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Objective: We investigated a decrease in antibiotic prescribing for respiratory illnesses in 2 academic urgent-care clinics during the coronavirus disease 2019 (COVID-19) pandemic using semistructured clinician interviews. Methods: We conducted a quality-improvement project from November 2020 to May 2021. We investigated provider antibiotic decision making using a mixed-methods explanatory design including interviews. We analyzed transcripts using a thematic framework approach to identify emergent themes. Our performance measure was antibiotic prescribing rate (APR) for encounters with respiratory diagnosis billing codes. We extracted billing and prescribing data from the electronic medical record and assessed differences using run charts, p charts and generalized linear regression. Results: We observed significant reductions in the APR early during the COVID-19 pandemic (relative risk [RR], 0.20; 95% confidence interval [CI], 0.17–0.25), which was maintained over the study period (P < .001). The average APRs were 14% before the COVID-19 pandemic, 4% during the QI project, and 7% after the project. All providers prescribed less antibiotics for respiratory encounters during COVID-19, but only 25% felt their practice had changed. Themes from provider interviews included changing patient expectations and provider approach to respiratory encounters during COVID-19, the impact of increased telemedicine encounters, and the changing epidemiology of non–COVID-19 respiratory infections. Conclusions: Our findings suggest that the decrease in APR was likely multifactorial. The average APR decreased significantly during the pandemic. Although the APR was slightly higher after the QI project, it did not reach prepandemic levels. Future studies should explore how these factors, including changing patient expectations, can be leveraged to improve urgent-care antibiotic stewardship.

Background and Purpose The purpose is to determine the impact of an academic neurohospitalist service on clinical outcomes. Methods We performed a retrospective, quasi-experimental study of patients discharged from the general neurology service before (August 2010–July 2014) and after implementation of a full-time neurohospitalist service (August 2016–July 2018) compared to a control group of stroke patients. Primary outcomes were length of stay and 30-day readmission. Using the difference-in-difference approach, the impact of introducing a neurohospitalist service compared to controls was assessed with adjustment of patients’ characteristics. Secondary outcomes included mortality, in-hospital complications, and cost. Results There were 2706 neurology admissions (1648 general; 1058 stroke) over the study period. The neurohospitalist service was associated with a trend in reduced 30-day readmissions (ratio of ORs: .52 [.27, .98], P = .088), while length of stay was not incrementally changed in the difference-in-difference model (-.3 [-.7, .1], P = .18). However, descriptive results demonstrated a significant reduction in mean adjusted LOS of .7 days (4.5 to 3.8 days, P < .001) and a trend toward reduced readmissions (8.9% to 7.6%, P = .42) in the post-neurohospitalist cohort despite a significant increase in patient complexity, shift to higher acuity diagnoses, more emergent admissions, and near quadrupling of observation status patients. Mortality and in-hospital complications remained low, patient satisfaction was stable, and cost was not incrementally changed in the post-neurohospitalist cohort. Conclusions Implementation of a neurohospitalist service at an academic medical center is feasible and associated with a significant increase in patient complexity and acuity and a trend toward reduced readmissions.

ImportanceStudies show that ChatGPT, a general purpose large language model chatbot, could pass the multiple-choice US Medical Licensing Exams, but the model’s performance on open-ended clinical reasoning is unknown.ObjectiveTo determine if ChatGPT is capable of consistently meeting the passing threshold on free-response, case-based clinical reasoning assessments.DesignFourteen multi-part cases were selected from clinical reasoning exams administered to pre-clerkship medical students between 2019 and 2022. For each case, the questions were run through ChatGPT twice and responses were recorded. Two clinician educators independently graded each run according to a standardized grading rubric. To further assess the degree of variation in ChatGPT’s performance, we repeated the analysis on a single high-complexity case 20 times.SettingA single US medical schoolParticipantsChatGPTMain Outcomes and MeasuresPassing rate of ChatGPT’s scored responses and the range in model performance across multiple run throughs of a single case.Results12 out of the 28 ChatGPT exam responses achieved a passing score (43%) with a mean score of 69% (95% CI: 65% to 73%) compared to the established passing threshold of 70%. When given the same case 20 separate times, ChatGPT’s performance on that case varied with scores ranging from 56% to 81%.Conclusions and RelevanceChatGPT’s ability to achieve a passing performance in nearly half of the cases analyzed demonstrates the need to revise clinical reasoning assessments and incorporate artificial intelligence (AI)-related topics into medical curricula and practice.

Background The reported incidence of adverse reactions following Coronavirus disease 2019 (COVID‐19) convalescent plasma (CCP) transfusion has generally been lower than expected based on the incidence of transfusion reactions that have been observed in studies of conventional plasma transfusion. This raises the concern for under‐reporting of adverse events in studies of CCP that rely on passive surveillance strategies. Materials and Methods Our institution implemented a protocol to actively identify possible adverse reactions to CCP transfusion. In addition, we retrospectively reviewed the charts of inpatients who received CCP at Stanford Hospital between May 13, 2020 and January 31, 2021. We determined the incidence of adverse events following CCP transfusion. Results A total of 49 patients received CCP. Seven patients (14%) had an increased supplemental oxygen requirement within 4 h of transfusion completion, including one patient who was intubated during the transfusion. An additional 11 patients (total of 18, 37%) had increased oxygen requirements within 24 h of transfusion, including 3 patients who were intubated. Six patients (12%) fulfilled criteria for transfusion‐associated circulatory overload (TACO). Conclusion Using an active surveillance strategy, we commonly observed adverse events following the transfusion of CCP to hospitalized patients. It was not possible to definitively determine whether or not these adverse events are related to CCP transfusion. TACO was likely over‐diagnosed given overlap with the manifestations of COVID‐19. Nevertheless, these results suggest that the potential adverse effects of CCP transfusion may be underestimated by reports from passive surveillance studies.

