Screening for Social Determinants of Health: Active and Passive Information Retrieval Methods.

Screening for social determinants of health (SDOH) is recommended, but numerous barriers exist to implementing SDOH screening in clinical spaces. In this study, the authors identified how both active and passive information retrieval methods may be used in clinical spaces to screen for SDOH and meet patient needs. The authors conducted a retrospective sequential cohort analysis comparing the active identification of SDOH through a patient-led digital manual screening process completed in primary care offices from September 2019 to January 2020 and passive identification of SDOH through natural language processing (NLP) from September 2016 to August 2018, among 1735 patients at a large midwestern tertiary referral hospital system and its associated outlying primary care and outpatient facilities. The percent of patients identified by both the passive and active identification methods as experiencing SDOH varied from 0.3% to 4.7%. The active identification method identified social integration, domestic safety, financial resources, food insecurity, transportation, housing, and stress in proportions ranging from 5% to 36%. The passive method contributed to the identification of financial resource issues and stress, identifying 9.6% and 3% of patients to be experiencing these issues, respectively. SDOH documentation varied by provider type. The combination of passive and active SDOH screening methods can provide a more comprehensive picture by leveraging historic patient interactions, while also eliciting current patient needs. Using passive, NLP-based methods to screen for SDOH will also help providers overcome barriers that have historically prevented screening.

Closing the Gap: A Comparison of Engagement Interventions to Achieve Equitable Breast Cancer Screening in Rural Illinois.

Mammography screening rates are typically lower in those with less economic advantage (EA). This study, conducted at an integrated health care system covering a mixed rurality population, assessed the ability of interventions (text messages linking to a Web microsite, digital health care workers, and a community health fair) to affect mammography screening rates and disparity in those rates among different EA populations. Payor type served as a proxy for greater (commercially insured) versus lower (Medicaid insured) EA. 4,342 subjects were included across the preintervention ("Pre") and postintervention ("Post") periods. Interventions were prospectively applied to all Medicaid subjects and randomly selected commercial subjects. Applying interventions only to lower EA subjects reversed the screening rate disparity (2.6% Pre vs. -3.7% Post, odds ratio [OR] 2.4 P < 0.01). When intervention arms ("Least," "More," "Most") were equally applied, screening rates in both EA groups significantly increased in the More arm (Medicaid OR = 2.04 P = 0.04, Commercial OR = 3.08 P < 0.01) and Most arm (Medicaid OR 2.57 P < 0.01, Commercial OR 2.33 P < 0.01), but not in the Least (text-only) arm (Medicaid OR 1.83 P = 0.11, Commercial OR 1.72 P = 0.09), although this text-only arm was inadequately powered to detect a difference. In summary, targeting interventions to those with lower EA reversed screening rate disparities, text messaging combined with other interventions improved screening rates in both groups, and future research is needed to determine whether interventions can simultaneously improve screening rates for all without worsening the disparity.

Medicaid expansion and accessibility to healthcare: The Illinois experience.

OBJECTIVE: The study examined the impact that the Medicaid expansion in Illinois had upon insurance rates, access to medical care, dental care, pharmaceuticals, and mental-health counseling between rural and urban counties. DESIGN AND SAMPLE: A serial cross-sectional design was used to assess the health perceptions of adults living in Illinois. MEASURES: Survey data were collected in 2012 (n = 6,149) before the Medicaid expansion in Illinois and in 2015 (n = 3,532) after the expansion from rural (n = 4) and urban counties (n = 4). INTERVENTION: Medicaid expansion reduced the uninsured rate in both rural (16.39%-4.87%) and urban counties (17.05%-5.2%) and improved self-reported health. It also increased access to all types of healthcare, with the biggest increase in dental coverage. RESULTS: Path analysis indicated that the Medicaid expansion ß = -1.03 (p < .01) and poor versus not poor ß = -1.50 (p < .01) were a significant predictor to no healthcare access. Rural verses urban location was not significant (ß = 0.04); however, race/ethnicity was significantly different (p < .01). CONCLUSION: Findings suggest that although the expansion has increased access to care overall, those who are the most vulnerable are still not benefiting equally from the expansion. Therefore, strategies to assist high-risk adults in enrolling and using their Medicaid coverage need to developed and implemented.