The ability to use data in strategic decisionmaking is one of the key competitive advantage differentiators in health and human services. As more consumer and population health data becomes available, the advantage will go to the organizations with the ability to turn that data into actionable insights. That is a consistent theme from many thought leaders in the field, including Andrew Wright, Vice President, Digital Medicine for Otsuka America Pharmaceutical, as he explained in his keynote presentation, Remaking Health Care With Wearable Technology & Digital Health – A View To The Future; Craig Rhinehart, Director of Innovation and Market Development at IBM Watson Health, as he discussed in his keynote presentation, Cognitive Computing & Big Data: How They Will Shape The Future Of Care Delivery; and David K. Kelley, M.D., Chief Medical Officer at the Office of Medical Assistance Programs, Pennsylvania Department of Human Services, as he described in his keynote presentation, Leveraging The Power Of Analytics To Shape Medicaid Policy & Practices For Complex Consumers: The Experience Of Pennsylvania’s Medicaid Program.
A recent article in NEJM Catalyst, Using It Or Losing It? The Case For Data Scientists Inside Health Care, put the value of health care data, just in its deployment to reduce costs, at $300 billion annually. The authors also make the point that most of that “potential” is lost because analytics activities are “under-resourced” in the field. They cite two reasons for this situation that our team sees frequently.
First, there is managerial reliance on instinct rather than on data. A primary reason for underutilization is cultural; both organizational culture and clinical culture. Using “metrics” to inform decisionmaking with actionable insights, and change care delivery practices on an enterprise and programmatic level, is “a foreign culture” to most organizations.
Second, there is the lack of internal demand for analytics capabilities due to immediate margin/cost pressures. Without understanding the “return” on an investment in metrics, analytics is just another unwelcome expense. As the field becomes more price sensitive, more consumer-oriented, and more focused on value, the “return” will become more apparent.
Assuming an increased recognition at the executive level, that metrics-based management tools are needed for long-term sustainability, the question is how to best develop an enterprise-wide operational capacity for nimble performance management. The authors refer to the current use of “interested” clinical professionals and ad hoc combinations of computer scientists and clinical personnel to fill this need on a smaller scale. But on a larger scale, their general recommendation is creating in-house analytics functions led by data scientists—described as, “locally customized and owned data science” and “large-scale personnel investments in data science.”
If health systems and hospitals depend on vendors to define the pertinence of information and to drive the agenda at clinical and administrative team meetings, then it is difficult to see how locally tailored, timely, and responsive strategic changes and tactical modifications can be made to innovations in, for example, care delivery. If we similarly allow external vendors to decide what data, information, and insights should be brought to the attention of senior leadership, then we disempower our own employees and internal domain experts.
I think that, except for some of the very largest health and human service systems, it is a better path for organizations to keep on staff the team members responsible for “strategic” analytics functions and outsource the “transactional” functions to skilled vendor partners. To a degree, the authors acknowledge this, noting:
Without locally customized and owned data science, delivery science can at best offer a standard, average utility service. But for many smaller hospitals and systems, relying on trustworthy vendors and mature external products is likely to be an acceptable and cost-effective solution.
I think this approach is best because of the rapid changes in the technology realm and the need to have expert partners keeping up with those on-going developments. In addition, there is also the critical shortage of data scientists—there is an estimated 6,000 data scientists in the U.S. (only 180 of whom work in health and human services).
But to make these vendor partnerships work, health and human service executive teams need to reframe their perspective of their enterprise, with the knowledge that there may be multiple on-going and significant relationships with partner organizations, both transactional and strategic. The purpose of those relationships is to acquire needed insights, knowledge, or skills to optimize organizational performance with a financial model that increases organizational value. Part of that competency in optimizing strategic relationships is also to understand the role of strategic partners. I don’t think it is wise for any organizations to allow external parties to “decide what data, information, and insights should be brought to the attention of senior leadership” or “drive the agenda at clinical and administrative team meetings.” Those decisions can be in consultation with external advisors, but the ultimate responsibility lies with the executive team.
This issue of strategic versus transactional functions in analytics reminds me of previous discussions about financial management (see Reinventing The CFO: The Enhanced Role Financial Officers Play In A Shifting Market) and information systems management (see After ‘Reinventing’ The CFO, It’s The CIO’s Turn). What we do know is that health and human service organizations will need to spend more on technology and analytics in the years ahead—the question is how. It is helpful to remember the words of computing pioneer, Richard W. Hamming: “The purpose of computing is insight, not numbers.”
To help you develop best practice analytics, these are our top resources on big data from the OPEN MINDS Industry Library:
- Big Data To Survive, Sustain & Succeed
- Big Data = More Provider Performance Reports
- ‘Big Data’ For Dummies
- Big Data Gets Practical
- Big Data In Action
- Why & Where ‘Big Data’ Matters In Health & Human Services
- What To Do If You Don’t Have ‘Big Data’ – Making The Most Of ‘Little Data’
- From Data Modeling To Data-Driven Decisions
- Can Data Fix It?
- Bringing Your Data To Life
For more, join Ken Carr, Senior Associate, OPEN MINDS; David C. Guth, Jr., Chief Executive Officer, Centerstone; and Don Savoie, Executive Vice President & Chief Operating Officer, Meridian Behavioral Healthcare, Inc. on February 15 at The 2018 OPEN MINDS Performance Management Institute for the session, KPI To Improve Performance & Manage The Market.