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Objectives We characterized real-time patient portal test result viewing among emergency department (ED) patients and described patient characteristics overall and among those not enrolled in the portal at ED arrival. Methods Our observational study at an academic ED used portal log data to trend the proportion of adult patients who viewed results during their visit from May 04, 2021 to April 04, 2022. Correlation was assessed visually and with Kendall's τ. Covariate analysis using binary logistic regression assessed result(s) viewed as a function of time accounting for age, sex, ethnicity, race, language, insurance status, disposition, and social vulnerability index (SVI). A second model only included patients not enrolled in the portal at arrival. We used random forest imputation to account for missingness and Huber-White heteroskedasticity-robust standard errors for patients with multiple encounters (α = 0.05). Results There were 60,314 ED encounters (31,164 unique patients). In 7,377 (12.2%) encounters, patients viewed results while still in the ED. Patients were not enrolled for portal use at arrival in 21,158 (35.2%) encounters, and 927 (4.4% of not enrolled, 1.5% overall) subsequently enrolled and viewed results in the ED. Visual inspection suggests an increasing proportion of patients who viewed results from roughly 5 to 15% over the study (Kendall's τ = 0.61 [p Conclusion We observed increased portal-based test result viewing among ED patients over the year since the 21st Century Cures act went into effect, even among those not enrolled at arrival. We observed disparities in those who viewed results.

Angiotensin-converting enzyme inhibitor (ACEI) and angiotensin receptor blocker (ARB) drugs may modify risk associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Therefore, we assessed whether baseline therapy with ACEIs or ARBs was associated with lower mortality, respiratory failure (noninvasive ventilation or intubation), and renal failure (new renal replacement therapy) in SARS-CoV-2-positive patients. This retrospective registry-based observational cohort study used data from a national database of emergency department patients tested for SARS-CoV-2. Symptomatic emergency department patients were accrued from January to October 2020, across 197 hospitals in the United States. Multivariable analysis using logistic regression evaluated end points among SARS-CoV-2-positive cases, focusing on ACEIs/ARBs and adjusting for covariates. Model performance was evaluated using the c statistic for discrimination and Cox plotting for calibration. A total of 13 859 (99.9%) patients had known mortality status, of whom 2045 (14.8%) died. Respiratory failure occurred in 2485/13 880 (17.9%) and renal failure in 548/13 813 (4.0%) patients with available data. ACEI/ARB status was associated with a 25% decrease in mortality odds (odds ratio [OR], 0.75; 95%CI, 0.59-0.94; P = .011; c = .82). ACEIs/ARBs were not significantly associated with respiratory failure (OR, 0.89; 95%CI, 0.78-1.06; P = .206) or renal failure (OR, 0.75; 95%CI, 0.55-1.04; P = .083). Adjusting for covariates, baseline ACEI/ARB was associated with 25% lower mortality in SARS-CoV-2-positive patients. The potential mechanism for ACEI/ARB mortality modification requires further exploration.

Study objective The goals of this study were to determine the current and projected supply in 2030 of contributors to emergency care, including emergency residency-trained and board-certified physicians, other physicians, nurse practitioners, and physician assistants. In addition, this study was designed to determine the current and projected demand for residency-trained, board-certified emergency physicians. Methods To forecast future workforce supply and demand, sources of existing data were used, assumptions based on past and potential future trends were determined, and a sensitivity analysis was conducted to determine how the final forecast would be subject to variance in the baseline inputs and assumptions. Methods included: (1) estimates of the baseline workforce supply of physicians, nurse practitioners, and physician assistants; (2) estimates of future changes in the raw numbers of persons entering and leaving that workforce; (3) estimates of the productivity of the workforce; and (4) estimates of the demand for emergency care services. The methodology assumes supply equals demand in the base year and estimates the change between the base year and 2030; it then compares supply and demand in 2030 under different scenarios. Results The task force consensus was that the most likely future scenario is described by: 2% annual graduate medical education growth, 3% annual emergency physician attrition, 20% encounters seen by a nurse practitioner or physician assistant, and 11% increase in emergency department visits relative to 2018. This scenario would result in a surplus of 7,845 emergency physicians in 2030. Conclusion The specialty of emergency medicine is facing the likely oversupply of emergency physicians in 2030. The factors leading to this include the increasing supply of and changing demand for emergency physicians. An organized, collective approach to a balanced workforce by the specialty of emergency medicine is imperative.

Objectives Patient knowledge deficits related to opioid risks, including lack of knowledge regarding addiction, are well documented. Our objective was to characterize patients' perceptions of signs of addiction. Methods This study utilized data obtained as part of a larger interventional trial. Consecutively discharged English-speaking patients, age >17 years, at an urban academic emergency department, with a new opioid prescription were enrolled from July 2015 to August 2017. During a follow-up phone interview 7 to 14 days after discharge, participants were asked a single question, "What are the signs of addiction to pain medicine?" Verbatim transcribed answers were analyzed using a directed content analysis approach and double coding. These codes were then grouped into themes. Results There were 325 respondents, 57% female, mean age 43.8 years, 70.1% privately insured. Ten de novo codes were added to the 11 DSM-V criteria codes. Six themes were identified: (1) effort spent acquiring opioids, (2) emotional and physical changes related to opioid use, (3) opioid use that is "not needed, (4) increasing opioid use, (5) an emotional relationship with opioids, and (6) the inability to stop opioid use. Conclusions Signs of addiction identified by opioid naive patients were similar to concepts identified in medical definitions. However, participants' understanding also included misconceptions, omissions, and conflated misuse behaviors with signs of addiction. Identifying these differences will help inform patient-provider risk communication, providing an opportunity for counseling and prevention.

