Journal of General Internal Medicine

Due to limitations in data collected through electronic health records, the social risk factors (SRF) that predate severe illness and restrict access to critical care services are poorly understood. This study explored the feasibility and utility of directly eliciting SRF in the ICU by implementing a screening program. 566 critically ill patients at the medical ICU of Robert Wood Johnson University Hospital from July 1, 2019, to September 31, 2021, were screened for seven SRF. We compared characteristics between those with and without each SRF through Chi-squared tests and Wilcoxon Rank Sum tests. Overall, 39.58% of critically ill patients reported at least one SRF. Age, socioeconomic status, insurance type, and severity score differed significantly depending on the SRF. Most notably, the prevalence of SRF, overall and individually, changed after March 2020 which represented the onset of the COVID-19 pandemic. Our findings indicate that SRF can induce low-risk severe illnesses and restrict access to critical care services.

BackgroundIn the US, over 200 lives are lost from opioid overdoses each day. Accurate and prompt diagnosis of opioid use disorders (OUD) may contribute substantially to prevention of overdose deaths. However, OUD research is limited, the specificity and sensitivity of OUD ICD codes are unknown, and the ICD codes are known to underestimate OUD prevalence. We developed and validated algorithms to identify OUD from EHR data and examine validity of ICD-based definitions for OUD. MethodsThrough multiple iterations, we developed EHR-based algorithms to identify OUD. These algorithms and ICD-based OUD definition were validated against a total of 169 independent gold standard EHR chart reviews conducted by an expert adjudication panel of eight pain and addiction medicine clinical experts across four large healthcare systems. The experts relied on clinical judgement and current Diagnostic and Statistical Manual of Mental Disorders-5 criteria for making OUD diagnoses. ResultsOf the 169 EHR charts, 81 (48%) were reviewed by more than one expert and exhibited 85% agreement between the reviewing experts. The OUD ICD codes alone had 10% sensitivity and 99% specificity, underscoring the strong potential for OUD underestimation in studies depending on ICD codes alone. In comparison, after four iterations, the algorithms identified OUD with a 23% sensitivity and 98% specificity. Conclusions and RelevanceThis is the first study to evaluate the validity of OUD ICD codes and develop validated EHR-based algorithms to address OUD underestimation. This work has the potential to inform future research on early intervention and prevention of OUD.

Importance Missed outpatient appointments, including no-shows and cancellations, may disrupt continuity of care and be associated with worse outcomes, but long-term system-wide patterns and clinical implications are not well characterized. Objective To characterize variation in missed appointment rates in the Veterans Health Administration (VHA) over time and by geographic location, visit modality, and preexisting conditions, and to evaluate associations between missed appointment rates and adverse outcomes among veterans with posttraumatic stress disorder (PTSD) or traumatic brain injury (TBI). Design Cohort study using VHA Corporate Data Warehouse outpatient appointment data from January 1, 2000, through December 31, 2024. Setting National integrated health care system of the VHA. Participants System analysis includes all scheduled outpatient appointments with a valid status, and outcome analysis includes veterans with PTSD (n = 1 429 890) or TBI (n = 554 553), diagnosed before 2023. Exposures For system -level analyses, missed appointment rates were calculated. In outcome analyses, 2023 missed appointment rates were categorized into tertiles within the cohort and appointment type. Main Outcomes and Measures One year risks of all-cause hospitalization, all-cause mortality, and hospitalization or death beginning January 1, 2024. Results Among 2,162,520,880 outpatient appointments from 2000 to 2024, 6.5% were no-shows and 25.4% were canceled. Across facilities, no-show rates ranged from 3.5% to 14.1%, patient-initiated cancellation rates from 9.7% to 26.0%, and clinic-initiated cancellation rates from 8.5% to 17.9%. In 2023, veterans with amputation, Parkinson disease, PTSD, or TBI had higher missed appointment rates than veterans without these conditions. Among veterans with PTSD, the highest no-show tertile, compared with none, was associated with higher mortality (HR, 1.91; 95% CI, 1.84-1.98) and hospitalization or death (HR, 1.07; 95% CI, 1.05-1.08). Among veterans with TBI, the highest no-show tertile was associated with hospitalization or death (HR, 1.65; 95% CI, 1.61-1.69). Conclusions and Relevance Missed outpatient appointments were common in the VHA and varied substantially across facilities and over time. Among veterans with PTSD or TBI, higher missed appointment rates, particularly no-shows, were associated with increased risks of hospitalization and mortality, suggesting that these patterns may help identify high-risk veterans for targeted outreach.

