Innovating American Healthcare: Integrating Public Health Goals and Healthcare Delivery

Covid-19 has magnified the awareness that health can touch all of us.

Those who are poor or have chronic conditions, as well as people of color, have disproportionately been affected. It has shown us how the ills of the system show up in the emergency rooms, the fragmentation of our organizations, the lack of real interoperability, and the impact that people in our community can have on messaging.

We also have seen firsthand in a stark way what we have known: the failures of nursing homes, long-term care, and support for those aging in America.

Overall, health inequities are a chronic condition in the United States. For example, I recently saw a patient. She is a single mother of two who contracted Covid-19 over the summer and lost her job as a result. She has experienced significant mental health challenges, and while she was able start a new part-time job, she is terrified of getting Covid-19 again.

This anecdote highlights the need for the integration of public health and healthcare delivery. I’ve highlighted three reasons below.

Forty-one percent of patients had delayed routine care during Covid-19. Although we have anecdotally seen the rise of virtual and telemedicine, after the initial surge, telemedicine remained plateaued.

Routine care is frequently inaccessible, and many fall on the services of a physician or ER which may not be the most cost-effective solution. Forty-six percent of adults don’t have a doctor they can call their own. We have a reactive model of care that needs to become a proactive medical care. Vaccinations are down, cancer screenings are down, and primary care visits are down.

In a stunning reversal of a decades-long trend, healthcare spending in 2020 is on course to be lower than in 2019, according to an analysis of available data by Kaiser Family Foundation experts. Although the decrease in spending is expected to be modest, this would be the first outright drop in spending on patient care since records started being kept in the 1960s, KFF President Drew Altman noted in a column accompanying the new report.

Furthermore, not managing mental health in the context of chronic disease makes it harder for people to engage in healthy behaviors.

Distrust is particularly high among Black and Latino communities, which is worrying given that people of color are being infected and dying at a disproportionately high rate in the pandemic. A Pew Research Center poll released in mid-September indicated only 32 percent of Black adults said they were definitely or probably going to be vaccinated.

The challenges of uptake of vaccination in communities has been attributed in part to the historic mistreatment of communities of color both in medicine and clinical research. Furthermore, there continue to be challenges in access and education because of fear, poor literacy, and a lack of culturally competent communication and outreach.

It is no secret that a complex web of social factors, such as socioeconomic status or location, can be correlated to the prevalence of chronic disease states. Using Geographic Information Systems (GIS), previous research has aggregated large datasets, both public and private, and tied them to physical locations, discovering novel spatial associations which inform policies and clinical interventions (1-3).

Despite methodological advancements, GIS is not widely used at the population level to understand patients’ outcomes. For example, instead of using the traditional applications of GIS to track the prevalence of disease, can we consider the end state complications of a particular condition or cluster of poor outcomes and work backward to understand how a condition quickly accelerates?

In other words, can we use existing tools and data to look at social factors that influence the worst possible outcomes of chronic disease (e.g., blindness, amputation, death)? If our goal is to reduce the impact of chronic disease on the healthcare system, then several interconnected ideas emerge:

1. The real impact of chronic disease on spending is driven by catastrophic emergency visits—not from patients who regularly see their doctor

2. Consequently, by aiming to decrease catastrophic visits, a more specific analysis at population level must center around end-stage outcomes, allowing for higher resolution results, more targeted interventions, more specific research, and more public health interventions that improve outcomes and reduce costs.

Persuading the public health community to adopt GIS technology requires engaging policy, systemic, and environmental levers. Over the past decade, a number of published papers have validated the impact of spatial modeling on understanding regional social determinants of health and developing corresponding strategies (4-7).

Geraghty et al. discovered, for example, that a neighborhood’s socioeconomic status was associated with difficulty in controlling glucose levels, but not lipids, for their diabetic patient population (2). Their GIS work led to the immediate development of increased diabetes self-management education, resulting in improved patient outcomes in underserved counties.

I posit four guiding principles in accelerating healthcare delivery innovation in the context of public health:

• Comprehensivity
  • Focus on total health, not just physical safety
  • Support the whole population
  • Help members navigate to the right care, increasingly virtual
• Proactivity
  • Engage members early
  • Target outreach based on risk and needs
  • Educate and support through personalized communication
• Integration
  • Support the whole person
  • Incorporate social determinants of health into your plans
  • Explore collaborative care models
  • Move toward integrated, distributed care
• Data Liquidity
  • Ensure you have access to intelligence at the point of strategy and care
  • Be able to design systems and incorporate analytics in a way that you have next week’s dashboard today
  • Check the output for algorithmic bias
  • Interoperability, place-based data, geospatial data, and data-wrangling are keys to success

Imagine a future where hospitals and health systems share responsibility with the community and cross-sector partners in addressing social determinants and social risks. This means all are working together to break down silos, consolidate and organize resources for collective action,  identify evidence-based strategies, and share best practices to bring those strategies to scale.

These approaches will help begin to cohesively achieve health equity, creating communities where healthcare disparities are greatly reduced (or no longer exist) and everyone has an opportunity to be healthy.

1. Geraghty EM, Balsbaugh T, Nuovo J, Tandon S. “Using Geographic Information Systems (GIS) to assess outcome disparities in patients with type 2 diabetes and hyperlipidemia.” The Journal of the American Board of Family Medicine. 2010 Jan 1;23(1):88-96.

2. Lyseen AK, Nøhr C, Sørensen EM, Gudes O, Geraghty EM, Shaw NT, Bivona-Tellez C. “A review and framework for categorizing current research and development in health-related geographical information systems (GIS) studies.” Yearb Med Inform. 2014 Aug 15;9(1):110-24.

3. McLafferty SL. “GIS and health care.” Annual Review of Public Health. 2003 May;24(1):25-42.

4. Clift K, Scott L, Johnson M, Gonzalez C. “Leveraging geographic information systems in an integrated health care delivery organization.” The Permanente Journal. 2014;18(2):71.

5. Baker J, White N, Mengersen K. “Spatial modeling of type II diabetes outcomes: a systematic review of approaches used.” Royal Society Open Science. 2015 Jun 1;2(6):140460.

6. Hipp JA. “Spatial analysis and correlates of county-level diabetes prevalence, 2009-2010.” Preventing Chronic Disease. 2015;12.

7. Curtis AB, Kothari C, Paul R, Connors E. “Using GIS and secondary data to target diabetes-related public health efforts.” Public Health Reports. 2013 May 1:212-20.