Importance:Pediatric acute care in the US is highly regionalized. Existing geographic frameworks to measure acute care patterns were developed based on adults and do not reflect pediatric use patterns. Objective:To develop a national US atlas of pediatric acute care regions. Design, Setting, and Participants:This was a cross-sectional study of Medicaid data from January 2021 to December 2022, with analysis from April 2024 to June 2025, of US acute care hospitals. Emergency and inpatient encounters among youth younger than 16 years enrolled in Medicaid or the Children's Health Insurance Program, comprising more than half of US youths, were included. Main Outcome and Measures:The primary outcome was modularity, a measure of how well an atlas fits pediatric use patterns. Using network analysis, pediatric emergency service areas (PESAs) were derived capturing home-to-hospital care seeking and pediatric emergency referral regions (PERRs) capturing interhospital referrals. Modularity, proportion of encounters staying within region, size, and hospital counts between atlases were compared. Local-level comparisons included PESAs, Dartmouth Atlas hospital service areas (HSAs), and pediatric HSAs (PHSAs) derived using Dartmouth methods. Referral-level comparisons compared PERRs with Dartmouth hospital referral regions (HRRs), pediatric HRRs (PHRRs), and states. Results:Using data from 27 817 736 encounters (median [IQR] age, 5 [2-10] years; 14 547 400 [52.3%] male) from 4830 hospitals, 835 PESAs and 105 PERRs were identified. PESAs were larger and included more hospitals and youths than HSAs or PHSAs. PERRs were larger (median [IQR], 17 595 [7781-33 251] square miles) and contained more hospitals (median [IQR], 39 [23-59]) and youths (median [IQR], 597 000 [358 000-864 000]) than HRRs (medians [IQRs]: 4822 [2044-9425] square miles, 11 [6-18] hospitals, 146 000 [74 000-282 000] youths) or PHRRs (medians [IQRs]: 8845 [4209-16 608] square miles, 21 [13-35] hospitals, 348 000 [26 703-70 282] youths). States were larger (median [IQR], 50 097 [211 000-638 000] square miles) and had more hospitals (median [IQR], 74 [41-113]), and youths (median [IQR], 961 000 [366 000-1 615 000]) than PERRs. PESAs had the highest local-level modularity. PERRs had the highest referral-level modularity. Among acute care encounters, 89.9% stayed within PESA, 68.6% within HSA, and 80.1% within PHSA. Among referrals, 92.3% stayed within PERR, 73.0% within HRR, 81.9% within PHRR, and 93.4% within state. Conclusions and Relevance:The Atlas of Pediatric Acute Care is the first empirically derived, pediatric-specific map of acute care regions spanning the US. Based on actual use patterns, this atlas may serve as a foundation for pediatric research, policy, regional planning, and quality improvement.