OBJECTIVE The only effective treatment for ischemic moyamoya disease (iMMD) is cerebral revascularization by an extracranial to intracranial bypass. The preferred revascularization method remains controversial: direct versus indirect bypass. The purpose of this study was to test the hypothesis that method choice should be personalized based on angiographic, hemodynamic, and clinical characteristics to balance the risk of perioperative major stroke against treatment efficacy. METHODS Patients with iMMD were identified retrospectively from a prospectively maintained database. Those with mild to moderate internal carotid artery or M1 segment stenosis, preserved cerebrovascular reserve, intraoperative M4 segment anterograde flow ≥ 8 ml/min, or the absence of frequent and severe transient ischemic attacks (TIAs) or stroke had been assigned to indirect bypass. The criteria for direct bypass were severe ICA or M1 segment stenosis or occlusion, impaired cerebrovascular reserve or steal phenomenon, intraoperative M4 segment retrograde flow or anterograde flow < 8 ml/min, and the presence of frequent and severe TIAs or clinical strokes. The primary study endpoint was MRI-confirmed symptomatic stroke ≤ 7 days postoperatively resulting in a decline in the modified Rankin Scale (mRS) score from preoperatively to 6 months postoperatively. As a secondary endpoint, the authors assessed 6-month postoperative DSA-demonstrated revascularization, which was classified as < 1/3, 1/3–2/3, or > 2/3 of the middle cerebral artery territory. RESULTS One hundred thirty-eight patients with iMMD affecting 195 hemispheres revascularized in the period from March 2016 to June 2018 were included in this analysis. One hundred thirty-three hemispheres were revascularized with direct bypass and 62 with indirect bypass. The perioperative stroke rate was 4.7% and 6.8% in the direct and indirect groups, respectively (p = 0.36). Degree of revascularization was higher in the direct bypass group (p = 0.03). The proportion of patients improving to an mRS score 0–1 (from preoperatively to 6 months postoperatively) tended to be higher in the direct bypass group, although the difference between the two bypass groups was not statistically significant (p = 0.27). CONCLUSIONS The selective use of an indirect bypass procedure for iMMD did not decrease the perioperative stroke rate. Direct bypass provided a significantly higher degree of revascularization. The authors conclude that direct bypass is the treatment of choice for iMMD.

Background Internal medicine residents face numerous career options after residency training. Little is known about when residents make their final career choice. Objective We assessed the timing and predictive factors of final career choices among internal medicine residents at graduation, including demographics, pre-residency career preferences, and rotation scheduling. Methods We conducted a retrospective study of graduates of an academic internal medicine residency program from 2014 to 2017. Main measures included demographics, rotation schedules, and self-reported career choices for residents at 5 time points: recruitment day, immediately after Match Day, end of postgraduate year 1 (PGY-1), end of PGY-2, and at graduation. Results Of the 138 residents eligible for the study, 5 were excluded based on participation in a fast-track program for an Accreditation Council for Graduate Medical Education subspecialty fellowship. Among the remaining 133 residents, 48 (36%) pursued general internal medicine fields and 78 (59%) pursued fellowship training. Career choices from recruitment day, Match Day, and PGY-1 were only weakly predictive of the career choice. Many choices demonstrated low concordance throughout training, and general medicine fields (primary care, hospital medicine) were frequently not decided until after PGY-2. Early clinical exposure to subspecialty rotations did not predict final career choice. Conclusions Early career choices before and during residency training may have low predictability toward final career choices upon graduation in internal medicine. These choices may continue to have low predictability beyond PGY-2 for many specialties. Early clinical exposure may not predict final career choice for subspecialties.

Purpose We describe the design of a longitudinal cohort study to determine SARS-CoV-2 incidence and prevalence among a population-based sample of adults living in six San Francisco Bay Area counties. Methods Using an address-based sample, we stratified households by county and by census-tract risk. Risk strata were determined by using regression models to predict infections by geographic area using census-level sociodemographic and health characteristics. We disproportionately sampled high and medium risk strata, which had smaller population sizes, to improve precision of estimates, and calculated a desired sample size of 3400. Participants were primarily recruited by mail and were followed monthly with PCR testing of nasopharyngeal swabs, testing of venous blood samples for antibodies to SARS-CoV-2 spike and nucleocapsid antigens, and testing of the presence of neutralizing antibodies, with completion of questionnaires about socio-demographics and behavior. Estimates of incidence and prevalence will be weighted by county, risk strata and sociodemographic characteristics of non-responders, and will take into account laboratory test performance. Results We enrolled 3842 adults from August to December, 2020, and completed follow-up March 31, 2021. We reached target sample sizes within most strata. Conclusions Our stratified random sampling design will allow us to recruit a robust general population cohort of adults to determine the incidence of SARS-CoV-2 infection. Identifying risk strata was unique to the design and will help ensure precise estimates, and high-performance testing for presence of virus and antibodies will enable accurate ascertainment of infections.

Background Preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2_ infections in healthcare workers (HCWs) is critical for healthcare delivery. We aimed to estimate and characterize the prevalence and incidence of coronavirus disease 2019 (COVID-19) in a US HCW cohort and to identify risk factors associated with infection. Methods We conducted a longitudinal cohort study of HCWs at 3 Bay Area medical centers using serial surveys and SARS-CoV-2 viral and orthogonal serological testing, including measurement of neutralizing antibodies. We estimated baseline prevalence and cumulative incidence of COVID-19. We performed multivariable Cox proportional hazards models to estimate associations of baseline factors with incident infections and evaluated the impact of time-varying exposures on time to COVID-19 using marginal structural models. Results A total of 2435 HCWs contributed 768 person-years of follow-up time. We identified 21 of 2435 individuals with prevalent infection, resulting in a baseline prevalence of 0.86% (95% confidence interval [CI], .53%–1.32%). We identified 70 of 2414 incident infections (2.9%), yielding a cumulative incidence rate of 9.11 cases per 100 person-years (95% CI, 7.11–11.52). Community contact with a known COVID-19 case was most strongly correlated with increased hazard for infection (hazard ratio, 8.1 [95% CI, 3.8–17.5]). High-risk work-related exposures (ie, breach in protective measures) drove an association between work exposure and infection (hazard ratio, 2.5 [95% CI, 1.3–4.8). More cases were identified in HCWs when community case rates were high. Conclusions We observed modest COVID-19 incidence despite consistent exposure at work. Community contact was strongly associated with infections, but contact at work was not unless accompanied by high-risk exposure.