Background and purpose There remains limited evidence for the clinical importance of most imaging findings in whiplash. However, it is possible the type and number of findings on Computed Tomography (CT) may contribute to prognostic recovery models. The purpose is to interpret cervical spine pathologies in the context of known factors influencing recovery. Materials and methods This is a secondary analysis from a database of 97 acutely injured participants enrolled in a prospective inception cohort study. Thirty-eight participants underwent standard of care cervical spine CT in the emergency medicine department. All 38 participants were assessed at Results Participants from both groups demonstrated at least one observable pathology. The group with persistent moderate/severe symptoms presented with significantly more pathology at baseline than those who later reported recovery or milder symptoms at 3-months post injury (p = 0.02). Conclusions This preliminary study, which needs replication in a larger cohort, provides foundation that the number of degenerative pathologies seen on initial post MVC CT may be associated with the subsequent clinical course of whiplash.

Objectives Recent guidelines advise limiting opioid prescriptions for acute pain to a three-day supply; however, scant literature quantifies opioid use patterns after an emergency department (ED) visit. We sought to describe opioid consumption patterns after an ED visit for acute pain. Design Descriptive study with data derived from a larger interventional study promoting safe opioid use after ED discharge. Setting Urban academic emergency department (>88,000 annual visits). Subjects Patients were eligible if age >17 years, not chronically using opioids, and newly prescribed hydrocodone-acetaminophen and were included in the analysis if they returned the completed 10-day medication diary. Methods Patient demographics and opioid consumption are reported. Opioid use is described in daily number of pills and daily morphine milligram equivalents (MME) both for the sample overall and by diagnosis. Results Two hundred sixty patients returned completed medication diaries (45 [17%] back pain, 52 [20%] renal colic, 54 [21%] fracture/dislocation, 40 [15%] musculoskeletal injury [nonfracture], and 69 [27%] “other”). The mean age (SD) was 45 (15) years, and 59% of the sample was female. A median of 12 pills were prescribed. Patients with renal colic used the least opioids (total pills: median [interquartile range {IQR}] = 3 [1–7]; total MME: median [IQR] = 20 [10–50]); patients with back pain used the most (total pills: median [IQR] = 12 [7–16]; total MME: median [IQR] = 65 [47.5–100]); 92.5% of patients had leftover pills. Conclusions In this sample, pill consumption varied by illness category; however, overall, patients were consuming low quantities of pills, and the majority had unused pills 10 days after their ED visit.

Patient-reported outcomes (PROs) are of increasing importance in clinical research because they capture patients' experience with well-being, illness, and their interactions with health care. Because PROs tend to focus on specific symptoms (e.g., pain, anxiety) or general assessments of patient functioning and quality of life that offer unique advantages compared to traditional clinical outcomes (e.g., mortality, emergency department revisits), emergency care researchers may benefit from incorporation of PRO measures into their research design as a primary or secondary outcome. Patients may also benefit from the ability of PROs to inform clinical practice and facilitate patient decision making, as PROs are obtained directly from the lived experience of other patients with similar conditions or health status. This review article introduces and defines key terminology relating to PROs, discusses reasons for utilizing PROs in clinical research, outlines basic psychometric and practical assessments that can be used to select a specific PRO measure, and highlights examples of commonly utilized PRO measures in emergency care research.

We sought to identify longitudinal trends in workforce entry and attrition among rural and urban emergency physicians, nonemergency physicians, and advanced practice providers.We performed a repeated cross-sectional analysis, from 2013 to 2019, of emergency clinicians who received reimbursement for at least 50 Evaluation and Management services [99281-99285] from Medicare part B within any study year. We calculated the emergency workforce's entry and attrition rates annually. Entry was defined as clinicians newly entering or re-entering the workforce, and attrition was defined as clinicians leaving permanently or temporarily. We stratified the analyses by rural designation and assessed the proportions and state-level changes in clinician density.In total, 82,499 unique clinicians performed at least 50 Evaluation and Management services within any of the 7 study years examined, including 47,000 emergency physicians, 9,029 nonemergency physicians, and 26,470 advanced practice providers. Emergency physicians made up a decreasing proportion of the workforce (68.1% in 2013; 65.5% in 2019), and advanced practice providers made up an increasing proportion of the workforce (20.9% in 2013; 26.1% in 2019). Annually, 5.9% to 6.8% (2,186 to 2,407) of emergency physicians newly entered and 0.8% to 1.4% (264 to 515) re-entered the workforce, whereas 3.8% to 4.9% (1,241 to 1,793) permanently left and 0.8% to 1.6% (276 to 521) temporarily left. Additionally, the total proportion of clinicians practicing in rural designations decreased, and advanced practice providers separately made up a substantially increasing proportion of the rural workforce (23.0% in 2013; 32.7% in 2019). Substantial state-level variation existed in the supply and demand of emergency clinician densities per 100,000 population.The annual rate of emergency physician attrition was collectively more than 5%, well above the 3% assumed in a recently publicized projection, suggesting a potential overestimation of the anticipated future clinician surplus. Notably, the attrition of emergency physicians has disproportionately affected vulnerable rural areas. This work can inform emergency medicine workforce decisions regarding residency training, advanced practice provider utilization, and clinician employment.

Increased body mass index (BMI) and metabolic syndrome (MetS) have been associated with adverse outcomes in viral syndromes. We sought to examine associations of increased BMI and MetS on several clinical outcomes in patients tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).The registry of suspected COVID-19 in emergency care (RECOVER) is an observational study of SARS-CoV-2-tested patients (n=27,051) across 155 United States emergency departments (EDs). We used multivariable logistic regression to test for associations of several predictor variables with various clinical outcomes.We found that a BMI ≥30 kg/mIn this large nationwide sample of ED patients, we report a significant association of both high BMI and composite MetS with poor outcomes in SARS-CoV-2-positive patients. Findings suggest that composite MetS profile may be a more universal predictor of adverse disease outcomes, while the impact of BMI is more heavily modulated by SARS-CoV-2 status.

