In the latest evidence supporting the use of technology tools to improve population health through value-based delivery, children covered under Medicaid realized a reduction in both hospital admissions and length-of-stay. The study authors concluded that registry-based information technology platforms used by an interdisciplinary team were a “scalable strategy.”
The study was published in the December issue of the Journal of the American Medical Association. According to the authors, it is the largest such study to measure the impact of population health strategies and tolls on hospital outcomes among Medicaid-enrolled children (population), according to HealthData Management.
Some 93,000 children in 31 in-network practices were tracked by an interdisciplinary team coordinate communication and care coordination. As a result, hospitalization was reduced by 50 fewer admissions a month, totaling 3,600 fewer bed days in a year.
To accomplish the improvement, the interdisciplinary team relied on electronic medical record registry development alongside a common longitudinal quality improvement framework to better communicate among clinicians and community health workers.
“Fundamentally, what we are talking about is identifying those patients who need the most proactive care, surrounding them with an expert team—from physicians to nurses, to social workers to navigators—and then equipping that team with supportive technology,” said David Rubin, MD, director of population health innovation and PolicyLab at Children’s Hospital of Philadelphia (CHOP) and lead author. “In doing that, we saw that we can deliver the type of care our patients want while providing value and efficiency throughout the whole healthcare system.”
Population Health Pros and Cons
Population health is defined as “the health outcomes of a group of individuals, including the distribution of such outcomes within the group.” Critics of the notion of population health claim the concept is too broad and not very useful in guiding specific research or policy. The reasoning goes that no one has overall responsibility for population health. Instead, policy managers have responsibility for a single population sector, while advocacy groups focus on a single disease.
However, the inherent value of population health is that it enables the integration of knowledge into the medical record of such factors as social determinates of Health (SDoH). SDoH Impacts up to 80 percent of health outcomes. It includes such factors as food and housing scarcity and domestic and environmental violence and neglect.
Integrated Data Platforms Key in Meeting Population Health Strategies
The idea of managing health in groups, as opposed to individually, gained traction in 2008 as part of the Triple Aim initiative. Triple Aim was proposed by Don Berwick, CEO of the Institute for Healthcare Improvement, a leader in the use of scientific methods, evidence-based medicine, and comparative effectiveness research. The initiative had three goals:
1: Improve the individual’s experience of care;
2: Improve the health of populations;
3: Reduce per capita costs of care to populations (value-based care).
As with all emerging healthcare delivery models, success depends on putting effective data analysis platforms in place to foster community-partnerships between social service agencies and hands-on providers. Such was the model of the just-released CHOP study, which follows a June 2019 study by NYU Langone School of Medicine. The NYU Langone study called for the need to integrate community partnerships and data insights to define and achieve population health targets.
Sam Basta, MD, Senior Medical Director, Clinical Integration, Sentara Health, recently laid out a “new care model for innovative organizations” in an article in Health Tech Outlook. Dr. Basta stressed that such models would be required to meet financial and reputational goals for care coordination. He labeled such a solution as an active population care delivery platform requiring several functions, including:
1: Population analytics to identify patients who have traits in common, such as SDoH factors. This aspect of the equation includes user analytics, measure analytics, and effectiveness analytics.
2: Intervention design to identify user engagement, user experience, behavioral change opportunities, and possible interventions. Such data helps to design individual, one-to-one care, rather than the traditional “one-to-many” approach.
3: Healthcare system intervention to incorporate factors such as SDoH to achieve population care delivery. Bring together all caregivers – from socials service providers to clinicians – helps alleviate fragmented care.