BACKGROUND: Pediatric hospital readmissions increase healthcare costs and highlight gaps in care. Social determinants of health (SDOH), such as housing and transportation insecurity, significantly impact outcomes but are underexplored in pediatric populations. OBJECTIVES: This study evaluates the impact of housing and transportation-related SDOH on pediatric readmissions, comparing structured ICD-10-CM Z-codes alone to a combination of structured and unstructured data extracted via natural language processing (NLP). MATERIALS AND METHODS: We conducted a retrospective cohort study of pediatric patients (ages 2-17) discharged from UCSF Benioff Children's Hospital between January 2018 and January 2023. SDOH exposure was identified using structured Z-codes and NLP-extracted data. The primary outcome was hospital readmission within 365 days. Cox proportional hazards models assessed associations between SDOH and readmission risk. RESULTS: Among 8928 patients, only 0.8% were identified as exposed using structured data, compared to 31.7% using combined data. Patients identified through combined data had a higher readmission risk (HR: 2.64, 95% CI: 2.34-2.98) compared to those identified with structured data alone (HR: 1.99, 95% CI: 1.27-3.13). ED utilization was also higher among exposed patients. In the structured-only analysis, exposed patients had a significantly higher hazard of ED readmission (HR: 2.26, 95% CI: 1.65-3.10), whereas the association was slightly attenuated in the combined analysis (HR: 1.49, 95% CI: 1.37-1.62). CONCLUSION: Leveraging unstructured data enhances SDOH identification and reveals stronger associations with hospital and ED readmissions. A hybrid approach enables improved risk stratification and targeted interventions to address pediatric health disparities.

OBJECTIVE: To measure hospital engagement in interoperable exchange of health-related social needs (HRSN) data. MATERIALS AND METHODS: This study combined national data from the 2022 American Hospital Association (AHA) Annual Survey, AHA IT Supplement, and the Centers for Medicare and Medicaid Services Impact File. Multivariable logistic regression was used to identify hospital characteristics associated with receiving HRSN data from external organizations. RESULTS: Of 2502 hospitals, 61.4% reported electronically receiving HRSN data from external sources, most commonly from health information exchange organizations. Hospitals participating in accountable care organizations or patient-centered medical homes and hospitals using Epic or Cerner electronic health records (EHRs) were more likely to receive external HRSN data. In contrast, for-profit hospitals and public hospitals were less likely to participate in HRSN data exchange. DISCUSSION: Hospital ownership, participation in value-based care models, and EHR vendor capabilities are important drivers in advancing HRSN data exchange. CONCLUSION: Additional policy and technological support may be needed to enhance HRSN data interoperability.

OBJECTIVES: National attention has focused on increasing clinicians' responsiveness to the social determinants of health, for example, food security. A key step toward designing responsive interventions includes ensuring that information about patients' social circumstances is captured in the electronic health record (EHR). While prior work has assessed levels of EHR "social risk" documentation, the extent to which documentation represents the true prevalence of social risk is unknown. While no gold standard exists to definitively characterize social risks in clinical populations, here we used the best available proxy: social risks reported by patient survey. MATERIALS AND METHODS: We compared survey results to respondents' EHR social risk documentation (clinical free-text notes and International Statistical Classification of Diseases and Related Health Problems [ICD-10] codes). RESULTS: Surveys indicated much higher rates of social risk (8.2%-40.9%) than found in structured (0%-2.0%) or unstructured (0%-0.2%) documentation. DISCUSSION: Ideally, new care standards that include incentives to screen for social risk will increase the use of documentation tools and clinical teams' awareness of and interventions related to social adversity, while balancing potential screening and documentation burden on clinicians and patients. CONCLUSION: EHR documentation of social risk factors currently underestimates their prevalence.

OBJECTIVE: Electronic health records (EHRs) are increasingly used to capture social determinants of health (SDH) data, though there are few published studies of clinicians' engagement with captured data and whether engagement influences health and healthcare utilization. We compared the relative frequency of clinician engagement with discrete SDH data to the frequency of engagement with other common types of medical history information using data from inpatient hospitalizations. MATERIALS AND METHODS: We created measures of data engagement capturing instances of data documentation (data added/updated) or review (review of data that were previously documented) during a hospitalization. We applied these measures to four domains of EHR data, (medical, family, behavioral, and SDH) and explored associations between data engagement and hospital readmission risk. RESULTS: SDH data engagement was associated with lower readmission risk. Yet, there were lower levels of SDH data engagement (8.37% of hospitalizations) than medical (12.48%), behavioral (17.77%), and family (14.42%) history data engagement. In hospitalizations where data were available from prior hospitalizations/outpatient encounters, a larger proportion of hospitalizations had SDH data engagement than other domains (72.60%). DISCUSSION: The goal of SDH data collection is to drive interventions to reduce social risk. Data on when and how clinical teams engage with SDH data should be used to inform informatics initiatives to address health and healthcare disparities. CONCLUSION: Overall levels of SDH data engagement were lower than those of common medical, behavioral, and family history data, suggesting opportunities to enhance clinician SDH data engagement to support social services referrals and quality measurement efforts.

