Community-level factors like economic status, availability of transportation, housing conditions, and basic food resources within the neighborhood are key determinants in patients’ health outcomes. Nevertheless, these social factors are usually under-emphasized or even skipped in clinical Electronic Health Records (EHR) systems, meaning that important non-clinical patient data are sometimes missed in healthcare delivery and seen as a driver of inequity in healthcare.
The Reason for Inclusion of Community-Level Data
While it is true that many healthcare professionals and organizations may be somewhat lacking in certain datasets due to the fact that historic EHR systems did not contain broad community contextual information. As a result, primary care physicians are blind to patient adherence issues, the existence of various barriers to care, and the social determinants of health disparities. This poses a great challenge on the healthcare systems to be able to effectively identify and manage patients’ broad needs outside of the clinical settings.
The following are the possible advantages of incorporating community level data in the health care management:
Enhancing the Decision-Making Process in Clinical Practice: Physicians can develop better treatment/care plans that take into consideration the patient’s socio-economic status. Improve Healthcare Resource Allocation: Determine patient needs and identify community resources that can alleviate issues and improve health outcomes. Improve Collaboration with the Community: Demonstrate collaboration with local organizations to extend healthcare services from the medical sector into community sites. Improve Health Equity: Develop plans that are directed towards the root causes of health disparities.
CDC and Other Organizations’ Suggestions
The National Academy of Medicine (NAM), the American Academy of Pediatrics, and the CDC recommend the integration of “community vital signs” into clinical practice. These vital signs include neighborhood deprivation, racial/ethnic segregation, and environmental exposure, which help the clinicians in identifying the at-risk population and managing the community’s health more effectively.
Methods for the Application of Community Data in Clinical Settings
Healthcare providers and organizations can do the following to ensure that community-level data is integrated with clinical processes:
- Geographic Information System (GIS) Mapping
- E-Prescribing Community Resources
- Neighborhood Risk Adjustment
- Building Community – Clinical Alignment
Limitations and the Need for Further Inquiry
Although the enthusiasm and evidence of feasibility of SDoH data are present, the current research has not yet provided strong evidence of the improvements in healthcare, patient outcomes, or improvement in health equity as a result of these integrations. Hence, healthcare organizations are urged to:
- Implement strict protocols for the assessment of the community-data integration projects.
- Evaluate the results of the intervention strategies developed with the help of both clinical and non-clinical data.
- Share and exchange ideas to expand the scientific discourse
Conclusion
The integration of community-level context with clinical EHR data is an important new frontier for primary care, which can help healthcare providers to better understand and manage SDoH issues that may be impacting their patient population in a negative manner. These tools can be used by healthcare providers to ensure that they deliver the best treatment plans, form good relationships with their community, and enhance the health of their patients. Healthcare organizations need to continue to incorporate local SDoH data into their community care models and also to work to demonstrate the positive impact of this on quality of care and patient outcomes. Such combined efforts can greatly enhance the delivery of healthcare and improve health equity in the United States Healthcare System.