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Background Physician fatigue increases the likelihood of medical errors. Eye-tracking technology offers an unobtrusive and objective way to measure fatigue but has only been implemented in controlled settings. Objective Our objective was to determine the feasibility of capturing physiological indicators of fatigue using eye-tracking technology in a real-world clinical setting. Methods A mixed-methods feasibility study was performed in a convenience sample of clinicians practicing in an urban, academic emergency department from November 11 to December 15, 2021. Outcomes included fatigue assessed at the beginning and end of each shift via eye-tracking (with low scores indicating greater fatigue) and self-report. Results Among 15 participants, self-reported fatigue and task load increased from the beginning to the end of their shift (fatigue visual analog scale [FVAS] 3.7–4.6, p = 0.04; physician task load [PTL] 97.7–154.3, p = 0.01). It was feasible to collect eye-tracking data at a fixed computer workstation with twice daily calibration and 61% capture of reliable data when the clinician was working at the study computer. Eye-tracking metrics did not change significantly from the beginning to the end of the shift. Eye metric fatigue score was associated with the change in PTL score (r 0.59, p = 0.02) but not FVAS. This association persisted after adjusting for age, gender, and role, with every 10-point increase in PTL, there was a 0.02-point increase in fatigue score (p = 0.04). Conclusion It is unclear whether the inability to detect fatigue via eye-tracking in routine clinical care was due to confounding factors, the technology, study design, sample size, or an absence of physiological fatigue. Further research and advances in functionality are needed to determine the eye-tracking technology's role in measuring clinician fatigue in routine care.

Pragmatic clinical trials (PCTs) focus on correlation between treatment and outcomes in real-world clinical practice, yet a guide highlighting key study considerations and design types for emergency medicine investigators pursuing this important study type is not available. Investigators conducting emergency department (ED)-based PCTs face multiple decisions within the planning phase to ensure robust and meaningful study findings. The PRagmatic Explanatory Continuum Indicator Summary 2 (PRECIS-2) tool allows trialists to consider both pragmatic and explanatory components across nine domains, shaping the trial design to the purpose intended by the investigators. Aside from the PRECIS-2 tool domains, ED-based investigators conducting PCTs should also consider randomization techniques, human subjects concerns, and integration of trial components within the electronic health record. The authors additionally highlight the advantages, disadvantages, and rationale for the use of four common randomized study design types to be considered in PCTs: parallel, crossover, factorial, and stepped-wedge. With increasing emphasis on the conduct of PCTs, emergency medicine investigators will benefit from a rigorous approach to clinical trial design.

Background Untreated opioid use disorder (OUD) is a significant public health problem. Buprenorphine is an evidence-based treatment for OUD that can be initiated in and prescribed from emergency departments (EDs) and office settings. Adoption of buprenorphine initiation among ED clinicians is low. The EMBED pragmatic clinical trial investigated the effectiveness of a clinical decision support (CDS) tool to promote ED clinicians’ behavior related to buprenorphine initiation in the ED. While the CDS intervention was not associated with increased rates of buprenorphine treatment for patients with OUD at intervention ED sites, attending physicians at intervention EDs were more likely to initiate buprenorphine at least once over the duration of the study compared to those in the usual care arms (44.4% vs 34.0%, P = 0.01). This suggests the CDS intervention may be associated with increased adoption of buprenorphine initiation. As a secondary aim, we sought to identify the determinants of CDS adoption, implementation, and maintenance in a variety of ED settings and geographic locations. Methods We purposively sampled and conducted semi-structured, in-depth interviews with clinicians across EMBED trial sites randomized to the intervention arm from five healthcare systems. Interviews elicited clinician experiences regarding buprenorphine initiation and CDS use. Interviews were analyzed using directed content analysis informed by the Practical, Robust Implementation and Sustainability Model (PRISM). We used a hybrid approach (a priori codes informed by PRISM and emergent codes) for codebook development. ATLAS.ti (version 9.0) was used for data management. Coded data were analyzed within individual interview transcripts and across all interviews to identify major themes. This process involved (1) combining, comparing, and making connections between codes; (2) writing analytic memos about observed patterns; and (3) frequent team meetings to discuss emerging patterns. Results Twenty-eight interviews were conducted. Major themes that influenced the successful adoption, implementation, and maintenance of the EMBED intervention and ED-initiated BUP were organizational culture and commitment, clinician training and support, the ability to connect patients to ongoing treatment, and the ability to tailor implementation to each ED. These findings informed the identification of implementation strategies (framed using PRISM domains) to enhance the ED initiation of buprenorphine. Conclusion The findings from this qualitative analysis can provide guidance to build better systems to promote the adoption of ED-initiated buprenorphine.

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This cross-sectional study investigates rates of patients who left emergency departments without being seen from 2017 to 2021.

This cross-sectional study uses national benchmarking data to evaluate hospital occupancy and emergency department boarding during the COVID-19 pandemic.

Objective To derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time. Materials and Methods A cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time. Results Five out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time8 was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time8 after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P Conclusions For every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement.

