Understanding the impact of social determinants of health in hematology: a scoping review of trends across journals and over time.

Addressing Social Determinants of Mental Health in Pediatrics During the Coronavirus Disease 2019 Pandemic.

How Race, Ethnicity, and Social Determinants of Health Are Reported in Three European Pediatric Journals

Academic health centers (AHCs) in the United States have had a leading role in educating the medical workforce, generating new biomedical knowledge, and providing tertiary and quaternary clinical care. Yet the health status of the U.S. population lags behind almost every other developed world economy. One reason is that the health care system is not organized optimally to address the major driver of health status, the social determinants of health (SDOH). The United States' overall poor health status is a reflection of dramatic disparities in health that exist between communities and population groups, and these are associated with variations in the underlying SDOH. Improving health status in the United States thus requires a fundamental reengineering of the health delivery system to address SDOH more explicitly and systematically. AHCs' tripartite mission, which has served so well in the past, is no longer sufficient to position AHCs to lead and resolve the intractable drivers of poor health status, such as unfair and unjust health disparities, health inequities, or differences in a population's SDOH.AHCs enjoy broad public support and have an opportunity-and an obligation-to lead in improving the nation's health. This Perspective proposes a new framework for AHCs to expand on their traditional tripartite mission of education, research, and clinical care to include explicitly a fourth mission of social accountability. Through this fourth mission, comprehensive community engagement can be undertaken, addressing SDOH and measuring the health impact of interventions by using a deliberate structure and process, yielding defined outcomes.

Clinicians are concerned about their patients' social determinants of health (SDH); yet, they are unsure how to effectively gather patient-level SDH data and intervene without adding to current administrative burdens. Designed properly, clinical registries offer solutions to integrate neighborhood SDH data with clinical data from electronic health records, enabling the understanding of community factors to guide patient care. Federal and state interest in adjusting reimbursements based on SDH further underscores the need for strategies that integrate SDH and clinical data. The Population Health Assessment Engine (PHATE) exemplifies a registry-based SDH data integration solution that adjusts payments, contributes to public health surveillance, organizes care around hot spots (gaps in quality or uncontrolled disease), assesses patient risk, and connects with community organizations. PHATE also permits residency training to meet community health competency milestones by incorporating the PHATE curriculum. These functions enhance value, and their utility in education and care delivery would benefit from further investigation.

Cardiovascular disease is the leading cause of mortality among breast cancer (BC) patients aged 50 and above. Machine Learning (ML) models are increasingly utilized as prediction tools, and recent evidence suggests that incorporating social determinants of health (SDOH) data can enhance its performance. This study included females ≥ 18 years diagnosed with BC at any stage. The outcomes were the diagnosis and time-to-event of major adverse cardiovascular events (MACEs) within two years following a cancer diagnosis. Covariates encompassed demographics, risk factors, individual and neighborhood-level SDOH, tumor characteristics, and BC treatment. Race-specific and race-agnostic Extreme Gradient Boosting ML models with and without SDOH data were developed and compared based on their C-index. Among 4309 patients, 11.4% experienced a 2-year MACE. The race-agnostic models exhibited a C-index of 0.78 (95% CI 0.76-0.79) and 0.81 (95% CI 0.80-0.82) without and with SDOH data, respectively. In non-Hispanic Black women (NHB; n = 765), models without and with SDOH data achieved a C-index of 0.74 (95% CI 0.72-0.76) and 0.75 (95% CI 0.73-0.78), respectively. Among non-Hispanic White women (n = 3321), models without and with SDOH data yielded a C-index of 0.79 (95% CI 0.77-0.80) and 0.79 (95% CI 0.77-0.80), respectively. In summary, including SDOH data improves the predictive performance of ML models in forecasting 2-year MACE among BC females, particularly within NHB.

Ten years have passed since the release of the final report of the World Health Organization (WHO) Commission on Social Determinants of Health (CSDH),1 a landmark document that provided a global blue‐print for the health promotion community and the stakeholders we work with. Three overarching recommendations were outlined, improving daily living conditions; tackling the inequitable distribution of power, money and resources; and measuring and understanding the problem and assessing the impact of action.1 The extent to which progress has been, and continues to be, made is contested. This editorial briefly reflects on what has been achieved over the past decade—in broad terms—about action on the social determinants of health (SDH) in Australia. We deliberately take a balanced view by highlighting the weaknesses and strengths in what has been achieved by governments, non‐government organisations, research institutions, peak bodies and civil society. We also reflect on the ongoing role that the Australian Health Promotion Association (AHPA) has played in advancing our understanding about, and action on, the SDH.

