Attending Physician Research Articles

Evaluating Performance of ChatGPT on MKSAP Cardiology Board Review Questions

Chat Generative Pretrained Transformer (ChatGPT) is a natural language processing tool created by OpenAI. Much of the discussion regarding artificial intelligence (AI) in medicine is the ability of the language to enhance medical practice, improve efficiency and decrease errors. The objective of this study was to analyze the ability of ChatGPT to answer board-style cardiovascular medicine questions by using the Medical Knowledge Self-Assessment Program (MKSAP).The study evaluated the performance of ChatGPT (versions 3.5 and 4), alongside internal medicine residents and internal medicine and cardiology attendings, in answering 98 multiple-choice questions (MCQs) from the Cardiovascular Medicine Chapter of MKSAP. ChatGPT-4 demonstrated an accuracy of 74.5 %, comparable to internal medicine (IM) intern (63.3 %), senior resident (63.3 %), internal medicine attending physician (62.2 %), and ChatGPT-3.5 (64.3 %) but significantly lower than cardiology attending physician (85.7 %). Subcategory analysis revealed no statistical difference between ChatGPT and physicians, except in valvular heart disease where cardiology attending outperformed ChatGPT (p = 0.031) for version 3.5, and for heart failure (p = 0.046) where ChatGPT-4 outperformed senior resident. While ChatGPT shows promise in certain subcategories, in order to establish AI as a reliable educational tool for medical professionals, performance of ChatGPT will likely need to surpass the accuracy of instructors, ideally achieving the near-perfect score on posed questions.

Assessment of decision-making autonomy in chronic pain patients: a pilot study

BackgroundPatient decision-making autonomy refers to the patients’ ability to freely exert their own choices and make their own decisions, given sufficient resources and information to do so. In pain medicine, it is accepted that appropriate beneficial management aims to propose an individualized treatment plan shared with the patients, as agents, to help them live as autonomously as possible with their pain. However, are patients in chronic pain centers sufficiently autonomous to participate in the therapeutic decisions that concern them? As this question still remains unanswered, a pilot study was set up to that aim.MethodsOver a 2-month period, first-time patients within a tertiary multidisciplinary pain center underwent a systematic evaluation of their autonomy using the MacArthur Competence Assessment Tool for Treatment (MacCAT-T), considered the benchmark tool for measuring a patient’s ability to consent to treatment. Demographic data and pain characteristics of the patients were collected and their respective attending pain physicians were asked to clinically assess their patients’ degree of autonomy. Another physician, who had not participated in the initial patient evaluation, subsequently administered the MacCAT-T questionnaire to the same patients.ResultsTwenty-seven patients were included during the study period (21 women and 6 men), with an average age of 50 years. The average duration of pain was 8 years. Based on their clinical experience, the 4 different pain physicians in charge of these patients considered that out of 25 assessed patients, 22 of them (89%) had full decision-making capacity, with no deficit in autonomy. According to the MacCAT-T results, only 13 of these 25 patients (48%) had no deficit, while 7 (26%) had a major deficit in autonomy. The only patient characteristic that appeared to be related to autonomy was pain type, specifically nociplastic pain. The average time taken to complete the test was 20 min, and patients were very satisfied with the interview.ConclusionResults from the present pilot study suggest that patients suffering from chronic pain do not appear to be entirely autonomous in their decision to consent to the proposed treatment plan according to the MacCAT-T questionnaire, and physicians seem to find it difficult to properly assess this competence in a clinical setting. Further studies with larger samples are needed to better evaluate this concept to improve the complex management of these patients.

Application of AI in the creation of discharge summaries in psychiatric clinics.

The integration of artificial intelligence (AI; ChatGPT 4.0) into medical workflows presents a great potential to enhance efficiency and quality. The use of artificial intelligence in the creation of discharge summaries seems particularly interesting and valid. The course of each hospitalization is described in the discharge summary, which is given to each patient and then to his general practitioner at the end of hospital treatment. An exploratory analysis of discharge summaries in psychiatric clinics underscores that these documents must fulfill diverse and specific requirements. Nevertheless, AI-generated discharge summaries offer the opportunity to optimize information transfer and alleviate the workload on physicians. The study evaluates the quality of discharge summaries produced by clinical staff and by an AI model (ChatGPT 4.0). The clinicians involved in writing of the discharge summaries were not informed about the study's purpose or methodology. The completed summaries were subsequently assessed by four attending physicians using predefined criteria. These physicians were also blinded to the study's objectives and were unaware of the individual authors of the summaries. The evaluation criteria included consistency, completeness, and comprehensibility. Additionally, the time required to prepare these summaries and its impact on overall quality were analyzed. The results of the study indicate that discharge summaries generated by AI are more efficient than discharge summaries prepared by clinic staff. The AI was particularly effective in terms of coherence and information structure. Further research, training and development is needed to improve the accuracy and reliability of AI-generated discharge summaries.