Objectives To measure nurse-perceived electronic health records (EHR) usability with a standardized metric of technology usability and evaluate its association with professional burnout. Methods A cross-sectional survey of a random sample of US nurses was conducted in November 2017. EHR usability was measured with the System Usability Scale (SUS; range 0–100) and burnout with the Maslach Burnout Inventory. Results Among the 86 858 nurses who were invited, 8638 (9.9%) completed the survey. The mean nurse-rated EHR SUS score was 57.6 (SD 16.3). A score of 57.6 is in the bottom 24% of scores across previous studies and categorized with a grade of “F.” On multivariable analysis adjusting for age, gender, race, ethnicity, relationship status, children, highest nursing-related degree, mean hours worked per week, years of nursing experience, advanced certification, and practice setting, nurse-rated EHR usability was associated with burnout with each 1 point more favorable SUS score and associated with a 2% lower odds of burnout (OR 0.98; 95% CI, 0.97–0.99; P < .001). Conclusions Nurses rated the usability of their current EHR in the low marginal range of acceptability using a standardized metric of technology usability. EHR usability and the odds of burnout were strongly associated with a dose-response relationship.

ObjectiveTo determine the effect of a user centered clinical decision support tool versus usual care on rates of initiation of buprenorphine in the routine emergency care of individuals with opioid use disorder.DesignPragmatic cluster randomized controlled trial (EMBED).Setting18 emergency department clusters across five healthcare systems in five states representing the north east, south east, and western regions of the US, ranging from community hospitals to tertiary care centers, using either the Epic or Cerner electronic health record platform.Participants599 attending emergency physicians caring for 5047 adult patients presenting with opioid use disorder.InterventionA user centered, physician facing clinical decision support system seamlessly integrated into user workflows in the electronic health record to support initiating buprenorphine in the emergency department by helping clinicians to diagnose opioid use disorder, assess the severity of withdrawal, motivate patients to accept treatment, and complete electronic health record tasks by automating clinical and after visit documentation, order entry, prescribing, and referral.Main outcome measuresRate of initiation of buprenorphine (administration or prescription of buprenorphine) in the emergency department among patients with opioid use disorder. Secondary implementation outcomes were measured with the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework.Results1 413 693 visits to the emergency department (775 873 in the intervention arm and 637 820 in the usual care arm) from November 2019 to May 2021 were assessed for eligibility, resulting in 5047 patients with opioid use disorder (2787 intervention arm, 2260 usual care arm) under the care of 599 attending physicians (340 intervention arm, 259 usual care arm) for analysis. Buprenorphine was initiated in 347 (12.5%) patients in the intervention arm and in 271 (12.0%) patients in the usual care arm (adjusted generalized estimating equations odds ratio 1.22, 95% confidence interval 0.61 to 2.43, P=0.58). Buprenorphine was initiated at least once by 151 (44.4%) physicians in the intervention arm and by 88 (34.0%) in the usual care arm (1.83, 1.16 to 2.89, P=0.01).ConclusionsUser centered clinical decision support did not increase patient level rates of initiating buprenorphine in the emergency department. Although streamlining and automating electronic health record workflows can potentially increase adoption of complex, unfamiliar evidence based practices, more interventions are needed to look at other barriers to the treatment of addiction and increase the rate of initiating buprenorphine in the emergency department in patients with opioid use disorder.Trial registrationClinicalTrials.govNCT03658642.

Objective To describe and benchmark physician-perceived electronic health record (EHR) usability as defined by a standardized metric of technology usability and evaluate the association with professional burnout among physicians. Participants and Methods This cross-sectional survey of US physicians from all specialty disciplines was conducted between October 12, 2017, and March 15, 2018, using the American Medical Association Physician Masterfile. Among the 30,456 invited physicians, 5197 (17.1%) completed surveys. A random 25% (n=1250) of respondents in the primary survey received a subsurvey evaluating EHR usability, and 870 (69.6%) completed it. EHR usability was assessed using the System Usability Scale (SUS; range 0-100). SUS scores were normalized to percentile rankings across more than 1300 previous studies from other industries. Burnout was measured using the Maslach Burnout Inventory. Results Mean ± SD SUS score was 45.9±21.9. A score of 45.9 is in the bottom 9% of scores across previous studies and categorized in the "not acceptable" range or with a grade of F. On multivariate analysis adjusting for age, sex, medical specialty, practice setting, hours worked, and number of nights on call weekly, physician-rated EHR usability was independently associated with the odds of burnout with each 1 point more favorable SUS score associated with a 3% lower odds of burnout (odds ratio, 0.97; 95% CI, 0.97-0.98; P Conclusion The usability of current EHR systems received a grade of F by physician users when evaluated using a standardized metric of technology usability. A strong dose-response relationship between EHR usability and the odds of burnout was observed.

This Viewpoint discusses the need for public funding for research that supports health workforce well-being and addresses occupational burnout among health care practitioners.

Physician turnover places a heavy burden on the healthcare industry, patients, physicians, and their families. Having a mechanism in place to identify physicians at risk for departure could help target appropriate interventions that prevent departure. We have collected physician characteristics, electronic health record (EHR) use patterns, and clinical productivity data from a large ambulatory based practice of non-teaching physicians to build a predictive model. We use several techniques to identify possible intervenable variables. Specifically, we used gradient boosted trees to predict the probability of a physician departing within an interval of 6 months. Several variables significantly contributed to predicting physician departure including tenure (time since hiring date), panel complexity, physician demand, physician age, inbox, and documentation time. These variables were identified by training, validating, and testing the model followed by computing SHAP (SHapley Additive exPlanation) values to investigate which variables influence the model’s prediction the most. We found these top variables to have large interactions with other variables indicating their importance. Since these variables may be predictive of physician departure, they could prove useful to identify at risk physicians such who would benefit from targeted interventions.

