Unlocking the full potential of SDOH in health care
Social determinants of health (SDOH) can help manage patients' existing health conditions and evaluate their propensity for certain diseases.
A person’s health and physical well-being is often attributed to factors like quality of food, levels of activity and genetics. However, studies have established that health and overall quality of life are affected by several other factors, such as place of residence, education level, income status, etc., which often go unnoticed during clinical evaluations.
These factors could be sociodemographic, like education and employment; behavioral, like dietary patterns and physical activity; or even psychological, like health literacy and stress.
These factors, known as the social determinants of health (SDOH), can not only help health care providers manage existing health conditions of patients, but also evaluate the possibility of an individual contracting certain diseases — even before they enter a medical facility.
The World Health Organization (WHO) defines SDOH as “the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life." Studies show that over 80% of an individual’s health and well-being is influenced by social determinants of health.
Importance of SDOH data in health care
A McKinsey survey from 2019 revealed people reporting food insecurity were 2.4x times more likely to report multiple ER visits and 2 times more IP visits over a period of 12 months. Similarly, people with unmet transportation needs were two times more likely to report an ER visit and 2.2 times more likely to report an IP visit over a period of 12 months.
Community level SDOH data sources, e.g. weather information, can be used to identify high risk zones where these non-clinical determinants like air pollution, etc., are likely to have a significant impact on health outcomes.
For example, in case of countries like India, a relationship can be established between pollution levels and health conditions like lung disorders or even chronic kidney disease. It can be determined that local environments where suspended particulate matter is over a certain level, people with asthma, lung affliction or anemia are more likely to face negative health consequences.
This information can be used to identify at-risk populations and to manage, prevent and restrict the onset of their diseases.
In this way, health care organizations can leverage social determinants of health for preventive care, ensuring better provision of preventive treatments to improve the overall health of the population.
Social determinants data can enable smarter investments and operational decisions for population health management programs. The insights generated through SDOH can help in designing effective interventions that reflect the needs and assets of the local community.
Top 3 data barriers to manage SDOH
The benefits of SDOH can only be availed if there is access to compiled, cleansed, synergized, integrated and readily available data for analysis and decision making. This can be made possible if we can overcome the following barriers.
Data availability: Availability of comprehensive, granulated data at the micro level is a significant challenge in using SDOH to make preventive health models. Information around sociodemographic factors like income, education, employment, etc., is more easily available relative to behavioral or psychological factors. The only reliable method of obtaining this information is through surveys on direct interactions, but this method is not easily scalable.
Data accuracy: Despite the data being available for socio-demographic factors, accuracy of available data is difficult to prove. As an individual grows personally, it is of importance that the SDOH data must be updated accordingly. If health care organizations aim to develop truly effective and personalized care plans for their members, ensuring accuracy of data at any point in time is crucial.
Data interoperability: It is often found that different companies use different screening tools and mathematical models or algorithms to gather relevant data. This further hinders the creation of a reliable, complete SDOH database of individuals, making it difficult to establish meaningful relationships and correlations between social determinants and actual health outcomes.
Making the best use of available data
Companies across the globe are getting creative in terms of efforts to determine these environmental factors, even though direct information is not available. Wearable devices and the Internet of Things (IoT) are playing a big part in piecing together the jigsaw puzzle.
Often, available factors can act as indirect indicators. For example, while it’s difficult to determine a person’s nutritional intake without a survey or a direct interaction, income information is found to correlate well with nutritional intake and could therefore be used as an indicator of nutrition. Piecing such data together immediately begins to build a picture that helps anticipate health care needs at the individual or community level.
Organizations are also leveraging advances in data sciences. Artificial intelligence (AI) and machine learning (ML) models can help impute information if it is not available and can fill in the missing data elements using whatever little information is available. Z-codes, launched as part of the ICD 10-CM, help to standardize and store nonclinical data.
The Fast Health Care Interoperability Resources (FHIR) platform was also launched with a similar intent of facilitating the exchange of health care information electronically between independent clinical systems.
However, these aforementioned tools still do not solve the issue of data availability and reliability, which is really the core of the problem. This can only be solved effectively through continued partnership of community organizations with health care payers and providers.
The road ahead
The potential of SDOH to improve health outcomes while reducing health care costs is recognized by stakeholders across the health care continuum. Health care organizations are increasingly adopting advanced analytical models to identify the right SDOH to focus upon and formulate measurable goals to demonstrate impact.
The key to unlocking the full potential of SDOH lies in overcoming the data barriers by leveraging innovative and standardized approaches to capture social determinant information at the community and individual level. This can help us deliver the right care in the right setting at the right time, thereby helping improve health outcomes.