Pennsylvanians' health is influenced by numerous social determinants of health (SDOH). Integrating SDOH data into electronic health records (EHRs) is critical to identifying health disparities, informing public health policies, and devising interventions. Nevertheless, challenges remain in its implementation within clinical settings. In 2018, the Pennsylvania Department of Health (PADOH) received the Centers for Disease Control and Prevention's DP18-1815 "Improving the Health of Americans Through Prevention and Management of Diabetes and Heart Disease and Stroke" grant to strengthen SDOH data integration in Pennsylvania practices. Quality Insights was contracted by PADOH to provide training tailored to each practice's readiness, an International Classification of Diseases, Tenth Revision (ICD-10) guide for SDOH, Continuing Medical Education on SDOH topics, and introduced the PRAPARE toolkit to streamline SDOH data integration and address disparities. Dissemination efforts included a podcast highlighting success stories and lessons learned from practices. From 2019 to 2022, Quality Insights and the University of Pittsburgh Evaluation Institute for Public Health (Pitt evaluation team) executed a mixed-methods evaluation. During 2019-2022, Quality Insights supported 100 Pennsylvania practices in integrating SDOH data into EHR systems. Before COVID-19, 82.8% actively collected SDOH data, predominantly using PRAPARE tool (62.7%) and SDOH ICD-10 codes (80.4%). Amidst COVID-19, these statistics shifted to 65.1%, 45.2%, and 42.7%, respectively. Notably, the pandemic highlighted the importance of SDOH assessment and catalyzed some practices' utilization of SDOH data. Progress was evident among practices, with additional contribution to other DP18-1815 objectives. The main challenge was the variable understanding, utilization, and capability of handling SDOH data across practices. Effective strategies involved adaptable EHR systems, persistent efforts by Quality Insights, and the presence of change champions within practices. The COVID-19 pandemic strained staffing in many practices, impeding SDOH data integration into EHRs. Addressing the diverse understanding and use of SDOH data requires standardized training and procedures. Customized support and sustained engagement by facilitating organizations are paramount in ensuring practices' efficient SDOH data collection and integration.

Background:Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality.Methods:We conducted a PubMed search using “SDOH” and “EHR” Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.Results:Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization.Discussion:Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

BackgroundIntegrating clinical, genomic, and social determinants of health (SDOH) data is essential for advancing precision medicine and addressing cancer health disparities. However, existing bioinformatics tools often lack the flexibility to perform equity-driven analyses or require significant programming expertise.ObjectiveWe developed AI-HOPE-PM (Artificial Intelligence Agent for High-Optimization and Precision Medicine in Population Metrics), a conversational artificial intelligence system designed to enable natural language–driven, multidimensional cancer analysis. This study describes the development, implementation, and application of AI-HOPE-PM to support hypothesis testing that integrates genomic, clinical, and SDOH data.MethodsAI-HOPE-PM leverages large language models and Python-based statistical scripts to convert user-defined natural language queries into executable workflows. It was evaluated using curated colorectal cancer datasets from The Cancer Genome Atlas and cBioPortal, enriched with harmonized SDOH variables. Accuracy of natural language interpretation, run time efficiency, and usability were benchmarked against cBioPortal and UCSC Xena.ResultsAI-HOPE-PM successfully supported case-control stratification, survival modeling, and odds ratio analysis using natural language prompts. In colorectal cancer case studies, the system revealed significant disparities in progression-free survival and treatment access based on financial strain, health care access, food insecurity, and social support, demonstrating the importance of integrating SDOH in cancer research. Benchmark testing showed faster task execution compared to existing platforms, and the system achieved 92.5% accuracy in parsing biomedical queries.ConclusionsAI-HOPE-PM lowers technical barriers to integrative cancer research by enabling real-time, user-friendly exploration of clinical, genomic, and SDOH data. It expands on prior work by incorporating equity metrics into precision oncology workflows and offers a scalable tool for supporting disparities-focused translational research. Five videos are included as multimedia appendices to demonstrate platform functionality in real-world scenarios.

