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The Truven Health Blog


The latest healthcare topics from a trusted, proven, and unbiased source.


MarketScan Trends in Spending for Mental Health and Substance Abuse Care


By Truven Staff/Friday, September 30, 2016

 

The most recent MarketScan® infographic leverages data from our MarketScan Research Databases to give an overview of MHSA trends for 2014 to 2015. We found that between 2014 and 2015:

●     Mental health and substance abuse costs increased 10%

●     Total substance abuse spending increased more quickly, rising 24.8% on a per member per month allowed amount basis

●     Services for substance abuse outpatient treatment (non-office) increased 22%

●     Inpatient substance abuse days per 1,000 admissions increased by 13%

 Find out more about MHSA trends here.



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

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