Importance:Beneficiaries diagnosed with cancer and enrolled in Medicare Advantage (MA) may face barriers to care due to MA plan network design. Given sizeable growth in MA enrollment, it is important to evaluate how well these plans serve patients with high health care needs. Objective:To examine the association between MA network breadth and changes in coverage among beneficiaries with new cancer diagnoses. Design, Setting, and Participants:This cohort used data from the 2019 to 2020 Surveillance, Epidemiology, and End Results (SEER)-Medicare and 2019 Ideon networks to examine MA plan network inclusion of American College of Surgeons Commission on Cancer-accredited facilities and National Cancer Institute-designated cancer centers. Beneficiaries enrolled in MA who were newly diagnosed with cancer in 2019 were included. Data were analyzed from October 2024 through September 2025. Exposure:The explanatory variable of interest was network breadth for these hospitals, measured as a continuous variable. Characteristics of patients in narrow networks (those with fewer than 25% of facilities in the network geographic service area that were in network) and nonnarrow networks were compared. Main Outcomes and Measures:The main outcome was any plan switch between diagnosis and January 2020, along with the kind of switch (MA to traditional Medicare or new MA plan). Multivariable logistic regression models stratified by plan type (ie, beneficiaries in employer-sponsored and retiree MA plans, plans with premiums, and plans without premiums) were used given potential differences in plan choices for these beneficiary groups. Mean marginal effects were calculated, and coefficients were scaled by 10 percentage points. Results:Among 24 444 MA beneficiaries, 13 216 individuals had plans with narrow networks (mean [SD] age, 72.2 [7.7] years; 6465 male [48.9%]; 2503 Black [18.9%], 1963 Hispanic [14.9%], and 7563 White [57.2%]) and 11 228 individuals had plans with nonnarrow networks (mean [SD] age, 73.4 [7.7] years; 5638 male [50.2%]; 1613 Black [14.4%], 1346 Hispanic [12.0%], and 7405 White [66.0%]); those in narrow networks were younger and more likely to be Black. A 10-percentage point increase in network breadth was associated with a 4.5-percentage point (95% CI, 5.5 to 3.5 percentage points; P < .001) decrease in the probability of switching to traditional Medicare or a new MA plan for beneficiaries enrolled in a nonemployer plan that charged premiums. It was not associated with enrollment changes among those in employer or retiree plans. Conclusions and Relevance:In this study, increased network breadth was not associated with decreased Medicare plan switching for enrollees in employer plans. This finding suggests that employer subsidization of coverage may outweigh concerns about network breadth for patients newly diagnosed with cancer.

Introduction:Beneficiaries enrolled in Medicare Advantage (MA) and newly diagnosed with cancer may be incentivized to switch coverage, particularly if their MA plan restricts their access to cancer care. Beginning January 2019, the deadline to disenroll from an MA plan changed from February 14 to March 31 and, for the first time, beneficiaries could switch to a different MA plan as opposed to having to enter traditional Medicare. Methods:We used 2016-2019 Surveillance, Epidemiology, and End Results (SEER) Medicare data to conduct a difference-in-differences analysis, estimating the effect of this new policy on rates of MA plan switching 1 month and 2 months after diagnosis. Results:For beneficiaries diagnosed with cancer in March, the policy was associated with a modest increase in both overall rates of switching and in switching to a new MA plan 1 month after diagnosis. Results indicated that the policy had a modest positive effect on changes in Medicare coverage for enrollees diagnosed in January when outcomes were measured 2 months after diagnosis. Conclusion:Relaxing enrollment rules for MA enrollees may not exacerbate adverse selection into traditional Medicare, given this evidence that beneficiaries with high-cost new diagnoses rarely exercised the option to change their MA plan.

Importance:For many US children, the nearest hospital may be out of state. Medicaid coverage differs by state, affecting access across state lines. Objective:To evaluate the frequency of out-of-state acute care use for pediatric patients. Design, Setting, and Participants:This cross-sectional study analyzed acute care hospital data for emergent and inpatient encounters among children younger than 16 years enrolled in Medicaid or the Children's Health Insurance Program (CHIP) in the 2021-2022 Transformed Medicaid Statistical Information System Analytic File database. Analyses were conducted January to July 2025. Exposure:Distance from a state border. Main Outcomes and Measures:The primary outcome was out-of-state care. The percentages of encounters occurring out of state were measured by state, city, and zip code. Logistic regression was used to evaluate the association of out-of-state care use with the log distance from a patient's zip code to the border between states. Results:This analysis included 28 952 692 acute care patient encounters (median [IQR] age, 5.3 [2.0-10.8] years, 52.3% male). Out-of-state care occurred among 820 972 encounters (2.8% [95% CI, 2.8%-2.8%]). Maryland (61 468 of 389 539 [15.8% (95% CI, 15.7%-15.9%)]), Vermont (3625 of 31 101 [11.7% (95% CI, 11.3%-12.0%)]), and West Virginia (18 455 of 168 151 [11.0% (95% CI, 10.8%-11.1%)]) had the highest percentages of out-of-state care. The city from which the highest number of children accessed care out of state was Kansas City, Missouri (13 327 of 84 181 encounters [15.8% (95% CI, 15.6%-16.1%)]). Out-of-state care use was more common in rural areas (4.4% [95% CI, 4.3%-4.4%]) compared with urban areas (2.7% [95% CI, 2.7,%-2.7%]). For every 2-fold increase in distance from a state border, crossing a border for care was 34.2% (95% CI, 34.2%-34.3%) less likely. Among children within 1 mile of a state border, 10.0% (95% CI, 9.9%-10.0%) received care out of state. Conclusions and Relevance:Findings from this cross-sectional study of Medicaid and CHIP enrollees indicated that out-of-state acute care use was uncommon overall but more common near state borders. Certain states and cities had high rates of out-of-state acute care use. Changes to Medicaid reimbursement could affect patients' ability to access cross-border care.

