Measuring What Matters for Community Health Improvement

A Virtual Conversation Across Two National Networks

By Clare Tanner, PhD, Michigan Public Health Institute; Soma Stout, MD, MS, Institute for Healthcare Improvement; and Peter Eckart, MA, Illinois Public Health Institute

Multi-sector approaches are an exciting and growing element of addressing health equity and community health improvement. It is somewhat ironic, then, that many federal agencies and private philanthropic organizations continue to perpetuate funding and service silos, even within their efforts to lift up multi-sector approaches. Within this dynamic environment, leaders of major national innovation networks are building new coalitions to magnify the impact and accelerate the progress of local collaboratives to measure and understand community health improvement.

In two co-occurring meetings in different parts of the country, participants from the All In: Data for Community Health National Meeting and the Institute for Healthcare Improvement’s 100 Million Healthier Lives Summit held a joint session to discuss what communities are learning about measuring health improvement and how we can align the lessons from our different support programs to address the important issue of measuring what matters.

All In: Data for Community Health, a national network of projects with the common goal of improving multi-sector data sharing and collaboration, gathered for its annual meeting in Denver in April, bringing together more than 260 people from across the nation representing local collaboratives, health systems, community partners, and subject matter experts. Simultaneously, the 100 Million Healthier Lives collaboration, which is working to create a community of solutions advance health, wellbeing and equity, was convened during the Institute for Healthcare Improvement (IHI) Summit in Orlando.Both communities are part of a growing national movement supporting data-driven, local and multi-sector collaborations focused on population health improvement at the community level, as described in a recent report from Academy Health. Although the two meetings were held separately, each group presented the other with a background presentation and responded to the same set of questions to kick off a virtual conversation around measurement.

Specifically, as we work across sectors for health at the community level, a crucial question that both groups will have to answer is this: “Whose lives are getting better because we are here?”

Are we making progress? If so, how do we know?

During the “Measuring our Progress” session at the All In National Meeting, attendees concluded that we cannot make get to a common measurement system by working in siloes, and bringing together two like-minded national groups was a step in the right direction.While energized by the many projects represented at the meeting, participants were also sober about the barriers in their work which, if we are to sustain and scale the progress, should not be left to be solved repeatedly community by community.In small group discussions, participants discussed both progress and challenges experienced by other communities.

Barriers to Scale

The Lack of Standards

Meeting participants realize that privacy regulations such as HIPPA, FERPA, and 42 CFR Part 2 are needed to protect people from unauthorized use of private information, and to clarify the rules of the road: defining when data sharing can take place, not just when it cannot. However, as communities pioneer new models of multi-sector collaboration, they find that risk aversion and lack of understanding often leads to more restrictive interpretation of the regulations than is truly necessary. Much of the dialogue about barriers at the meeting centered on the frustration, expense and time spent in obtaining legal guidance and negotiating contracts.

Legal Challenges

Most of the 40+ projects within the All In network have operated as collaborations for less than two years, so the conversations reflected a lack of established standards for data sharing. Due to the diverse nature of the projects – including partners from health care, public health, education, housing, criminal justice, local and state government – standards were not even identified as desirable, much less possible. Instead, the emphasis of these projects is documenting the lessons learned in this emerging field, with an eye to establishing eventual benchmarks.

1) Make the Value Case for Data Sharing

The presence of so many initiatives shows that getting to ‘yes’ is possible. Upfront work is needed to document and frame the value case to increase motivation to share data.

Issue: Different language and frames of reference across sectors can hamper communication of value.
Idea: Enhance communications capacity to frame the public discourse on social problems.

2) Elevate “Bright Spots”

Examples of successful data sharing are helpful, and participants asked each other to share legal strategies that were used.

Issue: Multisector data sharing to support collaboration is innovative and not the 'norm.'
Idea: Share examples and elevate bright spots to show the way.

Health departments can use governmental/public health authorities to facilitate the flow of data. For example, in one community, the city health department had convened community partners who were eager to find ways to prevent the catastrophic impacts and expense of falls in the elderly population. They wanted to learn where the falls were happening by looking at claims data, and overlay that with geographic information and housing/city infrastructure details to understand and mitigate fall risk. Legal challenges threatened the entire project, until a strategy was developed to create a city-wide ordinance permitting the health department to access needed data.“Opt-in” consents can seem like a magic bullet, overcoming legal barriers by getting participants to give permission to share their data across sectors. However, initiatives quickly find that consent management can become a governance and technical challenge outside of their ability to deploy effectively. Another community addressed this issue by providing a trust framework between the state’s health information exchange and initiative participants.

Limitations in Existing Data Infrastructure

Limitations were noted in the spread and capability of health information exchanges (HIEs). Participants have found that HIEs are not mature in many areas across the country, or are in competition and not sharing data with each other.Additionally, to make data sharing successful, it is important to maximize the extent to which technologies and workflows are supportive of service provision. However, the lack of data fields within electronic health records to store important data from other sectors impedes the ability to make data sharing to medical providers seamless.

Measuring What Matters

A major challenge to demonstrating progress in the field of multi-sector data sharing relates to finding appropriate metrics. Because most long-term outcome measures do not change quickly, or have a delay in reporting between one to three years, they are not helpful to track on a regular basis for improvement.