Background The challenging clinical dilemma of detecting pulmonary embolism (PE) in suspected patients is encountered in a variety of healthcare settings. We hypothesized that the optimal diagnostic approach to detect these patients in terms of safety and efficiency depends on underlying PE prevalence, case mix, and physician experience, overall reflected by the type of setting where patients are initially assessed. The objective of this study was to assess the capability of ruling out PE by available diagnostic strategies across all possible settings. Methods and findings We performed a literature search (MEDLINE) followed by an individual patient data (IPD) meta-analysis (MA; 23 studies), including patients from self-referral emergency care (n = 12,612), primary healthcare clinics (n = 3,174), referred secondary care (n = 17,052), and hospitalized or nursing home patients (n = 2,410). Multilevel logistic regression was performed to evaluate diagnostic performance of the Wells and revised Geneva rules, both using fixed and adapted D-dimer thresholds to age or pretest probability (PTP), for the YEARS algorithm and for the Pulmonary Embolism Rule-out Criteria (PERC). All strategies were tested separately in each healthcare setting. Following studies done in this field, the primary diagnostic metrices estimated from the models were the “failure rate” of each strategy—i.e., the proportion of missed PE among patients categorized as “PE excluded” and “efficiency”—defined as the proportion of patients categorized as “PE excluded” among all patients. In self-referral emergency care, the PERC algorithm excludes PE in 21% of suspected patients at a failure rate of 1.12% (95% confidence interval [CI] 0.74 to 1.70), whereas this increases to 6.01% (4.09 to 8.75) in referred patients to secondary care at an efficiency of 10%. In patients from primary healthcare and those referred to secondary care, strategies adjusting D-dimer to PTP are the most efficient (range: 43% to 62%) at a failure rate ranging between 0.25% and 3.06%, with higher failure rates observed in patients referred to secondary care. For this latter setting, strategies adjusting D-dimer to age are associated with a lower failure rate ranging between 0.65% and 0.81%, yet are also less efficient (range: 33% and 35%). For all strategies, failure rates are highest in hospitalized or nursing home patients, ranging between 1.68% and 5.13%, at an efficiency ranging between 15% and 30%. The main limitation of the primary analyses was that the diagnostic performance of each strategy was compared in different sets of studies since the availability of items used in each diagnostic strategy differed across included studies; however, sensitivity analyses suggested that the findings were robust. Conclusions The capability of safely and efficiently ruling out PE of available diagnostic strategies differs for different healthcare settings. The findings of this IPD MA help in determining the optimum diagnostic strategies for ruling out PE per healthcare setting, balancing the trade-off between failure rate and efficiency of each strategy.

Background: How diagnostic strategies for suspected pulmonary embolism (PE) perform in relevant patient subgroups defined by sex, age, cancer, and previous venous thromboembolism (VTE) is unknown. Purpose: To evaluate the safety and efficiency of the Wells and revised Geneva scores combined with fixed and adapted D-dimer thresholds, as well as the YEARS algorithm, for ruling out acute PE in these subgroups. Data Sources: MEDLINE from 1 January 1995 until 1 January 2021. Study Selection: 16 studies assessing at least 1 diagnostic strategy. Data Extraction: Individual-patient data from 20553 patients. Data Synthesis: Safety was defined as the diagnostic failure rate (the predicted 3-month VTE incidence after exclusion of PE without imaging at baseline). Efficiency was defined as the proportion of individuals classified by the strategy as "PE con -sidered excluded" without imaging tests. Across all strategies, efficiency was highest in patients younger than 40 years (47% to 68%) and lowest in patients aged 80 years or older (6.0% to 23%) or patients with cancer (9.6% to 26%). However, efficiency improved considerably in these subgroups when pretest probabil-ity-dependent D-dimer thresholds were applied. Predicted failure rates were highest for strategies with adapted D-dimer thresh-olds, with failure rates varying between 2% and 4% in the pre-defined patient subgroups. Limitations: Between-study differences in scoring predictor items and D-dimer assays, as well as the presence of differential verifica-tion bias, in particular for classifying fatal events and subsegmental PE cases, all of which may have led to an overestimation of the predicted failure rates of adapted D-dimer thresholds. Conclusion: Overall, all strategies showed acceptable safety, with pretest probability-dependent D-dimer thresholds having not only the highest efficiency but also the highest predicted failure rate. From an efficiency perspective, this individual-patient data meta-analysis supports application of adapted D-dimer thresholds. Primary Funding Source: Dutch Research Council. (PROSPERO: CRD42018089366)

BACKGROUND The Society for Academic Emergency Medicine Board of Directors convened a Task Force to elucidate the current state of workforce, operational, and educational issues being faced by academic medical centers related to advanced practice providers (APPs). The Task Force surveyed academic emergency department (ED) Chairs and Residency Program Directors. METHODS The survey was distributed to the Association of Academic Chairs of Emergency Medicine (AACEM)-member Chairs and their respective Residency Program Directors (PDs) in 2021. We surveyed 125 Chairs with their self-identified PDs. The survey sampled hiring, state independent practice laws, scope of practice, teaching and supervision, training opportunities, delegation of procedures between physician learners and APPs, and perceptions of the impact on resident and medical student education. RESULTS Of the AACEM-member Chairs identified, 73% responded and 47% of PDs responded. Most (98%) employ either physician assistants or nurse practitioners. Among responding departments, 86% report APPs working in fast track settings, 80% work in the main ED and 54% in the waiting room. In 44% of departments, APPs and residents evaluate patients concurrently, and 2% of respondents report that APPs manage high-acuity patients without attending involvement. Two-thirds of Chairs believe that APPs contribute positively to the quality of patient care, while 44% believe APPs contribute to the academic environment. One-third of PDs believe that the presence of APPs interferes with resident education. Although 75% of PDs believe residents require training to work effectively with APPs in the ED, almost half (49%) report zero hours of training around APP supervision or collaborative skills. CONCLUSIONS APPs are ubiquitous across academic EDs. Future research is required for academic ED leaders to balance physician and APP deployment across the academic emergency department within the context of patient care, resident education, institutional resources, professional development opportunities for APP staff, and standardization of APP EM training.