Importance:Recent trends in drug-related overdoses among adolescents have highlighted the need for mental health and substance use disorder (SUD) treatment. However, the extent of these treatment gaps is understudied. Objective:To characterize the factors associated with the diagnosis of and treatment for mental health and SUD for adolescents. Design, Setting, and Participants:This cross-sectional study used survey-weighted descriptive statistics and χ2 tests to estimate differences in characteristics and treatment receipt and included US adolescents and young adults aged 12 to 20 years who participated in the National Survey on Drug Use and Health in 2021 and 2022. Data were analyzed from February 2024 to February 2025. Main Outcomes and Measures:Primary outcomes included the prevalence of depression and suicidality-related mental health diagnoses, SUDs, and treatment rates for both conditions. Additional measures included treatment setting, socioeconomic and demographic characteristics, and health insurance-related factors. Results:From 2021 to 2022, 13% of participants had SUD and 24% had a mental health diagnosis during the previous year (mean [SD] age, 16.0 [2.5] years; 48.4% female individuals; 6.1% Asian, 13.9% Black, 25.7% Hispanic, and 49.9% White individuals). Only 10% of participants with SUD and 51% of adolescents with mental health diagnoses received treatment for their conditions, with higher rates of treatment among adolescents with comorbid SUD and mental health diagnoses. When comparing adolescents (aged 12-17 years) and young adults (aged 18-20 years) with SUD for treatment receipt, reductions were found in any mental health treatment (63% vs 51%; P = .03) and any SUD treatment (11% vs 8%; P = .01). Moreover, these lower rates were also found in more resource-intensive treatment settings, such as inpatient mental health care (14% vs 9%; P = .02) and specialty mental health facilities (47% vs 33%; P = .003). However, adolescents with opioid use disorder were less likely to receive medication treatment (11% vs 28%; P = .02). Treatment differences were associated with socioeconomic and insurance coverage factors. Compared with adolescents, young adults with SUD experienced increased poverty rates (20% vs 26%; P = .02), uninsurance rates (5% vs 10%; P = .05), and private insurance rates (49% vs 56%; P = .02) while receiving decreased Medicaid coverage (47% vs 33%; P < .001) and government assistance (34% vs 25%; P = .001). Conclusions and Relevance:The results of this cross-sectional survey study suggest that adolescents and young adults with SUDs rarely received treatment. Adolescents are especially vulnerable to treatment gaps once reaching young adulthood, and medications for opioid use disorder are systematically underused, especially for adolescents.

