How patients distinguish between clinical and administrative predictive models in health care.

OBJECTIVES: To understand patient perceptions of specific applications of predictive models in health care. STUDY DESIGN: Original, cross-sectional national survey. METHODS: We conducted a national online survey of US adults with the National Opinion Research Center from November to December 2021. Measures of internal consistency were used to identify how patients differentiate between clinical and administrative predictive models. Multivariable logistic regressions were used to identify relationships between comfort with various types of predictive models and patient demographics, perceptions of privacy protections, and experiences in the health care system. RESULTS: A total of 1541 respondents completed the survey. After excluding observations with missing data for the variables of interest, the final analytic sample was 1488. We found that patients differentiate between clinical and administrative predictive models. Comfort with prediction of bill payment and missed appointments was especially low (21.6% and 36.6%, respectively). Comfort was higher with clinical predictive models, such as predicting stroke in an emergency (55.8%). Experiences of discrimination were significant negative predictors of comfort with administrative predictive models. Health system transparency around privacy policies was a significant positive predictor of comfort with both clinical and administrative predictive models. CONCLUSIONS: Patients are more comfortable with clinical applications of predictive models than administrative ones. Privacy protections and transparency about how health care systems protect patient data may facilitate patient comfort with these technologies. However, larger inequities and negative experiences in health care remain important for how patients perceive administrative applications of prediction.

Structured and unstructured social risk factor documentation in the electronic health record underestimates patients' self-reported risks.

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.

Electronic connectivity between hospital pairs: impact on emergency department-related utilization.

OBJECTIVE: To use more precise measures of which hospitals are electronically connected to determine whether health information exchange (HIE) is associated with lower emergency department (ED)-related utilization. MATERIALS AND METHODS: We combined 2018 Medicare fee-for-service claims to identify beneficiaries with 2 ED encounters within 30 days, and Definitive Healthcare and AHA IT Supplement data to identify hospital participation in HIE networks (HIOs and EHR vendor networks). We determined whether the 2 encounters for the same beneficiary occurred at: the same organization, different organizations connected by HIE, or different organizations not connected by HIE. Outcomes were: (1) whether any repeat imaging occurred during the second ED visit; (2) for beneficiaries with a treat-and-release ED visit followed by a second ED visit, whether they were admitted to the hospital after the second visit; (3) for beneficiaries discharged from the hospital followed by an ED visit, whether they were admitted to the hospital. RESULTS: In adjusted mixed effects models, for all outcomes, beneficiaries returning to the same organization had significantly lower utilization compared to those going to different organizations. Comparing only those going to different organizations, HIE was not associated with lower levels of repeat imaging. HIE was associated with lower likelihood of hospital admission following a treat-and-release ED visit (1.83 percentage points [-3.44 to -0.21]) but higher likelihood of admission following hospital discharge (2.78 percentage points [0.48-5.08]). DISCUSSION: Lower utilization for beneficiaries returning to the same organization could reflect better access to information or other factors such as aligned incentives. CONCLUSION: HIE is not consistently associated with utilization outcomes reflecting more coordinated care in the ED setting.

Social and Medical Care Integration Practices Among Children's Hospitals.

OBJECTIVES: In response to evidence linking social risk factors and adverse health outcomes, new incentives have emerged for hospitals to screen for adverse social determinants of health (SDOH). However, little information is available about the current state of social risk-related care practices among children's hospitals. To address outstanding knowledge gaps, we sought to describe social risk-related care practices among a national sample of children's hospitals. METHODS: We analyzed responses to the 2020 American Hospital Association Annual Survey. Among children's hospitals, we calculated the prevalence of screening for social needs, strategies to address social risks/needs, partnerships with community-based organizations to address social risks/needs at the individual and community level, and rates of impact assessments of how social risk-related interventions affect outcomes. We also used χ2 tests to compare results by hospital characteristics. We weighted results to adjust for nonresponse. RESULTS: The sample included 82 children's hospitals. A total of 79.6% screened for and 96.0% had strategies to address at least 1 social risk factor, although rates varied by SDOH domain. Children's hospitals more commonly partnered with community-based organizations to address patient-level social risks than to participate in community-level initiatives. A total of 39.2% of hospitals assessed SDOH intervention effectiveness. Differences in social risk-related care practices commonly varied by hospital ownership and Medicaid population but not by region. CONCLUSIONS: We found wide variability in social risk-related care practices among children's hospitals based on the risk domain and hospital characteristics. Findings can be used to monitor whether social risk-related care practices change in the setting of new incentives.