Background COVID-19 has been associated with increased risk of thromboembolism in critically ill patients. Objectives We sought to examine the association of SARS-CoV-2 test positivity and subsequent acute vascular thrombosis, including venous thromboembolism (VTE) or arterial thrombosis (AT) in, a large nationwide registry of emergency department patients tested with a nucleic acid test for suspected SARS-CoV-2. Methods The RECOVER registry includes 155 emergency departments across the US. We performed a retrospective cohort study to produce odds ratios for COVID+ versus COVID- status as a predictor of 30 day VTE or AT, adjusting for age, biological sex, active cancer, intubation, hospital length of stay (LOS), and ICU care. Results Comparing 14,056 COVID+ patients with 12,995 COVID- patients, the overall 30-day prevalence of VTE events was 1.4% versus 1.3%, respectively (p=0.44, χ2). Multivariable analysis identified that testing positive for SAR-CoV-2 status was negatively associated with both VTE (OR 0.76, 95% CI: 0.61-0.94) and AT (0.51, 0.32-0.80), whereas intubation, ICU care, and age>=50 were positively associated with both VTE and AT. Conclusions In contrast to other reports, results from this large, heterogenous national sample of ED patients tested for SARS-CoV-2, showed no association between vascular thrombosis and COVID-19 test positivity.

Objectives Identification of patients with coronavirus disease 2019 (COVID‐19) at risk for deterioration after discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies patients with COVID‐19 at risk for return and hospital admission within 30 days of ED discharge. Methods We performed a retrospective cohort study of discharged adult ED patients (n = 7529) with SARS‐CoV‐2 infection from 116 unique hospitals contributing to the National Registry of Suspected COVID‐19 in Emergency Care. The primary outcome was return hospital admission within 30 days. Models were developed using classification and regression tree (CART), gradient boosted machine (GBM), random forest (RF), and least absolute shrinkage and selection (LASSO) approaches. Results Among patients with COVID‐19 discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine‐learning (ML) models (GBM, RF, and LASSO) performed similarly. The RF model yielded a test area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.71–0.78), with a sensitivity of 0.46 (95% CI, 0.39–0.54) and a specificity of 0.84 (95% CI, 0.82–0.85). Predictive variables, including lowest oxygen saturation, temperature, or history of hypertension, diabetes, hyperlipidemia, or obesity, were common to all ML models. Conclusions A predictive model identifying adult ED patients with COVID‐19 at risk for return for return hospital admission within 30 days is feasible. Ensemble/boot‐strapped classification methods (eg, GBM, RF, and LASSO) outperform the single‐tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize the allocation of follow‐up resources.

