Machine learning is getting better at predicting things. There are now algorithms that improve the detection of diabetic retinopathy, predict the onset of sepsis, and help determine a critically ill patient’s risk of dying. But a piece of wisdom from Warren Buffet comes to mind: “Predicting rain doesn’t matter. Building arks does.” Even the most impressive algorithm is relatively useless if it doesn’t allow us to build better “arks” to address the medical disorder or complications that the digital tool identifies. And building the best healthcare interventions requires that clinicians not just identify the right signs, symptoms, and biomarkers, whether they be high cholesterol levels, elevated A1c, or a lump in a woman’s breast. It requires we understand what’s happening in patients’ everyday lives outside the clinic, the so-called social determinants of health (SDOH), and then using that data to inform treatment.
A great deal has been written recently about SDOH. Health professionals are slowly beginning to realize that we cannot “remove health and illness from the social contexts in which they are produced,” according to Simukai Chigudu, Oxford Department of International Development, University of Oxford.1 That begs the questions: What social issues are mostly likely to influence our patients’ clinical course and what do we do about them? How can AI help alleviate the impact of these issues?
The Centers for Disease Control and Prevention (CDC) has numerous data sources to help incorporate SDOH into public health initiatives and medical practice. But as the agency points out, moving from data to action is the hard part. CDC has several programs designed to focus clinicians’ attention on key social issues, including socioeconomic status, educational level, and work history. One initiative, for instance, zeros in on the role of EHRs. Its purpose is to support the incorporation and use of structured work information into health IT systems. How might this SDOH element inform a physician’s different diagnosis? Consider a patient with hypertension who doesn’t respond to a low sodium diet or anti-hypertension medication. Awareness of his 10 year history as a house painter might point the clinician in the direction of lead poisoning, a possible cause of hypertension. Similarly, a nurse practitioner may be at a loss to figure out why a patient with type 2 diabetes has recently seen a spike in her A1c levels. If at EHR system is linked to work history, when the NP enters the reason for the clinic visit into the EHR field for chief complaint, this might trigger a pop up box that states that the patient works the night shift and that shift work can affect diabetes control. The system would then provide recommendations on diabetes management among shift workers. The same CDC program is also working on a work information data model, as well as national standards for vocabulary, system interoperability, and instructions for health IT system developers.
At Mayo Clinic, we are also studying the impact of SDOH on health and disease. Young Juhn, MD, MPH, Director of the AI Program and Precision Population Science Lab of Department of Pediatric & Adolescent Medicine at the Clinic, has studied the effects of socioeconomic status on health since in 2006 when his research work was supported by the NIH. With the support from the NIH, he developed and validated a housing-based socioeconomic measure called the Housing Based Index of Socioeconomic Status or HOUSES index, which is being used in epidemiologic research to help understand health disparities and differences in a variety of health outcomes in both adults and children. The index has enabled researchers to overcome the absence of socioeconomic measures in commonly used data sources (e.g., medical records or administrative data), conduct geospatial analysis in health disparities research, and apply a life course approach.
The HOUSES index is an objective way to measure the individual-level socioeconomic status of patients because it is based on real property data for individual (not aggregated) housing units and is derived from public records; it uses 4 data points: the number of bedrooms in a person’s residence, as well as the number of bathrooms, square footage of the unit, and estimated building value of the unit. The index can help target patients who are most at risk of poor health outcomes and inadequate access to health care , demonstrating the real value of adding SDOH into the mix by addressing the limitations of the existing SDOH. For example, Stevens et al have shown that patients with a higher HOUSES score (quartiles 2-4) had 53% lower risk of kidney transplant rejection (adjusted hazard ratio 0.47), when compared to those with the lowest score (quartile 1).2 Dr Juhn and his colleagues have found that HOUSES can predict 44 different health outcomes and behavioral risk factors in both adults and childreN.
Of course, clinicians still have to be reasonable in their expectations. Even if an algorithm were outfitted with every conceivable SDOH, it still may not reduce disparities in healthcare. Patients and providers may choose to ignore the recommendations of the improved algorithm because they believe the recommended diagnostic test is too expensive or unjustified, for example, because it is too difficult for patients to get to the testing facility, or because a patient’s lack of health literacy prevents them from seeing the value of said test.
Despite these shortcomings, SDOH-enhanced algorithms have the potential to improve patient care. While physicians and nurses have gained tremendous insights into health and disease by measuring countless clinical parameters during office visits, it’s clear now that’s not enough. The clinical picture generated with these metrics is too often hazy and needs to be supplemented by a long list of social metrics that can influence a patient’s access to care and their long-term outcomes.
What does this Articles mean to say. Summarized in 250 words.
This essay examines the significance of social determinants of health (SDOH) in patient care and the potential of algorithms and data analysis to enhance healthcare outcomes. It discusses the importance of understanding patients’ social contexts, the efforts made by organizations like the Centers for Disease Control and Prevention (CDC) and Mayo Clinic in incorporating SDOH into medical practice, and the benefits of utilizing SDOH data in clinical decision-making.
SDOH are social factors that influence individuals’ health outcomes and access to care. Recognizing their impact is crucial in providing comprehensive patient care. Factors such as socioeconomic status, educational level, work history, and housing conditions can significantly affect patients’ clinical course. By considering SDOH, clinicians can gain valuable insights into underlying causes, leading to more accurate diagnoses and tailored treatment plans.
Organizations like the CDC and Mayo Clinic have taken steps to incorporate SDOH into healthcare practices. The CDC has developed programs to focus clinicians’ attention on key social issues, including socioeconomic status and work history. By linking electronic health record (EHR) systems with work history data, clinicians can better understand patients’ health challenges. For instance, a history of exposure to lead through work might explain a patient’s hypertension. Mayo Clinic’s development of the Housing Based Index of Socioeconomic Status (HOUSES) is another example of integrating SDOH into research, allowing for a deeper understanding of health disparities and outcomes.
Algorithms and data analysis provide opportunities to leverage SDOH data for enhanced patient care. Algorithms that incorporate SDOH can offer valuable insights and recommendations to clinicians. However, it is essential to acknowledge that patient and provider factors, such as cost concerns, accessibility, and health literacy, may influence adherence to algorithm recommendations.
Integrating SDOH into healthcare practices through data analysis and algorithms has the potential to improve patient care. By considering patients’ social contexts alongside clinical parameters, clinicians can gain a comprehensive understanding of their patients’ health and well-being. Efforts made by organizations like the CDC and Mayo Clinic highlight the importance of incorporating SDOH into medical practice and research. While challenges remain, the integration of SDOH data offers opportunities to reduce disparities and enhance patient outcomes. By building a holistic understanding of patients’ lives outside the clinic, healthcare providers can tailor interventions that address the root causes of health issues, ultimately leading to better patient care and improved health outcomes.
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