It is widely recognized that social determinants of health (SDOH) can have a much greater impact than physical health in determining the overall well-being of individuals, especially in underserved communities. However, obtaining an understanding of how specific SDOH factors affect individuals is extremely difficult because SDOH data is not methodically collected by clinicians.
Though SDOH data does exist in patient records, it is just too difficult and time-consuming for clinicians to make sense of it because SDOH-related information is typically buried in patient notes. This problem ultimately inhibits their ability to consume data to inform decisions about individuals receiving care.
Natural Language Processing (NLP) – a key discipline of AI that uses computers to understand the written word — can overcome this challenge, and I encourage health & human services organizations and hospitals to explore this method to surface SDOH data, particularly as technological innovation in this area has matured significantly in the past several years.
Here are the top three reasons why health & human services organizations and hospitals should adopt NLP to make sense of SDOH.
1. Cost savings and more efficient care: As it stands today, clinicians, therapists and case workers spend an overwhelming amount of time reading typed or hand-written case notes to understand the status of their patients to identify potential courses of treatment. This is simply wasted time that could be better used for direct interaction with the patient.
The magic of NLP is that it can automatically highlight impactful indicators and trends across case or patient notes, and therefore quickly reveal SDOH to the case workers and clinicians on the case. An NLP platform can relieve health and social services workers of the time it takes to comb through the unmanageable amount of records by readily highlighting SDOH across a case.
2. Improved outcomes: NLP empowers caseworkers and clinicians with the information they need to make impactful decisions and allow supervisors to maximize quality of care delivered. This is because NLP provides a deeper understanding of a patient or case.
The Gravity Project is a national public collaborative creating diagnostic codes for SDOH factors with the goal of having those codes incorporated into the existing list of medical diagnosis codes. NLP can extract the information in unstructured data like case notes to support and translate that to SDOH-related diagnostic codes. These diagnoses would subsequently trigger interventions that improve outcomes.
3. Risk mitigation: NLP enables organizations to quickly identify patients at the highest level of risk so interventions can be targeted to those in most need of services. I firmly believe that you can only truly identify risk by understanding what is included in the narrative data. Most risk stratification systems today simply look at claims data to do this. But claims data is an incomplete picture. If care coordinators had a full picture through SDOH, then they would have a much better tool to identify those who are at most risk and where early interventions can prevent more serious health conditions from occurring.
The case for using NLP to make sense of SDOH is clear. Though it is now more widely understood that SDOH plays a major role in the overall health of individuals, we need to make it easier for hospitals and health & human services organizations to consume and make sense of this data to understand how SDOH affects individual patients. Doing so will only help providers make the best decisions for their patients, leading to more efficient delivery of care and improved outcomes.
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