This cross-sectional study compares patient electronic health record (EHR) inbox message volume in an ambulatory practice network in New England during the COVID-19 pandemic with prepandemic levels.

Key Points Question Can vendor-derived electronic health record (EHR) use data platforms be used to understand and explain physician turnover? Findings In this cohort study of 314 physicians in a large ambulatory care network, physician productivity and EHR use metrics were associated with physician departure. Counterintuitively, less time spent on the EHR (in particular inbox management) was associated with physician departure. Meaning These findings suggest that prospectively tracking these metrics along with standardizing metrics across vendor products could identify physicians at high risk of departure who would benefit from early, team-based, targeted interventions., This cohort study evaluates the association of physician productivity and electronic health record (EHR) use patterns with physician turnover., Importance Physician turnover takes a heavy toll on patients, physicians, and health care organizations. Survey research has established associations of electronic health record (EHR) use with professional burnout and reduction in professional effort, but these findings are subject to response fatigue and bias. Objective To evaluate the association of physician productivity and EHR use patterns, as determined by vendor-derived EHR use data platforms, with physician turnover. Design, Setting, and Participants This retrospective cohort study was conducted among nonteaching ambulatory physicians at a large ambulatory practice network based in New England. Data were collected from March 2018 to February 2020. Main Outcomes and Measures Physician departure from the practice network; 4 time-based core measures of EHR use, normalized to 8 hours of scheduled clinical time; teamwork, percentage of a physician’s orders that are placed by other members of the care team; and productivity measures of patient volume, intensity, and demand. Results Among 335 physicians assessed for eligibility, 314 unique physicians (89.2%) were included in the analysis (123 [39%] women; 100 [32%] aged 45-54 years), with 5663 physician-months of data. The turnover rate was 5.1%/year (32 of 314 physicians). Physicians completed a mean 2.6 appointments/hour (95% CI, 2.5-2.6 appointments/hour) and 206 appointments/month (95% CI, 197-215 appointments/month) with 5.5 hours (95% CI, 5.3-5.8 hours) of EHR time for every 8 hours of scheduled patient time. After controlling for gender, medical specialty, and time, the following variables were associated with turnover: inbox time (odds ratio [OR], 0.70; 95% CI, 0.61-0.82; P

Posterior strokes are frequently misdiagnosed as they present with non-specific complaints such as dizziness/vertigo. Emergency department (ED) practice often relies on CT/CTA to "exclude" infarct in such patients, providing false reassurance due to lower sensitivity of CT (42%) for stroke in the posterior circulation. We describe a pilot at our institution using a specialized MRI protocol with 95% sensitivity for posterior stroke, which may be used in place of CT/CTA or conventional MRI for stroke evaluation. Further development of this approach may help reduce the high rate of missed posterior stroke in patients presenting with dizziness.

Computerized clinical decision support (CDS) faces challenges to interoperability and scalability. Centralized, web-based solutions offer a mechanism to share the cost of CDS development, maintenance, and implementation across practices. Data standards have emerged to facilitate interoperability and rapid integration of such third-party CDS. This case report describes the challenges to implementation and scalability of an integrated, web-based CDS intervention for EMergency department-initiated BuprenorphinE for opioid use Disorder which will soon be evaluated in a trial across 20 sites in five healthcare systems. Due to limitations of current standards, security concerns, and the need for resource-intensive local customization, barriers persist related to centralized CDS at this scale. These challenges demonstrate the need and importance for future standards to support two-way messaging (read and write) between electronic health records and web applications, thus allowing for more robust sharing across health systems and decreasing redundant, resource-intensive CDS development at individual sites.

Objectives To analyze current practices in shared decision-making (SDM) in primary care and perform a needs assessment for the role of information technology (IT) interventions. Materials and Methods A mixed-methods study was conducted in three phases: (1) ethnographic observation of clinical encounters, (2) patient interviews, and (3) physician interviews. SDM was measured using the validated OPTION scale. Semistructured interviews followed an interview guide (developed by our multidisciplinary team) informed by the Traditional Decision Conflict Scale and Shared Decision Making Questionnaire. Field notes were independently coded and analyzed by two reviewers in Dedoose. Results Twenty-four patient encounters were observed in 3 diverse practices with an average OPTION score of 57.2 (0–100 scale; 95% confidence interval [CI], 51.8–62.6). Twenty-two patient and 8 physician interviews were conducted until thematic saturation was achieved. Cohen’s kappa, measuring coder agreement, was 0.42. Patient domains were: establishing trust, influence of others, flexibility, frustrations, values, and preferences. Physician domains included frustrations, technology (concerns, existing use, and desires), and decision making (current methods used, challenges, and patients’ understanding). Discussion Given low SDM observed, multiple opportunities for technology to enhance SDM exist based on specific OPTION items that received lower scores, including: (1) checking the patient’s preferred information format, (2) asking the patient's preferred level of involvement in decision making, and (3) providing an opportunity for deferring a decision. Based on data from interviews, patients and physicians value information exchange and are open to technologies that enhance communication of care options. Conclusion Future primary care IT platforms should prioritize the 3 quantitative gaps identified to improve physician–patient communication and relationships. Additionally, SDM tools should seek to standardize common workflow steps across decisions and focus on barriers to increasing adoption of effective SDM tools into routine primary care.