If measurement is not meaningful, it just creates work—and distracts from the work of meaningfully improving communities. The 100 Million Healthier Lives’ Measure What Matters platform enables communities to develop their theory of change and identify related process measures, including what they believe they need to measure on a day-to-day basis to create real improvement.

The platform offers users the opportunity to establish their own metrics while offering a small set of people-reported outcome metrics—all well-validated questions that are simple and apply to any community that are about whether mental, physical, social and spiritual well-being have improved. These metrics change quickly, are easy for citizen leaders across communities to understand, and are predictive of long-term morbidity and mortality. The platform is turning out to be a very powerful and useful tool to help change the conversation about what matters.

“If we change what we measure, it can help to change our perception of the world.”

Dr. Soma Stout, Executive Lead of 100 Million Healthier Lives, explained: “We believe this kind of increased use of person-reported outcomes—based on what matters to them—is an intervention. Data shapes our perception and our perception shapes data. It helps us to reshape what matters and reshape the process by which we value what real people whose lives are most affected are saying.”

Existing measures of community health are often mandated from outside the community and are not responsive to our efforts in the short-term.

Use the Measure What Matters platform to select from a vetted set of process and outcome metrics. Utilize a common set of questions on well-being as the ultimate metric of whether we are making a difference in people's lives.

Combatting the Effects of Power and Privilege through Community Engagement around Data

Referring back to the All In National Meeting’s opening plenary and keynote speaker Natalie Burke’s appeal to "use your data super powers for good, not evil,” participants in both meetings identified the need to prioritize transparency and community engagement around data and measurement. Here are some of the key takeaways from this discussion:

There is a wealth of data that shows there is a very real difference between communities that relates to historic and structural policies that promote inequity (redlining, etc.). What we need to do is commit to being in relationship with people and places that are not thriving.
We need to combine the data with stories of real people and real lives so that the data has context and color.
In general, there is a huge bias towards “evidence-based” interventions and measurements that can be harmful to communities.
Data collection needs to occur within trusted relationships, which rely on listening, transparency and respect.
It will be important to not just make the data available, but to share it within its local and historical context.
We need to trust and rely on “lived experience” as equal to or more important than the perception of subject-matter experts external to the community.

Conclusion

While 100 Million Healthier Lives and All In share a commitment to community collaboration and engagement to increase health and equity, it’s important to recognize the differences between our efforts. For example, while the All In model of loose affiliation has not yet produced a comprehensive framework for common measurement, 100 Million Healthier Lives has developed a rigorous understanding of well-being that is intended to be relevant across their participant communities. But that difference also illustrates the value of these cross-network relationships.

The All In National Meeting participants were asked how the relevance of measuring well-being as a project outcomes, and they responded that well-being could be an important measure. But they also asked, “could well-being be measured with the quantitative elements of their current evaluation frameworks? How would we distinguish between differing perceptions of well-being between communities? What do we even mean when we ask about this?” As if in answer to that question, the folks at the 100 Million Healthier Lives meeting were discussing their four domains of well-being: physical health, mental health, social well-being and spiritual well-being. Their measurement team has established well-validated questions that are simple to answer and apply across any community.

This question from All In and answer from 100 Million Healthier Lives illustrates a key principle held in common by both networks: answers to many of the questions currently being asked in communities across the country have probably been asked and answered in other contexts across the country. We believe together that the primary purpose of our cross-network cooperation is to help the communities we serve make new and useful connections that would be unavailable to them without this larger network of relationships.

“Do what we do best and partner for the rest.”

The scheduling of the All In and 100 Million Healthier Lives meetings illustrates that we cannot possibly gather under one organizational structure or even at the same place and time. But through a diligent and disciplined approach to connecting and sharing, we can begin to fulfill our shared commitment to unprecedented collaboration and the benefits of these relationships should ripple across the country. As one meeting participant said, “We’re all here to share.”

About the Authors

Clare Tanner, PhD, directs the Center for Data Management and Translational Research at the Michigan Public HealthInstitute (MPHI). MPHI is a non-profit organization based near Lansing, Michigan. Dr. Tanner’s current work focuses onissues related to high quality and accessible primary care, linkages between healthcare and public health, and therelationship between electronic documentation and the triple aim outcomes of improved health, better care, and lowercost.

Soma Stout, MD, MS is the Executive External Lead for Health Improvement for the Institute for Healthcare Improvement and serves as Executive Lead of 100 Million Healthier Lives, which brings together hundreds of partners across communities to support 100 million people globally to live healthier lives by 2020. Through unprecedented collaboration across sectors, innovative improvement strategies, and systems transformation, the initiative is creating a movement that redefines health and healthcare.

Peter Eckart’s career spans over twenty-five years in which he has successfully spearheaded operations and project management in not-for-profit organizations. In addition to his role as Director of Health and Information Technology at the Illinois Public Health Institute (IPHI), he is currently leading the National Program Office at IPHI for a Robert Wood Johnson Foundation initiative, Data Across Sectors for Health (DASH). DASH identifies barriers, opportunities, promising practices and indicators of progress for multi-sector collaborations to connect information systems and share data for community health improvement.