Accuracy of multi-task network based on vision Transformer in the three-dimensional upper airway analysis

Objective: To explore the accuracy of a multi-task model based on vision Transformer for analyzing the three-dimensional (3D) upper airway and its subregions, and to evaluate its clinical applicability. Methods: According to the inclusion and exclusion criteria, cone-beam CT (CBCT) data of 10 patients [4 males and 6 females, (20.8±2.7) years] who had their first visit to the Department of Orthodontics in the Hospital of Stomatology, Wuhan University from January 2012 to January 2020 were retrospectively selected. The 3D slicer software was used to segment the upper airway and pharyngeal airway and measure their volumes as the gold standard. The Dolphin 3D software was used to segment the pharyngeal airway and its subregions and measure their volumes as the gold standard. A multi-task model based on vision Transformer developed by the research team for automatic segmentation and volume measurement of the upper airway and its subregions. All the measurements were conducted by the same attending physician. The Bland-Altman analysis and intraclass correlation coefficient (ICC) were used to evaluate the consistency between the multi-task network and the gold standard in the upper airway segmentation and volume measurements, and the paired t test was used to compare the differences between the multi-tasking model and the gold standard. Results: The mean volume deviation of the upper airway segmented by multi-task model and 3D Slicer was -979.6 mm3, and the ICC was 0.97. The mean volume deviation of the pharyngeal airway, nasopharynx, velopharynx, glossopharynx and hypopharynx segmented by multi-task network and Dolphin 3D were 2 069.5, -950.1, -823.6, -813.9 and 4 003.4 mm3, respectively. In addition, ICC in pharyngeal airway, nasopharynx, velopharynx, glossopharynx and hypopharynx were 0.97, 0.94, 0.96, 0.96 and 0.69, respectively. Conclusions: The multi-task model based on vision Transformer produced different errors in the segmentation of 3D upper airway and its subregions. The segmentation of the nasopharynx, velopharynx and glossopharynx was in good agreement with the gold standard, while the segmentation of hypopharynx was poor, suggesting that the robustness and generalization of this model should be further enhanced.

Healthcare Provider Experiences With Unvaccinated COVID-19 Patients: A Qualitative Study.

In the setting of an active pandemic the impact of public vaccine hesitancy on healthcare workers has not yet been explored. There is currently a paucity of literature that examines how patient resistance to disease prevention in general impacts practitioners. The COVID-19 pandemic created unprecedented healthcare challenges with impacts on healthcare workers' wellbeing. Vaccine hesitancy added complexity to providing care for unvaccinated patients. Our study qualitatively explored experiences of healthcare providers caring for unvaccinated patients with severe COVID-19 infection in the intensive care setting. We used interview-based constructivist grounded theory methodology to explore experiences of healthcare providers with critically ill unvaccinated COVID-19 patients. Healthcare providers who cared for unvaccinated patients with severe COVID-19 respiratory failure following availability of severe acute respiratory syndrome coronavirus 2 vaccines were recruited from seven ICUs located within two large academic centers and one community-based hospital. We interviewed 24 participants, consisting of eight attending physicians, seven registered nurses, six critical care fellows, one respiratory therapist, one physiotherapist, and one social worker between March 2022 and September 2022 (approximately 1.5 yr after the availability of COVID-19 vaccines in Canada). Interviews were recorded, transcribed, de-identified, and coded to identify emerging themes. The final data was analyzed to generate the thematic framework. Reflexivity was employed to reflect upon and discuss individual pre-conceptions and opinions that may impact collection and interpretation of the data. Healthcare providers maintained dedication toward professionalism during provision of care, at the cost of suffering emotional turmoil from the pandemic and COVID-19 vaccine hesitancy. Evolving sources of stress associated with vaccine hesitancy included ongoing high volumes of critically ill patients, resource shortages, and visitation restrictions, which contributed to perceived emotional distress, empathy loss, and professional dissatisfaction. As a result, there were profound personal and professional consequences for healthcare professionals, with perceived impacts on patient care. Our study highlights struggles of healthcare providers in fulfilling professional duties while navigating emotional stressors unique to vaccine hesitancy. System-based interventions should be explored to help providers navigate biases and moral distress, and to foster resilience for the next major healthcare system strain.

"Dr. AI Will See You Now": How Do ChatGPT-4 Treatment Recommendations Align With Orthopaedic Clinical Practice Guidelines?