Background In order to streamline the emergency department (ED) referral process in a multi-network automated opioid treatment referral program, we performed a needs assessment of community providers for Medication for Opioid Use Disorder (MOUD) in the EMergency department-initiated BuprenorphinE for opioid use Disorder (EMBED) trial network. Methods A needs assessment was conducted in two phases: (1) key stakeholder meetings and (2) a survey of community sites offering MOUD. Stakeholder meetings were conducted with five key stakeholder groups: 1) ED clinicians and staff, 2) community sites offering MOUD, 3) the investigative team, 4) health system IT staff, and 5) medical ethics experts. Meetings continued until each stakeholder group stated that their priorities and needs were understood. Major categories of needs were extracted pragmatically based on recurrence across stakeholder groups. Informed by needs expressed by IT and MOUD site stakeholders, nineteen MOUD sites were surveyed to better characterize information needs of community sites offering MOUD when receiving an ED referral. Results Three major categories of needs for referral system were identified: 1) The system to be automated, flexible and allow multiple channels of referral, 2) Referral metrics are retrievable in a HIPAA compliant manner, 3) Patients are scheduled into community sites offering MOUD as urgently as possible. Of the MOUD sites surveyed, 68.4% (13/19) responded. Based on the responses, specific patient identifiers were required for most MOUD site referrals, and encrypted emails and EHR were the preferred methods of communication for the handoff. 53.8% (7/13) of the sites were able to accept patients within 3 days with only 1 site requiring >7 days. Conclusion These findings can inform IT solutions to address the discordant priorities of the ED (rapid and flexible referral process) and the community sites offering (referrals minimize variability and overbooking). To prevent drop-out in the referral cascade, our findings emphasize the need for increased availability and accessibility to MOUD on demand and protected communication channels between EDs and community providers of MOUD.

Despite basic federal requirements promoting a user-centered design approach to electronic health record (EHR) development and usability testing there have been usability and safety risks with EHR technology. Four EHR vendors were asked to provide written descriptions of their usability practices, and we reviewed these descriptions to identify areas where there has been advancement and areas for improvement. All 4 vendors described user-centered design processes and usability testing methods that demonstrate advancement from previous studies of vendor practices. Importantly, vendors are also beginning to address aspects of EHR implementation that play a critical role in shaping EHR usability. There are important areas for improvement in vendor practices including a greater focus on safety and on measurement and benchmarking. Vendors sharing their current usability practices demonstrates a step toward greater transparency which has typically been lacking.

Decision aids are patient-focused tools that have the potential to reduce the overuse of head computed tomography (CT) scans. The objective of this study was to create a consensus among Canadian mild traumatic brain injury and emergency medicine experts on modifications required to adapt two American decision aids about head CT use for adult and paediatric mild traumatic brain injury to the Canadian context. We invited 21 Canadian stakeholders and the two authors of the American decision aids to a Nominal Group Technique consensus meeting to generate suggestions for adapting the decision aids. This method encourages idea generation and sharing between team members. Each idea was discussed and then prioritised using a voting system. We collected data using videotaping, writing material and online collaborative writing tools. The modifications proposed were analysed using a qualitative thematic content analysis. Twenty-one participants took part in the meeting, including researchers and clinician researchers (n = 9; 43%), patient partners (n = 3; 14%) and decision makers (n = 2; 10%). A total of 84 ideas were generated. Participants highlighted the need to clarify the purpose of the decision aids, the nature of the problem being addressed and the target population. The tools require sociocultural adaptations, better identification of their target population, better description of head CT utility, advantages and related risks, modification of the visual and written representation of the risk of brain injury and head CT use, and locally adapted, patient follow-up plans. This study based on a Nominal Group Technique identified several adaptations for two American decision aids about head CT use for mild traumatic brain injury to support their use in Canada’s different healthcare, social, cultural and legal context. These adaptations concerned the target users of the decision aids, the information presented, and how the benefits and risks were communicated in the decision aids. Future steps include prototyping the two adapted decision aids, conducting formative evaluations with actual emergency department patients and clinicians, and measuring the impact of the adapted tools on CT scan use. A mild traumatic brain injury (also called concussion) can happen when the brain moves around in the skull after an impact to the head. A concussion is not a brain bleed and you cannot see a concussion. Concussions do not show up on a computed tomography (CT) scan. Brain bleeds do. Computed tomography scans are specialised X-ray machines that can detect serious brain injuries. Unfortunately, CT scan use also exposes patients to radiation and a future increased risk of cancer. Shared decision making involves health professionals and patients making decisions together based on the best available evidence, health professionals’ experience, and patients’ values and preferences. Shared decision making improves appropriate diagnostic test use. Two decision aids created in the USA are available to facilitate shared decision making regarding the use of head CT scans for patients with concussion. These decision aids are not fully adapted for use in Canada because the healthcare, social and legal context is different. Our study brought together patients and experts in the field of concussion and shared decision making to analyse these decision aids and propose adaptations that would increase their acceptance in Canadian emergency departments. We used a technique called the Nominal Group Technique to create a consensus about the most important changes to make to both original decision aids. The main adaptations needed for the Canadian context concerned avoiding information about cost and removing any information that does not change clinical management. This project will help us adapt two decision aids for clinical use in Canada and support appropriate CT scan use for patients with concussion.

