Health-related social needs information in the emergency department: clinician and patient perspectives on availability and use.

BACKGROUND: Patient health-related social needs (HRSN) complicate care and drive poor outcomes in emergency department (ED) settings. This study sought to understand what HRSN information is available to ED physicians and staff, and how HRSN-related clinical actions may or may not align with patient expectations. METHODS: We conducted a qualitative study using in-depth semi-structured interviews guided by HRSN literature, the 5 Rights of Clinical Decision Support (CDS) framework, and the Contextual Information Model. We asked ED providers, ED staff, and ED patients from one health system in the mid-Western United Stated about HRSN information availability during an ED encounter, HRSN data collection, and HRSN data use. Interviews were recorded, transcribed, and analyzed using modified thematic approach. RESULTS: We conducted 24 interviews (8 per group: ED providers, ED staff, and ED patients) from December 2022 to May 2023. We identified three themes: (1) Availability: ED providers and staff reported that HRSNs information is inconsistently available. The availability of HRSN data is influenced by patient willingness to disclose it during an encounter. (2) Collection: ED providers and staff preferred and predominantly utilized direct conversation with patients to collect HRSNs, despite other methods being available to them (e.g., chart review, screening questionnaires). Patients' disclosure preferences were based on modality and team member. (3) Use: Patients wanted to be connected to relevant resources to address their HRSNs. Providers and staff altered clinical care to account for or accommodate HRSNs. System-level challenges (e.g., limited resources) limited provider and staff ability to address patients HRSNs. CONCLUSIONS: In the ED, HRSNs information was inconsistently available, collected, or disclosed. Patients and ED providers and staff differed in their perspectives on how HSRNs should be collected and acted upon. Accounting for such difference in clinical and administrative decisions will be critical for patient acceptance and effective usage of HSRN information.

How legal problems are conceptualized and measured in healthcare settings: a systematic review.

Legal problems encompass issues requiring resolution through the justice system. This social risk factor creates barriers in accessing services and increases risk of poor health outcomes. A systematic review of the peer-reviewed English-language health literature following the PRISMA guidelines sought to answer the question, how has the concept of patients' "legal problems" been operationalized in healthcare settings? Eligible articles reported the measurement or screening of individuals for legal problems in a United States healthcare or clinical setting. We abstracted the prevalence of legal problems, characteristics of the sampled population, and which concepts were included. 58 studies reported a total of 82 different measurements of legal problems. 56.8% of measures reflected a single concept (e.g., incarcerated only). The rest of the measures reflected two or more concepts within a single reported measure (e.g., incarcerations and arrests). Among all measures, the concept of incarceration or being imprisoned appeared the most frequently (57%). The mean of the reported legal problems was 26%. The literature indicates that legal concepts, however operationalized, are very common among patients. The variation in measurement definitions and approaches indicates the potential difficulties for organizations seeking to address these challenges.

Non-response Bias in Social Risk Factor Screening Among Adult Emergency Department Patients.

Healthcare organizations increasingly use screening questionnaires to assess patients' social factors, but non-response may contribute to selection bias. This study assessed differences between respondents and those refusing participation in a social factor screening. We used a cross-sectional approach with logistic regression models to measure the association between subject characteristics and social factor screening questionnaire participation. The study subjects were patients from a mid-western state safety-net hospital's emergency department. Subjects' inclusion criteria were: (1) ≥ 18 years old, (2) spoke English or Spanish, and (3) able to complete a self-administered questionnaire. We classified subjects that consented and answered the screening questionnaire in full as respondents. All others were non-respondents. Using natural language processing, we linked all subjects' participation status to demographic characteristics, clinical data, an area-level deprivation measure, and social risk factors extracted from clinical notes. We found that nearly 6 out of every 10 subjects approached (59.9%), consented, and completed the questionnaire. Subjects with prior documentation of financial insecurity were 22% less likely to respond to the screening questionnaire (marginal effect = -22.40; 95% confidence interval (CI) = -41.16, -3.63; p = 0.019). No other factors were significantly associated with response. This study uniquely contributes to the growing social determinants of health literature by confirming that selection bias may exist within social factor screening practices and research studies.