ObjectiveTo identify the prevalence and predictors of burnout among academic family medicine faculty. DesignA comprehensive survey of academic family medicine faculty on burnout, perceptions of work life, and practice in 2011. SettingA large, distributed Department of Family and Community Medicine at the University of Toronto. ParticipantsAll 1029 faculty members were invited to participate. Main outcome measuresMaslach Burnout Inventory three subscales (emotional exhaustion, depersonalization, personal accomplishment). ResultsThe survey response rate was 66.8% (687/1029). The prevalence of high emotional exhaustion scores was 27.0% and high depersonalization was 9.2%, whereas the prevalence of high personal accomplishment scores was 99.4%. Bivariate analyses identified 27 variables associated with emotional exhaustion and 18 variables associated with depersonalization, including: ratings of the practice setting; leadership and mentorship experiences; job satisfaction; health status; and demographic variables. Multivariate analyses found four predictors of emotional exhaustion: lower ratings of job satisfaction, poorer ratings of workplace quality, working [≥]50 hrs/week, and poorer ratings of health status. Predictors of depersonalization included lower ratings of job satisfaction, [≤]5 years in practice, lower ratings of health status, and poor ratings of mentorship received. ConclusionsThis study describes the prevalence and predictors of burnout among physicians prior to the COVID-19 pandemic. Predictors that are potentially modifiable at local practice and systems levels include job satisfaction, workplace quality, hours worked, and mentorship received. New family physicians ([≤]5 years in practice) were at increased risk of depersonalization; strategies specific to this group may limit burnout and address the healthcare workforce crisis. Periodic studies are recommended to identify the impact of strategies implemented, emergent predictors, trends, and mitigating factors associated with burnout. The current crisis in family medicine indicates an urgent need to look back and plan forward.

Opioid dependence is a national crisis, with 30 million patients annually at risk of becoming persistent opioid users after receiving opioids for post-surgical pain management. Translational Pain Services (TPS) demonstrate effectiveness for behavioral health improvements but its effectiveness in preventing persistent opioid use is less established, especially amongst opioid exposed patients. Prohibitive costs and accessibility challenges have hindered TPS program adoption. To address these limitations, we designed and implemented a remote telehealth TPS protocol focusing on preventing continued opioid use while improving behavioral health. Licensed therapists trained in the opioid-tapering CBT protocol delivered sessions reimbursed through standard payer reimbursement. Our prospective study evaluated the protocols effectiveness on preventing persistent opioid use and behavioral health outcomes amongst both opioid naive and exposed patients. In an opioid-naive patient cohort (n=67), 100% completely tapered off opioids, while in an opioid-exposed cohort (n =19) 52% completely tapered off opioids, demonstrating promising results. In both cohorts, we observed significant improvements in behavioral health scores, including pain. This opioid-tapering digital TPS is effective, adoptable, and incurs no out-of-pocket cost for healthcare systems. We provide the opioid-tapering CBT protocol in the supplement to facilitate adoption. Trial Registration Impact of Daily, Digital and Behavioral Tele-health Tapering Program for Perioperative Surgical Patients Exposed to Opioids and Benzodiazepines registered at clinicaltrials.gov, NCT04787692. https://clinicaltrials.gov/ct2/show/NCT04787692?term=NCT04787692&draw=2&rank=1

ObjectivesTo characterize emergency department (ED) clinician engagement with electronic health record (EHR)-based social drivers of health (SDOH) data; test whether engagement differs in encounters with opioid use disorder (OUD); and, among OUD encounters, assess whether engagement is associated with medications for OUD (MOUD) treatment. Materials and MethodsWe conducted a cross-sectional study of adult ED encounters (January 2023-October 2024). OUD encounters, identified with a structured phenotype, were matched (1:2) to non-OUD encounters. Audit logs captured clinician engagement with structured SDOH questions ("SDOH Wheel"), ICD-10 Z codes in the Problem List, Social History free text, and social work notes. Engagement was any SDOH documentation or review of preexisting SDOH data during the encounter. Logistic regression estimated associations. ResultsAmong 17,103 encounters (5,701 OUD; 11,402 non-OUD), clinician SDOH documentation was rare (<1%). Clinicians most often reviewed Z codes (610/620; 98.4%), followed by the SDOH Wheel (1,103/3,953; 27.9%), social work notes (1,711/10,670; 16.0%), and Social History free text (232/6,942; 3.3%). Engagement occurred in 19.5% of encounters and was higher with OUD (26.6% vs 16.0%; adjusted odds ratio [aOR] 1.91, 95% CI 1.77-2.07). Among OUD encounters, engagement showed no clear association with MOUD (aOR 1.11, 95% CI 0.84-1.47), yet racial and ethnic disparities persisted. DiscussionED clinicians infrequently document but do review structured, accessible SDOH data. Engagement is higher in OUD encounters yet shows no definitive link with MOUD, while disparities persist. Interface designs that surface SDOH and targeted EHR decision support warrant evaluation to inform equitable, time-sensitive ED care.

