Challenges for Population Health: A Clinical and Technology Approach | Dave Levin, M.D. and Pete Rivera | Healthcare Blogs Skip to content Skip to navigation

Challenges for Population Health: A Clinical and Technology Approach

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Population health seems like the next logical step for organizations to leverage the wealth of information that they have collected.  It’s not about the amount of information but how to use  information as a resource and make it actionable. 

For years information technology has been looked at as a necessary evil for billing. Clinical information was used to augment or justify the level of complexity of the bill. Now with Patient Centered Medical Home, Meaningful Use and Accountable Care we are seeing a shift in how we view this information. Data is now a valuable asset to be used as a patient health tool, not just for payment and operations.

There is no doubt that the return on investment for Electronic Health Records is elusive. However, by augmenting the data in EHR’s with population health, organizations can better manage capitated contracts, preventive health screenings and provide a higher quality of care to their patients. This translates directly to higher reimbursements, lower costs, and higher patient satisfaction scores. 

The issue for most hospitals and physician groups is deciding what exactly they want to track, why, and just as important; how? The place to start is the development of a Population Health Management (PHM) Strategy that is in line with the local market and populations. Most population health programs are similar in that they seek to find and fill gaps in clinical care for similar populations. But reimbursement mechanisms can vary dramatically between markets depending on the amount of risk sharing and initial program design. Goals should carefully align with available payment mechanisms.

From a clinician’s point of view, PHM programs need to be relevant, integrated into workflow, and actionable. Ideally these programs should address clinical problems and patient populations that are at the core of the clinical practice. Program design needs to include careful attention to not just “what must be done” but “who will do it” and “when and how”. Care team design, workflow analysis and data requirements are just some of the key challenges to be worked out. 

Most vendors in this market will provide the conduit for measurement, but candidly admit that it is up to the clients to develop actual strategy, as well as guidance on what to do with the information.

A few things to consider:

  1. Readiness Assessment. What does the organization hope to achieve? What current tools are available? What resources will be required to: execute; maintain; and report metrics. 
  2. Population Health Strategy. What initial metrics will be tracked: HEDIS; PQRS; MU; ACO; PCMH. Build the requirements for the population health tool.
  3. Conduct a comprehensive RFP for Population Health Software Vendors
  4. Involve physicians in overall program planning and design including setting clinical objectives, clinical workflow, physician extender protocols and documentation requirements including both narrative and discrete data collection.
  5. Leverage your Business Intelligence tools and Clinical Intelligence tools and avoid reinventing effort. 
  6. Think about Data marts versus Data Warehouses. Not everything needs to be in one basket.
  7. Work with the selected Population Health Software Vendor to keep on target and focused on strategy. 
  8. Decide to get really good on one measure before moving on to another.
  9. Have a Physician champion (either internal or external to the organization) help with metric improvements. It will sometimes take some hard conversations to change behavior or to help change processes.
  10. Develop sound data governance policy and procedures. So that you are not chasing the data, you are chasing quality improvement outcomes.

 So where are we headed as an industry with all of this? If you have not figured it out, Population Health is the cross roads of all clinical data. It makes the data “actionable” and relevant, not just something that is stored for documenting treatment plans. It gets us to the point of doing data modeling and comparison of treatment options based on population outcomes and costs. Most importantly as a patient, it leverages our information to provide an improved clinical picture and wellness strategy.

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