BACKGROUND Electronic health record (EHR) usability and physician task load both contribute to physician professional burnout. The association between perceived EHR usability and workload has not previously been studied at a national level. Better understanding these interactions could give further information as to the drivers of extraneous task load. OBJECTIVE This study aimed to determine the relationship between physician-perceived EHR usability and workload by specialty and evaluate for associations with professional burnout. METHODS A secondary analysis of a cross-sectional survey of US physicians from all specialties was conducted from October 2017 to March 2018. Among the 1250 physicians invited to respond to the subsurvey analyzed here, 848 (67.8%) completed it. EHR usability was assessed with the System Usability Scale (SUS; range: 0-100). Provider task load (PTL) was assessed using the mental demand, physical demand, temporal demand, and effort required subscales of the National Aeronautics and Space Administration Task Load Index (range: 0-400). Burnout was measured using the Maslach Burnout Inventory. RESULTS The mean scores were 46.1 (SD 22.1) for SUS and 262.5 (SD 71.7) for PTL. On multivariable analysis adjusting for age, gender, relationship status, medical specialty, practice setting, hours worked per week, and number of nights on call per week, physician-rated EHR usability was associated with PTL, with each 1-point increase in SUS score (indicating more favorable) associated with a 0.57-point decrease in PTL score (P CONCLUSIONS A strong association was observed between EHR usability and workload among US physicians, with more favorable usability associated with less workload. Both outcomes were associated with the odds of burnout, with task load acting as a mediator between EHR usability and burnout. Improving EHR usability while decreasing task load has the potential to allow practicing physicians more working memory for medical decision making and patient communication.

This cross-sectional survey assesses the association of perceived electronic health record usability with patient interaction and work-life integration among US physicians.

Until we have a meaningful alternative, lockdown is the only thing we can do to prevent further catastrophic spread of the virus, says Edward R Melnick. But John PA Ioannidis argues that any benefits of lockdown depend on its effectiveness and the covid-19 burden—and that the harms are multifarious

Despite considerable progress in tackling cardiovascular disease over the past 50 years, many gaps in the quality of care for cardiovascular disease remain. Multiple missed opportunities have been identified at every step in the prevention and treatment of cardiovascular disease, such as failure to make risk factor modifications, failure to diagnose cardiovascular disease, and failure to use proper evidence based treatments. With the digital transformation of medicine and advances in health information technology, clinical decision support (CDS) tools offer promise to enhance the efficiency and effectiveness of delivery of cardiovascular care. However, to date, the promise of CDS delivering scalable and sustained value for patient care in clinical practice has not been realized. This article reviews the evidence on key emerging questions around the development, implementation, and regulation of CDS with a focus on cardiovascular disease. It first reviews evidence on the effectiveness of CDS on healthcare process and clinical outcomes related to cardiovascular disease and design features associated with CDS effectiveness. It then reviews the barriers encountered during implementation of CDS in cardiovascular care, with a focus on unintended consequences and strategies to promote successful implementation. Finally, it reviews the legal and regulatory environment of CDS with specific examples for cardiovascular disease.

ObjectiveClinical decision support (CDS) hard-stop alerts—those in which the user is either prevented from taking an action altogether or allowed to proceed only with the external override of a third party—are increasingly common but can be problematic. To understand their appropriate application, we asked 3 key questions: (1) To what extent are hard-stop alerts effective in improving patient health and healthcare delivery outcomes? (2) What are the adverse events and unintended consequences of hard-stop alerts? (3) How do hard-stop alerts compare to soft-stop alerts?Methods and MaterialsStudies evaluating computerized hard-stop alerts in healthcare settings were identified from biomedical and computer science databases, gray literature sites, reference lists, and reviews. Articles were extracted for process outcomes, health outcomes, unintended consequences, user experience, and technical details.ResultsOf 32 studies, 15 evaluated health outcomes, 16 process outcomes only, 10 user experience, and 4 compared hard and soft stops. Seventy-nine percent showed improvement in health outcomes and 88% in process outcomes. Studies reporting good user experience cited heavy user involvement and iterative design. Eleven studies reported on unintended consequences including avoidance of hard-stopped workflow, increased alert frequency, and delay to care. Hard stops were superior to soft stops in 3 of 4 studies.ConclusionsHard stops can be effective and powerful tools in the CDS armamentarium, but they must be implemented judiciously with continuous user feedback informing rapid, iterative design. Investigators must report on associated health outcomes and unintended consequences when implementing IT solutions to clinical problems.