Generalizability and portability of natural language processing system to extract individual social risk factors.

OBJECTIVE: The objective of this study is to validate and report on portability and generalizability of a Natural Language Processing (NLP) method to extract individual social factors from clinical notes, which was originally developed at a different institution. MATERIALS AND METHODS: A rule-based deterministic state machine NLP model was developed to extract financial insecurity and housing instability using notes from one institution and was applied on all notes written during 6 months at another institution. 10% of positively-classified notes by NLP and the same number of negatively-classified notes were manually annotated. The NLP model was adjusted to accommodate notes at the new site. Accuracy, positive predictive value, sensitivity, and specificity were calculated. RESULTS: More than 6 million notes were processed at the receiving site by the NLP model, which resulted in about 13,000 and 19,000 classified as positive for financial insecurity and housing instability, respectively. The NLP model showed excellent performance on the validation dataset with all measures over 0.87 for both social factors. DISCUSSION: Our study illustrated the need to accommodate institution-specific note-writing templates as well as clinical terminology of emergent diseases when applying NLP model for social factors. A state machine is relatively simple to port effectively across institutions. Our study. showed superior performance to similar generalizability studies for extracting social factors. CONCLUSION: Rule-based NLP model to extract social factors from clinical notes showed strong portability and generalizability across organizationally and geographically distinct institutions. With only relatively simple modifications, we obtained promising performance from an NLP-based model.

Natural language processing-driven state machines to extract social factors from unstructured clinical documentation.

Objective: This study sought to create natural language processing algorithms to extract the presence of social factors from clinical text in 3 areas: (1) housing, (2) financial, and (3) unemployment. For generalizability, finalized models were validated on data from a separate health system for generalizability. Materials and Methods: Notes from 2 healthcare systems, representing a variety of note types, were utilized. To train models, the study utilized n-grams to identify keywords and implemented natural language processing (NLP) state machines across all note types. Manual review was conducted to determine performance. Sampling was based on a set percentage of notes, based on the prevalence of social need. Models were optimized over multiple training and evaluation cycles. Performance metrics were calculated using positive predictive value (PPV), negative predictive value, sensitivity, and specificity. Results: PPV for housing rose from 0.71 to 0.95 over 3 training runs. PPV for financial rose from 0.83 to 0.89 over 2 training iterations, while PPV for unemployment rose from 0.78 to 0.88 over 3 iterations. The test data resulted in PPVs of 0.94, 0.97, and 0.95 for housing, financial, and unemployment, respectively. Final specificity scores were 0.95, 0.97, and 0.95 for housing, financial, and unemployment, respectively. Discussion: We developed 3 rule-based NLP algorithms, trained across health systems. While this is a less sophisticated approach, the algorithms demonstrated a high degree of generalizability, maintaining >0.85 across all predictive performance metrics. Conclusion: The rule-based NLP algorithms demonstrated consistent performance in identifying 3 social factors within clinical text. These methods may be a part of a strategy to measure social factors within an institution.

Adoption of Best Practices in Behavioral Health Crisis Care by Mental Health Treatment Facilities.