PurposeHigh clinical workload is associated with worse patient and hospital outcomes and is a well-established driver of clinician burnout. Trainees may be particularly exposed, shouldering both clinical and educational responsibilities. Evidence-based work design offers a data-driven approach to healthcare work but relies on robust workload measurements. Trainee workload remains poorly characterized, as commonly used metrics (e.g., duty hours, patient census) overlook cognitive and contextual dimensions. This pilot evaluated the feasibility of combining survey-based and electronic health record (EHR) data to characterize internal medicine (IM) trainees workload. MethodsA pilot study was conducted including IM and Medicine-Pediatrics residents (postgraduate years 1-4) between March 31 and June 22, 2025. Participants completed daily surveys during a seven-day inpatient schedule assessing workload and work experience domains, including environment, professional fulfillment, psychological safety, autonomy, and rounding experience, using validated instruments where available. Concurrently, EHR data captured chart review, documentation, orders, and secure messaging activity. Associations between survey and EHR data were assessed. ResultsAmong 37 eligible residents, 28 (76%) participated in the pilot capturing 166 shifts. Trainees spent 4.4 {+/-} 1.6 (mean {+/-} SD) minutes completing daily surveys and 8.6 {+/-} 2.3 minutes completing the final survey. Trainees reported working 11.6 {+/-} 1.0 hours/day and a median census of 9.0 (IQR 6.0-11.0). NASA-TLX score was 50.8 {+/-} 12.6. Positive shift ratings were associated with lower NASA-TLX scores and perceived rounding length. First-to-last EHR login duration was 15 {+/-} 2 hours/day, and EHR data showed 204 {+/-} 46 active minutes/day. Login duration correlated with self-reported hours (r=0.43, p<0.0001), and notes signed correlated with self-reported team (r=0.19, p=0.013) and personal census (r=0.34, p<0.0001). ConclusionsIntegrating survey-based and EHR-derived workload measures provides multidimensional insight into trainee work. This novel approach supports scalable measurement and evidence-based work design interventions to improve trainee well-being, education, and clinical efficiency.

ImportanceOutpatient physicians need guidance to support their clinical decisions regarding management of patients with COVID-19, specifically whether to hospitalize a patient or if managed as an outpatient, how closely to follow them. ObjectiveTo develop and prospectively validate a clinical prediction rule to predict the likelihood of hospitalization for outpatients with COVID-19 that does not require laboratory testing or imaging, including during the current Omicron wave. DesignDerivation and temporal validation of a clinical prediction rule, and prospective validation of two externally derived clinical prediction rules. SettingPrimary and urgent care clinics in a Pennsylvania health system. ParticipantsPatients 12 years and older presenting to outpatient clinics who had a positive polymerase chain reaction test for COVID-19. Main outcomes and measuresClassification accuracy (percentage in each risk group hospitalized) and area under the receiver operating characteristic curve (AUC). ResultsOverall, 4.0% of outpatients in the early derivation cohort (5843 patients presenting before 3/1/21), 4.2% in the late validation cohort (3806 patients presenting 3/1/21 to 9/30/21), and 1.9% in an Omicron cohort were ultimately hospitalized. We developed and temporally validated four simple risk scores. The base score included age, dyspnea, and the presence of a comorbidity, with the other scores adding fever, respiratory rate and/or oxygen saturation. All had very good overall accuracy (AUC 0.85-0.87) and classified at least half of patients into a low risk with a < 1% likelihood of hospitalization. Hospitalization rates in the Omicron cohort were 0.22%, 1.3% and 8.7% for the base score. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization than our novel risk scores. Conclusions and relevanceA simple risk score applicable to outpatient and telehealth settings can classify over half of COVID-19 outpatients into a very low risk group with a 0.22% hospitalization risk in the Omicron cohort. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/. Key pointsO_ST_ABSQuestionC_ST_ABSIs it possible to predict the eventual likelihood of hospitalization for outpatients with COVID-19 using simple non-laboratory based risk scores? FindingsWe created and temporally validated in the same population 4 risk scores with 3 to 5 predictors that do not require laboratory testing. Groups with low (0.34% to 0.89%), moderate (4.0% to 6.2%), and high-risk (19.2% to 25.2%) of hospitalization were identified. The risk scores were also accurate in an Omicron dominant cohort with hospitalization rates of 0.22% to 0.43% in the low-risk groups, 1.3% to 1.7% in the moderate risk groups, and 8.7% to 15.3% in the high risk groups. MeaningSimple risk scores can help support decisions about hospitalization in the outpatient setting.