IntroductionThe goal of this trial is to determine whether implementation of a user-centred clinical decision support (CDS) system can increase adoption of initiation of buprenorphine (BUP) into the routine emergency care of individuals with opioid use disorder (OUD).MethodsA pragmatic cluster randomised trial is planned to be carried out in 20 emergency departments (EDs) across five healthcare systems over 18 months. The intervention consists of a user-centred CDS integrated into ED clinician electronic workflow and available for guidance to: (1) determine whether patients presenting to the ED meet criteria for OUD, (2) assess withdrawal symptoms and (3) ascertain and motivate patient willingness to initiate treatment. The CDS guides the ED clinician to initiate BUP and facilitate follow-up. The primary outcome is the rate of BUP initiated in the ED. Secondary outcomes are: (1) rates of receiving a referral, (2) fidelity with the CDS and (3) rates of clinicians providing any ED-initiated BUP, referral for ongoing treatment and receiving Drug Addiction Act of 2000 training. Primary and secondary outcomes will be analysed using generalised linear mixed models, with fixed effects for intervention status (CDS vs usual care), prespecified site and patient characteristics, and random effects for study site.Ethics and disseminationThe protocol has been approved by the Western Institutional Review Board. No identifiable private information will be collected from patients. A waiver of informed consent was obtained for the collection of data for clinician prescribing and other activities. As a minimal risk implementation study of established best practices, an Independent Study Monitor will be utilised in place of a Data Safety Monitoring Board. Results will be reported in ClinicalTrials.gov and published in open-access, peer-reviewed journals, presented at national meetings and shared with the clinicians at participating sites via a broadcast email notification of publications.Trial registration numberNCT03658642; Pre-results.

Importance Systemic therapy and radiotherapy can be associated with acute complications that may require emergent care. However, there are limited data characterizing complications and the financial burden of cancer therapy that are treated in emergency departments (EDs) in the United States. Objectives To estimate the incidence of treatment-related complications of systemic therapy or radiotherapy, examine factors associated with inpatient admission, and investigate the overall financial burden. Design, Setting, and Participants A retrospective analysis of the Healthcare Cost and Utilization Project Nationwide Emergency Department Sample was performed. Between January 2006 and December 2015, there was a weighted total of 1.3 billion ED visits; of these, 1.5 million were related to a complication of systemic therapy or radiotherapy for cancer. Data analysis was conducted from February 22 to December 23, 2018. External cause of injury codes, Clinical Classifications Software,International Classification of Diseases, Ninth Revision, Clinical Modification, andInternational Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), Clinical Modificationcodes were used to identify patients with complications of systemic therapy or radiotherapy. Main Outcomes and Measures Patterns in treatment-related complications, patient- and hospital-related factors associated with inpatient admission, and median and total charges for treatment-related complications were the main outcomes. Results Of the 1.5 million ED visits included in the analysis, 53.2% of patients were female and mean age was 63.3 years. Treatment-related ED visits increased by a rate of 10.8% per year compared with 2.0% for overall ED visits. Among ED visits, 90.9% resulted in inpatient admission to the hospital and 4.9% resulted in death during hospitalization. Neutropenia (136 167 [8.9%]), sepsis (128 171 [8.4%]), and anemia (117 557 [7.7%]) were both the most common and costliest (neutropenia: $5.52 billion; sepsis: $11.21 billion; and anemia: $6.78 billion) complications diagnosed on presentation to EDs; sepsis (odds ratio [OR], 21.00; 95% CI, 14.61-30.20), pneumonia (OR, 9.73; 95% CI, 8.08-11.73), and acute kidney injury (OR, 9.60; 95% CI, 7.77-11.85) were associated with inpatient admission. Costs related to the top 10 most common complications totaled $38 billion and comprised 48% of the total financial burden of the study cohort. Conclusions and Relevance Emergency department visits for complications of systemic therapy or radiotherapy increased at a 5.5-fold higher rate over 10 years compared with overall ED visits. Neutropenia, sepsis, and anemia appear to be the most common complications; sepsis, pneumonia, and acute kidney injury appear to be associated with the highest rates of inpatient admission. These complications suggest that significant charges are incurred on ED visits.

Decision aids are evidenced-based tools designed to increase patient understanding of medical options and possible outcomes, facilitate conversation between patients and clinicians, and improve patient engagement. Decision aids have been used for shared decision-making (SDM) interventions outside of the ED setting for more than a decade. Their use in the ED has only recently begun to be studied. This article provides background on this topic and the conclusions of the 2016 Academic Emergency Medicine consensus conference SDM in practice work group regarding “Shared Decision Making in the Emergency Department: Development of a Policy-Relevant, Patient-Centered Research Agenda.” The goal was to determine a prioritized research agenda for the development and testing of shared decision-making interventions for use in emergency care that was most important to patients, clinicians, caregivers, and other key stakeholders. Using the nominal group technique, the consensus working group proposed prioritized research questions in six key domains: (1) content (i.e., clinical scenario or decision area), (2) level of evidence available, (3) tool design strategies, (4) risk communication, (5) stakeholders, and (6) outcomes.