1AbstractCOVID-19 has caused tremendous death and suffering since it first emerged in 2019. In response, models were developed to help predict the course of various disease metrics, and these models have been relied upon to help guide public health policy. Here we present a method called COVIDNearTerm to “forecast” hospitalizations in the short term, two to four weeks from the time of prediction. COVIDNearTerm is based on an autoregressive model and utilizes a parametric bootstrap approach to make predictions. We evaluated COVIDNearTerm on San Francisco Bay Area hospitalizations and compared it to models from the California COVID Assessment Tool (CalCAT). We found that that COVIDNearTerm pre-dictions were more accurate than the CalCAT ensemble predictions for all comparisons and any CalCAT component for a majority of comparisons. For instance, at the county level our 14-day hospitalization median absolute percentage errors ranged from 16% to 36%. For those same comparisons the CalCAT ensemble errors were between 30% and 59%. COVIDNearT-erm is also easier to use than some other methods. It requires only previous hospitalization data and there is an open source R package that implements the algorithm.

OBJECTIVES To determine the (1) frequency and visit characteristics of routine temperature measurement and (2) rates of interventions by temperature measurement practice and the probability of incidental fever detection. METHODS In this retrospective cohort study, we analyzed well-child visits between 2014–2019. We performed multivariable regression to characterize visits associated with routine temperature measurement and conducted generalized estimating equations regression to determine adjusted rates of interventions (antibiotic prescription, and diagnostic testing) and vaccine deferral by temperature measurement and fever status, clustered by clinic and patient. Through dual independent chart review, fever (≥100.4°F) was categorized as probable, possible, or unlikely to be incidentally detected. RESULTS Temperature measurement occurred at 155 527 of 274 351 (58.9%) well-child visits. Of 24 clinics, 16 measured temperature at >90% of visits (“routine measurement clinics”) and 8 at CONCLUSIONS Temperature measurement occurs at more than one-half of well-child visits and is a clinic-driven practice. Given the impact on subsequent interventions and vaccine deferral, the harm–benefit profile of this practice warrants consideration.

Surgical comanagement (SCM), in which surgeons and hospitalists share responsibility of care for surgical patients, has been increasingly utilized. In August 2012, we implemented SCM in Orthopedic and Neurosurgery services in which the same Internal Medicine hospitalists are dedicated year round to each of these surgical services to proactively prevent and manage medical conditions. In this article, we evaluate if SCM was associated with continued improvement in patient outcomes between 2012 and 2018 in Orthopedic and Neurosurgery services at our institution. We conducted regression analysis on 26,380 discharges to assess yearly change in our outcomes. Since 2012, the odds of patients with ≥1 medical complication decreased by 3.8% per year (P = .01), the estimated length of stay decreased by 0.3 days per year (P < .0001), and the odds of rapid response team calls decreased by 12.2% per year (P = .001). Estimated average direct cost savings were $3,424 per discharge.

Postoperative pain scores are widely monitored and collected in the electronic health record, yet current methods fail to fully leverage the data with fast implementation. A robust linear regression was fitted to describe the association between the log-scaled pain score and time from discharge after total knee replacement. The estimated trajectories were used for a subsequent K-medians cluster analysis to categorize the longitudinal pain score patterns into distinct clusters. For each cluster, a mixture regression model estimated the association between pain score and time to discharge adjusting for confounding. The fitted regression model generated the pain trajectory pattern for given cluster. Finally, regression analyses examined the association between pain trajectories and patient outcomes. A total of 3442 surgeries were identified with a median of 22 pain scores at an academic hospital during 2009–2016. Four pain trajectory patterns were identified and one was associated with higher rates of outcomes. In conclusion, we described a novel approach with fast implementation to model patients’ pain experience using electronic health records. In the era of big data science, clinical research should be learning from all available data regarding a patient’s episode of care instead of focusing on the “average” patient outcomes.

As a newly recognized subspecialty, understanding programmatic models for pediatric hospital medicine (PHM) programs is vital to lay the groundwork for a sustainable field. Although variability has been described within university-based PHM programs, there remains no national benchmark for community-based PHM programs. In this report, we describe the workload, clinical services, employment, and perception of sustainability of 70 community-based PHM programs in 29 states through a survey of community site leaders. The median hours for a full-time hospitalist was 1,882 hours/year with those employed by community hospitals working 8% more hours/year and viewing appropriate morning pediatric census as 20% higher than those employed by university institutions. Forty-three out of 70 (63%) site leaders perceived their programs as sustainable, with no significant difference by employer structure. Future studies should further explore root causes for workload discrepancies between community and academic employed programs along with establishing potential standards for PHM program development.

Importance Surveys in the US have found that Black and Latinx individuals have more reservations than their white counterparts about COVID-19 vaccination. However, little is known about the degree to which racial-ethnic differences in COVID-19 vaccination intentions are explained by differences in beliefs or perceptions about COVID-19 vaccines. Objective To compare intention to receive COVID-19 vaccination by race-ethnicity, to identify perceptional factors that may mediate the association between race-ethnicity and intention to receive the vaccine, and to identify the demographic and perceptional factors most strongly predictive of intention to receive a vaccine. Design Cross-sectional survey conducted from November, 2020 to January, 2021, nested within two longitudinal cohort studies of prevalence and incidence of SARS CoV-2 among the general population and healthcare workers. Setting Six San Francisco Bay Area counties. Study Cohort 3,161 participants in the Track COVID cohort (a population-based sample of adults) and 1,803 participants in the CHART Study cohort (a cohort of employees at three large medical centers). Results Rates of high vaccine willingness were significantly lower among Black (45.3%), Latinx (62.5%), Asian (65%), multi-racial (67.2%), and other race (61.0%) respondents than among white respondents (77.6%). Black, Latinx, and Asian respondents were significantly more likely than white respondents to endorse reasons to not get vaccinated, especially lack of trust. Participants’ motivations and concerns about COVID-19 vaccination only partially explained racial-ethnic differences in vaccination willingness. Being a health worker in the CHART cohort and concern about a rushed government vaccine approval process were the two most important factors predicting vaccination intention. Conclusions and Relevance Special efforts are required to reach historically marginalized racial-ethnic communities to support informed decision-making about COVID-19 vaccination. These campaigns must acknowledge the history of racism in biomedical research and health care delivery that has degraded the trustworthiness of health and medical science institutions among non-white population and may continue to undermine confidence in COVID-19 vaccines. Highlights Black, Latinx, Asian, Multi-racial, and Other race respondents were significantly less likely than white respondents to have high willingness to receive COVID-19 vaccination when surveyed around the time that the first COVID-19 vaccines received Emergency Use Authorization in the US. Racial-ethnic differences in vaccination intention were present for both health workers and the general population. Motivations and concerns about COVID-19 vaccination only partially explained racial-ethnic differences in COVID-19 vaccine willingness. The most important factors explaining COVID-19 vaccination intention were being a health worker, concern about government rushing the vaccine process, concern about having a bad reaction to the vaccine, motivation to protecting one’s self and family from COVID-19, and concern about getting COVID from the vaccine

