Many people immediately think of a doctor in a white coat carrying a stethoscope when they hear the word “health,” but in truth, the greatest opportunities to improve health happen pretty much everywhere but the doctor's office.
Rates of chronic conditions like obesity and type 2 diabetes are still increasing in the U.S. and are affected by diet, behavior, environment, and culture—factors that cannot be addressed by more doctor's visits.
Instead, collaborative programming that brings together strategies from housing, education, or labor to improve health outcomes may be one of the U.S.'s best strategies for diversifying and broadening its approaches to population health.
Embedding health-promoting opportunities where people live, work, and play—for example, providing mental health supports in a public school system or offering employee assistance programs at work—are just a few examples of cross-sector collaborations that can improve health.
The greatest opportunities to improve health happen pretty much everywhere but the doctor's office.
One of the best ways to solidify and drive cross-sector collaborations is to share patient/client data across organizations. For example, when doctors' offices find out that a patient lacks insurance, electricity, or other services that are foundational to improving health, they could share that data with a social worker whose job it would be to connect the patient to such services. Sharing data promotes accountability, enables the development of joint metrics across programs, advances decisionmaking, and increases transparency.
It's hard for collaborating organizations to know if they've achieved shared goals unless they also share data about outcomes. Service providers involved in the collaboration can react more quickly to and coordinate services better for the people they serve if they have complete information.
Collaborative work is a logical way to expand the types of services that could improve health, but without high-quality evidence demonstrating impact, it may be hard to convince funders that collaborations are worth the cost.
So why aren't collaborations sharing data more? The bottom line is that it's hard to do so. Barriers include competition between organizations about data ownership, hesitation over privacy considerations for the people being served, and a lack of enthusiasm to take on the risks of additional data being lost in a breach.
Why aren't collaborations sharing data more? The bottom line is that it's hard to do so.
Organizations may feel like they lack the expertise to make basic technical decisions: how data should be shared and stored, whose data and which data should be shared, how to keep data secure, and more.
Also, sharing data isn't free—it usually needs to be setup through a third-party vendor who maintains the integration of the systems. But the investment can be worth it if it's able to save collaborating organizations time and money by helping them figure out what's working and what isn't.
One of the biggest barriers to data sharing is fear of a data breach—the dread that sensitive data could be compromised, or worse yet, stolen. And yet an example of a long-standing effort to integrate data has focused on sharing the most sensitive data, thus far with great effectiveness.
The Ryan White HIV/AIDS Program (RWHAP) resides within the U.S. Department of Health and Human Services' Health Resources and Services Administration (HRSA) HIV/AIDS Bureau (HAB), and serves some of America's most vulnerable people, providing medical care and support services for uninsured and underinsured persons living with HIV/AIDS.
This population is more likely to experience housing instability or homelessness following diagnosis compared to the general population. In response, the U.S. Department of Housing and Urban Development's Office of HIV/AIDS Housing (OHH) established a program entitled Housing Opportunities for Persons With AIDS (HOPWA).
Both RWHAP and HOPWA have formula allocation programs, meaning that across states and metropolitan areas, they assign funds to local program administrators (often city or state government agencies) based on the size of the epidemic.
There is a great deal of overlap in populations served between these two programs, yet local programs in the same area rarely work together to coordinate their efforts, medical care and housing, which are often closely intertwined.
A person with HIV who has lost his or her apartment may no longer be able to reliably store medications in the refrigerator, for example, or may stay temporarily in a place far from their doctor. But if data were shared, each program could better understand and take steps to address the needs of shared clients.
To rectify this situation and encourage cross-sector collaboration, HAB and OHH jointly collaborated and wrote a letter (PDF) that they distributed to all their RWHAP and HOPWA grantees, encouraging them to “develop formal agreements to support data sharing processes and systems” so that they could share client-level data to improve coordination of services for persons living with HIV/AIDS who are unstably housed, at-risk for, or experiencing homelessness.
What is particularly striking about this recommendation is that HIV/AIDS data is considered some of the most sensitive data to share, given that its accidental release could be devastating for persons living with HIV who often experience stigma due to their HIV status. However, this letter was written based on experiences that data sharing can be done securely.
In fact, HAB and OHH have been working on joint informatics projects to improve care coordination for persons living with HIV/AIDS for the better part of the last decade. If organizations serving HIV-positive clients can share data securely and see health benefits, so can anyone else.
Vivian Towe is a policy researcher at the nonprofit, nonpartisan RAND Corporation.
This commentary originally appeared on The Hill on December 13, 2017. Commentary gives RAND researchers a platform to convey insights based on their professional expertise and often on their peer-reviewed research and analysis.