Artificial intelligence (AI) is engineered to emulate tasks that have historically required human interaction and intellect, including learning, pattern recognition, decision-making, and problem-solving. Although AI models like ChatGPT-4 have demonstrated satisfactory performance on medical licensing exams, suggesting a potential for supporting medical diagnostics and decision-making, no study of which we are aware has evaluated the ability of these tools to make treatment recommendations when given clinical vignettes and representative medical imaging of common orthopaedic conditions. As AI continues to advance, a thorough understanding of its strengths and limitations is necessary to inform safe and helpful integration into medical practice. (1) What is the concordance between ChatGPT-4-generated treatment recommendations for common orthopaedic conditions with both the American Academy of Orthopaedic Surgeons (AAOS) clinical practice guidelines (CPGs) and an orthopaedic attending physician's treatment plan? (2) In what specific areas do the ChatGPT-4-generated treatment recommendations diverge from the AAOS CPGs? Ten common orthopaedic conditions with associated AAOS CPGs were identified: carpal tunnel syndrome, distal radius fracture, glenohumeral joint osteoarthritis, rotator cuff injury, clavicle fracture, hip fracture, hip osteoarthritis, knee osteoarthritis, ACL injury, and acute Achilles rupture. For each condition, the medical records of 10 deidentified patients managed at our facility were used to construct clinical vignettes that each had an isolated, single diagnosis with adequate clarity. The vignettes also encompassed a range of diagnostic severity to evaluate more thoroughly adherence to the treatment guidelines outlined by the AAOS. These clinical vignettes were presented alongside representative radiographic imaging. The model was prompted to provide a single treatment plan recommendation. Each treatment plan was compared with established AAOS CPGs and to the treatment plan documented by the attending orthopaedic surgeon treating the specific patient. Vignettes where ChatGPT-4 recommendations diverged from CPGs were reviewed to identify patterns of error and summarized. ChatGPT-4 provided treatment recommendations in accordance with the AAOS CPGs in 90% (90 of 100) of clinical vignettes. Concordance between ChatGPT-generated plans and the plan recommended by the treating orthopaedic attending physician was 78% (78 of 100). One hundred percent (30 of 30) of ChatGPT-4 recommendations for fracture vignettes and hip and knee arthritis vignettes matched with CPG recommendations, whereas the model struggled most with recommendations for carpal tunnel syndrome (3 of 10 instances demonstrated discordance). ChatGPT-4 recommendations diverged from AAOS CPGs for three carpal tunnel syndrome vignettes; two ACL injury, rotator cuff injury, and glenohumeral joint osteoarthritis vignettes; as well as one acute Achilles rupture vignette. In these situations, ChatGPT-4 most often struggled to correctly interpret injury severity and progression, incorporate patient factors (such as lifestyle or comorbidities) into decision-making, and recognize a contraindication to surgery. ChatGPT-4 can generate accurate treatment plans aligned with CPGs but can also make mistakes when it is required to integrate multiple patient factors into decision-making and understand disease severity and progression. Physicians must critically assess the full clinical picture when using AI tools to support their decision-making. ChatGPT-4 may be used as an on-demand diagnostic companion, but patient-centered decision-making should continue to remain in the hands of the physician.

Об отдельных актуальных проблемах правового статуса врача и необходимости их законодательного решения.

Federal Law No. 323-FZ dated 11/21/2011 «On the Basics of Public Health protection in the Russian Federation» identifies two close, interrelated, but at the same time independent rights of a citizen: in article 21, the right to choose a doctor, taking into account the consent of a doctor, i. e. the right to choose any doctor at all and not only the attending physician, and in article 70 the patient’s right to choose the attending physician. At the same time, with the exception of the district service, the Basics do not disclose the mechanism for a citizen to choose a doctor, including a doctor, when providing him with primary specialized medical care and specialized medical care. This mechanism has not been disclosed in the procedures for providing medical care. In this regard, the authors proposed to supplement article 21 of the Fundamentals with instructions that the procedures for providing medical care, including the procedure for choosing a doctor by the patient. In addition, the publication noted that the absence of detailed and comprehensive medical care in the relevant procedures.

Механизмы внедрения технологий искусственного интеллекта в здравоохранение: новые этические вызовы

Currently, systems based on artificial intelligence (AI) are finding increasing application in medicine. Acting as assistants of both the attending physician and managing physician, they can be a good help in solving a number of problems in modern healthcare, such as staff shortage, professional burnout and, in some cases, insufficient staff qualification. However, this leads to increased requirements for reliability of such systems. Introduction of a new advanced technology raises a number of ethical issues and problems, the solution of which is necessary to gain trust of people and reduce distrust associated with the use of AI technologies. It seems that if ethical standards determine and set the progressive development of artificial intelligence, this will lead to the maximum benefit from the use of this technology in healthcare. The paper examines the ethical aspects of transition of software into the category of medical devices. At the same time, legal and organizational mechanisms for solving ethical problems at both the international and domestic levels are provided. The activities of both public and government organizations in this field are considered. The need to obtain the permission of ethical committees for conducting clinical trials and ensuring informed consent of patients is emphasized. It also highlights the importance of integrating medical data into structured datasets that can be registered as databases. This will contribute to improved quality of medical research and practice.