People receiving in-center hemodialysis (HD) have prioritized the need for more individualized health information and better communication with nephrologists. The most common setting for patient-nephrologist interactions is during the HD treatment, which is a time pressured setting that lacks privacy.To facilitate effective communication in the hemodialysis (HD) unit, we evaluated the usability of a web application (web app) from both the patient and physician perspective. The main aim of the web app was to support patients in prioritizing their dialysis concerns outside of the clinical HD encounter.Mixed method, parallel arm, multi-site, pilot randomized controlled trial.Two outpatient Canadian HD centers.Adult patients receiving in-center HD and their attending nephrologists.Patients were randomized to either a web application or an active control (paper form) for logging concerns to be addressed at weekly encounters with the nephrologist over 8 weeks. Topics included: HD treatment, symptoms, modality, and medications. The primary outcome was usability, defined as effectiveness (engagement with the tool, frequency of submitted concerns, whether the concern was satisfactorily addressed) and satisfaction with the tool using a priori thresholds and explored in interviews with patients and nephrologists.77 patients (30 women, median age 61, interquartile range [53,67], median 2 years [1,4] on dialysis) and 19 nephrologists (4 women, median age 46 [36,65]) were enrolled. Patient use of a digital device at baseline was low (20%). Engagement with the tool was 70% (web app) and 100% (paper) with a lower proportion of patients in the web app group submitting at least one concern over 8 weeks compared to the paper form group: 56.7% vs 87.9%. Weekly concerns were satisfactorily addressed in both groups and ≥70% of patients would continue to use the tools. For patients, both tools promoted preparation and participation in the encounter; however, only the web app facilitated greater privacy in relaying concerns. For most nephrologists, the tools were disruptive to their workflow and were perceived as unnecessary given existing processes and familiarity with patients. For future versions of the app, patients suggested more features to facilitate self-management and nephrologists suggested integration with health databases and multidisciplinary teams.Tertiary setting may limit generalizability.Both tools promoted fundamental components of self-management; however, patients in the paper form group submitted concerns more often and this tool was easier to remember to use. Although modifications would likely enhance web app usability, successful future adoption is limited by physician acceptance.Les personnes qui reçoivent l’hémodialyse (HD) en center hospitalier jugent nécessaire d’obtenir des informations de santé plus individualisées et d’avoir une meilleure communication avec les néphrologues. Les interactions entre les patients et les néphrologues ont plus souvent lieu pendant l’hémodialyse, mais ce contexte manque d’intimité et les parties sont souvent pressées par le temps.Pour aider à établir une communication plus efficace dans l’unité d’hémodialyse (HD), nous avons évalué la convivialité d’une application Web du point de vue du patient et du médecin. Cette application Web devait aider les patients à faire part de leurs préoccupations liées à la dialyze en dehors des séances d’HD.Essai multicentrique randomisé contrôlé avec groupes parallèles, réalisé par méthodes mixtes.Deux centers d’hémodialyse canadiens en consultation externe.Des adultes recevant des traitements d’HD en center hospitalier et leurs néphrologues traitants.Les patients ont été répartis aléatoirement pour utiliser l’application Web ou un témoin actif (formulaire papier) pour consigner, sur une période de huit semaines, les préoccupations à aborder lors des rencontres hebdomadaires avec leur néphrologue. Les sujets abordés concernaient le traitement d’HD, les symptômes, la modalité et les médicaments. Le principal critère d’évaluation était la facilité d’utilization, définie par l’efficacité (engagement avec l’outil, fréquence des soumissions, si le problème a été traité de façon satisfaisante). La satisfaction à l’égard de l’outil a été évaluée avec des seuils préétablis et explorée lors d’entrevues avec les patients et les néphrologues.Ont été inclus 77 patients (30 femmes) sous dialyze depuis 2 ans (durée médiane; intervalle interquartile [1,4]) et dont l’âge médian s’établissait à 61 ans [53-67]. Ont aussi été inclus 19 néphrologues (4 femmes; âge médian : 46 ans [36-65]). Au début de l’étude, l’utilization d’un dispositif numérique par les patients était faible (20 %). L’engagement avec l’outil était de 70 % (application Web) et de 100 % (formulaire). Les patients du groupe « application Web » sont moins nombreux à avoir soumis au moins une préoccupation au cours des huit semaines comparativement au groupe utilisant les formulaires papier (56,7 % c. 87,9 %). Les préoccupations hebdomadaires ont été abordées de façon satisfaisante dans les deux groupes et plus de 70 % des patients continueraient d’utiliser ces outils. Pour les patients, les deux outils ont favorisé la préparation et la participation à la rencontre, mais seule l’application Web a permis d’accroître la confidentialité dans la transmission des préoccupations. La plupart des néphrologues ont trouvé que ces outils perturbaient leur flux de travail et les ont perçus comme inutiles puisqu’ils jugent qu’un processus et une familiarité avec les patients existent déjà. Les patients ont suggéré que les futures versions de l’application aient plus de caractéristiques pour faciliter l’autogestion; les néphrologues ont quant à eux suggéré qu’elle soit intégrée aux bases de données sur la santé et aux équipes multidisciplinaires.Étude menée dans des centers de soins tertiaires, ceci pourrait limiter la généralisabilité des résultats.Les deux outils ont facilité des composantes fondamentales de l’autogestion. Cependant, les patients qui utilisaient des formulaires papier ont plus souvent fait part de leurs préoccupations. Il s’est également avéré qu’on pensait davantage à utiliser cet outil que l’application. Bien que des modifications puissent accroître la convivialité de l’application Web, son adoption demeure limitée par l’acceptation des médecins.

Objectives Despite increased focus on opioid prescribing, little is known about the influence of prescription opioid medication information given to patients in the emergency department (ED). The study objective was to evaluate the effect of an Electronic Medication Complete Communication (EMC2 ) Opioid Strategy on patients' safe use of opioids and knowledge about opioids. Methods This was a three-arm prospective, randomized controlled pragmatic trial with randomization occurring at the physician level. Consecutive discharged patients at an urban academic ED (>88,000 visits) with new hydrocodone-acetaminophen prescriptions received one of three care pathways: 1) usual care, 2) EMC2 intervention, or 3) EMC2 + short message service (SMS) text messaging. The ED EMC2 intervention triggered two patient-facing educational tools (MedSheet, literacy-appropriate prescription wording [Take-Wait-Stop]) and three provider-facing reminders to counsel (directed to ED physician, dispensing pharmacist, follow-up physician). Patients in the EMC2 + SMS arm additionally received one text message/day for 1 week. Follow-up at 1 to 2 weeks assessed "demonstrated safe use" (primary outcome). Secondary outcomes including patient knowledge and actual safe use (via medication diaries) were assessed 2 to 4 days and 1 month following enrollment. Results Among the 652 enrolled, 343 completed follow-up (57% women; mean ± SD age = 42 ± 14.0 years). Demonstrated safe opioid use occurred more often in the EMC2 group (adjusted odds ratio [aOR] = 2.46, 95% confidence interval [CI] = 1.19 to 5.06), but not the EMC2 + SMS group (aOR = 1.87, 95% CI = 0.90 to 3.90) compared with usual care. Neither intervention arm improved medication safe use as measured by medication diary data. Medication knowledge, measured by a 10-point composite knowledge score, was greater in the EMC2 + SMS group (β = 0.57, 95% CI = 0.09 to 1.06) than usual care. Conclusions The study found that the EMC2 tools improved demonstrated safe dosing, but these benefits did not translate into actual use based on medication dairies. The text-messaging intervention did result in improved patient knowledge.

