Updated: Feb 27, 2020
Population health is a nebulous word that means different things to different people, but essentially it is looking at multiple factors that determine a patient’s health, risk for disease, and cost of care and then finding a way to impact those metrics. Population health refers to aggregating multiple sources of data to gain a bigger, and better picture of a person’s health.
Only about 20% of what we know about a patient’s health comes from the exam room. Contributors to health happen outside the four walls of the clinical setting; it happens every day, in the real world! Exercise regimens, eating habits, medicine compliance, where you were born and where you live contribute to a person’s health.
By understanding more about the patient as a personal level, providers are better able to understand how to have better health outcomes at the aggregate, or population level. It is the switch from treating disease (sick care) to managing wellness (health care).
Some examples of data pertinent to population health:
· Clinical data, coming from a physician’s Electronic Medical Records system
· Claims data, coming from the person’s health insurer
· Pharmacy Benefit Data
· Wellness tracking Data, such as a FitBit or steps-tracking device
· Social determinants of health, such as transportation and access to care
· Remote patient monitoring equipment, such as a blood pressure monitor or scale
Why is population health important?
The goal is to aggregate as much data about a person and the environment in which they live, so that providers are better equipped to take more proactive care of the patient, rather than wait for sick care.
Louis Pasteur didn’t wait for someone to tell him what was growing in his beakers...he looked outside the confines of text books. Marie Currie didn’t wait for an application to radium...she pioneered it. Fleming wasn’t afraid of a fungus on his Petri dish...he saw the opportunity to stop disease.
None of these are serendipitous...they are insightful. Population health is applying common sense metrics to common problems to provide uncommon results.