There is a big movement toward appreciating the role of social determinants in the quality of care for individuals and, by extension, closing gaps in care and improving health equity across populations that may share similar challenges.
For example, access to quality food, transportation, income and cultural considerations are often cited as examples. More organizations are starting to collect this information to understand the gaps in care and get a more complete picture of the patient—both as an individual and as a member of populations that oftentimes share similar social determinants of health, or SDOH, profiles.
We look to technology to help bridge those gaps, but we know that, having implemented EHRs, there is a lot more to it than acquiring technology. Even if you implement and apply technology specifically to address SDOH, there are non-technical considerations that must be appreciated in order to achieve success.
In order to spark some conversation around the issue, we asked our members these questions: How can actionable intelligence address health equity? How can a better understanding of social determinants of health close the gaps in care?
“Actionable intelligence is used to address health equity through identification of at-risk groups through population health tools,” said Russell Thompson in the U.S. “It should be more prevalent in engagement as well, using data and statistics to target when and how to reach out to specific audiences.”
This is an excellent point. SDOH intelligence can actually help improve the way we collect more of the same, all while learning about how to engage patients in their care experience. “Part of the challenge in addressing equity,” he explained, “is getting patients engaged.” He suggests first using claims data and scheduling analysis to help identify the types of services that require more engagement. Good examples are emergency visits following outpatient services, or when patients frequently miss wellness visits. Still more information is found in data within the EMR, specifically, looking at contact history or preferences. “Augment this information,” he said, “with metrics on which channels are preferred by various demographic groups.” A bit of research from marketing databases can reveal more information such as the best time of day to contact people. What we learn from this makes our algorithms even better. This is just one aspect of the engagement process, and it reveals how complex the issue can be.
Discover how one health system has sought to learn more about its patients and their social determinants of health data to provide better—and more personalized—care.
Patient engagement starts long before encounters with the health system. M. Ahmed, PhD, pointed out that there are disparities in the United Kingdom, where they live, and a proactive approach is underway. “We are adding the patient voice to various National Health Services digital strategies and programs.” Dr. Ahmed noted, “We are developing several digital health training projects and awareness programs for the underprivileged,” and suggests this should be happening “not just at a local level but at an international level.” Rhushikesh Aherwal, in India, reinforced the premise that technology can help. “Technology plays a vital role in health equity, and there is still scope of improvement.” He noted that technology can enhance engagement by “improving delays in patient care, implementation of clinical guidelines, easy-to-understand patient safety policies, and overcoming language barriers.” Tim Marsh expanded on Aherwal’s comments, noting that technology allows us to create “feedback loops for data quality.” Marsh, who is based in Australia, suggests “hospital address books for [general practitioner] records,” to help identify where to send discharge summaries, “and feedback loops to the practices.”
Ashley Vanni, in the U.S., supplied a short list of must haves in order to address equity through technology and intelligence. Vanni shared we need “accuracy and security/privacy; reliable platforms; room for the atypical or rare data because atypical data is still data and can share value and insight; curiosity and willingness to learn; communication and human empathy alongside technology to work as a team.” Problems arise Vanni said, when we fail to acknowledge the atypical data, noting that “humans are evolving just like bacteria and virus. Sometimes,” Vanni added, “our once knowns change.” This was an interesting take on the social determinants of health viewed through data, and Vanni lead us to an important point: Innovation and insights often lie at the edges of what we already think we know.
“This is the one million dollar question that everyone is trying to figure out,” said Mervat Mina, in the U.S. “The current pandemic, COVID-19, is a great example. Many elements in our health system need to come together to close the gaps.” Mina includes socioeconomic, environment, race, policymakers, and legislators as stakeholders. “Leaders need to focus on designing and implementing health equity principles on a continuous basis, and not wait until a pandemic hits.”
Based on the comments, there is much more to social determinants of health and analytics than simply collecting data. Are there any shortcomings or gaps in the kind of data we really need to take a deep look at the equity issue?
Mehraneh Shantiaei, PhD, in Iran, said there are. “The healthcare system needs to collect much more about patients, including their level of health literacy, race, culture, socioeconomic factors, lifestyle, and also about their expectations and preferences.” Those expectations and preferences relate to “the healthcare services they want to receive in a very safe, private and systematic manner.” Dr. Shantiaei agreed that this means facilitating patient involvement and engagement to help close gaps in care. One of the best ways to support this, according to Dr. Shantiaei, is by “strengthening patient-physician and provider communication to collect and/or confirm data about many of these factors, as well as to get feedback about the quality of care.” There’s that feedback loop again, as referenced by Marsh earlier in the discussion.
How do we ensure that the data isn’t interpreted in such a way as to reinforce assumptions or bias about a particular individual or group? How do we “stay honest?” Dr. Shantiaei said data for “research, education and public health affairs is a critical need for society; it can also improve the quality of care. On the other hand, individual rights of patients should be respected.” Trust is key here, among the patients, the physicians, and the organizations. “Unethical use of patient data and reinforcement of bias/assumptions should be prevented,” added Dr. Shantiaei. Accordingly, we should be paying much more attention to patient autonomy while “strengthening reliability and privacy mechanisms of medical practice, digital tools,” and connection points.
Lygeia Ricciardi, EdM, in the U.S., raised an interesting point in the discussion. As more digital tools are developed, “we need to include diverse users directly in the process. Even if you’re very open-minded and empathetic,” Ricciardi suggested, “it’s hard to walk in someone else’s shoes if you don’t invite them to contribute to the process first-hand.” My takeaway from this: Creating tools without listening to diverse voices can actually reinforce bias that empathy cannot identify or overcome.
This diverse discussion was illuminating. Commenters raised great points from many perspectives, which is so important when discussing such complex issues at the intersection of SDOH, technology, and equity. In order to have actionable intelligence that can help close gaps in care, we must first collect and analyze data in ways that do not exacerbate the current situation. Solutions involve big picture, holistic approaches as well as granular details of algorithms and communication techniques.
What I really enjoy about this discussion, and where we are at with the transformation of health, is how the foundation of technology now allows us to address issues that extend far beyond mere data collection and sharing. A new age of intelligence is upon us, and we have the opportunity to apply what we learn to achieving better outcomes, and better health for all.
The issue of racism and the inequities people of color face must not be accepted and cannot continue to be ignored.