Importance:An increasing number of Medicare beneficiaries with cancer report Medicare Advantage (MA) coverage, but certain features of MA (eg, utilization management) may impede access to cancer care. MA beneficiaries may desire to switch to traditional Medicare (TM), which imposes little to no utilization restrictions, but switching may be challenging because access to Medigap-providing financial protections against high cost sharing in TM-is limited by medical underwriting of beneficiaries applying after initial Medicare enrollment in most states. Objective:To examine associations of Medigap guaranteed issue protections that prohibit medical underwriting with MA disenrollment among beneficiaries newly diagnosed with cancer. Design, Setting, and Participants:This retrospective cohort study examined Medicare beneficiaries 69 years and older who were newly diagnosed with cancer from 2014 to 2019 in the Surveillance, Epidemiology, and End Results Program-linked Medicare database. Beneficiaries continuously enrolled in Medicare Parts A and B for 4 years before to 1 year after diagnosis were included. Data were analyzed from October 2024 to April 2025. Exposure:A new cancer diagnosis. Main Outcomes and Measures:The outcome was switching to TM. Among those who were initially enrolled in MA, a difference-in-differences design was implemented to compare changes in the probability of MA disenrollment between beneficiaries diagnosed in 3 guaranteed issue states (New York, Connecticut, and Massachusetts) vs other 13 states contributing to the Surveillance, Epidemiology, and End Results Program registry, before and after diagnosis. Results:The study included 180 057 MA beneficiaries 69 years and older who were newly diagnosed with cancer (44.5% diagnosed at age 69-75 years; 51.5% male; 8.0% Hispanic; 7.4% non-Hispanic Black; 78.5% non-Hispanic White; and 6.1% another or unknown race and ethnicity). The rate of switching in guaranteed issue states increased from 2.1% to 4.7% following diagnosis but remained unchanged in other states (1.8% to 1.7%), corresponding to a difference-in-differences of 2.5 percentage points (95% CI, 1.9-3.2 percentage points; P < .001), or a 120% relative change. This differential increase was concentrated among beneficiaries who were younger, non-Hispanic White, diagnosed with distant-stage or rarer cancers, or enrolled in plans with more generous coverage (eg, PPO plans) or lower plan star ratings. Conclusions and Relevance:In this cohort study, state Medigap guaranteed issue protections were associated with higher rates of switching to TM among MA beneficiaries newly diagnosed with cancer. These findings underscore the protective association of state Medigap regulations in facilitating a switch to TM (especially among beneficiaries who likely desired more flexibility in accessing and receiving care) and illuminate potential disparities in switching that may reflect unequal abilities to compare and afford plans.

This study outlines methods for modeling disability-adjusted life-years (DALYs) in common decision-modeling frameworks. Recognizing the wide spectrum of experience and programming comfort level among practitioners, we outline 2 approaches for modeling DALYs in its constituent parts: years of life lost to disease (YLL) and years of life lived with disability (YLD). Our beginner approach draws on the Markov trace, while the intermediate approach facilitates more efficient estimation by incorporating non-Markovian tracking elements into the transition probability matrix. Drawing on an existing disease progression discrete time Markov cohort model, we demonstrate the equivalence of DALY estimates and cost-effectiveness analysis results across our methods and show that other commonly used "shortcuts" for estimating DALYs will not, in general, yield accurate estimates of DALY levels nor incremental cost-effectiveness ratios in a modeled population.HighlightsThis study introduces 2 DALY estimation methods-beginner and intermediate approaches-that produce similar results, expanding the toolkit available to decision modelers.These methods can be adapted to estimate other outcomes (e.g., QALYs, life-years) and applied to other common decision-modeling frameworks, including microsimulation models with patient-level attributes and discrete event simulations that estimate YLDs and YLLs based on time to death and disease duration.Our findings further reveal that commonly used shortcut methods for DALY calculations may lead to differing results, particularly for DALY levels and incremental cost-effectiveness ratios.

