We spend a lot of time putting together metrics that track what’s happening in the sectors of the health and human service field serving the most complex consumers. Over just the past year, our team has found that:
- An estimated 4.8 million consumers with SMI are enrolled in Medicaid (see Behavioral Health Coverage For The Medicaid SMI Population: A State-By-State Analysis)
- More than 163.8 million people suffer from the five most costly chronic conditions: mental disorders, heart conditions, diabetes mellitus, cancer, and COPD/asthma (see 2018 Chronic Care Management Market Update)
- Projected spending on child and family services in 2017 is $381.1 billion (see 2018 Children & Youth Services Market Update).
- 37 state Medicaid programs have integrated physical and behavioral health financing; 11 state Medicaid programs have a behavioral health carve-out to a care management organization (see State Medicaid Behavioral Health Carve-Outs: The OPEN MINDS 2018 Annual Update).
- Spending on behavioral health services is an estimated $253.8 billion in 2017 and spending is growing at a faster rate than general health care (see 2018 U.S. Behavioral Health Market Update).
But unless you’re putting together the details on these numbers, it isn’t apparent how bad the state of market metrics is in the health and human service space. To give you an example:
- There are no national spending numbers for the juvenile justice field
- There is no exact count of the number of addiction treatment beds and the number of psychiatric treatment beds.
- There is no data available for the spending on privatized child welfare services
- There is no single number of licensed behavioral health clinical professionals
- There is no “apples to apples” comparison data for Medicaid health plan capitation rates
- There isn’t a national definition for SMI—each state establishes their own definition
- There is no standard model for estimating the number of prisoners with SMI in federal, state, and county corrections facilities
The state of data on the provider organization side is not much better. Most of the provider organizations we work with don’t have an unduplicated count of the consumers that they served last year—or the total revenue generated across all service lines for serving a particular consumer. Or their unit cost by service location. Or whether their current consumers have been hospitalized (other than their system) in the past year.
This “state of the data” reminds me of a piece we ran last year that identified five key differences that make health care data “unique.” The data is complex, of course, and is housed in multiple places. Even more challenging, the data is recorded using changing definitions and it exists in both structured and unstructured formats. And, there are always the many regulatory requirements. (For more, see 5 Reasons Health Care Data Is Unique & Difficult To Measure.)
The good news is that there have been some changes in policy and practice that are making the “data void” easier to manage. A short list includes the Substance Abuse and Mental Health Administration’s (SAMHSA) final rule clarifying how consumer records related to addiction treatment may be used (see New SAMHSA Rule Clarifies Data Sharing For Addiction Treatment) and the 2017 Advanced APM tool where participants can now look up Qualifying APM Participant (QP) status based on claims (see Qualifying APM Participant (QP) Lookup Tool).
I reached out to a couple of my team members about what they are seeing in the changing state of data in the health and human service field. OPEN MINDS Senior Associate Sharon Hicks noted that in some ways, the exchange of health information has gotten better, but with one caveat:
At a broad level, the increase of health information exchanges, and the more sophisticated interpretations of the various data sharing and privacy rules have allowed increased data sharing between treating organizations and between payers. However, the industry is still working through the rules related to persons opting into data sharing (actively signing consents, etc.). My personal opinion is that privacy must win over data sharing because the risks outweigh the proven rewards.
My colleague Jim Gargiulo explained that, while the field isn’t quite where it needs to be, the future is looking both bright and imminent. He writes:
The amount of data generated by the health care ecosystem is growing all the time. It would be logical to assume that more of it is accessible to the provider organizations serving people with complex conditions, but that is not the case. The promise of predictive analytics to offer the right intervention at the right time to the right person is still just that, a promise. While Amazon, Google, and Facebook seem to know its users better than themselves, suggesting products, events, and friends based on past usage patterns, I’ve yet to see in the real world, self-service, on-demand technologies that suggest how to get the best outcome and value out of my health care experience.
There are lots of reasons for this, from confidentiality rules that inhibit data sharing, to the challenges of translating structured and unstructured data sets into actionable information, and the lack of consistent data standards across geographies and care settings. Perhaps the ultimate Holy Grail in our collective movement towards value-based reimbursement will be the fulfillment of this promise; of easy access to meaningful information in a way that improves care and saves lives.
But there is no question that many organizations are working with big datasets to improve care. In New York State and Missouri, large, claim-based data sets are being used to offer public-facing dashboards that inform providers and consumers about the state of their health care systems; sliced and diced by all types of demography, diagnosis, and provider characteristics. But these are lagging indicators that do a good job telling the story of past performance, but not providing actionable information about how to intervene or provide care in the present. The good news is many technology vendors are in the chase to demonstrate what is possible when combining claims, and clinical and social determinant data, to improve health care outcomes. It would be my expectation that the next time I am asked to point to an example of big data making an impact on client care, I will have to choose from many.
The race to aggregated and actionable data is on. It is enough of a race that the recent announcement by Amazon, JPMorgan Chase & Co., and Berkshire Hathaway to create a new health care entity has sent the stock market in a spin (see Amazon, Berkshire Hathaway & JPMorgan Chase & Co. To Partner On U.S. Employee Health Care). Every executive team needs a strategy to put together the data they need, however limited or expansive, to improve performance and remain relevant in a changing health and human service field.
For more, join me on August 16 at The 2018 OPEN MINDS Management Best Practices Institute for my plenary address, What Is A Best Practice? Exploring The Role Of Evidence-Based Practices, Practice-Based Evidence & Big Data.