Machine learning for targeted advance care planning in cancer patients: a quality improvement study

Context: Prognostication challenges contribute to delays in advance care planning (ACP) for patients with cancer near the end of life (EOL). Objectives: Examine a quality improvement mortality prediction algorithm intervention's impact on ACP documentation and EOL care. Methods: We implemented a validated mortality risk prediction machine learning model for solid malignancy patients admitted from the emergency department (ED) to a dedicated solid malignancy unit at Duke University Hospital. Clinicians received an email when a patient was identified as high-risk. We compared ACP documentation and EOL care outcomes before and after the notification intervention. We excluded patients with intensive care unit (ICU) admission in the first 24 hours. Comparisons involve chi-square/Fisher's exact tests and Wilcoxon rank sum tests; comparisons stratified by physician specialty employ Cochran-Mantel-Haenszel tests. Results: Pre-intervention and post-intervention cohorts comprised 88 and 77 patients, respectively. Most were White, non-Hispanic/Latino, and married. ACP conversations were documented for 2.3% of hospitalizations pre-intervention vs. 80.5% post-intervention (p<0.001), and if the attending physician notified was a palliative care specialist (4.1% vs. 84.6%) or oncologist (0% vs. 76.3%) (p<0.001). There were no differences between groups in length of stay (LOS), hospice referral, code status change, ICU admissions or LOS, 30-day readmissions, 30-day ED visits, and inpatient and 30-day deaths. Conclusion: Identifying patients with cancer and high mortality risk via machine learning elicited a substantial increase in documented ACP conversations but did not impact EOL care. Our intervention showed promise in changing clinician behavior. Further integration of this model in clinical practice is ongoing.

Subjective symptoms are triggers for the detection of immune checkpoint inhibitor-induced interstitial lung disease and associate with disease severity: a single-center retrospective study

BackgroundInterstitial lung disease (ILD) is one of the most common fatal immune-related adverse events (irAEs). ILD development adversely affects the continuation of anticancer drug therapy, including immune checkpoint inhibitor (ICI) therapy and prognosis. There are no established useful clinical indicators for the early detection of ILD. Furthermore, the factors that lead the attending physician to suspect ICI-induced ILD (ICI-ILD) remain unclear. This study aimed to investigate the ICI-ILD detection based on subjective symptoms and their relationship with disease severity in patients receiving anti-PD-1/PD-L1 antibody.MethodsThis was a retrospective observational study. We enrolled the patients who received anti-PD-1/PD-L1 antibody at Kyoto University Hospital between September 2014 and April 2021. Patients who developed ICI-ILD were stratified into two distinct groups based on factors that triggered the suspicion of ILD development. The “Subjective symptoms” group was defined as patients in whom ILD was detected based on subjective symptoms. Conversely, the “Routine examinations” group was defined as patients in whom ILD was suspected based on scheduled routine examinations. The severity of ILD in each group was assessed and its association with changes in the respiratory symptoms was examined.ResultsOf 926 patients who received anti-PD-1/PD-L1 antibody, 51 patients (5.5%) developed ICI-ILD. The incidence of ICI-ILD in patients with lung cancer was significantly higher than that in patients with other cancers (P < 0.001). Among the patients with ICI-ILD, 27 patients (52.9%) were classified into the “Subjective symptoms” group. The “Subjective symptoms” group exhibited a significantly higher proportion of Grade 3–5 ICI-ILD cases than the “Routine examinations” group (76.2% vs. 23.8%, P = 0.010). At the last visit, before the suspected onset of ILD, 21 of the 27 patients (77.8%) had no symptoms or no change in the respiratory symptoms.ConclusionSubjective symptoms triggered the suspicion of Grade 3–5 ICI-ILD. Enhanced monitoring and patient education could be essential for the early detection of ICI-ILD because ILD may develop rapidly. Our findings might help to manage ICI-ILD in clinical practice.

