During the past decade, finally, health care is starting to deal in a structured, measurable manner with the life factors and environments that play the biggest role in their patients’ health. A recent offering from LexisNexis Risk Solutions promises to greatly expand access to this data, called social determinants of health (SDoH), for payers, clinicians, public health agencies, pharma companies, and other researchers.

The company LexisNexis Risk Solutions is almost synonymous with the data banks of consumer data that financial firms and other businesses consult for identity verification and outreach. The company’s contributions to SDoH data were covered by a recent article in this publication along with this SDoH interview and this SDoH Maturity Model. But the new LexisNexis Health Equity and Inclusion Insights offering adds new data and improves its quality.

Data collection is undergoing more and more scrutiny by the press and legislators. A recently released book that critiques data collection on individuals, The Hank Show by McKenzie Funk, connects the history of tracking individuals with current trends in analytics, AI, and facial recognition. The final chapter on social determinants of health comes out more positive than most of the others. During the serious conversations going on currently about privacy and potential bias, we should also highlight the benefits of non-traditional data sources to improve health, as shown by the SDoH service from LexisNexis Risk Solutions.

SDoH data can allow clinicians and payers to identify patient needs, such as for food, transportation, or home visits. The data can direct resources in communities to services that address urgent problems. And it can allow clinicians to offer treatments with more sensitivity to patient preferences and capabilities.

The material about LexisNexis Health Equity and Inclusion Insights in this article is based on an email exchange I had with Diana Zuskov, associate vice president of healthcare strategy there.

Increasing Types of Available Data

The information traditionally available from LexisNexis Risk Solutions, as described in the previously-cited article, includes relatively straightforward items such as address and educational level, which LexisNexis can extract from public data sources. However, the new service also reports more complex information that doesn’t exist as fields in some source. For instance, to calculate the individual’s proximity to a healthcare provider and access to transportation, they need to combine addresses and other data points from multiple sources.

Calculations and modeling draw on available data about communities and the individual’s environment, as well as the usual information on individuals and households.

Quality and Diversity

Racial minorities and women have historically been harmed by both a dearth of data and the poor of that data. For instance, many people in the United States don’t have insurance, so there is no billing information from which to learn their health status. Similar gaps in information occur if they rarely go to a doctor.

Consequences, such as low participation in clinical trials, have seriously detrimental impacts on the health of excluded populations.

When data is collected, it might be done in a cavalier manner, such as bureaucratically lumping 60% of the world’s population into the category of “Asian.” This category masks important disparities between populations, recalling the old racist trope, “They all look alike anyway.”

When preparing LexisNexis Health Equity and Inclusion Insights, the company took conscious efforts to be inclusive and equitable. The organization achieved better population coverage by including non-traditional data sources to represent underserved populations that may not be adequately represented in surveys or marketing data sources.

Zuskov points out that much data collection in health care relies on visiting a clinical provider, which many people don’t do regularly. By looking at other publicly available data sources to round out the picture, they standardize data collection in a more continuous, holistic, and population-based approach. The resulting data provides rich insights on those who are most vulnerable and least likely to have regular healthcare interactions. Their data should help drug companies, researchers, and health care organizations meet health care quality goals and their own commitments to diversity.

Providing Answers

Databases can have excellent data, but effective use of that data requires effective delivery.

One of my previous articles on SDoH data listed a number of standards for data exchange, notably Gravity. Zuskov says that LexisNexis uses such standards where appropriate, but points out that they are limited because they “typically apply to SDoH data within a clinical interaction in an EHR system.” She says, “For true patient-centered care, I believe it’s critical to consider SDoH data outside of the 10-minute visit in a doctor’s office.”

As an example, she points to patients whose lack of transportation or requirements for work or child care lead them to miss their appointments. These problems must be addressed outside the visit, in advance. By evaluating each individual’s characteristics in the context of their environment—combining information about the patient’s location, the provider’s location, and public transportation—LexisNexis Risk Solutions can alert its clients to such issues.

Thus, the new service is based on an understanding of what questions clinicians, researchers, and others are asking. Zuskov listed some of the typical questions LexisNexis can answer now.

• Healthcare providers: What barriers to care exist for this patient or cohort of patients?
• Health care providers, payers, and pharmacists: What social care, care management, or other programs and benefits would be most relevant based on this patient’s barriers to care?
• Payers and health care systems: Are we reaching the appropriate vulnerable or diverse populations in our health equity strategy? Are there disparities in program participation or health outcomes?
• Life science and public health researchers: Is my research study inclusive and representative of the population?
• Life science and public health researchers: What social determinants might be affecting the outcomes I am seeing in my research?
• Public health researchers: What barriers in care can we address through patient assistance programs or other resources to support people throughout a multi-year research study (while also reducing loss of subjects during follow-up)?

The fields of medicine and public health know quite a bit about what causes illness. They also know that there’s a lot more we could do right now to reduce disparities and improve care for people who are currently left behind. I look forward to more data, wisely used.