OBJECTIVE:To pilot a system for deriving borders of pediatric regions, and to compare these to adult markets based on fit with pediatric utilization data. STUDY SETTING AND DESIGN:In this cross-sectional study, we studied all acute care encounters (emergency department visits and hospitalizations) for children less than 16 years old in Wisconsin 2021-2022. DATA SOURCES AND ANALYTIC SAMPLE:We used the Healthcare Cost and Utilization Project State Emergency Department and Inpatient Databases. We first counted how many patients from each ZIP code visited each hospital and mapped ZIP-hospital connections. Using a network analysis technique called community detection that clustered hospitals by their common connections, we grouped ZIP codes to form pediatric emergency service areas (PESAs). We counted patient referrals within and between PESAs and repeated the community detection procedure, resulting in pediatric emergency referral regions (PERRs). The primary outcome was modularity, a common network fit measure ranging from -1 to 1 (1 represents perfect clustering). We also compared demographics and network quality measures between PERRs, hospital referral regions (HRRs), core-based statistical areas, and Pittsburgh Trauma Atlas regions. PRINCIPAL FINDINGS:We analyzed 587,886 encounters, from which ZIP codes grouped into 24 PESAs. Based on referral patterns, there were 4 PERRs. PERRs had modestly higher modularity for interhospital referral patterns than all other systems (0.53, 95% confidence interval [CI] 0.52, 0.54 compared to 0.46, 95% CI 0.46, 0.47 for HRRs). PERRs were larger (median 11,361 mile2 vs. 3957 for HRRs), contained more children (median 265,222 vs. 49,667 for HRRs), and contained more hospitals (median 35 vs. 7 for HRRs) than all other systems. CONCLUSIONS:Using Wisconsin HCUP data, we derived pediatric acute care regions with a strong fit for pediatric utilization data. Future work should test this approach across the whole US, which would allow between-region cost and outcomes comparison.

Polygenic risk scores (PRS), risk prediction algorithms based on genome-wide association studies, show promising potential to predict disease risk and guide personalized screening and treatment. However, PRS risk prediction is uncertain and by extension has uncertain health economic value. It is unclear how previous cost-effectiveness analyses (CEA) handled the varied sources of uncertainty related to PRS risk prediction. This study aims to fill this knowledge gap and develop a framework to guide future CEA for PRS.

PURPOSE:Genomic screening to identify individuals with Lynch Syndrome (LS) and those with a high polygenic risk score (PRS) promises to personalize colorectal cancer (CRC) screening. Understanding its clinical and economic impact is needed to inform screening guidelines and reimbursement policies. METHODS:We developed a Markov model to simulate individuals over a lifetime. We compared LS+PRS genomic screening with standard of care (SOC) for a cohort of US adults at age 30. The Markov model included health states of no CRC, CRC stages (A-D), and death. We estimated incidence, mortality, and discounted economic outcomes of the population under different interventions. RESULTS:Screening 1000 individuals for LS+PRS resulted in 1.36 fewer CRC cases and 0.65 fewer deaths compared with SOC. The incremental cost-effectiveness ratio was $124,415 per quality-adjusted life year; screening had a 69% probability of being cost-effective using a willingness-to-pay threshold of $150,000/quality-adjusted life year . Setting the PRS threshold at the 90th percentile of the LS+PRS screening program to define individuals at high risk was most likely to be cost-effective compared with 95th, 85th, and 80th percentiles. CONCLUSION:Population-level LS+PRS screening is marginally cost-effective, and a threshold of 90th percentile is more likely to be cost-effective than other thresholds.

OBJECTIVE:To quantify the degree to which health care service area (HCSA) definitions captured hospitalizations and heterogeneity in social determinants of health (SDOH). DATA SOURCES AND STUDY SETTING:Geospatial data from the Centers for Medicare and Medicaid Services, the Census Bureau, and the Dartmouth Institute. Drive-time isochrones from MapBox. Area Deprivation Index (ADI) data. 2017 inpatient discharge data from Arizona, Florida, Iowa, Maryland, Nebraska, New Jersey, New York, and Wisconsin, State Emergency Department Databases and State Inpatient Databases, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality; and Fee-For-Service Medicare data in 48 states. STUDY DESIGN:Cross-sectional, descriptive analysis. DATA COLLECTION/EXTRACTION METHODS:The capture rate was the percentage of inpatient discharges occurring in the same HCSA as the hospital. We compared capture rates for each HCSA definition for different populations and by hospital type. We measured SDOH heterogeneity using the coefficient of variation of the ADI among ZIP codes within each HCSA. PRINCIPAL FINDINGS:HCSA definitions captured a wide range of inpatient discharges, ranging from 20% to 50% for Public Use Microdata Areas (PUMAs) to 93%-97% for Metropolitan Statistical Areas (MSAs). Three-quarters of inpatient discharges were from facilities within the same county as the patient's residential ZIP code, while nearly two-thirds were within the same Hospital Service Area. From the hospital perspective, 74.7% of inpatient discharges originated from within a 30-min drive and 90.1% within a 60-min drive. Capture rates were the lowest for teaching hospitals. PUMAs and drive-time-based HCSAs encompassed more homogenous populations while MSAs, Commuting Zones, and Hospital Referral Regions captured the most variation. CONCLUSIONS:The proportion of hospital discharges captured by each HCSA varied, with MSAs capturing the highest proportion of discharges and PUMAs capturing the lowest. Additionally, researchers face a trade-off between capture rate and population homogeneity when deciding which HCSA to use.

