Resident Physicians
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2021 ◽  
pp. 146144562110168
Paulien Harms ◽  
Tom Koole ◽  
Ninke Stukker ◽  
Jaap Tulleken

This paper examines how expertise is treated as a separable domain of epistemics by looking at simulated intensive care shift-handovers between resident physicians. In these handovers, medical information about a patient is transferred from an outgoing physician (OP) to an incoming physician (IP). These handovers contain different interactional activities, such as discussing the patient identifiers, giving a clinical impression, and discussing tasks and focus points. We found that with respect to (factual) knowledge about the patient, the OPs display an orientation to a knowledge imbalance, but with respect to (clinical) procedures, reasoning, and activities, they display an orientation to a knowledge balance. We use ‘expertise’ to refer to this latter type of knowledge. ‘Expertise’ differs from, and adds to, how knowledge is often treated in epistemics in that it is concerned with professional competence or ‘knowing how’. In terms of epistemics, the participants in the handovers orient to a steep epistemic or knowledge gradient when it concerns the patient, while simultaneously displaying an orientation to a horizontal expertise gradient.

2021 ◽  
pp. 000992282110442
Nishit H. Patel ◽  
Ameer Hassoun ◽  
Jennifer H. Chao

A chest radiograph (CXR) is not routinely indicated in children presenting with their first episode of wheezing; however, it continues to be overused. A survey was distributed electronically to determine what trainees are taught and their current practice of obtaining a CXR in children presenting with their first episode of wheezing and the factors that influence this practice. Of the 1513 trainees who completed surveys, 35.3% (535/1513) reported that they were taught that pediatric patients presenting with their first episode of wheezing should be evaluated with a CXR. In all, 22.01% (333/1513) indicated that they would always obtain a CXR in these patients, and 13.75% (208/1513) would always obtain a CXR under a certain age (4 weeks to 12 years, median of 2 years). Our study identifies a target audience that would benefit from education to decrease the overuse of CXRs in children.

2021 ◽  
Pankti P. Acharya ◽  
Brianna Fram ◽  
Jenna R. Adalbert ◽  
Ashima Oza ◽  
Prashanth Palvannan ◽  

Abstract Background The opioid epidemic is a multifactorial issue, which includes pain mismanagement. A recent study has shown that residents have received little training for opioid related patient care. Therefore, resident physician education is essential in addressing this issue. We aimed to analyze the effects of an educational intervention on the knowledge and potential prescribing habits of emergency medicine, general surgery, and internal medicine residents. Methods Resident physicians were provided with educational materials and were given pre- and posttests to complete. Descriptive statistics were used to analyze pre- and posttest responses. Chi-squared analysis was used to identify changes between the pre and posttests. A p < 0.05 value was considered statistically significant. Results Following the educational intervention, we observed improvement in correct prescribing habits for acute migraine management among emergency medicine residents (from 14.8–38.5%). Among general surgery residents, there was significant improvement in adherence to narcotic amounts determined by recent studies for sleeve gastrectomy (p = 0.01) and laparoscopic cholecystectomy (p = 0.002). Additionally, we observed a decrease in the number of residents who would use opioids as a first line treatment for migraines, arthritic joint pain, and nephrolithiasis. Conclusions Resident physicians have an essential role in combating the opioid epidemic. There was significant improvement in various aspects of opioid related pain management among emergency medicine, internal medicine, and general surgery residents following the educational interventions. We recommend that medical school and residency programs consider including opioid related pain management in their curricula.