Pregnancy and psychoemotional state of women after in vitro fertilization

Aim. To study the psycho-emotional state of pregnant women after IVF and to identify the factors influencing it.Materials and methods. A total of 104 pregnant women were included in a comparative study. They were divided into two groups: one group consisted of women whose pregnancy occurred after IVF (main group), and the other group consisted of women whose pregnancy occurred naturally (comparison group). Psychodiagnostic questionnaires were used to assess the psycho-emotional state of the pregnant women. Statistical processing was performed using the STATISTICA 10.0 program, and differences were considered significant at a significance level of p=0.05.Results and discussion. The results of the analysis showed that the mean age of pregnant women after IVF was 30.8±1.3 years, which was significantly higher than that of women with natural pregnancy (mean age 22.38±4.6 years). In the group of women who became pregnant after IVF, there were more women with a complicated obstetric and gynaecological history and with extragenital pathology. The results of psychodiagnostic studies showed that women in the IVF group had a high level of anxiety. Factors such as the age of the pregnant woman, history of infertility, longterm and unsuccessful infertility treatment, and the presence of somatic pathology negatively affect the mental health of pregnant women.Conclusion. Thus, a high level of anxiety, fear about the course of the pregnancy and the health of the unborn child is characteristic of women whose pregnancy occurred after IVF. Psycho-emotional reactions are associated with the nature of the pathology of the pregnancy, the duration of infertility treatment and a burdened obstetric and gynaecological history. It is important to create special consultation rooms for psychological support. The help of a perinatal psychologist is necessary. Collaboration between the psychologist and the attending physician can achieve the best results.

Live Streaming of the Professor's Ward Rounds in Undergraduate Neurology Education: Usability Study.

Although neurology department ward rounds are among the most important medical education exercises in Japan, they have several issues. Patients may find it unpleasant to undergo repeated neurological tests, especially when in the presence of several students. Only the front row of students can closely observe the examination findings; moreover, students were prohibited from contacting patients altogether during the COVID-19 pandemic. One possible solution is to use commercial videoconferencing systems. However, Japanese patients are reluctant to have their medical information or video footage of them sent outside of the hospital via the internet. The study aimed to confirm the feasibility of conducting remote teaching rounds using an in-house web conferencing system in which the patients' personal data are securely protected. This study also explored whether using remote rounds alongside face-to-face participation would enhance learning. We created an on-premises videoconferencing system using an open-source app. To perform video ward rounds, the professor wore a wireless microphone while leading routine in-person rounds and the attending physician carried a tablet device linked to a web conference, allowing students in another room to watch the rounds on a live stream. In total, 112 of 5th-year students who entered their 1-week neurology rotation between 2021 and 2022 were instructed to participate in 1-hour in-person and remote rounds. Students were given questionnaires to evaluate their satisfaction and the educational effects of the remote rounds. The remote ward rounds were conducted easily with no interference with the in-person rounds, nor any complaints from the patients. Each examination technique was explained by another teacher to the students who participated in remote rounds in the conference room. Characteristic neurological findings, such as plantar reflexes (Babinski sign), which are usually seen only by close observers during in-person rounds, could be visualized under magnification by all students. The postexperience survey (82/112, 73% response rate) showed that the mean score of participants' satisfaction was 3.94 (SD 0.83; excellent 5 and poor 1). No participant scoring 1 was noted. The proportion of students who observed 6 representative abnormal neurological findings (Babinski sign, hyperreflexia, cerebellar ataxia, involuntary movement, muscular weakness, and abnormality in sensory examination) increased by 22% (18/82, range 13-24) compared to in-person rounds alone. When self-rating the learning value, 43% (35/82) of the students answered that remote rounds are equally as valuable as in-person rounds, while 32% (26/82) preferred remote rounds. Live-streaming of neurology ward rounds using a secure in-house web conferencing system provides additional learning experience without concerns regarding leakage of patient information. This initiative could enhance neurology learning before entering a clinical clerkship.

Experience as a physician attending to traffic accident victims in hospitals in Managua, Nicaragua

Introduction: Every 23 seconds a person dies from a traffic accident in the world, that is about 3756 deaths per day, with the consequent impact on relatives and health personnel. Doctors are part of the health team that is on the front line of care. Although this is part of their daily work, their feelings and emotions are deeply affected. There is a deficit of research of this type carried out by doctors.Methodology: Qualitative study, under the constructivist paradigm with a phenomenological approach. The objective was; explore the lived experience of doctors who treat victims of traffic accidents. The sample was intentional with five participating physicians . The phenomenological interview was used as an information collection technique and analyses were carried out according to Van Manen.Results: From the analysis, 15 codes were obtained inductively, which gave rise to 6 sub-themes: Impacts of Tragedies; Communication of bad news; Becoming of emotion; Coping; Narratives that endure and Resignation that in turn made up the 3 central themes: Body Lived in the impact, Time Lived in the here and after and Human Relations lived: between narratives and resignation.Conclusion: The study reveals that doctors are indirect victims of adverse events in the face of damage to the patient and their family in the face of a traffic accident. Constant monitoring of their psychological and emotional well-being, training communication strategies and improving patient-centered curricular programs are recommended. Extending the study to other specialties would enrich this understanding..