OBJECTIVE:To develop a method of consistently identifying interfacility transfers (IFTs) in Medicare Claims using patients with ST-Elevation Myocardial Infarction (STEMI) as an example. DATA SOURCES/STUDY SETTING:100% Medicare inpatient and outpatient Standard Analytic Files and 5% Carrier Files, 2011-2020. STUDY DESIGN:Observational, cross-sectional comparison of patient characteristics between proposed and existing methods. DATA COLLECTION/EXTRACTION METHODS:We limited to patients aged 65+ with STEMI diagnosis using both proposed and existing methods. PRINCIPAL FINDINGS:We identified 62,668 more IFTs using the proposed method (86,128 versus 23,460). A separately billable interfacility ambulance trip was found for more IFTs using the proposed than existing method (86% vs. 79%). Compared with the existing method, transferred patients under the proposed method were more likely to live in rural (p < 0.001) and lower income (p < 0.001) counties and were located farther away from emergency departments, trauma centers, and intensive care units (p < 0.001). CONCLUSIONS:Identifying transferred patients based on two consecutive inpatient claims results in an undercount of IFTs and under-represents rural and low-income patients.

ImportanceFostamatinib, a spleen tyrosine kinase inhibitor, has been reported to improve outcomes of COVID-19.ObjectiveTo evaluate the efficacy and safety of fostamatinib in adults hospitalized with COVID-19 and hypoxemia.Design, Setting, and ParticipantsThis multicenter, phase 3, placebo-controlled, double-blinded randomized clinical trial was conducted at 41 US sites and 21 international sites between November 17, 2021, and September 27, 2023; the last follow-up visit was December 31, 2023. Participants were adults aged 18 years or older hospitalized with acute SARS-CoV-2 infection and hypoxemia. Data were analyzed between January 10 and March 8, 2024.InterventionsFostamatinib, 150 mg orally twice daily for 14 days, or placebo.Main Outcomes and MeasuresThe primary outcome was oxygen-free days, an ordinal outcome classifying a participant’s status at day 28 based on mortality and duration of supplemental oxygen use. An adjusted odds ratio (AOR) greater than 1.0 was considered to indicate superiority of fostamatinib over placebo. A key secondary outcome was 28-day all-cause mortality. Safety outcomes included elevated transaminase values, neutropenia, and hypertension.ResultsOf the 400 participants randomized (median age, 67 years [IQR, 58-76 years]; 210 [52.5%] men), 199 received fostamatinib and 201 received placebo. The mean (SD) number of oxygen-free days was 13.4 (12.4) in the fostamatinib group and 14.2 (12.1) in the placebo group (unadjusted mean difference, −1.26 days [95% CI, −3.52 to 1.00 days]; AOR, 0.82 [95% credible interval (CrI), 0.58-1.17]). Mortality at 28 days occurred in 22 of 195 patients (11.3%) in the fostamatinib group and 16 of 197 (8.1%) in the placebo group (AOR, 1.44; 95% CrI, 0.72-2.90). Aspartate aminotransferase elevation occurred more commonly in the fostamatinib group (23 [11.6%]) than in the placebo group (11 [5.5%]; AOR, 2.28; 95% CrI, 1.07-4.84). Other safety outcomes were similar between groups.Conclusions and RelevanceIn this randomized clinical trial of adults hospitalized with COVID-19 and hypoxemia, fostamatinib did not increase the number of oxygen-free days compared with placebo. These results do not support the hypothesis that fostamatinib improves outcomes among adults hospitalized with hypoxemia during the Omicron era.Trial RegistrationClinicalTrials.gov Identifier: NCT04924660

This Viewpoint discusses enrollment in Medicare Advantage vs traditional Medicare among older adults and common reasons for plan disenrollment, including the lack of in-network physicians and hospitals.