IntroductionPoint-of-care ultrasound (POCUS) may detect the cardiopulmonary manifestations of COVID-19 and expediently predict patient outcomes.MethodsWe conducted a prospective cohort study at four medical centers from 3/2020-1/2021 to evaluate POCUS findings and clinical outcomes with COVID-19. Our inclusion criteria included adult patients hospitalized for COVID-19 who received cardiac or lung POCUS with a 12-zone protocol. Images were interpreted by two reviewers blinded to clinical outcomes. Our primary outcome was ICU admission incidence. Secondary outcomes included intubation and supplemental oxygen usage.ResultsN=160 patients (N=201 scans) were included. Scans were collected a median 23 hours (IQR:7-80) from emergency department triage. Triage POCUS findings associated with ICU admission included B-lines (OR 4.41 [95% CI:1.71-14.30]; pConsolidations present on triage were associated with the need for oxygen at discharge (OR 2.16 [95% CI: 1.01-4.70]; p=0.047). A normal lung triage scan was protective for ICU admission (OR 0.28 [95% CI:0.09-0.75; pDiscussionLung POCUS findings detected early in the hospitalization may provide expedient risk stratification for important COVID-19 clinical outcomes, including ICU admission, intubation, or need for oxygen on discharge. A normal admission scan appears protective against adverse outcomes, which may aid in triage decisions of patients.

Background: Rotating medical consultants, hospitalists or geriatricians, are involved in the care of patients with hip fracture, often after medical complications have already occurred. In August 2012, we implemented a unique surgical comanagement (SCM) model in which the same Internal Medicine hospitalists are dedicated year-round to the orthopaedic surgery service. We examine whether this SCM model was associated with a decrease in medical complications, length of stay, and inpatient mortality in patients with hip fracture admitted at our institution, compared with the previous model. Methods: We included 2,252 admissions to the orthopaedic surgery service with a hip fracture between 2009 and 2018 (757 pre-SCM and 1495 post-SCM). We adjusted for age, Charlson comorbidity score, and operating time in all regression analyses. Results: Mean Charlson comorbidity score (1.6 versus 1.2) and median case mix index (2.1 versus 1.9) were higher in the post-SCM group. A 32% decrease was observed in the odds of having ≥1 medical complication(s) (odds ratio, 0.68 [95% confidence interval, 0.50 to 0.91], P = 0.009) post-SCM. No change was observed in length of stay or inpatient mortality despite an increase in medical complexity post-SCM. Conclusion: Having dedicated orthopaedic hospitalists may contribute to fewer medical complications in patients with hip fracture.

BackgroundHealthcare personnel (HCP) are prioritized for earliest SARS-CoV-2 vaccine administration, yet relatively few data exist on HCP’s knowledge, motivations, concerns, and intentions regarding COVID-19 vaccines.MethodsWe conducted a cross-sectional survey Nov.16-Dec.8, 2020 among HCP enrolled in a cohort study at three Northern California medical centers serving diverse roles including COVID-19 patient care. Eligible HCP were adult (age≥18) on-site employees of the University of California, San Francisco, San Francisco General Hospital, and Stanford Healthcare. A one-time electronically-administered survey was sent to cohort HCP on November 16, 2020 and responses analyzed.ResultsOverall, among 2,448 HCP invited, 2,135 completed the COVID-19 vaccine survey (87.2% response rate). HCPs had mean age 41 years, were 73% female, and had diverse jobs including COVID-19 patient contact. Enthusiasm for vaccination was overall strong, and more HCP (1,453, 69%) said they would definitely/likely receive vaccine if formally FDA-approved versus if approved via emergency use authorization only (785, 35%). While 541 (25%) respondents wanted to be among the earliest to receive vaccine, more desired vaccination after the first round (777, 36%) or >2 months after vaccinations began (389, 18%). Top factors increasing motivation for vaccination included perceiving risk from COVID-19 to self (1,382, 65%) or to family/friends (1355, 63%). Top concerns were vaccine side effects, cited by 596 (28%), and concerns about political involvement in FDA’s approval process (249, 12%).ConclusionsHCP were enthusiastic about COVID-19 vaccination for individual protection and protecting others, but harbored concerns about vaccine side effects. Our data may inform emerging vaccine education campaigns.Key PointsAmong 2,135 healthcare personnel surveyed, we found enthusiasm for COVID-19 vaccination both for individual benefit and protecting others. However, healthcare personnel rated their knowledge of COVID-19 vaccines as only moderate and harbored concerns about vaccine side effects. Education raising awareness of vaccine efficacy and side effects may help maximize vaccine uptake.

More than 3000 clinical trials related to COVID-19 have been registered through clinicaltrials.gov. With so many trials, there is a risk that many will be inconclusive due to being underpowered or due to an inability to recruit patients. At academic medical centers, multiple trials are competing for the same resources; the success of one may come at the expense of another. The COVID-19 Outpatient Pragmatic Protocol Study (COPPS) is a flexible phase 2, multi-site, randomized, blinded trial based at Stanford University designed to overcome these issues by simultaneously evaluating multiple COVID-19 treatments in the outpatient setting in one common platform with shared controls. This approach reduces the overall number of patients required for statistical power, while improving the likelihood that any enrolled patient receives active treatment. The platform study has two main domains designed to evaluate COVID-19 treatments by assessing their ability to reduce viral shedding (Viral Domain), measured with self-collected nasal swabs, or improve clinical outcomes (Clinical Domain), measured through self-reported symptomology data. Data are collected on both domains for all participants enrolled. Participants are followed over a 28-day period. COPPS has the advantage of pragmatism created around its workflow that is also appealing to potential participants because of a lower probability of inactive treatment. At the conclusion of this clinical trial we expect to have identified potentially effective therapeutic strategy/ies for treating COVID-19 in the outpatient setting, which will have a transformative impact on medicine and public health.