Background and ObjectivesSkilled nursing facility (SNF) hospitalization rates vary substantially across facilities serving comparable patient populations, yet the organizational factors underlying high performance remain poorly characterized. This study examines whether faith or mission-driven organizational identity is associated with lower-than-expected hospitalization rates in a national sample of Medicare-certified SNFs. DesignCross-sectional analysis of a stratified random sample of 618 Medicare-certified SNFs, drawn from a national cohort of 13,419 facilities with claims-based quality data. Facilities were classified by organizational identity (faith-affiliated, purpose-driven, or secular) using publicly available records. Performance was measured using CMS claims-based hospitalization and emergency department transfer rates adjusted for expected rates given patient case mix. Setting and ParticipantsMedicare-certified skilled nursing facilities in the United States, February 2026 CMS release. MethodsWe computed a composite performance gap as the mean of four z-scored observed-minus-expected measures (short-stay and long-stay hospitalization and ED transfer rates). We tested the association between faith affiliation and performance using Fishers exact test, logistic regression, OLS regression, propensity score matching, and causal mediation analysis. ResultsFaith-affiliated or purpose-driven facilities constituted 14.7% of significant overperformers (95% CI: 7.0-23.5%) and 0% of significant underperformers (95% CI: 0.0-4.4%), a monotonic gradient confirmed across all five performance zones. After propensity score matching on facility size, ownership type, and urbanicity (n=49 matched pairs), faith-affiliated facilities achieved 18.2% short-stay rehospitalization compared to 21.7% for matched secular facilities (3.5 percentage points fewer, p=0.019), and 1.30 long-stay hospitalizations per 1,000 resident-days compared to 1.71 (0.41 fewer per 1,000 days, p=0.019). Faith affiliation was associated with 61% more RN staffing hours per resident per day (0.96 vs. 0.60 hours, p<0.001), and formal mediation analysis confirmed that RN staffing hours substantially mediated the relationship between faith affiliation and hospitalization performance. Conclusions and ImplicationsFaith and mission-driven organizational identity is associated with superior hospitalization performance in a national SNF sample, mediated by elevated RN staffing intensity. These findings suggest that organizational culture and values are modifiable upstream determinants of nursing home quality, with implications for quality improvement, workforce policy, and value-based payment design.

IntroductionPrescribing naloxone is recommended by the Centers for Disease Control and Prevention to reduce the risk of death from an opioid overdose. Naloxone is rarely prescribed, even when indicated; improving our understanding of how primary care providers (PCP) perceive their role in naloxone prescribing is essential to increase opioid medication safety. The objectives of this study were to: (1) describe how PCPs perceive their role in prescribing naloxone for patients who are at high risk of an overdose and (2) describe PCP-reported barriers and facilitators of naloxone prescribing. MethodsCurrently practicing providers completed semi-structured interviews, based on Theory of Planned Behavior, to understand their attitudes toward naloxone, their perceived role in naloxone prescribing, and facilitators/barriers to prescribing naloxone. ResultsEleven interviews were conducted with physicians (n=2), physician assistants (n=8), and a nurse practitioner (n=1). Providers held generally positive attitudes toward naloxone as a rescue medication. Negative attitudes toward naloxone include the perception of facilitating risky opioid use. Providers suggested that whomever prescribes the opioid pain medication should be primarily responsible for prescribing naloxone. Providers noted that stigma may prevent them from discussing naloxone during clinic visits. Increasing visit time and receiving support/education from organizational and professional society leadership were identified as important facilitators of naloxone prescribing. ConclusionsWhile providers were aware of what naloxone was used for, there was reticence in discussing this medication with patients. Providers reported that whomever prescribes a pain medication should be primarily responsible for ensuring medication safety. If primary care organizations would like to improve opioid medication safety, ensuring that providers feel supported and receive needed education are essential.

