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What Data Will Be Available for Population Health Analytics?

Key Questions that Data Scientists Will Ask

By Truven Staff

This is the third in a series of three blogs that present key questions that must be answered before developing an analytic to support the business needs of Population Health Management (PHM) stakeholders or players, including health systems, practitioners, insurance companies, employers and government agencies.

The players agree they need cutting-edge analytics to make sense of their population, and the simplest definition of Population Health Management (PHM) that seems to be accepted by all the players is: Meeting the healthcare needs of a defined population of individuals, from the healthiest to the highest risk, with the right programs at the right time to ensure the best outcomes possible. On Tuesday I described the first question, who is the population that is to be managed; on Wednesday we turned to the “so what” question, what services can be offered to facilitate the management of the population.

The third important question is what data will be available on which to build the analytics?

Commonly utilized data sources for healthcare analytics include:

  • Information created for administrative purposes (administrative data)
  • Administrative data specifically created for reimbursement (claims data)
  • Information recorded to facilitate the process of delivering care (clinical data)
  • Self-reported information, such as survey data
  • Socio-economic data, either public or privately gathered
  • Device-generated data

Two aspects of this topic are important: what data will available to build the analytic and what data will the player have ongoing access to when applying the analytic?  In the ideal world of analytics development, each method is built using a comprehensive and representative data sample. In other words, the data should have a longitudinal view into a population’s healthcare experience using various data inputs, including administrative and EHR sourced content in addition to socioeconomic details; and, it should be inclusive of all types of individuals so that it is not biased toward certain demographics.

Answering questions about a population becomes more difficult when you don’t have all of the population’s information and need to infer certain aspects. Typically, the health systems or practitioners don’t have a comprehensive view of their patient population, but “they don’t know what they don’t know”.  On the other hand, typically, the insurers or employers do not have access to the clinical richness that lives within the medical records. And while many parties are optimistic about the value of socio-economic data, the process of obtaining that data and merging it into other data sources is not insignificant.

In summary, although on the surface it may appear that the same analytic solutions are desired by all the players, it’s highly unlikely that everyone can use precisely the same analytics due to different answers to three key questions: who is the population, what services can realistically be offered, and what data will be available. The job of Truven Health therefore becomes one of designing analytics that are specific to particular use cases, but with as much flexibility as possible to allow for applicability in various business and data situations. In later posts, I’ll discuss the various types of analytics that can be created once these three key questions are answered, along with some of the specific new analytics Truven Health is developing. 

Here are links to the two prior blogs on this topic: 

Anne Fischer
Senior Director, Advanced Analytics

 


Analytics for Population Health Management – First, Answer the Three Key Questions

Part 1: The first question

By Truven Staff

While the perspective on and effects of Population Health Management (PHM) differ according to the stakeholder or “player,” as I discussed in an earlier post, all the players agree they need cutting-edge analytics to make sense of their population.

To recap, in PHM the players include health systems, practitioners, insurance companies, employers and government agencies. Perhaps the simplest definition of PHM that seems to be accepted by all parties is this: Meeting the healthcare needs of a defined population of individuals, from the healthiest to the highest risk, with the right programs at the right time to ensure the best outcomes possible. Common stakeholder questions include:

  • What does my population look like and what are its overall healthcare needs?
  • How do I keep the healthy people healthy?
  • How do I best manage those that are already sick?
  • Whom do I need to target for care management/intervention?
  • Who is at highest risk for hospitalization, disease progression, higher costs, or other negative outcomes, and how can I best mitigate that risk?

Given those common questions, it may seem as if it would be a simple task to identify the analytic methods required and start churning out new analytics as fast as possible.  However, in the analytics world, nothing is as simple as it may first appear!

Developing an analytic to support these broad business needs requires answers to three key questions. First, who is the population that is to be managed? Depending on the perspective, this could be any of the following:

  • Individuals assigned to a particular physician or to an entity (e.g., Accountable Care Organization (ACO) or a Patient Centered Medical Home (PCMH)) for management
  • Individuals who have sought, or are likely to seek care from a particular health system
  • Individuals within a specific geographic community
  • Individuals enrolled in a particular health insurance plan
  • Employees of a given organization

As you might imagine, different populations may require very different analytics.  For example, a population of basically healthy fully employed young individuals may require analytics focused primarily on prevention and wellness, while a population of older, less healthy adults may require a more proactive disease management approach. True PHM requires analyzing different types of individuals in different ways.  There is no “one size fits all” approach in analytics.

Tomorrow, I will discuss the second and third key questions.

Anne Fischer
Senior Director, Advanced Analytics

Added later – here are links to the other two blogs:



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