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Population Health: Organizational and Data Governance, and Analytics Strategies

January 5, 2017
by Kevin Lamb, Health Catalyst
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Tom Burton at Health Catalyst talked about the work that Anne Milgram, former New Jersey Attorney General, is doing with analytics to reform the criminal justice system. Social determinants of health have a large overlap with the social determinants of crime. The economics that drive our criminal justice system thrive on crime not innocence. He wondered if anyone has started to experiment with bringing in criminal justice data to enhance predictive modeling and drill into root causes. This raised the prospect of medical ethics and bias in providing care, but it was also agreed that this type of data could prove helpful. We need to treat the whole person and the more information we know about the whole person the more accurate the care we can provide.

Tom Burton also shared a story of population health mimicking public health in New Ulm, Minnesota, where they conducted geospatial analysis to see hotspots of inactivity and chronic illness and disease. They then used that data to deploy community services and activities for promoting health and wellness, generating impressive results over a ten-year period.

The group agreed that not everything in population health is a big-data, high-cost issue. Sometimes it’s simply a matter of engaging with the community. For example, when Catholic Health Initiatives (CHI) found that teenage drivers in a small Midwestern town experienced a high number of accidents during the winter because they were driving to a nearby town to see movies, CHI built the town a movie theater. The result was a dramatic drop in auto accidents.

The group discussed patient-reported outcomes as another component of longitudinal data. More patients want more doctors to use measurements from their fitness devices. But this brings up more issues of data accuracy, validation, storage, liability and usage. Fitness devices and wellness programs are great motivational tools, but the consensus was that the popular devices lack the data quality required for population health. “We have a FitBit-type device whose heart-rate monitoring is unproven scientifically,” said Eric Yablonka, “but everybody talks about we need to get connected health data.”

The Need to Redesign Care Delivery

When participants were asked to describe the effectiveness and ROI of their care plans and workflows, and how they have incorporated such results into future risk models and care-plan design, the general consensus was that it’s hard to implement care plans consistently and it’s hard to determine the ROI.

For some, it’s difficult trying to define what the standard of care model is going to be. Outcomes for populations of people are lumped together and trying to correlate that overall population outcome with the exact care activities that took place over time is almost impossible to do. A lot of the current work is not about creating a predictive model or driving complicated analytics, but around getting clinical consensus on what the starting point will be. Healthcare systems could improve tremendously with care standards and the use of models to interpret what the outcomes will be using those standards.

Dr. John Pirolo: “We are trying to couple accountability with analytics. In the past, we treated analytics as a project, so it wasn’t necessarily connected to the people who were delivering clinical care or driving clinical redesign. What we are trying to do now is pursue analytics only in response to clinical leadership direction. When the request for analytics comes in, there’s a senior level discussion about what the expected clinical change will be in response to the analytic. This has made people more judicious about their requests because now they know they will have to generate operational change.”

Timothy Zeddies spoke about a specific care plan at Spectrum Health: “We had some interventions that were driven by the health plan. We had a lot of unnecessary care at the end of life. We created a model to care for some people individually, very one-on-one. To do that, we had to bring in a data-scientist-type person to accurately do healthcare evaluations. We found out there’s a certain kind of person who responds better to this kind of intervention.”

The group acknowledged while technology and discipline are key to creating care standards, there also needs to be a sense of urgency—which is very market-specific—people need to be afraid of failure before they make the commitment to require conformance.

Conclusion

Chief Data and Analytics Officers may be relative newcomers to the healthcare C-suite, but health systems smart enough to bring them to the senior-executive table will be the ones still standing when the dust settles on the tumultuous transition to value-based care. Because data is the lingua franca of population health, becoming a data-driven organization is imperative for a truly integrated delivery system. But the challenges of data are myriad and daunting: data quality, standards, integration, access, security, governance and management require often herculean human and technical effort. Building a data-driven culture in healthcare will demand not only system-wide discussions but industry-wide ones as well. We hope this SI Summit and report contributes to that effort.

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