Evaluating the comparability of patient-level social risk data extracted from electronic health records: A systematic scoping review.

Objective: To evaluate how and from where social risk data are extracted from EHRs for research purposes, and how observed differences may impact study generalizability. Methods: Systematic scoping review of peer-reviewed literature that used patient-level EHR data to assess 1 ± 6 social risk domains: housing, transportation, food, utilities, safety, social support/isolation. Results: 111/9022 identified articles met inclusion criteria. By domain, social support/isolation was most often included (N = 68/111), predominantly defined by marital/partner status (N = 48/68) and extracted from structured sociodemographic data (N = 45/48). Housing risk was defined primarily by homelessness (N = 39/49). Structured housing data was extracted most from billing codes and screening tools (N = 15/30, 13/30, respectively). Across domains, data were predominantly sourced from structured fields (N = 89/111) versus unstructured free text (N = 32/111). Conclusion: We identified wide variability in how social domains are defined and extracted from EHRs for research. More consistency, particularly in how domains are operationalized, would enable greater insights across studies.

Governance of Electronic Health Record Modification at U.S. Academic Medical Centers.

OBJECTIVES: A key aspect of electronic health record (EHR) governance involves the approach to EHR modification. We report a descriptive study to characterize EHR governance at academic medical centers (AMCs) across the United States. METHODS: We conducted interviews with the Chief Medical Information Officers of 18 AMCs about the process of EHR modification for standard requests. Recordings of the interviews were analyzed to identify categories within prespecified domains. Responses were then assigned to categories for each domain. RESULTS: At our AMCs, EHR requests were governed variably, with a similar number of sites using quantitative scoring systems (7, 38.9%), qualitative systems (5, 27.8%), or no scoring system (6, 33.3%). Two (11%) organizations formally review all requests for their impact on health equity. Although 14 (78%) organizations have trained physician builders/architects, their primary role was not for EHR build. Most commonly reported governance challenges included request volume (11, 61%), integrating diverse clinician input (3, 17%), and stakeholder buy-in (3, 17%). The slowest step in the process was clarifying end user requests (14, 78%). Few leaders had identified metrics for the success of EHR governance. CONCLUSION: Governance approaches for managing EHR modification at AMCs are highly variable, which suggests ongoing efforts to balance EHR standardization and maintenance burden, while dealing with a high volume of requests. Developing metrics to capture the performance of governance and quantify problems may be a key step in identifying best practices.

Physician Electronic Health Record Use After Changes in US Centers for Medicare & Medicaid Services Documentation Requirements.

This cohort study examines changes in physician electronic health record (EHR) documentation time before and after changes in Centers for Medicare & Medicaid evaluation and management requirements.

Perceptions of information continuity key to understanding quality of post-acute care transitions.

BACKGROUND: Skilled nursing facilities' (SNFs) ability to provide optimal post-acute care depends on effective receipt of information from hospitals ("information continuity"). Little is known about how SNFs perceive information continuity and how it may relate to upstream information sharing processes, organizational context, and downstream outcomes. PURPOSE: First, this study aims to identify how SNF perceptions of information continuity may be shaped by hospital information sharing practices, including measures of completeness, timeliness, and usability, as well as characteristics of the transitional care environment (i.e., integrated care relationships and/or consistency of information sharing practices across different hospital partners). Second, we analyze which of these characteristics are associated with quality of transitional care (measured by 30-day readmissions). APPROACH: A cross-sectional analysis of nationally representative SNF survey ( N = 212) linked to Medicare claims was performed. RESULTS: SNF perceptions of information continuity are strongly and positively associated with hospital information sharing practices. Adjusting for actual information sharing practices, SNFs that experienced discordance across hospitals reported lower perceptions of continuity (ß = -0.73, p = .022); evidence of stronger relationships with a given hospital partner appears to help facilitate resources and communication that helps to close this gap. Perceptions of information continuity, more so than the upstream information sharing processes reported, exhibited a more reliable and significant association with rates of readmissions as an indicator of transitional care quality. CONCLUSION: SNF perceptions of information continuity are strongly associated with patient outcomes and are reflective of both hospital information sharing practices as well as characteristics of the transitional care environment that can mitigate or amplify the cognitive and administration challenge of their work. PRACTICE IMPLICATIONS: Improving transitional care quality requires that hospitals improve information sharing behaviors but also invest in capacity for learning and process improvement in the SNF environment.

