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Big Data—and what to do with it

Big Data is all the rage. Everyone knows that it is huge.  But when you ask an operator what practical use all this data is being put to, the answer could be a blank stare.

Much focus is now on “population health management” or, more often, on recognition of population members demonstrating some risk of a healthcare event or unrecognized health condition.  Early detection of a health risk is always a good thing as treatment options are often more plentiful, less risky and affordable with fewer and diminished side effects.  Focus on clinical application of data and analytics to recognize opportunities for early intervention are great examples of meaningful use of Big Data to improve quality of life, reduce volume and acuity of clinical services, slow the pace of healthcare cost growth, making services financially accessible.

Medicare, Medicaid and private healthcare payers continue to develop reimbursement programs for providers making proactive, preventative care a compensated service which can replace revenues lost as demand for services which resulted from a “fix it as you go” approach to medical practice and patient health declines.

It is great that this application of Big Data and analytics is becoming understood and development (tools, processes, outcome expectations) is proceeding.  Do not ignore this evolution, it is likely to become an element of every provider’s future.

Big Data and analytics is not limited to clinical evaluation and risk detection, so what other value can be had?

How about demand forecasting?  Health insurance companies have been analyzing medical service demand of defined populations for decades.  Actuaries (insurance data scientists) are skilled at projecting the numbers of heart attacks, strokes, diabetes cases, COPD, CHF, you name it.  These estimates are the statistical basis for setting premium charges that will cover the cost of providing expected services.  What is not done at this level is identifying the specific individuals most likely to experience a medical event or a condition on set.

Much of hospital resource planning, investment and training is based on an intuitive expectation that what will be needed this year will be pretty much the same as in preceding years.  Adjustment might be made for recognition of some medical procedures evolving from inpatient services to outpatient or even office procedures.  Historically, if a management team needed to increase or replace revenues, capacity to an existing clinical service line might be increased or new clinical service added.  There can be a lot of enthusiasm for these sorts of investments. Recruiting one more cardiologist, internist or orthopedist is expensive, but the addition can leverage existing hospital clinical staff, medical equipment and facilities.  More of the same can be a very attractive idea.

Adding a service line (labor and delivery, oncology, behavioral health services, et cetera) can be a much more expensive investment but a popular one with local community leadership.   Leaders can get enthusiastic about improved local access, convenience and facility expansion.

But in either case, what happens if there is just not enough demand in the served community to support these investments?  This could result in compromise of existing services and medical staff practices.  A failed investment in new clinical services can add even more pressure to a challenging financial picture or saddle the institution with a clinical program lacking volume of practice necessary to maintain the very high standard of clinical quality that should be expected.

Analysis of Big Data to drive clinical investment should be a focus for every hospital board of directors and executive team.  Assumptions and intuition can no longer be the basis for crucial decision making. Utilize data to answer these questions:

  • What is the composition of your institution’s service population?
  • What risk factors are they exposed to and engaged in?
  • What are the medical services statistically likely to be required?
  • What is the profile of patients currently using your facility?

Once you have data available for these basic questions, there is another set of information that must be acquired and planned for:

  • If a portion of the served population is not using an existing service, why?
  • If a service is needed but cannot be provided safely and sustainably, what preparation and planning should be done to serve potential emergency needs?
  • How can patients gain access and support to needed services through the local hospital?

If a service is forecasted to be in high demand but a significant portion of that demand originates from patient types that are not currently served, this may be an opportunity for growth. The reasons for low utilization must be recognized and addressed before a service investment can be made.  In summary, Big Data should be applied to improve patient outcomes, clinical performance, avoidance/early intervention and strategic planning/investment.   Resources are often scarce.  Local care must be effective and sustainable to be available.

Call BrightWork Advisory, LLC to develop your Big Data practices today.

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