OBJECTIVE: The authors aimed to examine adoption of behavioral health crisis care (BHCC) services included in the Substance Abuse and Mental Health Services Administration's (SAMHSA's) best practices guidelines. METHODS: Secondary data from SAMHSA's Behavioral Health Treatment Services Locator in 2022 were used. BHCC best practices were measured on a summated scale capturing whether a mental health treatment facility (N=9,385) adopted BHCC best practices, including provision of these services to all age groups: emergency psychiatric walk-in services, crisis intervention teams, onsite stabilization, mobile or offsite crisis responses, suicide prevention, and peer support. Descriptive statistics were used to examine organizational characteristics (such as facility operation, type, geographic area, license, and payment methods) of mental health treatment facilities nationwide, and a map was created to show locations of best practices BHCC facilities. Logistic regressions were performed to identify facilities' organizational characteristics associated with adopting BHCC best practices. RESULTS: Only 6.0% (N=564) of mental health treatment facilities fully adopted BHCC best practices. Suicide prevention was the most common BHCC service, offered by 69.8% (N=6,554) of the facilities. A mobile or offsite crisis response service was the least common, adopted by 22.4% (N=2,101). Higher odds of adopting BHCC best practices were significantly associated with public ownership (adjusted OR [AOR]=1.95), accepting self-pay (AOR=3.18), accepting Medicare (AOR=2.68), and receiving any grant funding (AOR=2.45). CONCLUSIONS: Despite SAMHSA guidelines recommending comprehensive BHCC services, a fraction of facilities have fully adopted BHCC best practices. Efforts are needed to facilitate widespread uptake of BHCC best practices nationwide.

Accuracy of Electronic Health Record Food Insecurity, Housing Instability, and Financial Strain Screening in Adult Primary Care.

This study assesses the accuracy of electronic health record­based screening questionnaires about social risk factors using external single-domain questionnaires as a comparator.

Return visits to the emergency department: An analysis using group based curve models.

Stratification modeling in health services is useful to identify differential patient risk groups, or latent classes. Given the frequency and costs, repeated emergency department (ED) may be an appropriate candidate for risk stratification modeling. We applied a method called group-based trajectory modeling (GBTM) to a sample of 37,416 patients who visited an urban, safety-net ED between 2006 and 2016. Patients had up to 10 ED visits during the study period. Data sources included the hospital's electronic health record (EHR), the state-wide health information exchange system, and area-level social determinants of health factors. Results revealed three distinct trajectory groups. Trajectories with a higher risk of revisit were marked by more patients with behavioral diagnoses, injuries, alcohol & substance abuse, stroke, diabetes, and other factors. The application of advanced computational techniques, like GBTM, provides opportunities for health care organizations to better understand the underlying risks of their large patient populations. Identifying those patients who are likely to be members of high-risk trajectories allows healthcare organizations to stratify patients by level of risk and develop early targeted interventions.

Assessment of structured data elements for social risk factors.

OBJECTIVES: Computable social risk factor phenotypes derived from routinely collected structured electronic health record (EHR) or health information exchange (HIE) data may represent a feasible and robust approach to measuring social factors. This study convened an expert panel to identify and assess the quality of individual EHR and HIE structured data elements that could be used as components in future computable social risk factor phenotypes. STUDY DESIGN: Technical expert panel. METHODS: A 2-round Delphi technique included 17 experts with an in-depth knowledge of available EHR and/or HIE data. The first-round identification sessions followed a nominal group approach to generate candidate data elements that may relate to socioeconomics, cultural context, social relationships, and community context. In the second-round survey, panelists rated each data element according to overall data quality and likelihood of systematic differences in quality across populations (ie, bias). RESULTS: Panelists identified a total of 89 structured data elements. About half of the data elements (n = 45) were related to socioeconomic characteristics. The panelists identified a diverse set of data elements. Elements used in reimbursement-related processes were generally rated as higher quality. Panelists noted that several data elements may be subject to implicit bias or reflect biased systems of care, which may limit their utility in measuring social factors. CONCLUSIONS: Routinely collected structured data within EHR and HIE systems may reflect patient social risk factors. Identifying and assessing available data elements serves as a foundational step toward developing future computable social factor phenotypes.

Strategic use of health information exchange and market share, payer mix, and operating margins.

