6 Data Analytics Driving Successful Population Health Management

Tuesday, April 2nd, 2013
This post was written by Jessica Fornarotto

population health data analytics

Webinar Replay: Achieving Population Health Management Results in Value-Based Healthcare

The development of a successful population health management (PHM) effort starts with the data and the data analysis, states Patricia Curran, principal in Buck Consultants’ National Clinical Practice. Curran describes the role of data and data analysis, the six critical PHM data areas, and the “influences” and the “influencers” that affect a population’s road to better health.

Where are we today and where do we want to be in the future? All of the data that you can gather is carefully evaluated to consider several points: the culture of the company and the employees, the business objectives, the health literacy of the population, compliance and risk scores and the utilization trends.

Data is essential to understanding the population you wish to manage and designing programs to meet the needs of a specific population. Buck Consultants takes all the raw data that we can gather, analyzes it, and transforms it into knowledge. The ‘aha’ moment is when it all comes together and we use it to build a strategy for an organization’s PHM program. It’s important to use your own data to identify the population’s specific needs and target your program to those needs. There are six areas that form the foundation for a successful PHM program:

  • Clinical data is biometric data or lab data, and possibly health risk assessment (HRA) data, that helps identify risks and cost drivers and is used to monitor the program’s success.
  • Utilization data would be the utilization patterns. For example, how are people accessing their healthcare?
  • Adherence is beginning to replace the word ‘compliance.’ This refers to how well members and providers are adhering to evidence-based medicine guidelines. Are they filling their prescriptions consistently? Are they getting preventive care?
  • Operational data is participation data, productivity data, disability data and other information that helps to monitor and develop the programs.
  • Financial data shows how this healthcare activity that you’re offering translates to dollars and opportunities for real hard dollar savings. This data is key in order to get senior management support and finances to continue the program.
  • Satisfaction data is necessary to monitor how participants and your key stakeholders view your efforts.

Part of the data analysis also includes identifying all the things that influence the decisions people are making and the influencers that are affecting what you’re trying to accomplish. For example, influencers might be spouses, family members, friends, healthcare providers, and employer management staff. Influences might be a fear of financial issues, ignorance, indifference, and inconvenience.

Take this scenario as an example: an employer may have a goal to increase the level of mammogram participation or people getting mammograms on a regular basis. They bring in a mobile unit to provide on site mammograms. But after they do this, they find that there is still no change with mammogram compliance. They will then go back to their employee population and discover that the reason they didn’t have any improvement was because the supervisors on the line didn’t allow people off the line to participate. The line supervisor is the influencer that needs to be identified and rectified before there’s going to be any change.

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