Apr 13 2022
Patient-Centered Care

Population Health vs. Public Health: How They Differ and the Tech Behind Them

Both population health and public health focus on improving health outcomes for groups of patients, but how are they different and what technology powers them?

As the healthcare industry focuses on value-based care, tracking data on health outcomes among patient populations will be crucial. Both population health and public health focus on improving care and outcomes across large groups of patients. While they may sound similar, population and public health have important distinctions.

What Is the Population Health Approach to Patient Care?

Population health focuses on improving health for a group of individuals and understanding the drivers of both good and bad health outcomes, explains Dr. Howard Hu, chair and professor of population and public health sciences at the Keck School of Medicine of the University of Southern California (USC). The drivers consist of the “the characteristics, the lifestyle factors and environmental factors that actually influence health and the differences between individuals,” he says.

Under the population health umbrella, healthcare providers manage a well-defined population over a period of time, explains John Moore, founder and managing partner at Chilmark Research.

“The way we’ve defined population health today in the context of the U.S. market, we’ve tied it to a given population that will be cared for under a given contract,” Moore says. “So, when we look at population health management, it’s usually for a defined subset of a population, not everyone in a given region or a given locale.”

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What Is Public Health and How Does It Help Patients?

“Public health is managing or overseeing the health of a given defined geographical region,” Moore explains. “You have public health departments for towns and communities, cities, counties, states and then nations, so it slowly scales up.”

The dominant focus of public health is on prevention, which is a distinction from population health. In addition, public health systems are more siloed from hospital systems, which share electronic health records (EHRs). Physicians rarely interact with public health systems except when they need to report diseases like tuberculosis or sexually transmitted diseases, Hu says. 

Hu also explains that public health is more focused than population health on laws, policies and governance. The government guidance that the Centers for Disease Control and Prevention or state governments issue regarding COVID-19 vaccine and mask mandates are an example of public health and a focus on a larger societal context, he says.

Public health agencies use detailed population data to track the health of a population. They employ electronic reporting to boost the timeliness and accuracy of data required to spot disease outbreaks and monitor these diseases over time. This data helps public health agencies manage widespread disease outbreaks such as COVID-19. 

EXPLORE: Learn how AI helps healthcare organizations reduce avoidable patient harm.

EHRs and EMRs Store Data for Patient Populations

Both population health and public health are heavily dependent on EHRs and electronic medical records (EMRs). EHRs are health records that are shareable between healthcare systems, while EMRs are specific to patients in a particular health system, Hu says.

To make EHRs more relevant to public health as well as population health, Hu would like to see the types of data broadened to incorporate data that provides a larger social context. That would include, for example, geospatial data, information about air pollution exposure and data on the risk of living in a high poverty area or food desert (a location that provides poor access to food supply, such as fruits and vegetables). 

“These are all part of the understanding of the broader context of health that I think deserves to be in the EMR or EHR and that are much more responsive to the population and public health agenda,” Hu says. 

Moore notes the value of EHRs in tracking outbreaks such as the flu in a community. “In the public health context, they’re dependent on EHRs to know what’s going on in the community,” he says. 

Health Monitoring Improves Population and Public Health

Wearable devices, which monitor health markers such as lung function, blood oxygen level and blood sugar (to track conditions like diabetes), can fit into both population and public health, according to Hu. 

“I don’t think there’s really a distinction between the two,” he explains. “Both generate data that allows us to better understand what’s happening with individuals and what might be the difference between individuals and populations in terms of their response to environmental stressors.”

RELATED: Learn how to integrate remote patient monitoring data to improve health outcomes.

Wearables will not fit into the public policy aspect of public health, according to Hu, who notes that public health scientists sometimes use the devices as part of research to gauge people’s reactions to external stimuli by measuring parameters such as blood pressure, pulse and diaphoresis (sweating).

“In my view it’s really just for the benefit of researchers as well as individuals who want to monitor their own health. That’s really the purpose of wearable technology,” Hu says. “We do not see the use of wearable technology to generate data for policy implementation as being an appropriate use of technology.”

Moore notes that it’s “early days” as far as using data from wearables to monitor disease.

Using Patient Portals for Population Health

More healthcare organizations are offering access to patient portals to allow patients to pay bills, access lab results and schedule appointments. The portals fit more into population health, Hu notes.  

“It’s more population health because you’re not going to get patients accessing patient portals unless it’s through their own health provider or their own health system,” Hu says. 

Social Media and Search Aid Public Health Research

Hu notes that his research colleagues are using social media as an example of population health data analytics to spot disease trends before the public does, tracking COVID-19 testing rates as well as the onset of a flu epidemic. For example, Hu’s colleagues study when the population performs Google searches for terms such as “diarrhea,” “nasal congestion” and “flu,” which often happens before a viral disease outbreak is detected using standard disease reporting mechanisms. 

“That’s an interesting dimension to how social media interacts with public and population health,” Hu says.

Data Analytics Uncovers Health Risks

At USC, Hu and his colleagues are essentially conducting what could be called “precision population health,” which involves using data analytics to analyze the risk factors for different communities. “In precision population and public health, we target different subpopulations for policy interventions or educational interventions that are tailored to their needs,” Hu says.

This is in contrast to “precision medicine,” which typically entails drawing upon genetics and individual personal information to customize drug regimens for individual patients.

“The population precision agenda is really about understanding the differences between people and tailoring public health interventions, so they work better in some segments of the population that need a different type of intervention than others,” Hu says. 

READ MORE: Dr. Atul Butte explains the evolution of data analytics in healthcare.

He cites the COVID-19 vaccination rates of the African American and Latino communities as examples of using data analytics to target public health messages to get vaccination rates up.  

Health systems analyze clinical, claims, demographic and financial data among populations. As Moore notes, healthcare organizations analyze claims data to determine the risk of patient populations. They then negotiate with the Centers for Medicare and Medicaid Services on how much they should be paid based on this risk. 

Artificial intelligence and machine learning will also factor into population and public health by allowing researchers to analyze huge volumes of data from X-rays, MRIs and CAT scans for epidemiology studies, Hu says. 

“The theory is that will enable us to better interpret the kinds of data we’re getting in these large population studies to better understand what’s going to make people healthy and what strategies those might be,” Hu says. “But it’s still early for artificial intelligence and both population and public health.” 

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