BACKGROUND: Health information exchange (HIE) capabilities are tied to health care organizations' strategic and business goals. As a technology that connects information from different organizations, HIE may be a source of competitive advantage and a path to improvements in performance. PURPOSE: The aim of the study was to identify the impact of hospitals' use of HIE capabilities on outcomes that may be sensitive to changes in various contracting arrangements and referral patterns arising from improved connectivity. METHODOLOGY: Using a panel of community hospitals in nine states, we examined the association between the number of different data types the hospital could exchange via HIE and changes in market share, payer mix, and operating margin (2010-2014). Regression models that controlled for the number of different data types shared intraorganizationally and other time-varying factors and included both hospital and time fixed effects were used for adjusted estimates of the relationships between changes in HIE capabilities and outcomes. RESULTS: Increasing HIE capability was associated with a 13 percentage point increase in a hospital's discharges that were covered by commercial insurers or Medicare (i.e., payer mix). Conversely, increasing intraorganizational information sharing was associated with a 9.6 percentage point decrease in the percentage of discharges covered by commercial insurers or Medicare. Increasing HIE capability or intraorganizational information sharing was not associated with increased market share nor with operating margin. CONCLUSIONS: Improving information sharing with external organizations may be an approach to support strategic business goals. PRACTICE IMPLICATIONS: Organizations may be served by identifying ways to leverage HIE instead of focusing on intraorganizational exchange capabilities.

Sensitivity and Specificity of Real-World Social Factor Screening Approaches.

Health care organizations are increasingly documenting patients for social risk factors in structured data. Two main approaches to documentation, ICD-10 Z codes and screening questions, face limited adoption and conceptual challenges. This study compared estimates of social risk factors obtained via screening questions and ICD-10 Z diagnoses coding, as used in clinical practice, to estiamtes from validated survey instruments in a sample of adult primary care and emergency department patients at an urban safety-net health system. Financial strain, transportation barriers, food insecurity, and housing instability were independently assessed using instruments with published reliability and validity. These four social factors were also being collected by the health system in screening questions or could be mapped to ICD-10 Z code diagnosis code concepts. Neither the screening questions nor ICD-10 Z codes performed particularly well in terms of accuracy. For the screening questions, the Area Under the Curve (AUC) scores were 0.609 for financial strain, 0.703 for transportation, 0.698 for food insecurity, and 0.714 for housing instability. For the ICD-10 Z codes, AUC scores tended to be lower in the range of 0.523 to 0.535. For both screening questions and ICD-10 Z codes, the measures were much more specific than sensitive. Under real world conditions, ICD-10 Z codes and screening questions are at the minimal, or below, threshold for being diagnostically useful approaches to identifying patients' social risk factors. Data collection support through information technology or novel approaches combining data sources may be necessary to improve the usefulness of these data.

Primary Care-Based Case Conferences and Reductions in Health Care Utilization.

Case conferences are collaborative, interdisciplinary team meetings that facilitate consensus on individual patients' health management plans, coordinate services, and initiate referrals. This approach is well-suited to address the social needs and risks of complex patients. Evidence of this approach in primary care settings to change patient outcomes is limited. A panel of 976 patients from an urban, federally qualified health center were included in case conferences. Fixed-effects regression models estimated the effect of case conferences on admissions, emergency department (ED) visits, and missed outpatient appointments. Case conferencing was associated with a 6% reduction in the probability that the patient would have an ED visit in a given month and a 5% lower probability of an inpatient admission. The probability of missed primary care appointments increased. Case conferences are a potential strategy to address the multiple issues facing complex patients.

Practice and market factors associated with provider volume of health information exchange.

OBJECTIVE: To assess the practice- and market-level factors associated with the amount of provider health information exchange (HIE) use. MATERIALS AND METHODS: Provider and practice-level data was drawn from the Meaningful Use Stage 2 Public Use Files from the Centers for Medicare and Medicaid Services, the Physician Compare National Downloadable File, and the Compendium of US Health Systems, among other sources. We analyzed the relationship between provider HIE use and practice and market factors using multivariable linear regression and compared primary care providers (PCPs) to non-PCPs. Provider volume of HIE use is measured as the percentage of referrals sent with electronic summaries of care (eSCR) reported by eligible providers attesting to the Meaningful Use electronic health record (EHR) incentive program in 2016. RESULTS: Providers used HIE in 49% of referrals; PCPs used HIE in fewer referrals (43%) than non-PCPs (57%). Provider use of products from EHR vendors was negatively related to HIE use, while use of Athenahealth and Greenway Health products were positively related to HIE use. Providers treating, on average, older patients and greater proportions of patients with diabetes used HIE for more referrals. Health system membership, market concentration, and state HIE consent policy were unrelated to provider HIE use. DISCUSSION: HIE use during referrals is low among office-based providers with the capability for exchange, especially PCPs. Practice-level factors were more commonly associated with greater levels of HIE use than market-level factors. CONCLUSION: This furthers the understanding that market forces, like competition, may be related to HIE adoption decisions but are less important for use once adoption has occurred.