OBJECTIVES To describe the frequency and risk of return visit to the emergency department (ED) by older adults after prescription of any of four potentially inappropriate medication (PIM) classes included in the 2015 Beers Criteria commonly used for the relief of acute pain in the ED. DESIGN Retrospective cohort study. SETTING Large urban academic ED from January 1, 2013, to December 31, 2015. PARTICIPANTS Patients age 65 and older discharged from the ED with an initial pain score of 1 or higher (11 822 visits). MEASUREMENTS Prescriptions for PIM classes were collected from the medical record: nonsteroidal anti-inflammatory drugs (NSAIDs), benzodiazepines, skeletal muscle relaxants, and opioids. The proportion of patients with ED returns within 9 days were compared by medication class and pain severity (mild, moderate, or severe). Multivariable logistic regression was performed for each pain category to determine adjusted odds ratios (aORs) of ED return. RESULTS Of 11 822 included patients, PIMs were prescribed in 3392 (28.7%): 2550 (21.6%) opioids, 826 (7.0%) NSAIDs, 277 (2.3%) benzodiazepines, and 68 (0.6%) nonbenzodiazepine skeletal muscle relaxants. Total 9-day ED returns were 1125 (9.5%): mild 7.0%, moderate 8.3%, and severe pain 11.7%. Opioids were not associated with more frequent ED returns for mild or moderate pain, and they were associated with less frequent ED returns for severe pain (9.2% vs 12.7%; p < .001; aOR 0.69; 95% confidence interval [CI] = 0.54-0.87). Benzodiazepines were associated with more frequent ED returns for patients with moderate pain (15.5% vs 8.2%; p < .01; aOR = 2.01; 95%CI = 1.10-3.70). CONCLUSIONS These results are consistent with recommendations to limit benzodiazepine prescriptions for older adults and that among older adults with severe pain, opioid prescribing is associated with less frequent ED visits within 9 days of discharge. However, this study was not designed to evaluate safety, adverse events, or other important patient-centered outcomes. J Am Geriatr Soc 67:719-725, 2019.

PURPOSE: To determine whether emergency department (ED) visit history prior to cancer diagnosis is associated with ED visit volume after cancer diagnosis. METHODS: This was a retrospective cohort study of adults (≥ 18 years) with an incident cancer diagnosis (excluding nonmelanoma skin cancers or leukemia) at an academic medical center between 2008 and 2018 and a safety-net hospital between 2012 and 2016. Our primary outcome was the number of ED visits in the first 6 months after cancer diagnosis, modeled using a multivariable negative binomial regression accounting for ED visit history in the 6-12 months preceding cancer diagnosis, electronic health record proxy social determinants of health, and clinical cancer-related characteristics. RESULTS: Among 35,090 patients with cancer (49% female and 50% non-White), 57% had ≥ 1 ED visit in the 6 months immediately following cancer diagnosis and 20% had ≥ 1 ED visit in the 6-12 months prior to cancer diagnosis. The strongest predictor of postdiagnosis ED visits was frequent (≥ 4) prediagnosis ED visits (adjusted incidence rate ratio [aIRR]: 3.68; 95% CI, 3.36 to 4.02). Other covariates associated with greater postdiagnosis ED use included having 1-3 prediagnosis ED visits (aIRR: 1.32; 95% CI, 1.28 to 1.36), Hispanic (aIRR: 1.12; 95% CI, 1.07 to 1.17) and Black (aIRR: 1.21; 95% CI, 1.17 to 1.25) race, homelessness (aIRR: 1.95; 95% CI, 1.73 to 2.20), advanced-stage cancer (aIRR: 1.30; 95% CI, 1.26 to 1.35), and treatment regimens including chemotherapy (aIRR: 1.44; 95% CI, 1.40 to 1.48). CONCLUSION: The strongest independent predictor for ED use after a new cancer diagnosis was frequent ED visits before cancer diagnosis. Efforts to reduce potentially avoidable ED visits among patients with cancer should consider educational initiatives that target heavy prior ED users and offer them alternative ways to seek urgent medical care.

ObjectivesAccurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction rule for SARS-CoV-2 infection that uses simple criteria widely available at the point of care.MethodsData came from the Registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical predictors and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction rule was derived from a 50% random sample (n=9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables.ResultsMultivariable regression yielded a 13-variable score, which was simplified to 13-point rule: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation75% probability with +5 or more points).ConclusionCriteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decision about isolation and testing at high throughput checkpoints.Key pointsQuestionCan clinical criteria, derived solely from interview and vital signs accurately estimate the probability of infection from the novel coronavirus (SARS-CoV-2) that causes COVID-19?FindingsFrom derivation sample (n=9,925), we derived a set of 13 clinical criteria that produced an area under the receiver operating characteristic curve of 0.80 (0.79-0.81) in a validation sample (n=9,925). At a score of zero, the simplified version of the criteria produced sensitivity of 95.6% (94.8 to 96.3%), and specificity of 20.0% (19.0 to 21.0%).MeaningClinical criteria can estimate the probability of SARS-CoV-2 infection.

PURPOSE: Patients with cancer undergoing treatment frequently visit the emergency department (ED) for commonly anticipated complaints (eg, pain, nausea, and vomiting). Nearly all Medicare Oncology Care Model (OCM) participants prioritized ED use reduction, and the OCM requires that patients have 24-hour telephone access to a clinician, but actual reductions in ED visits have been mixed. Little is known about the use of telephone triage for acute care. METHODS: We identified adults aged 18+ years newly diagnosed with cancer, linked to ED visits from a single institution within 6 months after diagnosis, and then analyzed the telephone and secure electronic messages in the preceding 24 hours. We coded interactions to classify the reason for the call, the main ED referrer, and other attempted management. We compared the acuity of patient self-referred versus clinician-referred ED visits by modeling hospitalization and ED visit severity. RESULTS: From 2011 to 2018, 3,247 adults made 5,371 ED visits to the university hospital and self-referred to the ED 58.5% of the time. Clinicians referred to outpatient or oncology urgent care for 10.3% of calls but referred to the ED for 61.3%. Patient self-referred ED visits were likely to be hospitalized (adjusted Odds Ratio [aOR], 0.89, 95% CI, 0.64 to 1.22) and were not more severe (aOR, 0.75, 95% CI, 0.55 to 1.02) than clinician referred. CONCLUSION: Although patients self-referred for six of every 10 ED visits, self-referred visits were not more severe. When patients called for advice, clinicians regularly recommended the ED. More should be done to understand barriers that patients and clinicians experience when trying to access non-ED acute care.