Association Between Billing Patient Portal Messages as e-Visits and Patient Messaging Volume.

This study evaluates the adoption of clinician billing for patient portal messages as e-visits, prompted by significant increases in patient messaging after the onset of the COVID-19 pandemic.

Characterizing the relative frequency of clinician engagement with structured social determinants of health data.

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.

Association of Medicare Beneficiary and Hospital Accountable Care Organization Alignment With Surgical Cost Savings.

Importance: Although Medicare accountable care organizations (ACOs) account for half of program expenditures, whether ACOs are associated with surgical spending warrants further study. Objective: To assess whether greater beneficiary-hospital ACO alignment was associated with lower surgical episode costs. Design, Setting, and Participants: This retrospective cohort study was conducted between 2020 and 2022 using US Medicare data from a 20% random sample of beneficiaries. Individuals 18 years of age and older and without kidney failure who had a surgical admission between 2008 and 2015 were included. For each study year, distinction was made between beneficiaries assigned to an ACO and those who were not, as well as between admissions to ACO-participating and nonparticipating hospitals. Exposures: Time-varying binary indicators for beneficiary ACO assignment and hospital ACO participation and an interaction between them. Main Outcomes and Measures: Ninety-day, price-standardized total episode payments. Multivariable 2-way fixed-effects models were estimated. Results: During the study period, 2 797 337 surgical admissions (6% of which involved ACO-assigned beneficiaries) occurred at 3427 hospitals (17% ACO participating). Total Medicare payments for 90-day surgical episodes were lowest when ACO-assigned beneficiaries underwent surgery at a hospital participating in the same ACO as the beneficiary ($26 635 [95% CI, $26 426-$26 844]). The highest payments were for unassigned beneficiaries treated at participating hospitals ($27 373 [95% CI, $27 232-$27 514]) or nonparticipating hospitals ($27 303 [95% CI, $27 291-$27 314]). Assigned beneficiaries treated at hospitals participating in a different ACO and assigned beneficiaries treated at nonparticipating hospitals had similar payments (for participating hospitals, $27 003 [95% CI, $26 739-$27 267] and for nonparticipating hospitals, $26 928 [95% CI, $26 796-$27 059]). A notable factor in the observed differences in surgical episode costs was lower spending on postacute care services. Conclusions and Relevance: In this cohort study evaluating hospital and beneficiary ACO alignment and surgical spending, savings were noted for beneficiaries treated at hospitals in the same ACO. Allowing ACOs to encourage or require surgical procedures in their own hospitals could lower Medicare spending on surgery.

Management Opportunities and Challenges After Achieving Widespread Health System Digitization.

The adoption of electronic health records (EHRs) and digitization of health data over the past decade is ushering in the next generation of digital health tools that leverage artificial intelligence (AI) to improve varied aspects of health system performance. The decade ahead is therefore shaping up to be one in which digital health becomes even more at the forefront of health care delivery - demanding the time, attention, and resources of health care leaders and frontline staff, and becoming inextricably linked with all dimensions of health care delivery. In this chapter, we look back and look ahead. There are substantive lessons learned from the first era of large-scale adoption of enterprise EHRs and ongoing challenges that organizations are wrestling with - particularly related to the tension between standardization and flexibility/customization of EHR systems and the processes they support. Managing this tension during efforts to implement and optimize enterprise systems is perhaps the core challenge of the past decade, and one that has impeded consistent realization of value from initial EHR investments. We describe these challenges, how they manifest, and organizational strategies to address them, with a specific focus on alignment with broader value-based care transformation. We then look ahead to the AI wave - the massive number of applications of AI to health care delivery, the expected benefits, the risks and challenges, and approaches that health systems can consider to realize the benefits while avoiding the risks.