Investigation of Prevalence, Etiological Factors and Risk Factors of Urinary Tract Infection in Spinal Cord Injury Patients

Background: Urinary tract infection (UTI) is a common infection that constitutes a significant portion of all hospital infections. Objectives: Given the importance of spinal cord trauma, this study aimed to investigate the prevalence, etiological factors, and risk factors of UTI in spinal cord injury (SCI) patients. Methods: In this study, patients were examined daily. If a diagnosis of UTI was confirmed, based on laboratory results and the attending physician's opinion, they were included in the study. A researcher-made checklist was then completed by the researchers. Results: The results showed that 89 (67.4%) of the patients were male, and 101 (76.5%) were married. Additionally, 44.7% of the patients were hospitalized due to traffic accidents. The results also indicated that 30.3% of patients had hematuria, 35.5% had fever, and 12.9% had scrotal pain or turbid urine. Regarding the microorganisms identified, 55.3% of patients had Escherichia coli, 11.4% had Proteus, 9.1% had Klebsiella, and 7.6% had Candida spp. Conclusions: Given the significant prevalence of UTI in patients, necessary preventive measures should be implemented to prevent infections.

The Use of Smartphone-Based Highly Realistic MCI Training as an Adjunct to Traditional Training Methods.

Training for mass casualty incident (MCI) response is critical to ensure that resource allocation and treatment priorities limit preventable mortality. Previous research has investigated the use of immersive virtual environments as an alternative to high fidelity MCI training, which is expensive and logistically challenging to implement. While these have demonstrated positive early results, they still require complex technology deployment, dedicated training facilities, and significant time from instructors and facilitators. This study explores the feasibility of a smartphone-based application for trauma care training and MCI triage to fill the gap between classroom learning and high-fidelity simulation. The goals of this investigation were to evaluate clinician perceptions of a virtual MCI training simulator's usability, acceptability, fidelity, functionality, and pacing. This study used a smartphone-based training simulation called Extensible Field and Evacuation Care Training in a Virtual Environment (EFECTIVE), which presents virtual patient scenarios in a gamified, but visually high-fidelity environment. A total of 21 participants were recruited as a convenience sample of medical students, paramedics, nurses, and emergency medicine resident and attending physicians at University of Massachusetts Memorial Medical Center, an urban tertiary care medical center. Participants completed a brief tutorial and then performed a series of virtual patient scenarios and 1 MCI scenario on the simulator, each of which was 5 minutes in duration. Then, each participant completed a survey assessing the perceived usability, acceptability, fidelity, functionality, and pacing of the virtual training simulator. The research protocol was approved by the University of Massachusetts Chan Medical School Institutional Review Board. 48% of participants disagreed that a virtual simulator could completely replace live MCI training, though 71% agreed that app-based simulations could effectively supplement live MCI training and 67% felt that they could be used to learn how to order medical interventions in care under fire scenarios. 80% of participants agreed that the simulation could be used to practice MCI triage and to gain experience with coordinating movement of casualties to casualty collection points. 67% of participants believed that use of virtual simulators would increase their MCI preparedness. 76% agreed that the clinical cases depicted were medically realistic and that the clinical cases presented accurately represented the scenarios described. In addition, despite being presented on a smartphone as opposed to virtual reality, 62% of participants rated the experience immersive. This study provides encouraging evidence that easy to deploy smartphone-based simulations may be an effective way to supplement MCI and care under fire training. Although the study is limited by a small sample size, there was strong agreement among participants from a wide variety of emergency medicine roles that such a simulation could train core topics associated with MCI triage. Because app-based simulations are easily deployable and can be executed quickly and frequently, they could be used as a more flexible training model compared to large scale live or virtual reality-based simulations. The results of this investigation also indicate that a sufficient level of medical realism can be achieved without live simulation.

Emergency Department Slit Lamp Interdisciplinary Training Via Longitudinal Assessment in Medical Practice.

Eye emergencies make up nearly 3% of US emergency department (ED) visits. While emergency physicians (EP) should diagnose and treat these ophthalmologic emergencies, many trainees report limited ocular exposure and insufficient training throughout their residency to confidently conduct a thorough slit-lamp exam. We created an interdisciplinary, simulation-based mastery learning (SBML) curriculum to teach emergency attending physicians how to operate the slit lamp with multimodal learning methodology at a tertiary academic center. The EPs first demonstrate their initial slit-lamp competency with a 20-item checklist, and they then review the necessary curricular content to pass their independent readiness test before completing their in-person teaching and demonstration session with an ophthalmology attending to demonstrate procedural mastery (minimal passing score >90%). Fifteen EPs were enrolled; all completed the final exam of the curriculum. The pre- and post-curriculum checklist scores increased by an average of seven points (P = .002); 86.7% of EPs felt confident in completing a slit-lamp exam after the curriculum, compared to 20% at the beginning. Five of 15 reported teaching learners within the two-month post-curricular period, ranging from 5-30 students. The hands-on teaching was the most positively reviewed element of the curriculum. The SBML program successfully trained EPs on performing a comprehensive slit-lamp exam with promising results of downstream education to junior learners. We encourage other institutions to leverage SBML as a teaching modality for procedural-based training and advocate cross-discipline education initiatives.