Mandated Benefits for Diabetes Education and the Availability of Accredited Programs and Certified Diabetes Educators.

States have the latitude to mandate coverage of diabetes self-management education (DSME) services for privately insured and Medicaid patients. The impact of these mandates on the supply of DSME resources is unknown. This study compared changes in the supply of DSME programs and program sites accredited by the American Association for Diabetes Educators (AADE) and certified diabetes educators (CDE) between states that did and did not mandate benefits for DSME. Using a unique combination of legal and programmatic data sources, the authors employed fixed effects regression models with clustered robust standard errors to compare changes in the supply of AADE-accredited DSME programs, program sites, and CDEs in states that mandated benefits with states that did not. Given the variation in state mandates, models also estimated the impact of "flexible" reimbursement provisions on the supply of resources among adopting states. The supply of DSME resources has increased over time, but results indicate that mandated benefits were not a significant driver of these changes in the supply. The impact of flexible reimbursement provisions varied. Interestingly, provisions of the Affordable Care Act were associated with an increased supply of resources. Results suggest that extending benefits to previously insured patients does not increase the supply of DSME resources, but a rapid increase in patients entering the health system does encourage growth.

Content analysis of behavior change techniques in maternal and infant health apps.

Maternal and infant health (MIH) mobile applications (apps) are increasingly popular and frequently used for health education and decision making. Interventions grounded in theory-based behavior change techniques (BCTs) are shown to be effective in promoting healthy behavior changes. MIH apps have the potential to be useful tools, yet the extent to which they incorporate BCTs is still unknown. The objective of this study was to assess the presence of BCTs in popular MIH apps available in the Apple App and Google Play stores. Twenty-nine popular MIH apps were coded for the presence of 16 BCTs using the mHealth app taxonomy. Popular MIH apps whose purpose was to provide health education or decision-making support to pregnant women or parents/caregivers of infants were included in the final sample. On an average, the reviewed apps included seven BCTs (range 2-16). Techniques such as personalization, review of general or specific goals, macro tailoring, self-monitoring of goals, and health behavior linkages were most frequently present. No differences in the presence of BCTs between paid and free apps were observed. Popular MIH apps typically included only a minority of BCTs found to be useful for health promotion. However, apps developed by healthcare developers incorporated a higher number of BCTs within the app content. Therefore, app developers and policymakers may consider strategies to increase health expert involvement in app design and content delivery.

Primary Care Case Conferences to Mitigate Social Determinants of Health: A Case Study from One FQHC System.

Objective: Given the increasing difficulty healthcare providers face in addressing patients' complex social circumstances and underlying health needs, organizations are considering team-based approaches including case conferences. We sought to document various perspectives on the facilitators and challenges of conducting case conferences in primary care settings. Study Design: Qualitative study using semi-structured telephone interviews. Methods: We conducted 22 qualitative interviews with members of case conferencing teams, including physicians, nurses, and social workers from a Federally Qualified Health Clinic, as well as local county public health nurses. Interviews were recorded, transcribed, and reviewed using thematic coding to identify key themes/subthemes. Results: Participants reported perceived benefits to patients, providers, and healthcare organizations including better care, increased inter-professional communication, and shared knowledge. Perceived challenges related to underlying organizational processes and priorities. Perceived facilitators for successful case conferences included generating and maintaining a list of patients to discuss during case conference sessions and team members being prepared to actively participate in addressing tasks and patient needs during each session. Participants offered recommendations for further improving case conferences for patients, providers, and organizations. Conclusions: Case conferences may be a feasible approach to understanding patient's complex social needs. Participants reported that case conferences may help mitigate the effects of these social issues and that they foster better inter-professional communication and care planning in primary care. The case conference model requires administrative support and organizational resources to be successful. Future research should explore how case conferences fit into a larger population health organizational strategy so that they are resourced commensurately.