OBJECTIVE: The use of controlled medications such as opioids, stimulants, anabolic steroids, depressants, and hallucinogens has led to an increase in addiction, overdose, and death. Given the high attributes of abuse and dependency, prescription drug monitoring programs (PDMPs) were introduced in the United States as a state-level intervention. MATERIALS AND METHODS: Using cross-sectional data from the 2019 National Electronic Health Records Survey, we assessed the association between PDMP usage and reduced or eliminated controlled substance prescribing as well as the association between PDMP usage and changing a controlled substance prescription to a nonopioid pharmacologic therapy or nonpharmacologic therapy. We applied survey weights to produce physician-level estimates from the survey sample. RESULTS: Adjusting for physician age, sex, type of medical degree, specialty, and ease of PDMP, we found that physicians who reported "often" PDMP usage had 2.34 times the odds of reducing or eliminating controlled substance prescriptions compared to physicians who reported never using the PDMP (95% confidence interval [CI] 1.12-4.90). Adjusting for physician age, sex, type of doctor, and specialty, we found that physicians who reported "often" use of the PDMP had 3.65 times the odd of changing controlled substance prescriptions to a nonopioid pharmacologic therapy or nonpharmacologic therapy (95% CI: 1.61-8.26). DISCUSSION: These results support the continued use, investment, and expansion of PDMPs as an effective intervention for reducing controlled substance prescription and changing to nonopioid/pharmacologic therapy. CONCLUSION: Overall, frequent usage of PDMPs was significantly associated with reducing, eliminating, or changing controlled substance prescription patterns.

Healthcare organizations are increasing social determinants of health (SDH) screening and documentation in the electronic health record (EHR). Physicians may use SDH data for medical decision-making and to provide referrals to social care resources. Physicians must be aware of these data to use them, however, and little is known about physicians' awareness of EHR-based SDH documentation or documentation capabilities. We therefore leveraged national physician survey data to measure level of awareness and variation by physician, practice, and EHR characteristics to inform practice- and policy-based efforts to drive medical-social care integration. We identify higher levels of social needs documentation awareness among physicians practicing in community health centers, those participating in payment models with social care initiatives, and those aware of other advanced EHR functionalities. Findings indicate that there are opportunities to improve physician education and training around new EHR-based SDH functionalities.

Dear JAMIA editors and readers: We appreciate Shachak’s letter regarding our article entitled “A Call for Social Informatics.”1 In it, he argues that social informatics is a poor term to describe a field dedicated to the application of information technologies to capture and apply social data in conjunction with health data in order to advance health.2 We continue to believe that it is a useful term, however, and value this opportunity to share our reasoning. In selecting the term social informatics, we were aware of the existence of a field of social informatics, which focuses on the study of the interaction of various aspects of information and communication technologies with institutional and cultural contexts.3 However, this use of the term social informatics is rarely found in the biomedical informatics literature. In a PubMed search for the term social informatics, we identified 67 publications (not including our article) with the term. We were able to retrieve 64 of these articles; only 6 used the term in the article itself. In the remaining 58, the term appeared as part of the author affiliation or institutional description (eg, from a School or Department of Social Informatics). We believe that this offers strong evidence that the term is not in widespread use in the biomedical informatics literature.

OBJECTIVE: Electronic Health Records (EHRs) increasingly include designated fields to capture social determinants of health (SDOH). We developed measures to characterize their use, and use of other SDOH data types, to optimize SDOH data integration. MATERIALS AND METHODS: We developed 3 measures that accommodate different EHR data types on an encounter or patient-year basis. We implemented these measures-documented during encounter (DDE) captures documentation occurring during the encounter; documented by discharge (DBD) includes DDE plus documentation occurring any time prior to admission; and reviewed during encounter (RDE) captures whether anyone reviewed documented data-for the newly available structured SDOH fields and 4 other comparator SDOH data types (problem list, inpatient nursing question, social history free text, and social work notes) on a hospital encounter basis (with patient-year metrics in the Supplementary Appendix). Our sample included all patients (n = 27 127) with at least one hospitalization at UCSF Health (a large, urban, tertiary medical center) over a 1-year period. RESULTS: We observed substantial variation in the use of different SDOH EHR data types. Notably, social history question fields (newly added at study period start) were rarely used (DDE: 0.03% of encounters, DBD: 0.26%, RDE: 0.03%). Free-text patient social history fields had higher use (DDE: 12.1%, DBD: 49.0%, RDE: 14.4%). DISCUSSION: Our measures of real-world SDOH data use can guide current efforts to capture and leverage these data. For our institution, measures revealed substantial variation across data types, suggesting the need to engage in efforts such as EHR-user education and targeted workflow integration. CONCLUSION: Measures revealed opportunities to optimize SDOH data documentation and review.

As evidence of the associations between social factors and health outcomes continues to mount, capturing and acting on social determinants of health (SDOH) in clinical settings has never been more relevant. Many professional medical organizations have endorsed screening for SDOH, and the U.S. Office of the National Coordinator for Health Information Technology has recommended increased capacity of health information technology to integrate and support use of SDOH data in clinical settings. As these efforts begin their translation to practice, a new subfield of health informatics is emerging, focused on the application of information technologies to capture and apply social data in conjunction with health data to advance individual and population health. Developing this dedicated subfield of informatics-which we term social informatics-is important to drive research that informs how to approach the unique data, interoperability, execution, and ethical challenges involved in integrating social and medical care.