Background: People with disabilities (PWD) represent over one-quarter of the US population and disproportionately experience chronic pain, yet limited research explores disparities they face in opioid use disorder (OUD) treatment. Objective: To examine disparities across disability status regarding opioid use disorder (OUD)-related outcomes and understand how chronic pain interacts with these associations. Methods: We completed a cross-sectional, secondary analysis of data from the All of Us Research Program, including 370,722 adults with electronic health record data available between January 2021-September 2023. We identified prevalence of disability, chronic pain, OUD, receipt of medications for OUD (MOUD), and OUD remission using diagnostic codes. We performed interaction analyses between chronic pain, disability subtype, and MOUD receipt in affecting OUD outcomes. Results: OUD was more common among individuals with physical (aOR: 2.74, 95% CI: 2.54-2.95), cognitive (2.19, 1.94-2.45), and multiple disabilities (2.43, 2.19-2.68), compared to those without disabilities. Among patients with OUD, those with physical disabilities were less likely to receive MOUD (0.81, 0.69-0.94). Compared to those without disabilities, chronic pain was associated with higher probabilities of OUD diagnosis and lower probabilities of MOUD and OUD remission across all subjects. These relationships were stronger for OUD diagnosis in cognitive disabilities, MOUD in multiple disabilities, and OUD remission in physical disabilities. Conclusions: Disability and chronic pain jointly shape disparities in OUD treatment and underscore the urgent need for care models that integrate OUD treatment with pain management and address the unique access challenges faced by people with disabilities.

BackgroundTransgender and non-binary communities continue to be underserved in healthcare. This study seeks to better understand the barriers and difficulties faced by transgender and non-binary patients in accessing primary care and hormone therapy in Utah. MethodsAn online survey was developed for transgender and non-binary identifying adults and was advertised via social media and the University of Utah Hospitals website. ResultsThere were 123 respondents from Utah including 39 trans women, 49 trans men, and 35 non-binary individuals. The age ranged from 18-67 (average 30 years), and 93% were Caucasian. The majority (84%) were insured, yet 67% of respondents reported difficulty accessing primary care. Fear of discrimination and being unable to find trans-friendly providers were reported as the two largest barriers. Non-binary respondents reported fear of discrimination as a barrier to primary care at the highest percentage (92%). Nearly 3 in 4 respondents who have hormone therapy reported difficulty paying for it. One in four trans women reported accessing hormones online or from a friend. ConclusionUtah is currently drastically underequipped to provide for the healthcare needs of transgender and non-binary communities. There needs to be an increase in trans-friendly primary care providers to curb discrimination. More resources and efforts must go into training primary care providers with necessary knowledge to properly serve transgender and non-binary patients. Finally, clear anti-discrimination laws are needed for insurance companies to reduce the financial barrier to transgender health services in Utah.

PurposePrior studies have identified risk factors which prognosticate severity of SARS-CoV-2 illness among hospitalized patients. Since the majority of patients first present to ambulatory care sites, there is a need to identify early predictors of disease progression in this population. MethodsThis retrospective cohort study investigated the impact of underlying comorbid conditions on SARS-CoV-2 infection severity in the ambulatory setting. All patients who presented to a single federally qualified health center (FQHC) between March-May 2020 with a positive SARS-CoV-2 test were reviewed for inclusion. Patient demographics, symptomology, prior medical history, and outcomes were collected. Results301 patients were included, with nearly equal numbers of patients with (n=151) and without (n=150) underlying comorbidities. Overall, 269 patients (89%) had a mild outcome and 32 patients (11%) had a severe outcome. Advanced age (OR: 9.4 [95% CI: 3.4-27.4], p < 0.001) and male gender (OR: 3.2 [95% CI: 1.2-9.8], p = 0.02) were significant predictors of severe outcomes. Additionally, every obesity category (1: BMI = 30.0-34.9; 2: BMI = 35-39.9; 3: BMI = 40.0+) was associated with more severe outcomes compared to non-obese (OR: 3.5, p = 0.05; OR: 5.2, p = 0.03; OR: 13.9, p = 0.01). Compared to an HbA1C < 6, an HbA1C of 7.1-8.0 showed a clinically significant association. ConclusionSARS-CoV-2 severity can be prognosticated in the ambulatory population by the presence and severity of pre-existing comorbidities. Early identification and risk stratification of these comorbidities will allow clinicians to develop plans for closer monitoring to prevent severe illness.