Healthcare information technologies are now a routine component of patient–clinician interactions. Originally designed for operational functions including billing and regulatory compliance, these systems have had unintended consequences including increased exam room documentation, divided attention during the visit, and use of scribes to alleviate documentation burdens. In an age in which technology is ubiquitous in everyday life, we must re-envision healthcare technology to support both clinical operations and, above all, the patient–clinician relationship. We present 6 habits for designing user-centered health technologies: (1) put patient care first, (2) assemble a team with the right skills, (3) relentlessly ask WHY, (4) keep it simple, (5) be Darwinian, and (6) don’t lose the forest for the trees. These habits should open dialogues between developers, implementers, end users, and stakeholders, as well as outline a path for better, more usable technology that puts patients and their clinicians back at the center of care.

BACKGROUND Emergency departments (EDs) frequently care for individuals with opioid use disorder (OUD). Buprenorphine (BUP) is an effective treatment option for patients with OUD that can safely be initiated in the ED. At present, BUP is rarely initiated as a part of routine ED care. Clinical decision support (CDS) could accelerate adoption of ED-initiated BUP into routine emergency care. OBJECTIVE This study aimed to design and formatively evaluate a user-centered decision support tool for ED initiation of BUP for patients with OUD. METHODS User-centered design with iterative prototype development was used. Initial observations and interviews identified workflows and information needs. The design team and key stakeholders reviewed prototype designs to ensure accuracy. A total of 5 prototypes were evaluated and iteratively refined based on input from 26 attending and resident physicians. RESULTS Early feedback identified concerns with the initial CDS design: an alert with several screens. The timing of the alert led to quick dismissal without using the tool. User feedback on subsequent iterations informed the development of a flexible tool to support clinicians with varied levels of experience with the intervention by providing both one-click options for direct activation of care pathways and user-activated support for critical decision points. The final design resolved challenging navigation issues through targeted placement, color, and design of the decision support modules and care pathways. In final testing, users expressed that the tool could be easily learned without training and was reasonable for use during routine emergency care. CONCLUSIONS A user-centered design process helped designers to better understand users’ needs for a Web-based clinical decision tool to support ED initiation of BUP for OUD. The process identified varying needs across user experience and familiarity with the protocol, leading to a flexible design supporting both direct care pathways and user-initiated decision support.

Background The validated Pediatric Emergency Care Applied Network (PECARN) rule helps determine the relevance of a head computerized tomography (CT) for children with mild traumatic brain injury (mTBI). We sought to estimate the potential overuse of head CT within two Canadian emergency departments (EDs). Methods We conducted a retrospective chart review of children seen in 2016 in a paediatric Level I (site 1) and a general Level II (site 2) trauma centre. We reviewed charts to determine the appropriateness of head CT use according to the PECARN rule in a random subset of children presenting with head trauma. Simple descriptive statistics were applied. Results One thousand five hundred and forty-six eligible patients younger than 17 years consulted during the study period. Of the 203 randomly selected cases per setting, 16 (7.9%) and 24 (12%), respectively from sites 1 and 2 had a head CT performed. Based on the PECARN rule, we estimated the overuse for the younger group ( Conclusion Both EDs demonstrated overuse rates below 10% although it was higher for the older group at site 2. Such low rates can potentially be explained by the university affiliation of both hospitals and by two Canadian organizations working to raise awareness among physicians about the overuse of diagnostic tools and dangers inherent to radiation.

ObjectiveThe aim of this study was to evaluate the association between physician and nurse self-reported empathy and burnout and the number of annual primary care visits per patient under their care.MethodsDesign: A cross-sectional survey study was conducted from January 2013 to July 2014. Site: The 22 primary care centres of the Lleida Health Region in Spain. Main outcome measures: The Jefferson Scale of Physician Empathy and the Maslach Burnout Inventory were used to measure empathy and burnout, respectively. The number of visits and the number of diagnoses coded per visit were obtained through the Region’s electronic health record.ResultsTwo hundred and sixty-seven healthcare professionals (physicians and nurses, 52.6% participation of the total in the region) with 301 657 patients under their care. Healthcare professionals’ degree of burnout and empathy was associated with the number of annual visits per patient under their care. Burned out nurses and physicians received fewer visits (4.5vs3.7 in nurses and 18.1vs18.9 in physicians), whereas more empathic physicians received more visits per patient (19.4vs17.2, pConclusionsThe number of annual primary care visits per patient that healthcare professionals receive is closely associated with healthcare professionals’ empathy and burnout. These results should serve to promote empathic skills and establish organisational changes that promote efficiency in the practice and, in turn, reduce the degree of burnout of healthcare professionals.

The purpose of this study is to use detailed electronic health record data to profile the use of condition-specific, risk-standardized imaging by emergency physicians.CT utilization in four emergency departments in a single health care system was retrospectively analyzed. The primary outcome for analysis was indication-specific, risk-standardized CT utilization. We constructed seven clinical cohorts on the basis of the presence or absence of a traumatic indication for the most frequently performed CT studies. Risk standardization was performed using machine learning algorithms and hierarchic logistic regression models. Variation in CT utilization for each cohort was analyzed using coefficients of variation and box plots, the effect of risk standardization on physician profiling was determined using slope diagrams and kappa values, and within-physician correlation was assessed using correlation coefficients and matrices.For the seven cohorts, the number of physicians ordering more than 25 CT studies for a particular indication ranged from 70 to 88, and the number of ED visits ranged from 17,458 to 117,489. The unadjusted variation was large for each indication (coefficient of variation, 30.2-57.9). Risk standardization resulted in reduced but persistent variation for all indications (coefficient of variation, 12.3-22.3). Among indication-specific models, risk standardization resulted in reclassification by two or more deciles for 14.0-39.1% of physicians. The R value for within-physician correlation varied from 0.02 to 0.80 and was highest between chest and abdominal imaging for trauma.In this multisite study of CT utilization, risk standardization had a substantial impact on variation in CT utilization and emergency physician profiling. Administrators and payers should include risk standardization in future measures of physician imaging to ensure valid assessment of performance and achieve improvements in emergency care value.