Background Self-help group (SHG) interventions have been widely studied in low and middle income countries. However, there is little data on specific impacts of health layering, or adding health education modules upon existing SHGs which were formed primarily for economic empowerment. We examined three SHG interventions from 2012-2017 in Bihar, India to test the hypothesis that health-layering of SHGs would lead to improved health-related behaviours of women in SHGs. Methods A model for health layering of SHGs – Parivartan – was developed by the non-governmental organisation (NGO), Project Concern International, in 64 blocks of eight districts. Layering included health modules, community events and review mechanisms. The health layering model was adapted for use with government-led SHGs, called JEEViKA+HL, in 37 other blocks of Bihar. Scale-up of government-led SHGs without health layering (JEEViKA) occurred contemporaneously in 433 other blocks, providing a natural comparison group. Using Community-based Household Surveys (CHS, rounds 6-9) by CARE India, 62 reproductive, maternal, newborn and child health and nutrition (RMNCHN) and sanitation indicators were examined for SHGs with health layering (Pavivartan SHGs and JEEViKA+HL SHGs) compared to those without. We calculated mean, standard deviation and odds ratios of indicators using surveymeans and survey logistic regression. Results In 2014, 64% of indicators were significantly higher in Parivartan members compared to non-members residing in the same blocks. During scale up, from 2015-17, half (50%) of indicators had significantly higher odds in health layered SHG members (Parivartan or JEEViKA+HL) in 101 blocks compared to SHG members without health layering (JEEViKA) in 433 blocks. Conclusions Health layering of SHGs was demonstrated by an NGO-led model (Parivartan), adapted and scaled up by a government model (JEEViKA+HL), and associated with significant improvements in health compared to non-health-layered SHGs (JEEViKA). These results strengthen the evidence base for further layering of health onto the SHG platform for scale-level health change. Study registration ClinicalTrials.gov number NCT02726230

Background The Government of Bihar (GoB) in India, the Bill and Melinda Gates Foundation and several non-governmental organisations launched the Ananya program aimed to support the GoB to improve reproductive, maternal, newborn and child health and nutrition (RMNCHN) statewide. Here we summarise changes in indicators attained during the initial two-year pilot phase (2012-2013) of implementation in eight focus districts of approximately 28 million population, aimed to inform subsequent scale-up. Methods The quasi-experimental impact evaluation included statewide household surveys at two time points during the pilot phase: January-April 2012 (“baseline”) including an initial cohort of beneficiaries and January-April 2014 (“midline”) with a new cohort. The two arms were: 1) eight intervention districts, and 2) a comparison arm comprised of the remaining 30 districts in Bihar where Ananya interventions were not implemented. We analysed changes in indicators across the RMNCHN continuum of care from baseline to midline in intervention and comparison districts using a difference-in-difference analysis. Results Indicators in the two arms were similar at baseline. Overall, 40% of indicators (20 of 51) changed significantly from baseline to midline in the comparison districts unrelated to Ananya; two-thirds (n = 13) of secular indicator changes were in a direction expected to promote health. Statistically significant impact attributable to the Ananya program was found for 10% (five of 51) of RMNCHN indicators. Positive impacts were most prominent for mother’s behaviours in contraceptive utilisation. Conclusions The Ananya program had limited impact in improving health-related outcomes during the first two-year period covered by this evaluation. The program’s theories of change and action were not powered to observe statistically significant differences in RMNCHN indicators within two years, but rather aimed to help inform program improvements and scale-up. Evaluation of large-scale programs such as Ananya using theory-informed, equity-sensitive (including gender), mixed-methods approaches can help elucidate causality and better explain pathways through which supply- and demand-side interventions contribute to changes in behaviour among the actors involved in the production of population-level health outcomes. Evidence from Bihar indicates that deep structural constraints in health system organisation and delivery of interventions pose substantial limitations on behaviour change among health care providers and beneficiaries. Study registration ClinicalTrials.gov number NCT02726230.

Background: Clinical trials, conducted efficiently and with the utmost integrity, are a key component in identifying effective vaccines, therapies, and other interventions urgently needed to solve the COVID-19 crisis. Yet launching and implementing trials with the rigor necessary to produce convincing results is a complicated and time-consuming process. Balancing rigor and efficiency involves relying on designs that employ flexible features to respond to a fast-changing landscape, measuring valid endpoints that result in translational actions and disseminating findings in a timely manner. We describe the challenges involved in creating infrastructure with potential utility for shared learning. Methods: We have established a shared infrastructure that borrows strength across multiple trials. The infrastructure includes an endpoint registry to aid in selecting appropriate endpoints, a registry to facilitate establishing a Data & Safety Monitoring Board, common data collection instruments, a COVID-19 dedicated design and analysis team, and a pragmatic platform protocol, among other elements. Results: The authors have relied on the shared infrastructure for six clinical trials for which they serve as the Data Coordinating Center and have a design and analysis team comprising 15 members who are dedicated to COVID-19. The authors established a pragmatic platform to simultaneously investigate multiple treatments for the outpatient with adaptive features to add or drop treatment arms. Conclusion: The shared infrastructure provides appealing opportunities to evaluate disease in a more robust manner with fewer resources and is especially valued during a pandemic where efficiency in time and resources is crucial. The most important element of the shared infrastructure is the pragmatic platform. While it may be the most challenging of the elements to establish, it may provide the greatest benefit to both patients and researchers.

