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Bridging the Divide: Using Data Sharing to Bring Collaborators Together

This installment of the Payment Reform for Population Health blog series focuses on the value of data sharing as a driver for collaboration – both internally and across sectors.

With support from the Robert Wood Johnson Foundation, AcademyHealth is working to identify the challenges and barriers in linking the health care payment system to community-wide population health. This four-part blog series features diverse perspectives from members of the Payment Reform for Population Health initiative’sGuiding Committee on the critical need for collaboration across sectors. The first three posts highlight promising approaches to collaboration with a focus on health care system and public health systems, using data sharing to bring collaborators together, and setting the right incentives to spur population health investments. The four-week series will wrap up with a post by AcademyHealth staff:

  1. The Sweet Spot in Health Care and Public Health
  2. Using Data Sharing to Bring Collaborators Together
  3. Supplementing Payment Reform to Promote Needed Investments in Population Health
  4. Addressing the Elements in the Room

As Karen Hacker notes in an earlier Payment for Population Health blog, there are several areas where health care systems can collaborate across sectors on population health. One of those critical areas is related to the use of data to support the interests of health care delivery systems as well as the broader community.  It is becoming increasingly recognized that multi-sector collaboration is key to creating healthier communities with each sector playing an important role. Health care delivery systems possess the business intelligence and analytic capacity to help them understand the needs of their own populations. As collaborators, by working in concert with others who are looking more broadly at community needs and resources, they can use data to support more rational targeting of evidence-based solutions.

A key driver for the success of new payment models for health care systems and health plans is the organizational capability to use business intelligence, including data analytics, to improve performance in quality and safety as well as in costing, pricing, and decision support.  For example, health care organizations need to continually improve not just their data collection in such areas as costs of adverse events, financial implications of readmissions, and the fiscal effects of waste in care processes, but there is growing recognition that many of those cost drivers are based on broader non-clinical needs of their patients. To fully realize the potential of business intelligence in creating value to patients and communities, healthcare organizations will have to reach beyond their walls to collaborate with community-based organizations, government agencies, and other providers on the collection, sharing, and analysis of data. 

In creating a data use strategy, the information must be actionable and it must be credible. And the credibility of information depends on several factors. First, all interested stakeholders must agree that what needs to be measured is being measured. Second, there must be assurance that metrics are being reported and recorded consistently—both within a health care system and across the sectors, where possible. Third, information needs context for meaning—understanding broader population needs can help give context to the medical conditions patients are experiencing.

As certain metrics are increasingly prescribed by government and private purchasers as a condition of payment, there is a growing imperative to control costs and improve quality. In other instances, health care systems and plans will want to define and track their own metrics to gauge the success of an initiative or assess the quality or cost of care. Collaborating with the public health and social services sectors also can help health care systems embed those metrics within a broader understanding of population health. In all cases, it is important that both finance and clinicians understand and agree upon the metrics that should be tracked, where and how the information should be collected, and how the data should be calculated and reviewed. Organizations must ensure that information is being collected and reported consistently if that information is to be credible, comparable, and, ultimately, actionable.

Healthcare Financial Management Association’s Value Project has identified many key insights into working with both clinicians and finance to develop a data strategy. While these insights relate specifically to internal collaboration, they also have much to teach us about cross-sector collaboration as well.

Take, for example, a large health system that served as a pilot site for a bundled payment program. Within this particular payment model, the bundled payment rate included an “allowance” for potentially avoidable conditions—the more these conditions are avoided, the greater the potential shared savings for the health system. As these finance and clinicians worked together on the payment methodology, they learned several lessons including:

  • Words matter. A term like “potentially avoidable” may seem perfectly acceptable to finance, but suggests a failing to clinicians. Finance leaders may want to work with a small group of physician champions on the language used to describe a value initiative and the metrics involved before engaging with a broader clinical audience. This language issue will certainly also be an issue when collaborating across sectors as well.
  • Be selective. Don’t try to measure—and improve upon—everything at once. Identify a few metrics that seem most significant, and that clinicians perceive as within their control, and focus efforts on improving these.
  • Lead with quality; follow with cost. Clinicians will engage more readily with metrics that relate to the quality and safety of patient care. When working across sectors, make sure you pick metrics that matter to your partners as well.

Healthcare organizations are clearly leaders in the evolving field of data analytics. As they gain access to more quality data, the ability to share these data with external partners, integrate them with other population-based data, and collectively convert them into actionable investments for community-wide health is growing. Collaboration also can provide a framework within which to overcome some of the data fragmentation that exists across plans and providers within a single community. Finally, collaboration around data collection, analysis, and use can benefit both health care systems and the communities within which those systems exist.

Stay tuned here, at the AcademyHealth blog, for updates on our initiative’s progress and emerging findings. If you are doing related work, please contact Enrique Martinez-Vidal; we’d like to include you in our efforts!

Richard Gundling
Committee Member

Richard Gundling, FHFMA, CMA

Senior Vice President - Healthcare Financial Management Association

Richard Gundling, FHFMA, CMA, is Senior Vice President for the Healthcare Financial Management Association (HF... Read Bio

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