Electronic Connectivity Among US Hospitals Treating Shared Patients.

BACKGROUND: Increasing electronic health information exchange (HIE) between provider organizations is a top policy priority that has been pursued by establishing varied types of networks. OBJECTIVES: To measure electronic connectivity enabled by these networks, including community, electronic health record vendor, and national HIE networks, across US hospitals weighted by the volume of shared patients and identify characteristics that predict connectivity. RESEARCH DESIGN: Cross-sectional analysis of 1721 hospitals comprising 16,344 hospital pairs and 6,492,232 shared patients from 2018 CareSet Labs HOP data and national hospital surveys. SUBJECTS: Pairs of US acute care hospitals that delivered care to 11 or more of the same fee-for-service Medicare beneficiaries in 2018. MEASURES: Whether a patient was treated by a pair of hospitals connected through participation in the same HIE network ("connected hospitals") or not connected because the hospitals participated in different networks, only 1 participated, or both did not participate. RESULTS: Sixty-four percent of shared patients were treated by connected hospitals. Of the remaining shared patients, 14% were treated by hospital pairs that participated in different HIE networks, 21% by pairs in which only 1 hospital participated in an HIE network, and 2% by pairs in which neither participated. Patients treated by pairs with at least 1 for-profit hospital, and by pairs located in competitive markets, were less likely to be treated by connected hospitals. CONCLUSIONS: While the majority of shared patients received care from connected hospitals, remaining gaps could be filled by connecting HIE networks to each other and by incentivizing certain types of hospitals that may not participate because of competitive concerns.

Physician awareness of social determinants of health documentation capability in the electronic health record.

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.

Association of Primary Care Engagement in Value-Based Reform Programs With Health Services Outcomes: Participation and Synergies.

Importance: Policy makers envision synergistic benefits from primary care reform programs that advance infrastructure and processes in the context of a supportive payment environment. However, these programs have been operationalized and implemented separately, raising the question of whether synergies are achieved. Objective: To evaluate associations between primary care engagement in voluntary delivery system and/or payment reform programs and health services outcomes. Design Setting and Participants: This was an observational longitudinal analysis of US ambulatory primary care organizations (PCOs) with attributed Medicare fee-for-service beneficiaries (1.6-1.9 million unique beneficiaries annually) using data for 2009, 2010, and 2015 to 2017; PCOs included multispecialty practices that delivered primary care. Data analyses were performed from January 2020 to December 2021. Exposures: Annual PCO participation in or recognition by (1) the Centers for Medicare & Medicaid's meaningful use (MU) program, (2) the National Committee for Quality Assurance's Patient-Centered Medical Home (PCMH) program, and/or (3) the Medicare Shared Savings Program (MSSP), an Accountable Care Organizations program. Main Outcomes and Measures: Independent and joint associations between an additional year of participation by a PCO in each of the 3 reform programs, and 3 types of outcomes: (1) hospital utilization (all-cause admissions, ambulatory care sensitive admissions, all-cause readmissions, all-cause emergency department visits); (2) evidence-based diabetes guideline adherence (≥1 annual glycated hemoglobin test, low-density lipoprotein cholesterol test, nephropathy screening, and eye examination); and (3) Medicare spending (total, acute inpatient, and skilled nursing facility). Results: The study sample comprised 47 880 unique PCOs (size ≤10 beneficiaries, 50%; ≤1-2 clinicians, 65%) and approximately 5.61 million unique Medicare beneficiaries (mean [SD] age, 71.4 [12.7] years; 3 207 568 [57.14%] women; 4 474 541 [79.71%] non-Hispanic White individuals) across the study years (2009, 2010, 2015-2017). Of the hospital utilization measures, only ambulatory care sensitive admission was associated with improved performance, showing a statistically significant marginal effect size for joint participation in MU and MSSP (-0.0002; 95% CI, -0.0005 to 0.0000) and MSSP alone (-0.0003; 95% CI, -0.0005 to -0.0001). For diabetes adherence, joint participation in PCMH and MU was associated with 0.06 more measures met (95% CI, 0.03 to 0.10) while participation in all 3 programs was associated with 0.05 more measures met (95% CI, 0.02 to 0.09). Stand-alone PCMH and stand-alone MU participation were also associated with improved performance. Joint participation in MU and MSSP was associated with $33.89 lower spending (95% CI, -$65.79 to -$1.99) as was stand-alone MSSP participation (-$37.04; 95% CI, -$65.73 to -$8.35). Conclusions and Relevance: This longitudinal observational study found that participation by PCOs in single or multiple reform programs was associated with better performance for only a subset of health services outcomes. More consistent and larger synergies may be realized with improved alignment of program requirements and goals.