BackgroundLung ultrasound (LUS) may be used in the diagnostic evaluation of patients with COVID-19. An abnormal LUS is associated with increased risk for ICU admission in COVID-19. Previously described LUS manifestations for COVID-19 include B-lines, consolidations, and pleural thickening. The interrater reliability (IRR) of these findings for COVID-19 is unknown.Research QuestionWhat is the interrater reliability of lung ultrasound findings in patients with RT-PCR confirmed COVID-19?Study Design and MethodsThis study was conducted at conducted at two academic medical centers between 03/2020-06/2020. Nine physicians (hospitalists: n=4; emergency medicine: n=5) independently evaluated n=20 LUS scans (n=180 independent observations) collected from RTPCR confirmed COVID-19 patients. These studies were randomly selected from an image database consisting of COVID-19 patients evaluated in the emergency department with portable ultrasound devices. Physicians were blinded to any patient information or previous LUS interpretation. Kappa values (κ) were used to calculate IRR.ResultsThere was substantial IRR on the following items: normal LUS scan (κ=0.79 [95% CI: 0.72-0.87]), presence of B-lines (κ=0.79 [95% CI: 0.72-0.87]), >=3 B-lines observed (κ=0.72 [95% CI: 0.64-0.79]). Moderate IRR was observed for the presence of any consolidation (κ=0.57 [95% CI: 0.50-0.64]), subpleural consolidation (κ=0.49 [95% CI: 0.42-0.56]), and presence of effusion (κ=0.49 [95% CI: 0.41-0.56]). Fair IRR was observed for pleural thickening (κ=0.23 [95% CI: 0.15-0.30]).InterpretationMany LUS manifestations for COVID-19 appear to have moderate to substantial IRR across providers from multiple specialties utilizing differing portable devices. The most reliable LUS findings with COVID-19 may include the presence/count of B-lines or determining if a scan is normal. Clinical protocols for LUS with COVID-19 may require additional observers for the confirmation of less reliable findings such as consolidations.Clinicaltrials.gov Registration:NCT04384055DisclosuresAndre Kumar, MD, MEd is a paid consultant for Vave Health, which manufactures one of the ultrasound devices used in this study. His consultant duties include providing feedback on product development. The other authors do not have any items to disclose.

IntroductionPoint-of-care ultrasound (POCUS) has the potential to transform healthcare delivery in the era of COVID-19 with its diagnostic and therapeutic expediency. It can be performed by clinicians already at the bedside, which permits an immediate and augmented assessment of a patient. Although lung ultrasound can be used to accurately diagnose a variety of disease states such as pneumothorax, pleural effusions, pneumonia and interstitial lung disease2, there are limited reports on the sonographic manifestations of COVID-19. There is an urgent need to identify alternative diagnostic modalities that can be immediately employed at the bedside of COVID-19 patients.MethodsThis study was conducted at two medical centers in the United States from 3/21/2020-6/01/2020. Any adult who was hospitalized with COVID-19 (based on symptomatology and a confirmatory RT-PCR for SARS-CoV-2) and received a pulmonary POCUS examination was included. Providers were instructed to use a 12-zone scanning protocol for pulmonary views and save 6 second clips of each lung zone. This study utilized several POCUS devices, including Butterfly IQ, Vave, Lumify, and Sonosite. The collected images were interpreted by the study researchers based on a consensus document developed by the study authors and previously accepted definitions of lung POCUS findings.ResultsA total of 22 eligible patients who received 36 lung scans were included in our study. Eleven (50%) patients experienced clinical deterioration (defined as either ICU admission, invasive mechanical ventilation, or death within 28 days from the initial symptom onset). Among the 36 lung scans collected, only 3 (8%) were classified as normal. The remaining scans had the following abnormalities: presence of B-lines (n=32, 89%), consolidations (n=20, 56%), pleural thickening (n=17, 47%), and pleural effusion (n=4, 11%). Out of 20 scans with consolidations, 14 (70%) were subpleural and 5 (25%) were translobar. A-lines were present in 26 (72%) of patients, although they were only observed in the majority of the collected lung zones in 5 (14%) of patients. Ultrasound findings were stratified by time from symptom onset to the scan based on the following time periods: early (0-6 days), middle (7-13 days), and late (14-28 days). B-lines appeared early after symptom onset and persisted well into the late disease course. In contrast, pleural thickening increased in frequency over time (early: 25%, middle: 47%, late: 67%). Subpleural consolidations also appeared in higher frequency later in the disease course (early: 13%, middle 42%, late: 56%).Discussioncertain lung ultrasound findings may be common in Covid-19, while others may appear later in the disease course or only occur in patients who experience clinical deterioration. Future efforts should investigate the predictive utility of consolidations, pleural thickening and B-lines for clinical deterioration and compare them to traditional radiological studies such as X-rays or CTs.

Background There is insufficient knowledge about how personal access to handheld ultrasound devices (HUDs) improves trainee learning with point-of-care ultrasound (POCUS). Objective To assess whether HUDs, alongside a yearlong lecture series, improved trainee POCUS usage and ability to acquire images. Methods Internal medicine intern physicians (n = 47) at a single institution from 2017 to 2018 were randomized 1:1 to receive personal HUDs (n = 24) for patient care/self-directed learning vs no-HUDs (n = 23). All interns received a repeated lecture series on cardiac, thoracic, and abdominal POCUS. Main outcome measures included self-reported HUD usage rates and post-intervention assessment scores using the Rapid Assessment of Competency in Echocardiography (RACE) scale between HUD and no-HUD groups. Results HUD interns reported performing POCUS assessments on patients a mean 6.8 (SD 2.2) times per week vs 6.4 (SD 2.9) times per week in non-HUD arm (P = .66). There was no relationship between the number of self-reported examinations per week and a trainee's post-intervention RACE score (rho = 0.022, P = .95). HUD interns did not have significantly higher post-intervention RACE scores (median HUD score 17.0 vs no-HUD score 17.8; P = .72). Trainee confidence with cardiac POCUS did not correlate with RACE scores. Conclusions Personal HUDs without direct supervision did not increase the amount of POCUS usage or improve interns' acquisition abilities. Interns who reported performing more examinations per week did not have higher RACE scores. Improved HUD access and lectures without additional feedback may not improve POCUS mastery.