Objectives Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. Methods Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. Results Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation75% probability with +5 or more points). Conclusion Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.

e18661 Background: The 30-day rate of readmission, a standard metric for many common acute medical conditions, has also been proposed as a quality measure in cancer. However, hospitalizations for cancer patients are more heterogeneous than traditional groups (pneumonia, heart failure patients). We analyzed the pattern and timing of inpatient readmissions for a population-based sample of adults newly diagnosed with cancer. Methods: We identifiably linked adults diagnosed with cancer (2008-2018) from the UT Southwestern tumor registry to comprehensive hospital visits from a regional all-payer health exchange covering a 100-mile radius of Dallas, TX. Our primary outcome was first inpatient readmission within 30 days of discharge among patients hospitalized within 1 year of cancer diagnosis. Subsequent index periods began > 30 days from index discharge to create non-overlapping readmission windows. We grouped primary diagnosis codes into 15 clinically relevant conditions, noting concordance between index and readmission diagnosis. We separately grouped hospital diagnoses deemed preventable by Medicare’s systemic therapy (chemo or immunotherapy) quality measure. We defined early readmission as occurring on days 0-7 and late as days 8-30. Results: Our cohort of 19,608 patients (49.5% female, 71.0% commercial/Medicare insurance) were most diagnosed with gastrointestinal (14.7%), breast (13.9%), and lung cancer (13.6%), with 28.9% advanced stage at diagnosis. Of those, 53.3% had received systemic therapy for their initial treatment. Patients generated 15,066 index admissions and 1,783 readmissions (11.8% 30-day readmission rate). The reason for readmission was the same as index hospitalization in 22.9% of early readmissions and 15.3% of late readmissions. Neoplasm and infection were the most common conditions; their frequencies were similar in early readmissions (12.7% and 12.8% respectively), but infections were higher in late admissions (12.0% and 15.7%). The next most common were gastrointestinal symptoms - modestly more frequent in systemic therapy patients vs. non-systemic therapy (11.4% and 9.3%); and complications of medical care, modestly more frequent in early readmissions vs. late (9.8% and 7.5%). The proportion of early readmissions for preventable diagnoses was 21.7% following receipt of systemic therapy vs 18.0% not receiving systemic therapy, and 29.2% vs. 22.5% respectively for late readmissions. Conclusions: The 30-day readmission rate in patients with cancer was 11.8% (lower than many inpatient conditions) and was usually due to reasons different than the index admission. More work is needed to validate/refine the concept of readmissions to cancer patients as an indicator of quality of care due heterogeneity of patients with cancer, the reasons they are hospitalized and rehospitalized, and relatively low rates of potential preventability.

IntroductionChronic kidney disease (CKD) is a global-health problem. A significant proportion of referrals to nephrologists for CKD management are early and guideline-discordant, which may lead to an excess number of referrals and increased wait-times. Various initiatives have been tested to increase the proportion of guideline-concordant referrals and decrease wait times. This paper describes the protocol for a systematic review to study the impacts of quality improvement initiatives aimed at decreasing the number of non-guideline concordant referrals, increasing the number of guideline-concordant referrals and decreasing wait times for patients to access a nephrologist.Methods and analysisWe developed this protocol by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols (2015). We will search the following empirical electronic databases: MEDLINE, Embase, Cochrane Library, CINAHL, Web of Science, PsycINFO and grey literature for studies designed to improve guideline-concordant referrals or to reduce unnecessary referrals of patients with CKD from primary care to nephrology. Our search will include all studies published from database inception to April 2021 with no language restrictions. The studies will be limited to referrals for adult patients to nephrologists. Referrals of patients with CKD from non-nephrology specialists (eg, general internal medicine) will be excluded.Ethics and disseminationEthics approval will not be required, as we will analyse data from studies that have already been published and are publicly accessible. We will share our findings using traditional approaches, including scientific presentations, open access peer-reviewed platforms, and appropriate government and public health agencies.PROSPERO registration numberCRD42021247756.

OBJECTIVES The COVID-19 pandemic has placed acute care providers in demanding situations in predicting disease given the clinical variability, desire to cohort patients, and high variance in testing availability. An approach to stratifying patients by likelihood of disease based on rapidly available emergency department (ED) clinical data would offer significant operational and clinical value. The purpose of this study was to develop and internally validate a predictive model to aid in the discrimination of patients undergoing investigation for COVID-19. METHODS All patients greater than 18 years presenting to a single academic ED who were tested for COVID-19 during this index ED evaluation were included. Outcome was defined as the result of COVID-19 polymerase chain reaction (PCR) testing during the index visit or any positive result within the following 7 days. Variables included chest radiograph interpretation, disease-specific screening questions, and laboratory data. Three models were developed with a split-sample approach to predict outcome of the PCR test utilizing logistic regression, random forest, and gradient-boosted decision tree methods. Model discrimination was evaluated comparing area under the receiver operator curve (AUC) and point statistics at a predefined threshold. RESULTS A total of 1,026 patients were included in the study collected between March and April 2020. Overall, there was disease prevalence of 9.6% in the population under study during this time frame. The logistic regression model was found to have an AUC of 0.89 (95% confidence interval [CI] = 0.84 to 0.94) when including four features: exposure history, temperature, white blood cell count (WBC), and chest radiograph result. Random forest method resulted in AUC of 0.86 (95% CI = 0.79 to 0.92) and gradient boosting had an AUC of 0.85 (95% CI = 0.79 to 0.91). With a consistently held negative predictive value, the logistic regression model had a positive predictive value of 0.29 (0.2-0.39) compared to 0.2 (0.14-0.28) for random forest and 0.22 (0.15-0.3) for the gradient-boosted method. CONCLUSION The derived predictive models offer good discriminating capacity for COVID-19 disease and provide interpretable and usable methods for those providers caring for these patients at the important crossroads of the community and the health system. We found utilization of the logistic regression model utilizing exposure history, temperature, WBC, and chest X-ray result had the greatest discriminatory capacity with the most interpretable model. Integrating a predictive model-based approach to COVID-19 testing decisions and patient care pathways and locations could add efficiency and accuracy to decrease uncertainty.