Expert recommendations for internet medical care treatment of tuberculosis

Tuberculosis, a chronic infectious disease caused by Mycobacterium tuberculosis, has long posed significant challenges to both medical professionals and patients in its diagnosis and treatment. The need for regular follow-up visits has resulted in a significant economic and logistical burden for patients and a strain on healthcare resources. However, the recent rapid development of Internet technology has significantly enhanced the role of telemedicine in the health care of tuberculosis. This proposal aims to provide a comprehensive overview of the various aspects of telemedicine in the management of tuberculosis. It covers functional positioning, medical qualifications, scope of application, management team, operational model, medical standards, medication evaluation, precautions, risk management. It is intended to serve as a reference for healthcare professionals in the practical application of telemedicine in tuberculosis care.【Expert Recommendation 1】: To provide the further online services for tuberculosis patients who have already had a face-to-face consultation and been diagnosed with tuberculosis. Telemedicine in tuberculosis care includes confirmation of diagnosis, treatment, health counseling, management, educational initiatives, and the initial assessment and management of new symptoms and signs.【Expert Recommendation 2】: The qualifications for telemedicine in tuberculosis care require that the attending physician has a valid medical license and has at least three years of clinical experience in tuberculosis.【Expert Recommendation 3】: The primary target group for online tuberculosis care are patients who are undergoing routine follow-up visits. Certain examinations and test results are mutually recognized within certain regions.【Expert Recommendation 4】: The management team for telemedicine in tuberculosis care is consist of specialist physicians from tuberculosis medical institutions or third-party internet medical organizations, together with information technology staff.【Expert Recommendation 5】: The informatization framework for telemedicine in tuberculosis care includes referrals between higher and lower-level institutions, as well as online and offline referrals. Methods for online follow-up consultations include text-based and video consultations.【Expert Recommendation 6】: Standards for telemedicine in tuberculosis care must be in line with government documents. Regular monitoring and evaluation of the safety and efficacy of anti-tuberculosis therapy is needed, with guidance for patients to seek further treatment at an offline medical institution if necessary.【Expert Recommendation 7】: The attending physician must comply with regulations when documenting patient care and issuing prescriptions, which are valid only after review and approval by a pharmacist.【Expert Recommendation 8】: Healthcare providers are encouraged to use digital platforms, including social media, short video platforms, and mini-programs, to disseminate health information to patients and their families.【Expert Recommendation 9】: If the attending physician discovers adverse events in internet medical treatment, it is necessary to report them immediately to the adverse reaction reporting system of the hospital relying on internet medical treatment.【Expert Recommendation 10】: The attending physician should handle any disputes or complaints about online diagnosis and treatment in accordance with applicable laws, regulations, and guidelines.【Expert Recommendation 11】: Strict confidentiality of patient information must be maintained in online tuberculosis care, and patient consent before data can be used for educational or research purposes.【Expert Recommendation 12】: For primary care facilities that do not have the necessary resources for online diagnosis and treatment, we recommend establishing satellite centers, implementing simplified remote care systems, and utilizing mobile devices in collaboration with nearby hospitals that can provide remote care. This approach aims to increase the capacity of primary care facilities to provide telemedicine service.

Small airway dysfunction links asthma exacerbations with asthma control and health-related quality of life

BackgroundSmall airway dysfunction not only affects asthma control, but also has adverse effects on the psychological and/or social activities of asthma patients. However, few long-term observational studies have explored the complex relationship between small airway dysfunction and asthma control and health-related quality of life in patients with asthma exacerbations.MethodsThe study recruited 223 patients with exacerbations of asthma (i.e. those with at least one asthma attack over the past year) and 228 patients without exacerbations of asthma (i.e. those without asthma attacks over the past year). We evaluated SAD in patients with asthma exacerbations using impulse oscillometry method. At each evaluation time point within one year of follow-up, the attending physician conducts a case investigation of the patients. We analyzed the correlation between SAD and general characteristics (age, obesity, smoking history), type 2 inflammation (blood eosinophils, exhaled nitric oxide), FEV1, as well as asthma control (ACT) and health-related quality of life (mini-AQLQ) in patients with asthma exacerbations, and constructed a structural equation model to evaluate the causality of these clinical variables.ResultsThe SAD prevalence in patients with asthma exacerbation is as high as 75%. SAD is connected with poor asthma control and poor health-related quality of life. The structural equation model indicates that age, obesity, FeNO, and FEV1 are independent predictive factors of SAD. SAD is the main determinant factor of asthma control, which in turn affected health-related quality of life. FEV1 and age directly affect asthma control and affect health-related quality of life through asthma control. In addition, there is a bidirectional relationship between FEV1 and small airway dysfunction and between asthma control and health-related quality of life.ConclusionsSmall airways are involved from an early stage in asthma. Abnormal function of the small airways can significantly increase airway resistance in asthma patients, while worsening their clinical symptoms. In addition, aging is also a key risk factor for asthma control. Especially, small airway dysfunction links asthma control with health-related quality of life.