OBJECTIVE Osteoarthritis (OA) is a common cause of joint pain and disability, and effective treatments are lacking. Extracellular adenosine has antiinflammatory effects and can prevent and treat OA in animal models. Ticagrelor and clopidogrel are both used in patients with coronary artery disease, but only ticagrelor increases extracellular adenosine levels. This study was undertaken to determine whether treatment with ticagrelor was associated with a lower risk of OA. METHODS We conducted a 1:2 propensity score-matching analysis using data from 2011-2017 in the Optum Clinformatics Data Mart. Patients who had received either ticagrelor or clopidogrel for ≥90 days were included in our study, and patients with a prior diagnosis of OA or inflammatory arthritis were excluded. OA was identified using International Classification of Diseases codes. The primary outcome was the time to diagnosis of OA after treatment with ticagrelor versus clopidogrel. RESULTS Our propensity score-matched cohort consisted of 7,007 ticagrelor-treated patients and 14,014 clopidogrel-treated patients, with a median number of days receiving treatment of 287 and 284, respectively. For both groups, the mean age was 64 years, and 73% of the patients were male. Multivariate Cox regression analysis estimated a hazard ratio for developing OA of 0.71 (95% confidence interval 0.64-0.79) (P < 0.001) after treatment with ticagrelor compared to clopidogrel. CONCLUSION Treatment with ticagrelor was associated with a 29% lower risk of developing OA compared to treatment with clopidogrel over 5 years of follow-up. We hypothesize that the reduction in OA seen in patients who received ticagrelor may in part be due to increased extracellular adenosine levels.

Objectives Quality and safety improvement are global priorities. In the last two decades, the United States has introduced several payment reforms to improve patient safety. The Agency for Healthcare Research and Quality (AHRQ) developed tools to identify preventable inpatient adverse events using administrative data, patient safety indicators (PSIs). The aim of this study was to assess changes in national patient safety trends that corresponded to U.S. pay-for-performance reforms. Methods This is a retrospective, longitudinal analysis to estimate temporal changes in 13 AHRQ's PSIs. National inpatient sample from the AHRQ and estimates were weighted to represent a national sample. We analyzed PSI trends, Center for Medicaid and Medicare Services payment policy changes, and Inpatient Prospective Payment System regulations and notices between 2000 and 2013. Results Of the 13 PSIs studied, 10 had an overall decrease in rates and 3 had an increase. Joinpoint analysis showed that 12 of 13 PSIs had decreasing or stable trends in the last 5 years of the study. Central-line blood stream infections had the greatest annual decrease (-31.1 annual percent change between 2006 and 2013), whereas postoperative respiratory failure had the smallest decrease (-3.5 annual percent change between 2005 and 2013). With the exception of postoperative hip fracture, significant decreases in trends preceded federal payment reform initiatives. Conclusions National in-hospital patient safety has significantly improved between 2000 and 2015, as measured by PSIs. In this study, improvements in PSI trends often proceeded policies targeting patient safety events, suggesting that intense public discourses targeting patient safety may drive national policy reforms and that these improved trends may be sustained by the Center for Medicare and Medicaid Services policies that followed.

Background mHealth technology holds promise for improving the effectiveness of frontline health workers (FLWs), who provide most health-related primary care services, especially reproductive, maternal, newborn, child health and nutrition services (RMNCHN), in low-resource - especially hard-to-reach - settings. Data are lacking, however, from rigorous evaluations of mHealth interventions on delivery of health services or on health-related behaviors and outcomes. Methods The Information Communication Technology-Continuum of Care Service (ICT-CCS) tool was designed for use by community-based FLWs to increase the coverage, quality and coordination of services they provide in Bihar, India. It consisted of numerous mobile phone-based job aids aimed to improve key RMNCHN-related behaviors and outcomes. ICT-CCS was implemented in Saharsa district, with cluster randomization at the health sub-center level. In total, evaluation surveys were conducted with approximately 1100 FLWs and 3000 beneficiaries who had delivered an infant in the previous year in the catchment areas of intervention and control health sub-centers, about half before implementation (mid-2012) and half two years afterward (mid-2014). Analyses included bivariate and difference-in-difference analyses across study groups. Results The ICT-CCS intervention was associated with more frequent coordination of AWWs with ASHAs on home visits and greater job confidence among ASHAs. The intervention resulted in an 11 percentage point increase in FLW antenatal home visits during the third trimester (P = 0.04). In the post-implementation period, postnatal home visits during the first week were increased in the intervention (72%) vs the control (60%) group (P

Background The American Society of Anesthesiologists Physical Status (ASA-PS) classification system was developed to categorize the fitness of patients before surgery. Increasingly, the ASA-PS has been applied to other uses including justification of inpatient admission. Our objectives were to develop and cross-validate a statistical model for predicting ASA-PS; and 2) assess the concurrent and predictive validity of the model by assessing associations between model-derived ASA-PS, observed ASA-PS, and a diverse set of 30-day outcomes. Methods Using the 2014 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Participant Use Data File, we developed and internally cross-validated multinomial regression models to predict ASA-PS using preoperative NSQIP data. Accuracy was assessed with C-Statistics and calibration plots. We assessed both concurrent and predictive validity of model-derived ASA-PS relative to observed ASA-PS and 30-day outcomes. To aid further research and use of the ASA-PS model, we implemented it into an online calculator. Results Of the 566,797 elective procedures in the final analytic dataset, 8.9% were ASA-PS 1, 48.9% were ASA-PS 2, 39.1% were ASA-PS 3, and 3.2% were ASA-PS 4. The accuracy of the 21-variable model to predict ASA-PS was C = 0.77 +/− 0.0025. The model-derived ASA-PS had stronger association with key indicators of preoperative status including comorbidities and higher BMI (concurrent validity) compared to observed ASA-PS, but less strong associations with postoperative complications (predictive validity). The online ASA-PS calculator may be accessed at https://s-spire-clintools.shinyapps.io/ASA_PS_Estimator/ Conclusions Model-derived ASA-PS better tracked key indicators of preoperative status compared to observed ASA-PS. The ability to have an electronically derived measure of ASA-PS can potentially be useful in research, quality measurement, and clinical applications.