This paper summarizes the methodology for the registry of suspected COVID‐19 in emergency care (RECOVER), a large clinical registry of patients from 155 United States (US) emergency departments (EDs) in 27 states tested for SARS‐CoV‐2 from March–September 2020. The initial goals are to derive and test: (1) a pretest probability instrument for prediction of SARS‐CoV‐2 test results, and from this instrument, a set of simple criteria to exclude COVID‐19 (the COVID‐19 Rule‐Out Criteria—the CORC rule), and (2) a prognostic instrument for those with COVID‐19. Patient eligibility included any ED patient tested for SARS‐CoV‐2 with a nasal or oropharyngeal swab. Abstracted clinical data included 204 variables representing the earliest manifestation of infection, including week of testing, demographics, symptoms, exposure risk, past medical history, test results, admission status, and outcomes 30 days later. In addition to the primary goals, the registry will provide a vital platform for characterizing the course, epidemiology, clinical features, and prognosis of patients tested for COVID‐19 in the ED setting.

Background Subdural hematoma (SDH) is the most common form of traumatic intracranial hemorrhage. Orlando and colleagues derived a prediction tool for neurosurgical intervention, the “Orlando Tool,” consisting of (a) maximum thickness of hematoma, and (b) presence of acute-on-chronic (AOC) hematoma. This study externally validated the Orlando Tool. Methods We performed a retrospective chart review of consecutive patients aged ≥16 years with a Glasgow Coma Scale score ≥13, and a computed tomography–documented isolated, traumatic SDH, who presented to a university-affiliated, urban, 100,000-annual-visit emergency department from 2009–2015. The primary outcome was neurosurgical intervention. Thickness of hematoma and presence of AOC hematoma were abstracted from cranial computed tomography scan reports by 2 trained physician abstractors. Results A total of 607 patients with isolated SDH were included in the validation dataset. Median hematoma thickness was 6 mm. AOC hematoma was noted in 13% of patients. Mortality was 2.5%, and 15.7% of patients underwent neurosurgery. The Orlando Tool had an area under the curve of 0.93 in the validation, comparable to 0.94 reported in their derivation set. At the prespecified cutoff of 9.96% risk, the tool had a 88% (95% CI, 80–94) sensitivity in the validation cohort compared with 94% in the derivation cohort. The specificity of 82% (95% CI, 78–85) was comparable with 84% in the derivation group. Negative likelihood ratio was 0.14 (95% CI, 0.08–0.25), compared with 0.09 in derivation. Conclusions The Orlando Tool accurately predicts neurosurgical intervention in patients with isolated, traumatic SDH and preserved consciousness.

Study Objectives: During the COVID-19 pandemic, the incidence of critical patient encounters decreased significantly presumably due to shelter in place orders With the reopening of the economy and an associated increase in patients presenting to the ED, we fear utilizing staffing models established prior to the advent of COVID-19 will be inadequate to handle both the increasing overall patient volume and workflows necessary to evaluate patients in the COVID-19 era Our objective was to identify whether length of stay (LOS) and left without being seen (LWBS) rates are sensitive to physician staffing changes when simulating for both previous standard patient volume and the expected volume of patients presenting with COVID-19-like illness (CLI) Our research question sought to identify whether the addition of physicians to CLI care areas, standard care areas, or some hybrid could significantly improve these throughput measures Methods: We built a discrete-event simulation model to capture patient, provider, and nurse flow through the ED system and gauge its throughput We analyzed data of actual standard patient (SP) and CLI patient (CP) encounters to define the model’s input The model was validated by ensuring its output replicated the data’s statistical features To assess the impact of CLI on the ED’s throughput, we modified the validated simulation model to capture the capacity and features of both standard evaluation and CLI-dedicated care areas We identified five different staffing options and compared their performance to our baseline staffing model and performed a statistical analysis under multiple scenarios representing different encounter volumes Results: We fit specific probability distributions to represent the interarrival times of 28,454 SP and 1,693 CP encounters We evaluated the impact of alternative staffing options under 16 different permutations of SP and CP, focusing on improvements in LOS and LWBS rates Adding one provider floating between standard care spaces led to the highest observed average reduction in LOS for SP, equal to 24 34% or 69 min for discharged patients (LOS: 300 min, 95% CI: 299 - 300, n=100), under a mix of 75% (21,318) of SP and 100% (5,932) of CP (Mix 1), and 13 91% or 88 min for admitted patients (LOS: 547 min, 95% CI: 545 - 548, n=100), under a mix of 100% (29,722) of SP and 100% (5,932) of CP (Mix 2), as well as to the highest observed average reduction in LWBS rate, equal to 84 57% or 50/week (LWBS: 9 12/week, 95% CI: 8 53 - 9 32, n=100), under Mix 1 Further, adding one provider floating between CLI-dedicated care spaces led to the largest average improvement in LOS for CP, equal to 10 44% or 38 min for discharged patients (LOS: 323 min, 95% CI: 322 - 324, n=100), and 8 11% or 36 min for admitted patients (LOS: 408 min, 95% CI: 407 - 409, n=100), all under Mix 2 These CLI-area improvements were smaller than those achieved by adding one provider floating between standard care spaces, suggesting that the baseline staffing of a single provider per CLI care area provided enough capacity to satisfy the CLI demand Conclusion: We assessed the performance of five different staffing options, focusing on LOS and LWBS rates, and found that adding an additional provider floating between standard patient ED care spaces lead to the most robust decrease in LOS for both discharged and admitted patients Interestingly, the improvements of adding an extra provider to CLI-dedicated ED care spaces had an insignificant impact compared to the baseline staffing model