The Truven Health Blog

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What Data Will Be Available for Population Health Analytics?

Key Questions that Data Scientists Will Ask

Thursday, September 15, 2016

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,  that is to be managed; on Wednesday we turned to the “so what” question,  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


What Services Can Be Offered for Population Health Management

The Second Question When You're About to Build Analytics for Population Health Management

Wednesday, September 14, 2016

Yesterday, I noted that all the players in Population Health management (PHM), including health systems, practitioners, insurance companies, employers and government agencies, agree they need cutting-edge analytics to make sense of their population. The simplest definition of Population Health Management (PHM) that seems to be accepted by all the players: "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." And then I described the first () of three key questions that must be answered before developing an analytic to support the business needs of the players.

The second key question is, what services can be offered to facilitate the management of the population?  This could include some combination of:

  • Wellness programs
  • Specific disease prevention programs
  • Ongoing care/disease/case management
  • Educational programs
  • Targeted individual outreach
  • Treatment guidance
  • Clinical services (e.g., free clinics, screenings)

This question is often overlooked when building analytics. I think of it as the “so what” question.  What are you, the key stakeholder, going to do with the information that this analytic provides to you?  What action will you take based on its results?  If you are an employer who is primarily interested in managing the health of your employees, it is fairly unlikely that you are investing in clinical care managers who can guide a patient through the treatment options available to them when they are newly diagnosed with a serious condition. However, if you are a health system or a physician practice, analytics that identify these patients at the earliest point of care may be of interest to you.  Similarly, a health system is unlikely to have significant influence over the culture of wellness present at a given employer.  Understanding the “so what’ of an analytic is absolutely key to developing a practical solution.

Tomorrow, I'll focus on the third key question that data scientists must ask before building population health analytics.

Anne Fischer
Senior Director, Advanced Analytics

Added later - here are links to the other two blogs in this series:

  • What is the population to be managed?
  • What data will be available for population health analytics?

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

    Part 1: The first question

    Tuesday, September 13, 2016

    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:

    A Data Scientist Thinks About Population Health Management

    Wednesday, August 24, 2016

    (The Truven Health Advanced Analytics team is tasked with building new and differentiating analytic methods. Asked to explain some interesting new analytics that are important for managing populations, the Advanced Analytics team wanted first to explain how they’re thinking about Population Health Management.)

    What is Population Health Management (PHM)? Much like the adage about the blind men and the elephant, Population Health Management can mean completely different things to different audiences. Hospital systems, practitioners, government, and private insurers all have different interpretations of what the term means. And, in fact, its implications are very different to each of these players.

    For most health systems, PHM represents a complete paradigm shift from their traditional way of doing business. Think of it like this: Imagine you own an auto-repair business. Perhaps you have a single facility, perhaps a chain of facilities. You are generally responsible for fixing a car when it’s damaged, and perhaps also performing routine maintenance on that vehicle. Now imagine you are being told that:

    • You are no longer simply responsible for the car when it is in your shop, but you are responsible for the car’s general care and maintenance for its lifetime.
    • The insurance company is no longer paying for the specific services you provide, they are paying you based on the overall “health” of the cars that you service. You now need to know what happens to that car outside the walls of your facilities.
    • You are no longer simply repairing the car when it needs it, you are being paid to keep the car “healthy” and out of your repair shop.

    Imagine how foreign that would seem. You have no information about the drivers of the car other than what you can gather publicly. You have no idea what kind of driving record a person has, what kind of routine maintenance they perform on their car (except that which happens to occur in one of your shops), or what kinds of roads they drive on. In short, you have no knowledge of what kind of risk they bring to the table.

    Hospital systems are in this situation. Historically, they have not needed to know much about their patients outside of what occurs within their facilities. They don’t have much information on where their patients are seeking care outside of their facilities, what kind of preventive care they are taking, what their social determinants of health are, nor how risky each patient is in terms of lifestyle and overall health, and they don’t have any input to their patients’ health benefit programs.

    Now imagine you are the auto mechanic. Your repair shop owner is now asking you to understand the entire spectrum of a given vehicle you are servicing. Perhaps your specialty is body work, but you have to start thinking about the gas mileage and the health of the exhaust system in every car you see. Similarly, practitioners – particularly those who are not primary care physicians and are not used to thinking about “the whole patient” – struggle with the concept of population health because their focus is typically on one patient and one problem at a time.

    Taking the analogy further, imagine you are the auto insurer (payer). You have historically managed payment for all the expenses for a given driver (and adjusted your rates to that driver based on their record/perceived risk). However, in this analogy as a healthcare insurer, your ability to refuse coverage to someone is diminishing, and your ability to assess risk is out of date, given that all drivers seem to be getting progressively worse and consequently more expensive. You are eager to shift some of that payment risk to the auto mechanics who are far more “hands on” with the cars, but there is no framework in which to plan and agree to terms. Plus you are still expected to maintain the risk for random “Acts of Nature” such as trees falling on cars, lightning strikes, and accidents caused by others. You are used to thinking about risk stratification and management at the group level, less so at the individual level.

    Finally, imagine that you are the civil engineer responsible for designing the infrastructure on which the cars travel. You design roads to accommodate certain volumes, speeds, and types of vehicles, and support laws to enforce speed limits and construction zones. (Besides being the largest healthcare payer, this is the other role government plays in healthcare.) But now you’re being asked to help understand and contribute to improving the overall “health” of the vehicles on your roads, to do this in a way that minimizes the frequency and scope of needed repairs, and to do it all on a reduced budget. Oh, and at the same time, you have to be thinking about how to ensure safe roadways and service for new kinds of cars – self driving, connected, and beyond. . .

    So how can Truven Health help? Our job as the analytics specialists is to help provide the information needed to expand the view of patients, and to present the information so that it’s actionable. Providing information on the full spectrum of care, even for something as specific as a surgical patient receiving a joint replacement (as our Bundled Care consultants do), can be invaluable in helping facilities, practitioners and payers understand the downstream implications of the care that is delivered. Helping them understand which patients are at high risk for “collision” (such as our new Risk of Hospitalization models) can lead to timely, cost-effective interventions. Identifying which segments of the population could most benefit from management (such as our forthcoming population classification method) can help focus activities for guiding patients and members towards health and well-being. Bringing valuable analytics to life can only happen if we first understand where our clients are coming from, and second, where they need to go to continue to be successful.

    In coming blog posts, I will offer insights into the work of data scientists and into the analytics we are developing to help our clients continue to be successful.


    Anne Fischer
    Senior Director, Advanced Analytics