Emily M. Hayden

Early bedside care during preclinical medical education: can technology-enhanced patient simulation advance the Flexnerian ideal?

Flexner wanted medical students to study at the patient bedside—a remarkable innovation in his time—so that they could apply science to clinical care under the watchful eye of senior physicians. Ever since his report, medical schools have reserved the latter years of their curricula for such an “advanced” apprenticeship, providing clinical clerkship experiences only after an initial period of instruction in basic medical sciences. Although Flexner codified the segregation of preclinical and clinical instruction, he was committed to ensuring that both domains were integrated into a modern medical education. The aspiration to fully integrate preclinical and clinical instruction continues to drive medical education reform even to this day. In this article, the authors revisit the original justification for sequential preclinical–clinical instruction and argue that modern, technology-enhanced patient simulation platforms are uniquely powerful for fostering simultaneous integration of preclinical–clinical content in a way that Flexner would have applauded. To date, medical educators tend to focus on using technology-enhanced medical simulation in clinical and postgraduate medical education; few have devoted significant attention to using immersive clinical simulation among preclinical students. The authors present an argument for the use of dynamic robot-mannequins in teaching basic medical science, and describe their experience with simulator-based preclinical instruction at Harvard Medical School. They discuss common misconceptions and barriers to the approach, describe their curricular responses to the technique, and articulate a unifying theory of cognitive and emotional learning that broadens the view of what is possible, feasible, and desirable with simulator-based medical education.

Simulation center accreditation and programmatic benchmarks: a review for emergency medicine.

Simulation-based education has grown significantly over the past 10 years. As a result, more professional organizations are developing or implementing accreditation processes to help define minimum standards and best practices in simulation-based training. However, the benefits and potential pitfalls of sponsoring and implementing such programs have yet to be fully evaluated across specialties. The board of directors of the Society for Academic Emergency Medicine (SAEM) requested an evaluation of the potential to create an emergency medicine (EM)-based Simulation Consultation and Accreditation Service. In response to this request, the Simulation Accreditation and Consultation Work Group, a subgroup of the Committee on Technology in Medical Education (now Simulation Academy), was created. The work group was charged with: 1) reviewing current benchmarks and standards set by existing simulation accreditation programs; 2) analyzing current EM simulation program structures, including leadership, administrative, and financial components; and 3) proposing a potential model for EM-based simulation accreditation. This article outlines currently existing and proposed accreditation models and identifies components that support best practices. It then goes on to describe three general programmatic models to better understand how simulation training can be operationalized in EM. Finally, the work group uses this collective information to propose how an accreditation process, in concert with the SAEM Simulation Consultation Service, can enhance and advance EM simulation training.

Neurologic complaints in a patient with infective endocarditis.

BACKGROUND Infectious intracranial aneurysms constitute a small subgroup of all intracranial aneurysms, but are an important cause of neurologic complaints in patients with infective endocarditis. OBJECTIVE To describe a potentially fatal cause of neurologic complaints in patients with endocarditis. CASE REPORT We report a case of a 33-year-old woman with Streptococcus sanguinis endocarditis and several neurologic complaints including right arm numbness, confusion, and occasional word-finding difficulty that were found to be secondary to infectious intracranial aneurysm. CONCLUSION Early consideration of intracranial infectious aneurysm in patients with infective endocarditis and neurologic symptoms is critical. Therapeutic intervention is often effective, and risk of aneurysm rupture is high.

How we implemented a resident-led medical simulation curriculum in a large internal medicine residency program

Abstract Mannequin-based simulation in graduate medical education has gained widespread acceptance. Its use in non-procedural training within internal medicine (IM) remains scant, possibly due to the logistical barriers to implementation of simulation curricula in large residency programs. We report the Massachusetts General Hospital Department of Medicine’s scale-up of a voluntary pilot program to a mandatory longitudinal simulation curriculum in a large IM residency program (n = 54). We utilized an eight-case curriculum implemented over the first four months of the academic year. An intensive care unit curriculum was piloted in the spring. In order to administer a comprehensive curriculum in a large residency program where faculty resources are limited, thirty second-year and third-year residents served as session facilitators and two senior residents served as chairpersons of the program. Post-session anonymous survey revealed high learner satisfaction scores for the mandatory program, similar to those of the voluntary pilot program. Most interns believed the sessions should continue to be mandatory. Utilizing residents as volunteer facilitators and program leaders allowed the implementation of a well-received mandatory simulation program in a large IM residency program and facilitated program sustainability.