ImportanceDiscriminatory language in clinical documentation impacts patient care and reinforces systemic biases. Scalable tools to detect and mitigate this are needed. ObjectiveDetermine utility of a frontier large language model (GPT-4) in identifying and categorizing biased language and evaluate its suggestions for debiasing. DesignCross-sectional study analyzing emergency department (ED) notes from the Mount Sinai Health System (MSHS) and discharge notes from MIMIC-IV. SettingMSHS, a large urban healthcare system, and MIMIC-IV, a public dataset. ParticipantsWe randomly selected 50,000 ED medical and nursing notes from 230,967 MSHS 2023 adult ED visiting patients, and 500 randomly selected discharge notes from 145,915 patients in MIMIC-IV database. One note was selected for each unique patient. Main Outcomes and MeasuresPrimary measure was accuracy of detection and categorization (discrediting, stigmatizing/labeling, judgmental, and stereotyping) of bias compared to human review. Secondary measures were proportion of patients with any bias, differences in the prevalence of bias across demographic and socioeconomic subgroups, and provider ratings of effectiveness of GPT-4s debiasing language. ResultsBias was detected in 6.5% of MSHS and 7.4% of MIMIC-IV notes. Compared to manual review, GPT-4 had sensitivity of 95%, specificity of 86%, positive predictive value of 84% and negative predictive value of 96% for bias detection. Stigmatizing/labeling (3.4%), judgmental (3.2%), and discrediting (4.0%) biases were most prevalent. There was higher bias in Black patients (8.3%), transgender individuals (15.7% for trans-female, 16.7% for trans-male), and undomiciled individuals (27%). Patients with non-commercial insurance, particularly Medicaid, also had higher bias (8.9%). Higher bias was also seen in health-related characteristics like frequent healthcare utilization (21% for >100 visits) and substance use disorders (32.2%). Physician-authored notes showed higher bias than nursing notes (9.4% vs. 4.2%, p < 0.001). GPT-4s suggested revisions were rated highly effective by physicians, with an average improvement score of 9.6/10 in reducing bias. Conclusions and RelevanceA frontier LLM effectively identified biased language, without further training, showing utility as a scalable fairness tool. High bias prevalence linked to certain patient characteristics underscores the need for targeted interventions. Integrating AI to facilitate unbiased documentation could significantly impact clinical practice and health outcomes.

BackgroundAccess to medications for opioid use disorder (MOUD) in the U.S. is highly restricted. In March 2020, to reduce transmission of COVID-19, SAMHSA issued emergency regulations allowing up to two weeks of take-home doses for most patients. ObjectivesWe evaluated the benefits and unintended consequences of these new regulations expanding take-home eligibility to inform MOUD policy post-pandemic MethodsWe conducted a mixed-methods evaluation of an opioid treatment program in San Francisco caring for a diverse, low-income urban population. We assessed clinic-level intake, retention, and take-home prescribing; individual-level acute care utilization and mortality; and patient/provider perceptions of benefits, harms and challenges of the new regulations. ResultsClinic volume, intake and retention were largely unchanged after implementation of the new regulations, though the average monthly proportion of individuals receiving take-homes significantly increased from 31% to 47% (p<0.001). Among 506 established patients ([&ge;]90 days of care), the 10-month mortality was 2.7% among those who never received take-homes versus 3.2% among those newly started (p=0.79) and 0.8% among those with increases in take-homes (p=0.24). Individuals who never received take-homes had higher rates of emergency department visits (47.0%) and hospitalizations (19.7%) versus those with new starts (ED visits 29.2%, p<0.001; hospitalizations 14.3%, p=0.19) or increases in take-homes (ED visits 17.5%, p<0.001; hospitalizations 10.0%, p=0.02). Both patients and providers reported increased treatment flexibility, leading to increased engagement and stabilization. ConclusionsGiven the benefit and lack of appreciable harms, policymakers should consider extending expanded MOUD take-home eligibility after COVID-19, with careful monitoring for unintended outcomes.