This pilot study assessed the feasibility of implementing electronic health record (EHR) tools for collecting, reviewing, and acting on patient-reported social determinants of health (SDH) data in community health centers (CHCs). We believe it is the first such US study. We implemented a suite of SDH data tools in 3 Pacific Northwest CHCs in June 2016, and used mixed methods to assess their adoption through July 2017. We modified the tools at clinic request; for example, we added questions that ask if the patient wanted assistance with SDH needs. Social determinants of health data were collected on 1,130 patients during the study period; 97% to 99% of screened patients (n = 1,098) had ≥1 SDH need documented in the EHR, of whom 211 (19%) had an EHR-documented SDH referral. Only 15% to 21% of patients with a documented SDH need indicated wanting help. Examples of lessons learned on adoption of EHR SDH tools indicate that clinics should: consider how to best integrate tools into existing workflow processes; ensure that staff tasked with SDH efforts receive adequate tool training and access; and consider that timing of data entry impacts how and when SDH data can be used. Our results indicate that adoption of systematic EHR-based SDH documentation may be feasible, but substantial barriers to adoption exist. Lessons from this study may inform primary care providers seeking to implement SDH-related efforts, and related health policies. Far more research is needed to address implementation barriers related to SDH documentation in EHRs.

Social determinants of health data in solid organ transplantation: National data sources and future directions

Background Social determinants of health (SDOH) affect around 70% of health outcomes. However, it is not clear how to integrate SDOH into clinical practice and health care policy. This quality improvement project engaged stakeholders to identify SDOH factors relevant in an Alaska Native/American Indian health system and how to integrate SDOH data into electronic health records (EHRs). Methods The authors utilized an internal steering committee of clinical leadership; conducted focus groups with patients, practitioners, administrative staff, and clinical leaders; developed programmatic workgroups to engage with the health system; and coordinated with allied health systems. Results The Steering Committee members prioritized uses of SDOH data. Focus groups grounded work in local community values and refined SDOH subdomains. Workgroups developed data visualizations, such as EHR dashboards, to automate data collection for reporting and assess performance metrics. External stakeholders helped innovate ways to utilize SDOH data through community partnerships and advocacy work. Stakeholders liked how the holistic approach of SDOH looks at whole-person wellness and how it can improve patient-practitioner relationships and reduce health disparities. They were concerned about outdated SDOH data and how some sensitive SDOH could lead to unanticipated harms. Leaders emphasized developing an actionable, strengths-based SDOH framework. Conclusions Many initiatives call for integrating SDOH into health care and EHRs. Engaging diverse audiences helps guide the work. This engagement may be particularly helpful for minority-serving health systems. SDOH data collection can be stigmatizing for patients. Stakeholder engagement can mitigate that by identifying which SDOH data elements to prioritize, and how to utilize them.

Open Data Challenge to Examine the Impact of Social Determinants of Health on Stroke.

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. 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. 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%). 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. 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.

PURPOSEDevelop a cancer-specific machine learning (ML) model that accurately predicts 30-day unplanned readmissions in patients with solid tumors.METHODSThe initial cohort included patients 18 years or older diagnosed with a solid tumor. Two distinct cohorts were generated: one with and one without detailed social determinants of health (SDOHs) data. For each cohort, data were temporally partitioned in 70% (training), 20% (validation), and 10% (testing). Tree-based ML models were developed and validated on each cohort. The metrics used to evaluate the model's performance were receiver operating characteristic curve (ROC), area under the ROC curve, precision, recall (R), accuracy, and area under the precision-recall curve.RESULTSWe included 13,717 patients in this study in two cohorts (5,059 without SDOH data and 8,658 with SDOH data). Unplanned 30-day readmission occurred in 21.3% of the cases overall. The five main non-SDOH factors most highly associated with an unplanned 30-day readmission (R, 0.74; IQR, 0.58-0.76) were: number of previous unplanned readmissions; higher Charlson comorbidity score; nonelective index admission; discharge to anywhere other than home, hospice, or nursing facility; and higher anion gap during the admission. Neighborhood crime index, neighborhood median home values, annual income, neighborhood median household income, and wealth index were the main five SDOH factors important for predicting a high risk for an unplanned hospital readmission (R, 0.66; IQR, 0.56-0.72). The models were not directly comparable.CONCLUSIONKey drivers of unplanned readmissions in patients with cancer are complex and involve both clinical factors and SDOH. We developed a cancer-specific ML model that with reasonable accuracy identified patients with cancer at high risk for an unplanned hospital readmission.