Big Data? So What?

What good is “Big Data” if you can’t make any sense of it? Just because the go-live date of a giant new system is when the builders get paid, does that mean turning it on is the ultimate goal? Or is there more to “Big Data” than size and speed?

We should evaluate and pay for “Big Data” based on what it accomplishes. Not what it promises.

There has been a lot of talk about “Big Data” in the Medicaid industry for the past 10 years. While the technologies surrounding data analysis for Medicaid are exciting, “Big data” has become just another buzz word in healthcare. And with other buzzwords, “Big data” is often used to sell more than it is to actually deliver value. This article provides a review of recent Medicaid data investments and offers tips on how to avoid getting caught up in the hype.

The promise of Big Data

Big data in Healthcare can positively impact people’s lives and the cost of healthcare.  And it could save a lot of money- some studies show as much as $400 billion can be saved by leveraging big data, but adoption is complex and slow.

How? Healthcare professionals can leverage a large volume of data and then personalize a treatment to suit an individual patient in a timely manner. Organizations can strategically use health information to improve operational efficiency, reduce costs and enhance the safety and quality of patient care.

The need for Big Data

The challenge is the overwhelming amount of data to be stored, managed, analyzed, and protected.  Healthcare systems generate an astounding number of transactions. Every doctor visit, hospital stay, prescription and lab test results in multiple transactions just for the claims processing. And that doesn’t even count the eligibility data. A healthplan with 100,000 member lives will easily have millions of data records each year to manage. A Medicaid program will have billions and billions. It’s a lot of data to manage, let alone pull out insights that are useful to improve outcomes or reduce cost.

The data is complex and difficult to manage for numerous reasons. It must be captured from divergent complex systems all under different and changing regulatory requirements.  There is also inconsistency in definitions of data.  “Ask two clinicians what criteria are necessary to identify someone as a diabetic and you may get three different answers. “  There are multiple different end-users –  what a doctor needs to determine treatment for a cancer patient is very different from what a budget analyst needs to track trends. But its all running off of the same data.

There’s even more data coming. The analyst firm Gartner projects that by 2020 there will be more than 25 billion connected devices. For healthcare, any device that generates data about a person’s health and submits that data plays a part.  There are medical devices such as blood pressure monitors, glucose monitors, wearables that track such activity as heartrate, weight, how many steps taken, and countless others.  This data is already at an unprecedented volume.  A health system’s ability to manage this new volume of data is crucial.

There have been major Big Data Medicaid efforts recently

Its not all hype. There are many case studies showing the promise of Big Data for those able to move beyond the vision and concept stage.

  • In the North Carolina Medicaid program, the state’s Health Information Exchange system connects EHRs in more than 1,200 facilities. This system allows doctors to see a comprehensive medical record for each patient.
  • CMS has been working to integrate the data of 51 different Medicaid programs in recent years. While success has been slow in coming, without technologies that emerged in Big Data, there’s no way the initial lift of putting all the Medicaid data together would be possible.
  • The National Governor’s association started an 8-state health data exchange project in the summer of 2018 that seems promising. States participating will collaborate on best practices for Big Data analysis and governance. Part of the project design includes actual outcomes analysis of data efforts related to value-based care models and new substance abuse treatment programs.
  • CMS and states are already planning to use data more and more to recover wasted money. This summer CMS started requiring states to submit “enhanced data” to help audit whether payments were made in compliance with regulations. NY launched a review of its DSRIP program using a new Big Data approach with Boston University. C. Medicaid officials are wrapping up a recent data project where they identified $8M in savings due to ineligible members on rolls.

Tips on making most of Big Data

  1. Keep the end in mind. The goal is not Big Data. The goal is an insight that can change people’s lives. In a way, small data is the goal.
  2. Do not underestimate the effort to integrate multiple sources from multiple vendors and systems. This will be large at first and significant ongoing. The integration work never stops.
  3. Invest in meta data management. You need to know the sources of all your data, as well as important high-level information about each source.
  4. Don’t underestimate the need for consultants. You will think you are buying a self-service system, but you need to budget for help.
  5. Don’t underestimate the disruption of implementation.
  6. Invest in visualization that makes sense to a wide range of people within your organization.
  7. Don’t buy into the hype. Ask bidders for specific, granular case studies of what you will be able to do once the Big Data solution is built. Then determine if its worth it before you buy.

Reach out today for help

Healthcare Data Management is the process of collecting, storing, and analyzing data pulled from numerous sources.  Analytics and business intelligence are made possible with visualization and query tools that can separate information from insights. Here at Paragon, we provide solutions for both data management and analytics. Reach out today to learn more.

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