BackgroundAccess to specialty care, including cardiology, in the Veterans Health Administration (VHA) varies widely across geographic regions. VHAs clinical resource hub (CRH) model of care offers mostly-virtual specialty care to individuals in low access regions and has recently been implemented in cardiology. How implementation of this predominantly virtual cardiology program affects the reach of cardiology specialty care in VHA is not known. This study describes the association between patient characteristics and use of CRH cardiology care in VHAs Sierra Pacific region (Northern California, Nevada, and the Pacific Islands). MethodsWe compared patients who used CRH cardiology services between 7/15/2021 and 3/31/2023 to non-CRH Sierra Pacific cardiology patients, then used multivariate logistic regression to estimate the association between patient-level factors and odds of being a CRH user. ResultsThere were 804 CRH users over the study period with 1,961 CRH encounters, and 19,583 non-CRH users with 83,489 encounters. Among CRH users, 8% were women and 41% were [&ge;]75 years, compared to 5% and 49% respectively among non-CRH users. Similar proportions in both groups were rural (26% for both CRH and non-CRH), highly-disabled (48% CRH, 47% non-CRH), and low-income (21% CRH, 20% non-CRH). In multivariate logistic models, adjusted odds of using CRH were higher for women (adjusted odds ratio [AOR] 1.70 [95% CI 1.46-1.98]) and lower for older Veterans (AOR 0.33 for [&ge;]75 [95% CI 0.23-0.48]). Highly rural Veterans also had higher adjusted odds of using CRH (AOR 1.88 [95% CI 1.30-2.69]). ConclusionsThe Sierra Pacific CRH cardiology program served a disproportionately high number of women and highly rural Veterans and similar proportions of highly-disabled and low-income Veterans as conventional VA care in its first two years of operation. This predominately-virtual model of cardiology care may be an effective strategy for overcoming access barriers for certain individuals, though targeted efforts may be required to reach older Veterans.

BackgroundPrimary care (PC) attachment improves healthcare access and prevention and management of chronic conditions. Yet, growing proportions of Canadians are unattached, signing-up on provincial waitlists. Understanding variations in healthcare utilization during COVID-19, and among potentially vulnerable unattached patients, is needed. This study compares emergency department (ED) utilization and hospitalization among those on and off a provincial PC waitlist, during the first two waves of COVID-19. MethodsWaitlist and administrative health data were linked to describe persons ever/never on the waitlist between January 1, 2017, and December 24, 2020. ED utilization and ambulatory care sensitive conditions (ACSC) hospitalization rates by current waitlist status were quantified from physician claims and hospitalization data. Relative differences during COVID-19 first and second waves were compared with the previous year. ResultsDuring the study period, 100,867 Nova Scotians (10.1%) were on the waitlist. Those on the waitlist had higher ED utilization and ACSC hospitalizations. ED utilization was higher overall for individuals [&ge;]65 years and females; lowest during first two COVID-19 waves; and differed more by waitlist status for those <65 years. ED contacts and ACSC hospitalizations decreased during COVID-19 relative to the previous year, and for ED utilization this difference was more pronounced for those on the waitlist. InterpretationNova Scotians seeking PC attachment utilize hospital-based services more frequently than those not on the waitlist. Both groups had lower utilization during the COVID-19 pandemic than the year before. The degree to which forgone services produces downstream health burden remains to be seen.

Purpose: Nonresponse to routinely collected patient-reported outcome measures (PROMs) threatens the representativeness of aggregated data. We characterized patient-, provider-, and clinic-level factors associated with PROMIS Global-10 nonresponse in routine radiation oncology care. Methods: In this retrospective cohort study, all adults seen at five Mass General Brigham radiation oncology clinics over one year were included. The primary outcome was patient-level nonresponse, defined as never completing the portal-administered Global-10 versus completing it at least once. Using iterative mixed-effects logistic regression, we modeled patient-, provider-, and clinic-level factors. Results: Among 12,214 patients, 71 providers, and five clinics, patient- and appointment-level response rates were 35.4% and 10.9%, with patient-level response ranging nearly fivefold across clinics (12.8% to 66.2%). In Model 1, male sex, lower education, not working, and recent surgery had higher odds of nonresponse, and longer time since diagnosis lower odds. After provider- and clinic-level factors were added, patient sex, education, and employment became nonsignificant, whereas recent surgery (adjusted odds ratio [aOR] 1.97) and longer time since diagnosis (aOR 0.46 for >12 months) persisted. A provider's historical collection rate was protective but attenuated at the clinic level. There, a later program launch (aOR 0.29) and higher historical collection rate (aOR 0.79) correlated with lower nonresponse, whereas academic versus community setting did not. Conclusions: Nonresponse to routinely collected PROMs is a multilevel phenomenon driven substantially by clinic-level implementation factors, not patient characteristics alone. Because response rate is only a proxy for representativeness, PROMs programs and PRO-based performance measures should prioritize representative collection over volume.

