Some Obvious Things about Healthcare Data

Some musings from a panel I was on the other day @ Xconomy’s Healthcare Summit.  It took place in Lincoln, MA, a suburb of Waltham :).

The Topic was the “Healthcare Innovation Pipeline” – a subject worthy of days of discussion, but wisely relegated by Bob Budieri to 30 minutes,  Joining me were Joe Kvedar, Founder/Director of Partners’ Center for Connected Health, Jill Seidman Director of Healthbox, and our moderator, Richard Dale, COO of Optum Labs.

Some takeaways:

I was glad to cut to the chase of  the “Obvious Things” w/r/t to the current state of Healthcare change:

  • Providers and Payers: It’s nearly impossible for them to maintain their operations while attempting to comply with regulatory deadlines.  Meaningful Use-2, HIPPA Omnibus, the Affordable Care Act itself – each place enormous pressure on the Healthcare system. Yet each continue to create incredible opportunities for disruptive technology solutions to help them cope with the urgent, tectonic pressure of compliance.
  • For docs, Medicare/Medicaid reimbursements are decreasing, private insurance payments are flat, and costs are increasing.  It’s getting incredibly-challenging for hospitals to maintain their operations while preparing for massive change.  And near-impossible for practices large and small to run effectively.
  • MU-2: while software is still being certified, hospital IT has no choice but to run non-certified apps.
  • ACA: while the transition from episodic sickness care to continuous wellness care is accelerating, the progress of building private patient data exchanges and outcomes registries plods on.
  • Some say that if system doesn’t move fast enough, even more regulations will increase, causing all stakeholders to move faster. Or not. Haste makes waste.

hpe & change

Still more “Obvious Things” result:

  • As Denny Ausiello challenged the group from the audience, we need to move away from the buzz term of “big data”, as the label implies that healthcare operation solutions lie only in mining large, broad pools of patient information.

We at Polaris wholeheartedly agree.  Unlike the commercial enterprise, the healthcare enterprise as a whole hasn’t had the benefit of 35 years of structured, workflow-derived data aggregation.

Real disruption will occur only with the accessibility of the deep, patient and cohort-level data that’s only beginning to emerge – not just the slicing and dicing of holistic population data.

While the healthcare industry may not be in a position to reclassify the moniker that has become the über buzz phrase across the commercial enterprise, we are in fact well-positioned to identify opportunities that lie across a healthcare system in obvious need of fixing.

  • The tremendous opportunity to standardize and integrate multiple sources of information on behalf of a single patient.

Only with this improved quality flow of information comes the ability to conduct predictive analytics across larger pools of patient populations which significantly improve outcomes.

  • The increased availability of data, and the ability to integrate this massive volume explosion into standard and usable forms is key. Among other things, this means integration into clinical workflows.

Clinical, claims, patient history, and genetic profiling data – in workable formats – integrated into clinical workflows – enables benchmarking of patients and care levels against population levels that can truly inform.

The prospect for disruption in the healthcare system has perhaps never been greater.

  •  But effecting and managing disruption is an art form – not a science.  And having the technology to disrupt is not enough.
  • Disruption best occurs when technology, urgency and at the same time, metered patience converge.

As Joe Kvendar put it, creating new models of care delivery, by developing innovative strategies to move care from hospitals and doctor’s offices into the day-to-day lives of patients, is a level of disruption which requires value to, and patience by, both patients and caregivers.

The rate of both clinician AND patient adoption of healthcare technology – and their motivations – will in many ways meter the adoption of innovation within the system.

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