An Agenda for Increasing Grant Funding of Emergency Medicine Education Research

Funding is a perennial challenge for medical education researchers. Through a consensus process, the authors developed a multifaceted agenda for increasing funding of education research in emergency medicine (EM). Priority agenda items include developing resources to increase the competitiveness of medical education research faculty in grant applications, identifying means by which departments may bolster their facultys grant writing success, taking long-term steps to increase the number of grants available to education researchers in the field, and encouraging a shift in cultural attitudes toward education research.

Fellowship Training in Simulation

As the field of medical simulation grows, there is increasing demand for experts and leaders in the field. Simulation fellowships are one venue for intensive training and faculty development for a focused academic career. We describe here a model for such training, typically offered as a 1–2-year program of professional development. These formalized programs are designed to extend focused training beyond individual faculty development courses and continuing medical education programs, offering an advanced career pathway in the field. As more simulation fellowships and associated training opportunities are offered in the USA and abroad, a well-trained community of simulation leaders will increasingly be available to help guide and sustain the field.

3EMF Characterization of Telemedicine Use Among US Emergency Departments

Study Objectives: Acute kidney injury (AKI) is strongly associated with adverse clinical outcomes including prolonged hospitalization, progression to CKD, and death. Diagnosis of AKI relies on detection of changes in serum creatinine (sCr) and urine output, both of which lag days behind renal injury and are unreliable at initial presentation. Here, we utilized data mining and machine learning methods to develop a predictive model for AKI with capacity for identifying ED patients at high risk for development of AKI within 7 days of their ED visit. Methods: A retrospective cross-sectional cohort of ED visits from 3 hospitals over 2 years was generated and used for model derivation and out-of-sample validation. Clinical data for all adult ED visits where initial sCr measurements were available at index visit and again within 7 days of EDdeparturewere extracted froma relational database that underlies our electronic health record (EHR) by an experienced data user. Primary outcome for prediction was Stage 2 AKI within 7 days of ED visit, defined according to sCr-based Kidney Disease Improving Global Outcomes (KDIGO) criteria (sCr increase to 2 times baseline). Secondary outcomes included KDIGO Stage 1 AKI (sCr increase of 0.3mg/dl above baseline or 1.5 times baseline) and Stage 3 AKI (sCr increase to 3 times baseline or to 4.0 mg/dl). Predictor variables extracted from the EHR included vital signs, laboratory results, chief complaints, demographics, past medical history, active problems, home medications and EDmedication administrations. Only EHR data available prior to prediction, made at time of first metabolic panel result, was included. Predictor variables were normalized as follows: ED vital signs and laboratory results were processed to minimum and maximum values, nephrotoxic and nephroprotective medications were grouped by pharmacologic class and least absolute shrinkage and selection operator (LASSO) feature selection processing applied to chief complaints and active problems identify variables with predictive value for AKI.Multiple machine learning models (logistic regression, decision tree, linear discriminant analysis, support vector machine, and random forest) were generated and tested in the prediction of our primary outcome. All were developed using a training dataset comprised of 90% of encounters and evaluated in the remaining encounters using 10-fold cross validation. Performance of each model was assessed using binary classification measures and receiver operator curve (ROC) analyses. Results: Our final cohort included 127,183 ED visits by 72,539 unique patients. Median age was 58 years (IQR: 43-71) and most common high-risk comorbidities were hypertension (51.8%) and heart failure (9.8%). Incidence of AKI in our cohort was as follows: Stage 1: 12.4%, Stage 2: 1.5%, Stage 3: 1.0%. Predictive model performance as measured by area under the ROC analysis ranged from 0.661 (95% CI: 0.637 0.685) using decision tree to 0.771 (95% CI: 0.759 0.783) using random forest. Conclusions: Machine learning methods applied to EHR data identified ED patients at high risk for AKI well before patients met diagnostic criteria. The model developed here, when paired with nephroprotective point-of-care clinical decision support, has potential to improve outcomes for this patient population.