BackgroundCurrent literature on direct primary care (DPC) is largely composed of opinion-based arguments that the model may exacerbate healthcare inequity. Objective data are needed to assess how DPC practices are distributed and whether they contribute to disparities in access to care. Methods1. Visited DPC Frontier website (https://mapper.dpcfrontier.com). 2. Extracted clinic information from the website using a custom web-socket script (ws.py) that requested each entry using its practice-key (scraped from raw html). 3. Duplicate entries removed by R script (No-Dups.R) unique.data.frame() (unique rows) (Data_Columns = "Postal","City","Region","Name","Lat","Lng","Website") 3. Clinic data was then merged (by zipcode) with the metadata from the zipcodeR database and the Rural-Urban Commuting Area Codes (RUCA) coding table from the US Department of Agriculture to produce the Mergd-with-RUCA.csv spreadsheet (a comprehensive geospatial database of DPC practice locations and their associated demographic and socioeconomic characteristics). 4. Data Analysis: Data-Analysis-Recommendations.pdf Sections 2,4, and 5 (authored by Sr. Lugo Capera) ResultsDPC practices were more common in urban and suburban zip codes, positively correlated with higher total housing units, lower occupancy rates, and lower median household incomes. Family medicine was more prevalent in lower-income zip codes, while specialties such as dermatology and cardiology clustered in middle- and higher-income areas. DiscussionWhile DPC practices appear to favor more commercial or suburban settings, their presence in lower-income zip codes suggests potential to serve populations with limited access to traditional care. However, specialty care appears less equitably distributed. ConclusionThe GINI coefficient of 0.22 for DPC practice distribution indicates modest inequality, with most zip codes hosting one or no DPC practices. While geographic access to primary DPC appears relatively even, disparities in specialty DPC and potential quality differences merit further investigation.

ImportanceThe clinical workforce composition in U.S. hospitals is shifting, with advanced practice practitioners (APPs) - nurse practitioners and physician assistants -assuming larger roles in inpatient care. How this mix relates to patient mortality is unclear. ObjectiveTo investigate the association between hospital-level physician proportion and 30-day risk standardized mortality rate (RSMRs) for six common inpatient conditions. DesignCross-sectional national study linking 2022 CMS Physician & Other Practitioners, Facility Affiliation, Hospital Compare Complications and Deaths, and American Hospital Association Annual Survey data. Analyses were completed October 17, 2025. SettingNation-wide U.S. hospitals treating traditional Medicare beneficiaries in 2022. ParticipantsMedicare beneficiaries hospitalized for acute myocardial infarction, Chronic Obstructive Pulmonary Disease (COPD), Coronary Artery Bypass Graft (CABG) Surgery, heart failure, pneumonia, or stroke. Exposure(s) (for observational studies)Hospital-level physician proportion, defined as the number of physicians divided by the sum of affiliated physicians and APPs. Main Outcome(s) and Measure(s)Hospital-level condition-specific 30-day RSMRs - case-mix adjusted outcome measures presented as proportions. ResultsAmong 3,487 hospitals (mean physician proportion 79.7% [SD, 9.4%]), mean physician proportions across quartiles ranged from 67.8% (Q1; n=872; range, 25.0-73.5%) to 91.6% (Q4; n=871; range, 86.7-99.6%). Mean hospital-level RSMRs (%, 95% CI) were 12.63 (12.57-12.69) for acute myocardial infarction, 2.93 (2.88-2.99) for CABG surgery, 9.50 (9.44-9.56) for COPD, 12.03 (11.96-12.11) for heart failure, 18.12 (18.02-18.21) for pneumonia, and 13.74 (13.66-13.82) for stroke. Hospitals in the highest physician proportion quartile (Q4) had lower RSMRs than hospitals [p value, 95% CI] in the lowest quartile (Q1) for all conditions - acute myocardial infarction (12.54 vs. 12.86 [p<0.001, 0.16-0.48]), CABG (2.92 vs. 3.05 [p=0.106, -0.29-0.30]), COPD (9.11 vs. 9.83 [p<0.001, 0.56-0.88]), heart failure (11.24 vs. 12.73 [p<0.001, 1.29-1.70]), pneumonia (17.51 vs. 18.64 [p<0.001, 0.87-1.39]), and stroke (13.41 vs. 14.31 [p<0.001, 0.67-1.12]). Generalized additive models identified significant non-linear associations between the physician proportion and the RSMRs for COPD, acute myocardial infarction, heart failure, pneumonia, and stroke, respectively explaining 4.77-11.82% of the deviance. Conclusions and RelevanceHigher relative physician staffing was modestly but consistently associated with lower hospital-level mortality across common and high-burden medical conditions. Workforce composition may be a key structural determinant of hospital quality and warrants consideration in workforce and quality improvement strategies.