The use of many services is lower in Medicare Advantage (MA) compared with traditional Medicare, generating cost savings for insurers, whereas the quality of ambulatory services is higher. This study examined the role of selective contracting with providers in achieving these outcomes, focusing on primary care physicians. Assessing primary care physician costliness based on the gap between observed and predicted costs for their traditional Medicare patients, we found that the average primary care physician in MA networks was $433 less costly per patient (2.9 percent of baseline) compared with the regional mean, with less costly primary care physicians included in more networks than more costly ones. Favorable selection of patients by MA primary care physicians contributed partially to this result. The quality measures of MA primary care physicians were similar to the regional mean. In contrast, primary care physicians excluded from all MA networks were $1,617 (13.8 percent) costlier than the regional mean, with lower quality. Primary care physicians in narrow networks were $212 (1.4 percent) less costly than those in wide networks, but their quality was slightly lower. These findings highlight the potential role of selective contracting in reducing costs in the MA program.

This cross-sectional study quantifies Medicaid and the Patient Protection and Affordable Care Act (ACA) Marketplace overlap among primary care physicians.

ImportanceThe 21st Century Cures Act includes an information-blocking rule (IBR) that requires health systems to provide patients with immediate access to their health information in the electronic medical record upon request. Patients accessing their health information before they receive an explanation from their health care team may experience confusion and may be more likely to share unsolicited patient complaints (UPCs) with their health care organization.ObjectiveTo evaluate the quantity of UPCs about physicians before and after IBR implementation and to identify themes in UPCs that may identify patient confusion, fear, or anger related to the release of information.Design, Setting, and ParticipantsThis retrospective cohort study was conducted with an interrupted time-series analysis of UPCs spanning January 1, 2020, to June 30, 2022. The data were obtained from a single academic medical center, Vanderbilt University Medical Center, at which the IBR was implemented on January 20, 2021. Data analysis was performed from January 11 to July 15, 2023.ExposureImplementation of the IBR on January 20, 2021.Main Outcomes and MeasuresThe primary outcome was the monthly rate of UPCs before and after IBR implementation. A qualitative analysis was performed for UPCs received after IBR implementation. The Wilcoxon rank-sum test was used to compare monthly complaints between the pre- and post-IBR groups. The Pearson chi 2 test was used to compare proportions of complaints by UPC category between time periods.ResultsThe medical center received 8495 UPCs during the study period: 3022 over 12 months before and 5473 over 18 months after institutional IBR implementation. There was no difference in the monthly proportions of UPCs per 1000 patient encounters before (median, 0.81 [IQR, 0.75-0.88]) and after (median, 0.83 [IQR, 0.77-0.89]) IBR implementation (difference in medians, -0.02 [95% CI, -0.12 to 0.07]; P =.86). Segmented regression analysis revealed no difference in monthly UPCs (beta [SE], 0.03 [0.09]; P =.72).Conclusions and RelevanceIn this cohort study, implementation of the Cures Act IBR was not associated with an increase in monthly rates of UPCs. These findings suggest that review of UPCs identified as IBR-specific complaints may allow clinicians and organizations to prepare patients that their test and procedure results may be available before clinicians are able to review them and respond.