Mannequin-based Telesimulation: Increasing Access to Simulation-based Education

A telesimulation platform utilizes communications technology to provide mannequin-based simulation education between learners and instructors located remotely from one another. Specifically, the instructor controls the mannequin and moderates the debriefing remotely. During these sessions, the instructor observes the learners in real time and provides immediate feedback during the debriefing. This platform obviates the need to have instructors, learners, and mannequins in the same place at the same time, potentially allowing simulation-based educational sessions to occur with greater frequency for institutions not located proximate to formal simulation centers. Additionally, the telesimulation platform enables an experienced simulation instructor to observe and directly help new simulation instructors at remote simulation locations. Readily available Web-conferencing, screen-sharing software, microphones, and webcams makes telesimulation possible. Mannequin-based telesimulation is relatively new and not well represented in the literature, but could facilitate systems changes, providing educational experiences to health care professionals in locations not currently benefiting from mannequin-based simulation opportunities. Several research questions need to be addressed in future studies to better develop this educational approach, including technical feasibility, logistic issues, a comparison of telesimulation to other simulation approaches, and assessing limitations of the telesimulation platform.

Characterizing New England Emergency Departments by Telemedicine Use

Introduction Telemedicine connects emergency departments (ED) with resources necessary for patient care; its use has not been characterized nationally, or even regionally. Our primary objective was to describe the prevalence of telemedicine use in New England EDs and the clinical applications of use. Secondarily, we aimed to determine if telemedicine use was associated with consultant availability and to identify ED characteristics associated with telemedicine use. Methods We analyzed data from the National Emergency Department Inventory-New England survey, which assessed basic ED characteristics in 2014. The survey queried directors of every ED (n=195) in the six New England states (excluding federal hospitals and college infirmaries). Descriptive statistics characterized ED telemedicine use; multivariable logistic regression identified independent predictors of use. Results Of the 169 responding EDs (87% response rate), 82 (49%) reported using telemedicine. Telemedicine EDs were more likely to be rural (18% of users vs. 7% of non-users, p=0.03); less likely to be academic (1% of users vs. 11% of non-users, p=0.01); and less likely to have 24/7 access to neurology (p<0.001), neurosurgery (p<0.001), orthopedics (p=0.01), plastic surgery (p=0.01), psychiatry (p<0.001), and hand surgery (p<0.001) consultants. Neuro/stroke (68%), pediatrics (11%), psychiatry (11%), and trauma (10%) were the most commonly reported applications. On multivariable analysis, telemedicine was more likely in rural EDs (odds ratio [OR] 4.39, 95% confidence interval [CI] 1.30–14.86), and less likely in EDs with 24/7 neurologist availability (OR 0.21, 95% CI [0.09–0.49]), and annual volume <20,000 (OR 0.24, 95% CI [0.08–0.68]). Conclusion Telemedicine is commonly used in New England EDs. In 2014, use was more common among rural EDs and EDs with limited neurology consultant availability. In contrast, telemedicine use was less common among very low-volume EDs.

Simulation in preclinical medical student education: getting started

High-fidelity patient simulation allows deliberate practice1 of clinical skills, and is now commonly deployed as an educational platform for trainees, staff and teams across healthcare. In our medical school simulation laboratory, we focus primarily on the preclinical education of medical students, prior to their patient-care rotations. This approach may seem counterintuitive at such an early stage of training, but we find it powerful as a platform to teach basic biomedical science.2 ,3 In this brief practice report, we review core principles of our approach for readers who wish to pursue further work at the interface of healthcare simulation and basic science education. Many medical educators have historically viewed mannequin simulation as useful only for code cases; however, we use such cases sparingly and instead run simple cases of common medical symptoms (eg, shortness of breath). When we write our cases, we keep them as ‘textbook’ as possible and avoid the temptation to make the case more complicated which can distract the novice student from the pathophysiology. Even when viewed as an adjunct to teaching basic biomedical concepts, it is important to account for the emotional impact of simulation on the student. In the beginning of our simulation programme, we would allow the patient to die during …