According to a paper published in JAMA Network Open, health systems should use health IT solutions that capture and analyze social determinants of health (SDOH) EHR data to enhance population health. Using artificial intelligence methods like machine learning to link SDOH EHR data with community-level data could assist improve population health and health inequalities.
- Inequities: Elham Hatef, MD, MPH, of the Johns Hopkins School of Medicine and Bloomberg School of Public Health, said in a JAMA op-ed that health IT could present chances to enhance health equity by addressing the underlying issues that lead to care inequities.
- Common case: Type 2 diabetes is more common among Black individuals than non-Hispanic White adults, according to studies, Hatef stated, citing type 2 diabetes inequalities as an example. Higher rates of type 2 diabetes have also been linked to lower socioeconomic levels.
- EHR Data: “The use of real-time EHR data on a large population of patients, compared with the use of survey data with limited scope and claims data with the time lag, provides a source of high-volume data, the potential of which has not been fully exercised in health care systems,” Hatef emphasized.
- Survey: In comparison to survey and claims data, Hatef believes that using EHR data in population health research will assist give more reliable results. Furthermore, only a few research have looked into the link between “place-based determinants of health” and the risk of type 2 diabetes. Living in a food desert or having access to fast-food restaurants are examples of such circumstances.
- Capabilities: “The study is a great example of the capabilities of HIT to provide a comprehensive assessment of a person’s health, which goes beyond just documenting clinical diseases and medical interventions,” Hatef pointed out. Other IT advancements, such as machine learning, could help health systems promote population health.