Population Health Management

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Population health management is an approach to health care that expands the traditional model of care to a community model of care and focuses on the outcomes of groups of people.

Introduction

As organizations begin to move toward an Accountable Care Organization or other shared risk payment model, population health management is becoming an important function of EHR systems [1] [2]. Organizations seek to reduce costs for a covered group of patients by improving care and therefore reducing negative outcomes. By systematically focusing on the healthcare needs of every patient, emphasizing preventive medicine and managing established chronic disease, a health care system can accomplish these goals.[1] Processes that focus on care coordination and identifying high risk individuals are central to population health management.[2] The management of care changes from a reactive model to a proactive or preventative model. [3] The ultimate goal is to improve both the cost of care and the quality of care, two parts of the Triple Aim.

Five Principles of Population Health Management

There are five fundamental principles of population health management. [4][5]

  1. Community perspective– The definition of community can vary from all the people living in a geographic area to the individuals in a health plan. It includes subgroups that can be defined by diseases, demographics, or other characteristics.
  2. Epidemiological perspective – Care teams need to be trained to think of group experience in addition to that of individuals. Computerized data provides statistics about disease, disability, and death. The goal is to obtain the best possible outcome for a population at the lowest cost.
  3. Evidence-Based Medicine (EBM) as foundation– Care practices need to be based on guidelines that are backed by sound research and interventions that are demonstrated to be effective. See also Fulfilling the promise of evidence-based medicine
  4. Emphasis on outcomes – Quality measures that can be tracked over time with metrics to demonstrate improvement are important to the initiative.
  5. Preventative care foundation – The focus of the care team shifts from a disease orientation to a prevention focus at both the individual and community level.


Steps for Population Health Management

Successful population health management initiatives have had several steps in common.[4] [5]

  1. Create a registry – This is a list of patients with designated characteristic or disease in common.
  2. Establish guidelines and treatment algorithms – It is critical to use evidence based literature review to develop an algorithm. Clinical decision support (CDS) developed based upon the algorithms needs to be provided real time at the point of care.
  3. Identify patients who have care gaps and determine responsible care team.
  4. Develop interventions and create reminders – These can be targeted at either patients or care teams. Outcomes need to be reviewed to determine effectiveness of the intervention.
  5. Monitor performance and outcomes. Benchmark these results to national guidelines and peers. Provide individual providers or care teams with results over time.
  6. Change intervention if outcomes are not showing improvement or reaching goals.

Technological Tools Necessary for Success

  • Structured clinical data in an EHR in order to run reports.[6] See Structured Data Entry
  • Database with the ability to track outcomes and health status of patients [5]
  • Disease Registry to identify patients with selected diseases in order to identify gaps in care[6]
  • Ability to show data in multiple forms including electronic reports, printed reports, graphic displays. Trends over time are also important to demonstrate effectiveness of interventions. [7] A dashboard should be available for individual providers and for the entire organization.[1]
  • Integrated system with data from multiple sources including inpatient, outpatient, and outside sources [5]
  • Ability to risk stratify patients by current health status, allowing increased intensity of intervention to those at highest risk.[1]
  • Ability to generate reports indicating individual patient's care gaps.[1]

Challenges remaining

  • Many EHR’s do not have functionality necessary for population health management, possibly requiring the purchase of additional software.[1][7]
  • Interoperability of disparate electronic systems is necessary for organizations to obtain data from other sites of care, including outside organizations, payers, and pharmacies.[1][7]
  • An integrated means of communication with patients regarding gaps of care that is also efficient and fits into the workflow is needed.[1] A patient portal is one way to accomplish this.
  • Ability to mine data real time rather than in batches. This is especially true for data from outside organizations, which can be delivered covering multiple time-frames or with significant delay.[2]
  • Many organizations need to hire and train staff to coordinate care. Processes may also need to be developed in order to allow staff to improve care processes and communicate with patients.[2]
  • Patient engagement in the process is necessary and can be challenging.[2][4]
  • There remain multiple barriers to defining the population, including documentation, data that was developed for other purposes, and availability of data from outside sources.[4]
  • Incentives are not yet fully aligned with outcomes rather than the volume of care. That alignment may be made more difficult by regulations that restrict collaboration between payers, hospitals, and physicians.[8][7]
  • Acquiring the right analytical techniques to mine data from a large EHR database

Examples of successful population health management

  • Grant, R. W., & Meigs, J. B. (2002, July). The Use of Computers in Population-Based Diabetes Management. JCOM, 9(7), 390-6.
  • Kupersmith, J., Francis, J., Kerr, E., Krein, S., Pogach, L., Kolodner, R. M., & Perlin, J. B. (2007, January). Advancing Evidence-Based Care for Diabetes: Lessons From the Veterans Health Administration. Health Affairs, 26(2), 156-68.
  • Paulus, R. A., Davis, K., & Steele, G. D. (2001). Continuous Innovation in Health Care: Implications of the Geisinger Experience. Health Affairs, 27(5), 1235-45
  • Williams, J. (2013, March). a new model for care populaion management. healthcare financial management, 69-76

References

  1. 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 Matthews, M. B., & Hodach, R. (2012, April). automation is key to managing a population's health. Healthcare Financial Management, 74-80
  2. 2.0 2.1 2.2 2.3 2.4 Williams, J. (2013, March). a new model for care population management. healthcare financial management, 69-76.
  3. NCQA. (2013). NCQA Accreditation of Accountable Care Organizations.
  4. 4.0 4.1 4.2 4.3 Grant, R. W., & Meigs, J. B. (2002, July). The Use of Computers in Population-Based Diabetes Management. JCOM, 9(7), 390-6
  5. 5.0 5.1 5.2 5.3 Ibrahim, M. A., Savitz, L. A., Carey, T. S., & Wagner, E. H. (2001). Population-Based Health Principles in Medical and Public Health Practice. J Public Health Management Practice, 7(3)
  6. 6.0 6.1 Kupersmith, J., Francis, J., Kerr, E., Krein, S., Pogach, L., Kolodner, R. M., & Perlin, J. B. (2007, January). Advancing Evidence-Based Care for Diabetes: Lessons From the Veterans Health Administration. Health Affairs, 26(2), 156-68.
  7. 7.0 7.1 7.2 7.3 Cusak, C. M., Knudson, A. D., Kronstadt, J. L., Singer, R. F., & Brown, A. L. (2010). Practice-Based Population Health: Information Technology to Support Transformation to Proactive Primary Care. AHRQ.
  8. Paulus, R. A., Davis, K., & Steele, G. D. (2001). Continuous Innovation in Health Care: Implications of the Geisinger Experience. Health Affairs, 27(5), 1235-45


Submitted by Joanna Brelvi, MD