Precision Health-Enabled Machine Learning to Identify Need for Wraparound Social Services Using Patient- and Population-Level Data Sets: Algorithm Development and Validation.

BACKGROUND: Emerging interest in precision health and the increasing availability of patient- and population-level data sets present considerable potential to enable analytical approaches to identify and mitigate the negative effects of social factors on health. These issues are not satisfactorily addressed in typical medical care encounters, and thus, opportunities to improve health outcomes, reduce costs, and improve coordination of care are not realized. Furthermore, methodological expertise on the use of varied patient- and population-level data sets and machine learning to predict need for supplemental services is limited. OBJECTIVE: The objective of this study was to leverage a comprehensive range of clinical, behavioral, social risk, and social determinants of health factors in order to develop decision models capable of identifying patients in need of various wraparound social services. METHODS: We used comprehensive patient- and population-level data sets to build decision models capable of predicting need for behavioral health, dietitian, social work, or other social service referrals within a safety-net health system using area under the receiver operating characteristic curve (AUROC), sensitivity, precision, F1 score, and specificity. We also evaluated the value of population-level social determinants of health data sets in improving machine learning performance of the models. RESULTS: Decision models for each wraparound service demonstrated performance measures ranging between 59.2%% and 99.3%. These results were statistically superior to the performance measures demonstrated by our previous models which used a limited data set and whose performance measures ranged from 38.2% to 88.3% (behavioural health: F1 score P<.001, AUROC P=.01; social work: F1 score P<.001, AUROC P=.03; dietitian: F1 score P=.001, AUROC P=.001; other: F1 score P=.01, AUROC P=.02); however, inclusion of additional population-level social determinants of health did not contribute to any performance improvements (behavioural health: F1 score P=.08, AUROC P=.09; social work: F1 score P=.16, AUROC P=.09; dietitian: F1 score P=.08, AUROC P=.14; other: F1 score P=.33, AUROC P=.21) in predicting the need for referral in our population of vulnerable patients seeking care at a safety-net provider. CONCLUSIONS: Precision health-enabled decision models that leverage a wide range of patient- and population-level data sets and advanced machine learning methods are capable of predicting need for various wraparound social services with good performance.

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review.

BACKGROUND: Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients' nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health. OBJECTIVE: This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources. METHODS: We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported. RESULTS: A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location. CONCLUSIONS: A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.

Prediction of emergency department revisits using area-level social determinants of health measures and health information exchange information.

INTRODUCTION: Interoperable health information technologies, like electronic health records (EHR) and health information exchange (HIE), provide greater access to patient information from across multiple organizations. Also, an increasing number of public data sources exist to describe social determinant of health factors. These data may help better inform risk prediction models, but the relative importance or value of these data has not been established. This study assessed the performance of different classes of information individually, and in combination, in predicting emergency department (ED) revisits. METHODS: In a sample of 279,611 adult ED encounters. We compared the performance of Two-Class Boosted Decision Trees machine learning algorithm using 5 classes of information: 1) social determinants of health measures only, 2) current visit EHR information only, 3) current and historical EHR information, 4) HIE information only, and 5) all available information combined. RESULTS: The social determinants of health measure only model had the overall worst performance with an area under the curve AUC of 0.61. The model using all information classes together had the best performance (AUC = 0.732). The model using HIE information only performed better than all other single information class models. CONCLUSIONS: Broad information sources, which are reflective of patients' reliance on multiple organizations for care, better support risk prediction modeling in the emergency department.