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What Data Will Be Available for Population Health Analytics?

Key Questions that Data Scientists Will Ask


By Anne Fischer/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, 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

 


What Services Can Be Offered for Population Health Management

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


By Anne Fischer/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 (who is the population to be managed) 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


    By Anne Fischer/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:



    Focus on Value Based Care Leads to Scrutiny of Supply Chain Management


    By Truven Staff/Wednesday, September 7, 2016

    The shift to value based care continues to move risk from healthcare payers to healthcare providers, through contracts increasingly based on population health outcomes.  As hospitals and health systems take on more and more of this risk, they are examining all aspects of how to improve the efficiency of their clinical and financial operations.  

    In this RevCycle Intelligence article, CEO Laura Easton describes how Caldwell UNC Healthcare, working with Simpler Consulting, a Truven Health company, implemented Lean supply chain management strategies to save 2.62 million dollars through increased efficiency, productivity and reduced waste:  http://www.truven.info/j9R4N

    The Five Key Components of ACO Analytics


    By John Azzolini/Tuesday, August 30, 2016

    Accountable Care Organizations (ACOs) were created to provide financial incentives for providers to control costs and improve the quality of care. As they continue to advance, it is important for both providers and payers to ensure that risk is being appropriately shared between the two. This creates a unique set of challenges in determining the best way to design, manage and evaluate these programs. Whether you are running an ACO or contracting with one, data is integral to determining the best model. Without the proper data, those providing the care, and those paying for it, are flying blind.

    What’s more, not all ACOs are created equal, with three general types of models accounting for the bulk of ACOs: employer-sponsored, employer-direct contracted, and those leveraging existing insurer relationships. The analytic tools used to evaluate performance will depend upon which type of relationship a payer has with the ACO.

    The ACO Analytic “Tool Box”

    The five analytic methods listed below are key for ACOs managing program performance, and for employers and health plans assessing the value they are obtaining from these programs.

    1.       Attribution

    All measurement depends on a connection made between the ACO and/or its providers and enrollees. As a result, we need to uncover who the enrollees are, and for whom the ACO is bearing risk.

    Often, explicit patient assignment does not exist. Where it does, the evaluation models need to incorporate it into analytic databases. In the cases where it doesn’t, the ACO needs to perform that attribution based upon the observed pattern of care received by the patient population.

    2.       Population Health Management

    There are multiple tools available to identify and stratify patients, such as predictive modeling, where risk scores based on age, gender, and diagnosis are employed. Other methods employ biometric or health risk assessment information. Examples of these include Health and Longevity Scores, Health and Productivity Indexes, and Health Status/Opportunity Scores, that can be used to segment patient risk levels.

    3.       Network Management

    If an ACO is at financial risk for the management of individuals, it’s imperative to know where people are receiving health services, what kind of utilization is taking place out of network, and where those out-of-network services are being given.

    Many beneficiaries are not locked into the ACO network, which makes knowing whether these services are being given by high quality, efficient providers paramount.

    4.       Program Evaluation

    It’s important for everyone involved through the continuum of care that an assessment be made on the effectiveness of the ACO. As anyone who has been involved in care evaluation can tell you, there are a host of methodological pitfalls that can throw a wrench into measuring program evaluation. Controlling for differences between populations – specifically those who use the ACO and those who do not – is exceedingly important to determine the effectiveness of that ACO.

    5.       Quality Measurement

    In addition to evaluating ACOs on the basis of financial performance, establishing core quality measures for ACOs enables us to glean insights we would otherwise not have. Metrics such as potentially-avoidable admissions, screening rates, and specific process and care measures give us a baseline for quality measurement that is imperative in defining how well the ACO is performing.

    Embrace the Risk

    Risk is a fact of life in healthcare; it always has been. But in this new landscape, the ways in which both providers and payers are sharing that risk has undergone a drastic shift. Everyone will assume risk, but as we’ve outlined above, the key is to understand and properly allocate that risk between providers, patients and payers. The data is there; to guide these decisions, the key is employing the appropriate tools to establish this balance.

    John Azzolini
    Senior Consulting Scientist


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