ImportanceThe effect of higher-dose fluvoxamine in reducing symptom duration among outpatients with mild to moderate COVID-19 remains uncertain.ObjectiveTo assess the effectiveness of fluvoxamine, 100 mg twice daily, compared with placebo, for treating mild to moderate COVID-19.Design, Setting, and ParticipantsThe ACTIV-6 platform randomized clinical trial aims to evaluate repurposed medications for mild to moderate COVID-19. Between August 25, 2022, and January 20, 2023, a total of 1175 participants were enrolled at 103 US sites for evaluating fluvoxamine; participants were 30 years or older with confirmed SARS-CoV-2 infection and at least 2 acute COVID-19 symptoms for 7 days or less.InterventionsParticipants were randomized to receive fluvoxamine, 50 mg twice daily on day 1 followed by 100 mg twice daily for 12 additional days (n = 601), or placebo (n = 607).Main Outcomes and MeasuresThe primary outcome was time to sustained recovery (defined as at least 3 consecutive days without symptoms). Secondary outcomes included time to death; time to hospitalization or death; a composite of hospitalization, urgent care visit, emergency department visit, or death; COVID-19 clinical progression scale score; and difference in mean time unwell. Follow-up occurred through day 28.ResultsAmong 1208 participants who were randomized and received the study drug, the median (IQR) age was 50 (40-60) years, 65.8% were women, 45.5% identified as Hispanic/Latino, and 76.8% reported receiving at least 2 doses of a SARS-CoV-2 vaccine. Among 589 participants who received fluvoxamine and 586 who received placebo included in the primary analysis, differences in time to sustained recovery were not observed (adjusted hazard ratio [HR], 0.99 [95% credible interval, 0.89-1.09]; P for efficacy = .40]). Additionally, unadjusted median time to sustained recovery was 10 (95% CI, 10-11) days in both the intervention and placebo groups. No deaths were reported. Thirty-five participants reported health care use events (a priori defined as death, hospitalization, or emergency department/urgent care visit): 14 in the fluvoxamine group compared with 21 in the placebo group (HR, 0.69 [95% credible interval, 0.27-1.21]; P for efficacy = .86) There were 7 serious adverse events in 6 participants (2 with fluvoxamine and 4 with placebo) but no deaths.Conclusions and RelevanceAmong outpatients with mild to moderate COVID-19, treatment with fluvoxamine does not reduce duration of COVID-19 symptoms.Trial RegistrationClinicalTrials.gov Identifier: NCT04885530

OBJECTIVE To quantify shared patient relationships between primary care physicians (PCPs) and cardiologists and oncologists and the degree to which those relationships were captured within insurance networks. DATA SOURCES Secondary analysis of Vericred data on physician networks, CareSet data on physicians' shared Medicare patients, and insurance plan attributes from Health Insurance Compare. Data validation exercises used data from Physician Compare and IQVIA. STUDY DESIGN Cross-sectional study of the PCP-to-specialist in-network shared patient percentage (primary outcome). We also categorized networks by insurance market segment (Medicare Advantage, Medicaid managed care, small-group or individually purchased), insurance plan type, and network breadth. DATA EXTRACTION We analyzed data on 219,982 PCPs, 29,400 cardiologists, and 22,745 oncologists who, in 2021, accepted Medicare Advantage (n=941 networks), Medicaid managed care (n=293), and individually-purchased (n=332) and small-group (n=501) plans PRINCIPAL FINDINGS: Networks captured, on average, 64.6% of PCP-cardiology shared patient ties, and 61.8% of PCP-oncologist ties. Less than half of in-network ties (44.5% and 38.9%, respectively) were among physicians with a common organizational affiliation. After adjustment for network breadth, we found no evidence of differences in the shared patient percentage across insurance market segments or networks of different types (p-value>0.05 for all comparisons). An exception was among national vs. local and regional networks, where we found that national plans captured fewer shared patient ties, particularly among the narrowest networks (58.4% for national networks vs. 64.7% for local and regional networks for PCP-cardiology). CONCLUSIONS Given recent trends towards narrower networks, our findings underscore the importance of incorporating additional and nuanced measures of network composition to aid plan selection (for patients) and to guide regulatory oversight. This article is protected by copyright. All rights reserved.