IntroductionWe evaluated the impact of a ‘Team-Based Goals and Incentives’ (TBGI) intervention in Bihar, India, designed to improve front-line (community health) worker (FLW) performance and health-promoting behaviours related to reproductive, maternal, newborn and child health and nutrition.MethodsThis study used a cluster randomised controlled trial design and difference-in-difference analyses of improvements in maternal health-related behaviours related to the intervention’s team-based goals (primary), and interactions of FLWs with each other and with maternal beneficiaries (secondary). Evaluation participants included approximately 1300 FLWs and 3600 mothers at baseline (May to June 2012) and after 2.5 years of implementation (November to December 2014) who had delivered an infant in the previous year.ResultsThe TBGI intervention resulted in significant increases in the frequency of antenatal home visits (15 absolute percentage points (PP), p=0.03) and receipt of iron-folic acid (IFA) tablets (7 PP, p=0.02), but non-significant changes in other health behaviours related to the trial’s goals. Improvements were seen in selected attitudes related to coordination and teamwork among FLWs, and in the provision of advice to beneficiaries (ranging from 8 to 14 PP) related to IFA, cord care, breast feeding, complementary feeding and family planning.ConclusionResults suggest that combining an integrated set of team-based coverage goals and targets, small non-cash incentives for teams who meet targets and team building to motivate FLWs resulted in improvements in FLW coordination and teamwork, and in the quality and quantity of FLW–beneficiary interactions. These improvements represent programmatically meaningful steps towards improving health behaviours and outcomes.Trial registration numberNCT03406221

Importance Infusion reactions occur in 7% to 20% of patients receiving biologics. Home infusions are convenient and incur lower costs but may be associated with more adverse events; the safety of receiving biologic infusions for immune-mediated diseases at home remains unclear. Objective To assess whether patients receiving home biologic infusions have increased adverse events requiring emergency department (ED) or hospital admission compared with patients receiving facility infusions. Design, Setting, and Participants This retrospective cohort study used administrative claims data from a large national insurer for adult patients who received biologic infusions for immune-mediated disease between January 2007 and December 2017. Patients with hematologic malignant neoplasms or bone marrow transplantation were excluded. Data were analyzed from August 2019 to October 2020. Main Outcomes and Measures ED or hospital admission on the same or next day after administration of a biologic infusion at home vs at a facility; secondary outcomes included discontinuation of the biologic after an ED or hospital admission and postinfusion mortality. Results Of a total of 57 220 patients (mean [SD] age, 50.1 [14.8] years; 512 314 [68.1%] women) who received 752 150 biologic infusions (34 078 home infusions [4.5%] to 3954 patients and 718 072 facility infusions [95.5%] to 54 770 patients), patients who received home infusions were younger (mean [SD] age, 43.2 [13.2] vs 51.3 [14.8] years), more likely to be men (14 031 [41.2%] vs 225 668 [31.4%]), and had a lower Charlson comorbidity score compared with patients who received facility infusions (mean [SD] score, 0.5 [1.0] vs 1.1 [1.3]). Home infusions were associated with 25% increased odds of ED or hospital admission on the same or next day after the infusion (odds ratio [OR], 1.25; 95% CI, 1.09-1.44;P = .002) and 28% increased odds of discontinuation of the biologic after the ED or hospital admission (OR, 1.28; 95% CI, 1.08-1.51;P = .005). There was no difference in postinfusion mortality between home or facility infusions. The rates of adverse events were highest with home infusions of tocilizumab (48 of 481 infusions [10.0%]), vedolizumab (150 of 2681 infusions [5.6%]), and infliximab (1085 of 20 653 infusions [5.3%]), although the number of tocilizumab and vedolizumab infusions was low. Conclusions and Relevance In this study, biologic infusions administered at home, compared with those administered at a facility, were associated with increased adverse events requiring escalation of care. Because the number of home infusions has increased and is expected to continue to rise, the safety implications of administering biologic infusions at home needs to be further assessed.

BACKGROUND: Existing universal and procedure-specific surgical risk prediction models of death and major complications after elective total joint arthroplasty (TJA) have limitations including poor transparency, poor to modest accuracy, and insufficient validation to establish performance across diverse settings. Thus, the need remains for accurate and validated prediction models for use in preoperative management, informed consent, shared decision-making, and risk adjustment for reimbursement. QUESTIONS/PURPOSES: The purpose of this study was to use machine learning methods and large national databases to develop and validate (both internally and externally) parsimonious risk-prediction models for mortality and complications after TJA. METHODS: Preoperative demographic and clinical variables from all 107,792 nonemergent primary THAs and TKAs in the 2013 to 2014 American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) were evaluated as predictors of 30-day death and major complications. The NSQIP database was chosen for its high-quality data on important outcomes and rich characterization of preoperative demographic and clinical predictors for demographically and geographically diverse patients. Least absolute shrinkage and selection operator (LASSO) regression, a type of machine learning that optimizes accuracy and parsimony, was used for model development. Tenfold validation was used to produce C-statistics, a measure of how well models discriminate patients who experience an outcome from those who do not. External validation, which evaluates the generalizability of the models to new data sources and patient groups, was accomplished using data from the Veterans Affairs Surgical Quality Improvement Program (VASQIP). Models previously developed from VASQIP data were also externally validated using NSQIP data to examine the generalizability of their performance with a different group of patients outside the VASQIP context. RESULTS: The models, developed using LASSO regression with diverse clinical (for example, American Society of Anesthesiologists classification, comorbidities) and demographic (for example, age, gender) inputs, had good accuracy in terms of discriminating the likelihood a patient would experience, within 30 days of arthroplasty, a renal complication (C-statistic, 0.78; 95% confidence interval [CI], 0.76-0.80), death (0.73; 95% CI, 0.70-0.76), or a cardiac complication (0.73; 95% CI, 0.71-0.75) from one who would not. By contrast, the models demonstrated poor accuracy for venous thromboembolism (C-statistic, 0.61; 95% CI, 0.60-0.62) and any complication (C-statistic, 0.64; 95% CI, 0.63-0.65). External validation of the NSQIP- derived models using VASQIP data found them to be robust in terms of predictions about mortality and cardiac complications, but not for predicting renal complications. Models previously developed with VASQIP data had poor accuracy when externally validated with NSQIP data, suggesting they should not be used outside the context of the Veterans Health Administration. CONCLUSIONS: Moderately accurate predictive models of 30-day mortality and cardiac complications after elective primary TJA were developed as well as internally and externally validated. To our knowledge, these are the most accurate and rigorously validated TJA-specific prediction models currently available (http://med.stanford.edu/s-spire/Resources/clinical-tools-.html). Methods to improve these models, including the addition of nonstandard inputs such as natural language processing of preoperative clinical progress notes or radiographs, should be pursued as should the development and validation of models to predict longer term improvements in pain and function. LEVEL OF EVIDENCE: Level III, diagnostic study.

Additional file 1: Table S1. Coefficients for the Multinomial Model to Predict ASA Class by Major Pre-operative Factors.