2021 ◽  
Vol 6 (2) ◽  
pp. 66-71
Nazlı BATAR ◽  
Ceren PAK ◽  
Rivayet Nükra TÜFEKÇİ ◽  
Betül KOÇAK ◽  
Rümeysa ÖZÇALKAP ◽  

2021 ◽  
Vol 12 (04) ◽  
pp. 721-728
A. Jay Holmgren ◽  
Brenessa Lindeman ◽  
Eric W. Ford

Abstract Background Electronic health records (EHRs) demand a significant amount of physician time for documentation, orders, and communication during care delivery. Resident physicians already work long hours as they gain experience and develop both clinical and socio-technical skills. Objectives Measure how much time resident physicians spend in the EHR during clinic hours and after-hours, and how EHR usage changes as they gain experience over a 12-month period. Methods Longitudinal descriptive study where participants were 622 resident physicians across postgraduate year cohorts (of 948 resident physicians at the institution, 65.6%) working in an ambulatory setting from July 2017 to June 2018. Time spent in the EHR per patient, patients records documented per day, and proportion of EHR time spent after-hours were the outcome, while the number of months of ambulatory care experience was the predictor. Results Resident physicians spent an average of 45.6 minutes in the EHR per patient, with 13.5% of that time spent after-hours. Over 12 months of ambulatory experience, resident physicians reduced their EHR time per patient and saw more patients per day, but the proportion of EHR time after-hours did not change. Conclusion Resident physicians spend a significant amount of time working in the EHR, both during and after clinic hours. While residents improve efficiency in reducing EHR time per patient, they do not reduce the proportion of EHR time spent after-hours. Concerns over the impact of EHRs on physician well-being should include recognition of the burden of EHR usage on early-career physicians.

Chiara Costa ◽  
Michele Teodoro ◽  
Giusi Briguglio ◽  
Ermanno Vitale ◽  
Federica Giambò ◽  

Since the novel coronavirus (SARS-CoV-2) has spread worldwide, healthcare workers—resident physicians in particular—have been hugely involved in facing the COVID-19 pandemic, experiencing unprecedented challenges in fighting the disease. We aimed to evaluate the prevalence of poor sleep quality, daytime sleepiness, and alterations in mood state profiles in this category. This cross-sectional study, conducted in 2020, enrolled 119 subjects from a university hospital in southern Italy. Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), and Profile of Mood States (POMS) questionnaires were administered to physicians divided into four areas: anesthesiology, medicine, service, and surgery. In the overall sample, approximately 45% reported poor sleep quality, although only nine subjects (8%) reported an ESS score that suggested excessive daytime sleepiness. Alterations in mood profiles were also observed; the Vigor and Fatigue factors were the most altered. In particular, anesthesiologists seem to be the most affected category, showing a profound decrease in Vigor with a concomitant increase in Fatigue. Considering the possible consequences of the COVID-19 pandemic, preventive measures should be adopted, especially those aimed at facilitating a better turnover of physicians, optimizing the working schedule, and improving the organization of work.

2021 ◽  
Vol 10 (15) ◽  
pp. 3198
Hayoung Byun ◽  
Sangjoon Yu ◽  
Jaehoon Oh ◽  
Junwon Bae ◽  
Myeong Seong Yoon ◽  

The present study aimed to develop a machine learning network to diagnose middle ear diseases with tympanic membrane images and to identify its assistive role in the diagnostic process. The medical records of subjects who underwent ear endoscopy tests were reviewed. From these records, 2272 diagnostic tympanic membranes images were appropriately labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), or cholesteatoma and were used for training. We developed the “ResNet18 + Shuffle” network and validated the model performance. Seventy-one representative cases were selected to test the final accuracy of the network and resident physicians. We asked 10 resident physicians to make diagnoses from tympanic membrane images with and without the help of the machine learning network, and the change of the diagnostic performance of resident physicians with the aid of the answers from the machine learning network was assessed. The devised network showed a highest accuracy of 97.18%. A five-fold validation showed that the network successfully diagnosed ear diseases with an accuracy greater than 93%. All resident physicians were able to diagnose middle ear diseases more accurately with the help of the machine learning network. The increase in diagnostic accuracy was up to 18% (1.4% to 18.4%). The machine learning network successfully classified middle ear diseases and was assistive to clinicians in the interpretation of tympanic membrane images.

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