Pop health can decrease hypertension, diabetes: 3 takeaways

San Diego-area healthcare organizations are using population health strategies to improve hypertension treatment across the region.

The “Be There San Diego Data for Quality” (DFQ) group is a collaborative effort between health systems, data analysts, and clinical staff that began in 2012 with a goal to prevent and reduce heart attacks and strokes. In 2015, the group received funding from the CDC to beef up analytics and technical structure to make interoperability easier.

Eight healthcare organizations participate: Arch Health Partners, Council of Community Clinics, Kaiser Permanente, MultiCultural Primary Care Medical Group, Scripps Clinic Medical Group, Scripps Coastal Medical Center, Sharp Rees-Stealy, and University of California San Diego Medical Group. They share aggregated data concerning hypertension, diabetes and cardiovascular disease.

Top challenges:

1. Obtaining institutional support

When the group began working together, it was not as common for medical groups to report quality data to outside entities, says Kitty Bailey, MSW, executive director of DFQ. “While the clinical leadership was engaged and interested in sharing quality data with each other it was more difficult to obtain institutional support for the data request.”

2. Working with competitors

Bailey says that getting sometimes competing healthcare organizations to collaborate on a data project was a concern in the initial phase of the project. The group, she says, focused on the principle of “coopetition,” a hybrid of cooperation and competition. “For San Diego’s healthcare organizations, this means that while we are competitors we can also benefit from cooperating in certain arenas. In this case the healthcare organizations are coming together to compete against the disease instead of each other.”

3. Coordinating different technology abilities

It was also a challenge aligning the larger, multispecialty medical groups with more advanced technology with the smaller or midsized organizations with less sophisticated technology.

Also, though most of the practices have patients covered by Medicare or commercial insurers, one healthcare organization focuses primarily on uninsured patients.

Next: Promising results

Promising results

DQF was featured in Rand Health in June 2017, and researchers found that the group overcame challenges by committing to three action-oriented steps to improve hypertension in the region:

1. Implementing simplified guideline algorithms for treating patients with hypertension,

2. Promoting the use of the American College of Cardiology and American Heart Association atherosclerotic CVD risk estimator, and

3. Using an evidence-based medication protocol for prescribing a bundle of medications to prevent heart attacks for at-risk populations.

In 2014, the group’s shared data represented approximately 26% of patients with hypertension and 53% of people with diabetes in San Diego, according to the Rand Health study. By 2015, the group could report on outcomes according to age, gender, payers and ZIP code.

“Collecting quality measures at the level of patients' ZIP codes will enable the DFQ Group to explore geographic trends, identify ‘hot spots’ of poor health outcomes, and incorporate salient community characteristics, such as poverty rates and the distribution of historically underserved racial and ethnic groups,” the authors of the Rand Health study said.

Bailey says that because of the population health strategies agreed upon across healthcare organizations, in 2016 DQF was able to reach its regional target of 80% blood pressure control in patients with diabetes across participating organizations. 

“This was a goal for the past several years, and we were excited to see the improvements across the board that got us there,” Bailey says.

In the next few months, Bailey says that the data group will continue to create partnerships with healthcare organizations in the region, and will finalize a series of recommendations to other organizations on how to tackle hypertension and prediabetes using real-world examples.