Comparison of effect and safety of phacoemulsification surgery performed by resident and attending physicians.

The objective of this study is to compare the effect and safety of phacoemulsification surgery performed by resident and attending physicians. This was a retrospective study. Eyes with cataract who underwent phacoemulsification surgery at the First Affiliated Hospital of Ningbo University between January 2021 and December 2023 were reviewed. All the patients were followed up for at least 12 months and were divided into two groups according to the surgery performer. SPSS was used to analyze data, considering p < 0.05 significant. Overall, 316 patients with cataract in group 1 (surgery performed by resident physician, n = 181) and group 2 (surgery performed by attending physician, n = 135) were reviewed. There were no statistically significant differences in patient demographics variables and cataract grade between the groups. The resident surgeon used more cumulative dissipate energy (15.00 ± 7.25 vs. 10.83 ± 6.52, p < 0.001) and operation time (20.46 ± 5.69 vs. 12.59 ± 4.61 min, p < 0.001) to complete the surgery. Also, the ECL in group 1 was higher (14.87 ± 5.00 vs. 10.77 ± 4.46, p < 0.001). The eyes had significant visual improvement in both groups postoperatively (p < 0.05), but at the 12-month follow-up, eyes in group 2 had better best-corrected visual acuity [0.10 (0.00, 0.22) vs. 0.10 (0.10, 0.22) logMAR, p = 0.039]. Except for month 1, the intraocular pressure was no statistical difference in group 1 and group 2 (14.65 ± 2.52 vs. 15.30 ± 2.34 mmHg, p = 0.019). Cases in group 1 were more likely to undergo intraoperative and postoperative complications (37 vs. 14, p = 0.031), including cornea edema (p = 0.025), capsule tear (p = 0.044), and posterior capsular opacification (p = 0.027). The effect of phacoemulsification surgery performed by the resident physician is satisfying, but compared to the attending physician, the higher probability of complications should be paid more attention.

Large Language Model Influence on Management Reasoning: A Randomized Controlled Trial.

Large language model (LLM) artificial intelligence (AI) systems have shown promise in diagnostic reasoning, but their utility in management reasoning with no clear right answers is unknown. To determine whether LLM assistance improves physician performance on open-ended management reasoning tasks compared to conventional resources. Prospective, randomized controlled trial conducted from 30 November 2023 to 21 April 2024. Multi-institutional study from Stanford University, Beth Israel Deaconess Medical Center, and the University of Virginia involving physicians from across the United States. 92 practicing attending physicians and residents with training in internal medicine, family medicine, or emergency medicine. Five expert-developed clinical case vignettes were presented with multiple open-ended management questions and scoring rubrics created through a Delphi process. Physicians were randomized to use either GPT-4 via ChatGPT Plus in addition to conventional resources (e.g., UpToDate, Google), or conventional resources alone. The primary outcome was difference in total score between groups on expert-developed scoring rubrics. Secondary outcomes included domain-specific scores and time spent per case. Physicians using the LLM scored higher compared to those using conventional resources (mean difference 6.5 %, 95% CI 2.7-10.2, p<0.001). Significant improvements were seen in management decisions (6.1%, 95% CI 2.5-9.7, p=0.001), diagnostic decisions (12.1%, 95% CI 3.1-21.0, p=0.009), and case-specific (6.2%, 95% CI 2.4-9.9, p=0.002) domains. GPT-4 users spent more time per case (mean difference 119.3 seconds, 95% CI 17.4-221.2, p=0.02). There was no significant difference between GPT-4-augmented physicians and GPT-4 alone (-0.9%, 95% CI -9.0 to 7.2, p=0.8). LLM assistance improved physician management reasoning compared to conventional resources, with particular gains in contextual and patient-specific decision-making. These findings indicate that LLMs can augment management decision-making in complex cases. ClinicalTrials.gov Identifier: NCT06208423; https://classic.clinicaltrials.gov/ct2/show/NCT06208423.