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.

Dispersion in the hospital network of shared patients is associated with less efficient care.

BACKGROUND: There is growing recognition that health care providers are embedded in networks formed by the movement of patients between providers. However, the structure of such networks and its impact on health care are poorly understood. PURPOSE: We examined the level of dispersion of patient-sharing networks across U.S. hospitals and its association with three measures of care delivered by hospitals that were likely to relate to coordination. METHODOLOGY/APPROACH: We used data derived from 2016 Medicare Fee-for-Service claims to measure the volume of patients that hospitals treated in common. We then calculated a measure of dispersion for each hospital based on how those patients were concentrated in outside hospitals. Using this measure, we created multivariate regression models to estimate the relationship between network dispersion, Medicare spending per beneficiary, readmission rates, and emergency department (ED) throughput rates. RESULTS: In multivariate analysis, we found that hospitals with more dispersed networks (those with many low-volume patient-sharing relationships) had higher spending but not greater readmission rates or slower ED throughput. Among hospitals with fewer resources, greater dispersion related to greater readmission rates and slower ED throughput. Holding an individual hospital's dispersion constant, the level of dispersion of other hospitals in the hospital's network was also related to these outcomes. CONCLUSION: Dispersed interhospital networks pose a challenge to coordination for patients who are treated at multiple hospitals. These findings indicate that the patient-sharing network structure may be an overlooked factor that shapes how health care organizations deliver care. PRACTICE IMPLICATIONS: Hospital leaders and hospital-based clinicians should consider how the structure of relationships with other hospitals influences the coordination of patient care. Effective management of this broad network may lead to important strategic partnerships.

Longitudinal participation in delivery and payment reform programs among US Primary Care Organizations.

OBJECTIVE: To assess longitudinal primary care organization participation patterns in large-scale reform programs and identify organizational characteristics associated with multiprogram participation. DATA SOURCES: Secondary data analysis of national program participation data over an eight-year period (2009-2016). STUDY DESIGN: We conducted a retrospective, observational study by creating a unique set of data linkages (including Medicare and Medicaid Meaningful Use and Medicare Shared Savings Program Accountable Care Organization (MSSP ACO) participation from CMS, Patient-Centered Medical Home (PCMH) participation from the National Committee for Quality Assurance, and organizational characteristics) to measure longitudinal participation and identify what types of organizations participate in one or more of these reform programs. We used multivariate models to identify organizational characteristics that differentiate those that participate in none, one, or two-to-three programs. DATA EXTRACTION METHODS: We used Medicare claims to identify organizations that delivered primary care services (n = 56 ,287) and then linked organizations to program participation data and characteristics. PRINCIPAL FINDINGS: No program achieved more than 50% participation across the 56,287 organizations in a given year, and participation levels flattened or decreased in later years. 36% of organizations did not participate in any program over the eight-year study period; 50% participated in one; 13% in two; and 1% in all three. 14.31% of organizations participated in five or more years of Meaningful Use while 3.84% of organizations participated in five years of the MSSP ACO Program and 0.64% participated in at least five years of PCMH. Larger organizations, those with younger providers, those with more primary care providers, and those with larger Medicare patient panels were more likely to participate in more programs. CONCLUSIONS AND RELEVANCE: Primary care transformation via use of voluntary programs, each with their own participation requirements and approach to incentives, has failed to broadly engage primary care organizations. Those that have chosen to participate in multiple programs are likely those already providing high-quality care.

Documentation and review of social determinants of health data in the EHR: measures and associated insights.

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.