significant emphasis on healthcare analytics to inform quality improvement activities in addition to managing cost and utilization control efforts. The use of structured and routine analysis of healthcare claims-based information enables both established organizations and newly developed PCMHs to receive ongoing feedback on process effectiveness and health outcomes, facilitating rapid-cycle process improvement across the organization. PCMHs typically focus their analytic resources on operational process improvements and patient outcomes, with the goal of driving improvements in support of the Triple Aim. Successful organizations understand that routine and actionable information is the key to driving improvements. Examples of PCMH-focused analytic approaches being used across the country, which typically focus on cost, utilization, and quality, include but are not limited to the following:

  • Increased Use of Generic Pharmaceuticals: Pharmacy claims data is analyzed to identify areas of opportunity for transitioning members to generic equivalents as a cost-reduction initiative.
  • Appropriate Emergency Department (ED) Usage: Utilization patterns in the ED are evaluated to identify high utilization members (and attributed providers) and high frequency conditions. High rates of ED visits may be related to access and availability issues with primary care physicians, underutilization of urgent care services, or lack of understanding that many conditions are more appropriately addressed by a physician office visit rather than an ED visit.
  • Reduced Hospital Admissions and Readmissions: The disease burden of a population is assessed and abnormal utilization rates are identified to assist primary care providers in focusing on specific conditions and/or groups of patients so they can better manage healthcare status and prevent unnecessary hospitalizations and readmissions using outpatient resources.
  • Consistent Practices across Providers: Provider service patterns are compared to identify variations in care practices that could affect all three categories – cost, utilization, and quality. This information can be used to drive adherence to clinical guidelines and identify re-education opportunities for providers who are more expensive than their peers when treating specific cases (e.g., compare costs to treat diabetes patients across providers within a practice to identify discrepancies in care practices and cost savings opportunities). This type of benchmarking analysis can also be used to identify providers who may have high rates of ED usage, or hospital admissions and readmissions within their patient panel.
  • Targeted Care Management Services: Service consumption and diagnosis patterns are analyzed to identify members in need of care management services, for example, members with multiple chronic conditions, mental health issues, or poly-pharmacy usage. Risk scores and clinical drivers can be used to understand individual patient needs and priorities.
  • Preventive Care and Evidence-Based Measures: Claims data is used to assess whether members are consistently receiving preventive care, such as depression screenings, cancer screenings, diabetic eye exams, HbA1c testing, lipid profiles, and blood pressure screenings. Evidence-based care guidelines are implemented for chronic conditions, such as diabetes and cardiovascular disease, and compliance is tracked through claims data.

Experts in the field cite “a growing body of evidence…that the patient-centered medical home is an effective model to transform primary care and serve as a foundation”3 for other accountable care models. Leveraging data analysis is a crucial component of success for organizations pursuing this type of delivery model transformation. Sources 1Deborah Piekes et al. Early Evidence on the Patient-Centered Medical Home. Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services 2012; 12-0020-EF. 2Kurt C Stange et al. Defining and Measuring the Patient-Centered Medical Home. J Gen Intern Med 2010; 25(6):601-12. 3Harbrecht, Marjie G, and Lisa M. Latts. Colorado’s Patient-Centered Medical Home Pilot Met Numerous Obstacles, Yet Saw Results Such as Reduced Hospital Admissions. Health Affairs 2012; 31(9):2010-2017.

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