BACKGROUND Clinical practice guidelines (CPGs) have been published by the American College of Emergency Physicians (ACEP) since 1990 to advance evidence-based emergency care. ACEP clinical policies have drawn anecdotal criticism for bias, yet the overall quality of these guidelines has not previously been quantified. We sought to examine ACEP clinical policies using a recognised, validated appraisal instrument: Appraisal of Guidelines for Research & Evaluation (AGREE II). METHODS Systematic assessment of current ACEP clinical policies was conducted using the AGREE II instrument, which contains 23 appraisal items (scored on a 1-7 scale) in six domains and two overall assessments. Each policy was independently appraised by five trained appraisers. Primary outcomes were AGREE II ratings for each item, domain and "Overall Assessment," and scores were reported as standardised percentages from all five appraisers. Secondary analyses examined associations between AGREE II ratings and policy publication date, strength of underlying evidence and strength of recommendations. Additional analysis examined relationships between domain and "Overall Assessment" ratings. RESULTS Twenty guidelines published from April 2007 to November 2017 were included. Of the six domains, "Scope and Purpose" scored highest (mean 90%) and "Applicability" scored lowest (mean 35%). The four remaining domains ("Stakeholder Involvement," "Rigor of Development," "Clarity of Presentation" and "Editorial Independence") had mean scores of 53%-78%. The mean "Overall Assessment" rating was 69% and was not associated with policy publication date, strength of underlying evidence or strength of recommendations. We found positive associations between "Overall Assessment" ratings and two domains: "Rigor of Development" (r = 0.70) and "Clarity of Presentation" (r = 0.70). CONCLUSIONS Based on validated AGREE II criteria, ACEP clinical policies can be most improved by addressing their application in practice. ACEP clinical policies' overall quality did not improve over the assessed time period and is not explained by the quality of underlying evidence.

Objectives Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data–driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. Methods This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics. Results There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0.86 (95% CI = 0.82 to 0.90); CART model, 0.69 (95% CI = 0.62 to 0.77); logistic regression model, 0.76 (95% CI = 0.69 to 0.82); CURB-65, 0.73 (95% CI = 0.67 to 0.80); MEDS, 0.71 (95% CI = 0.63 to 0.77); and mREMS, 0.72 (95% CI = 0.65 to 0.79). The random forest model AUC was statistically different from all other models (p ≤ 0.003 for all comparisons). Conclusions In this proof-of-concept study, a local big data–driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions.

Background Overuse of computed tomography (CT) for minor head injury continues despite developed and rigorously validated clinical decision rules like the Canadian CT Head Rule (CCHR). Adherence to this sensitive and specific rule could decrease the number of CT scans performed in minor head injury by 35%. But in practice, the CCHR has failed to reduce testing, despite its accurate performance. Objectives The objective was to identify nonclinical, human factors that promote or inhibit the appropriate use of CT in patients presenting to the emergency department (ED) with minor head injury. Methods This was a qualitative study in three phases, each with interview guides developed by a multidisciplinary team. Subjects were recruited from patients treated and released with minor head injuries and providers in an urban academic ED and a satellite community ED. Focus groups of patients (four groups, 22 subjects total) and providers (three groups, 22 subjects total) were conducted until thematic saturation was reached. The findings from the focus groups were triangulated with a cognitive task analysis, including direct observation in the ED (>150 hours), and individual semistructured interviews using the critical decision method with four senior physician subject matter experts. These experts are recognized by their peers for their skill in safely minimizing testing while maintaining patient safety and engagement. Focus groups and interviews were audio recorded and notes were taken by two independent note takers. Notes were entered into ATLAS.ti and analyzed using the constant comparative method of grounded theory, an iterative coding process to determine themes. Data were double-coded and examined for discrepancies to establish consensus. Results Five core domains emerged from the analysis: establishing trust, anxiety (patient and provider), constraints related to ED practice, the influence of others, and patient expectations. Key themes within these domains included patient engagement, provider confidence and experience, ability to identify and manage patient anxiety, time constraints, concussion knowledge gap, influence of health care providers, and patient expectations to get a CT. Conclusions Despite high-quality evidence informing use of CT in minor head injury, multiple factors influence the decision to obtain CT in practice. Identifying and disseminating approaches and designing systems that help clinicians establish trust and manage uncertainty within the ED context could optimize CT use in minor head injury.

Due to a miscommunication during the process of transferring this manuscript from our editorial team to Production, the Members of the American College of Emergency Physicians Clinical Policies Committee (Oversight Committee) were not properly indexed in PubMed. This has now been corrected online. The publisher would like to apologize for any inconvenience caused.