Experienced Physicians Research Articles

Revealing administrative staff roles in primary care during the COVID-19 pandemic: a qualitative study of family physicians’ perspectives

BackgroundAdministrative staff in primary care undertake numerous tasks to support patient care delivery. Although their roles are often overlooked, administrative staff are essential to the coordination and operations of primary care clinics. The COVID-19 pandemic introduced additional clinical and administrative tasks, including transitioning to virtual appointments and triaging patients for urgency, changing typical workflows. In Canada, existing pandemic plans for primary care did not account for these administrative tasks, nor the support that family physicians would require to continue to provide patient access to primary care. This research seeks to describe and understand the perceptions and experiences of family physicians of their administrative staff roles in primary care during the COVID-19 pandemic, to help inform future pandemic planning.MethodsWe present findings from a qualitative case study across four regions in Canada: Vancouver Coastal health region in British Columbia, Ontario Health West region, the province of Nova Scotia, and the Eastern Health region of Newfoundland and Labrador. We conducted semi-structured qualitative interviews with family physicians (n = 68) across the four regions and thematically analysed the data.ResultsWe identified five salient themes in the data, including (1) applying public health guidelines, (2) educating patients on COVID-19 and COVID-19 services, (3) re-organizing patient visits, (4) maintaining adequate staffing, and (5) recognizing administrative staff contributions. During the COVID-19 pandemic, family physicians took on numerous additional roles to reduce the risk of transmission of the virus with the support of their administrative staff. Family physicians emphasized the challenges of maintaining adequate staffing, and the importance of administrative staff in enabling the provision of primary care.ConclusionsExisting pandemic plans do not account for increased administrative roles taken on by primary care administrative staff. Pandemic plans must include guidance for the roles taken on by primary care administrative staff, such as clinical tasks, as they will continue to play an important role in pandemic recovery. Supporting administrative staff would enhance primary care providers’ ability to manage care during pandemics, facilitate resilience, and decrease provider and administrative burnout.

TIRADS-based artificial intelligence systems for ultrasound images of thyroid nodules: protocol for a systematic review.

The thyroid imaging reporting and data system (TIRADS) was developed as a standard global term to describe thyroid nodule risk features, aiming to address issues such as variability and low reproducibility in nodule feature detection and interpretation by different physicians. The objective of this study is to comprehensively study articles that utilize AI techniques to design and develop decision support systems for classifying thyroid nodule risk on the basis of various TIRADS guidelines from ultrasound images. This protocol includes five steps: identification of key research questions of the review, descriptions of the systematic literature search strategies, criteria for study inclusion and exclusion, study quality measures, and the data extraction process. We designed a complete search string using PubMed, Scopus, and Web of Sciences to retrieve all relevant English language studies up to January 2024. A PRISMA diagram was constructed, inclusion and exclusion criteria were defined, and after a quality assessment of the included papers, relevant data were extracted. The protocol of this systematic review was registered in the PROSPERO database (CRD42024551311). We anticipate that our findings will assist researchers in creating higher-quality systems with increased efficiency, reducing unnecessary biopsies, improving the reproducibility and reliability of thyroid nodule diagnostics, and providing good educational opportunities for less experienced physicians. In this study, a protocol was used for performing a systematic review to evaluate the diagnostic performance and other various aspects used in the design and development of artificial intelligence CAD systems based on various thyroid imaging reporting and data systems (TI-RADSs).

Physician experiences of team-based clinical microsystems: implications for the future of inpatient interprofessional communication

ABSTRACT Healthcare clinical microsystems are small, goal-oriented groups of healthcare professionals that work together on a regular basis to provide care to discrete patient populations. They often include team-based frameworks such as bedside interprofessional rounds, geographic colocation of healthcare team members, interprofessional staff leadership, and unit-level data sharing. In inpatient settings, microsystems improve interprofessional communication and collaboration. In this qualitative study, we examined physician experiences of clinical microsystems and the mechanisms underpinning interprofessional communication in this environment. We interviewed 51 resident and hospitalist physicians. Thematic analysis of physician experiences revealed three key themes: (a) Clinical microsystem effect on workflow, (b) Open forum and work relationships, and (c) Face-to-face vs technology-based communication. Physicians described strong interprofessional relationships based on frequent face-to-face communication. Familiarity with healthcare team members, often resulting from being co-located to the same units, created a comfortable work environment, advanced patient care, and accelerated workflow. Physicians preferred in-person communication with interprofessional colleagues over technology-based communicationand described how in-person interactions enriched subsequent technology-led interactions. As the use of technology-based communication grows in inpatient settings, decreasing face-to-face interactions and facilitating dispersed care, understanding and implementing optimal conditions for effective interprofessional communication is essential.

Weathering the Storm

The provision of palliative care has been seen to have a significant impact on the interprofessional health care team. Being emotionally present to support patients and families at their most stressful points in life can result in compassion satisfaction or compassion fatigue. Providing palliative care through the COVID-19 pandemic added another layer of complexity to this professional caregiving experience. Although efforts have been made by employers to support health care providers in the practices of self-care and mindfulness, the true experience of care providers was not known. This study was conducted from June 2020 to October 2020 to better understand the experiences of physicians, nurses, social workers, and spiritual care providers who were providing palliative care during the first wave of the pandemic. Twenty-five participants completed the Professional Quality of Life (The Concise ProQOL Manual: The Concise Manual for the Professional Quality of Life Scale) measure and open-ended questions to evaluate their experiences as health care providers. The findings resulted in an overarching theme of “Weathering the Storm” with 6 themes describing the experiences of the interprofessional team.

The study on ultrasound image classification using a dual-branch model based on Resnet50 guided by U-net segmentation results

In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonography has become a routine diagnostic tool for thyroid nodules due to its high real-time capabilities and low invasiveness. However, thyroid images obtained from current ultrasound tests often have low resolution and are plagued by significant noise interference. Regional differences in medical conditions and varying levels of physician experience can impact the accuracy and efficiency of diagnostic results. With the advancement of deep learning technology, deep learning models are used to identify whether a nodule in a thyroid ultrasound image is benign or malignant. This helps to close the gap between doctors’ experience and equipment differences, improving the accuracy of the initial diagnosis of thyroid nodules. To cope with the problem that thyroid ultrasound images contain complex background and noise as well as poorly defined local features. in this paper, we first construct an improved ResNet50 classification model that uses a two-branch input and incorporates a global attention lightening module. This model is used to improve the accuracy of benign and malignant nodule classification in thyroid ultrasound images and to reduce the computational effort due to the two-branch structure.We constructed a U-net segmentation model incorporating our proposed ACR module, which uses hollow convolution with different dilation rates to capture multi-scale contextual information for feature extraction of nodules in thyroid ultrasound images and uses the results of the segmentation task as an auxiliary branch for the classification task to guide the classification model to focus on the lesion region more efficiently in the case of weak local features. The classification model is guided to focus on the lesion region more efficiently, and the classification and segmentation sub-networks are respectively improved specifically for this study, which is used to improve the accuracy of classifying the benign and malignant nature of the nodules in thyroid ultrasound images. The experimental results show that the four evaluation metrics of accuracy, precision, recall, and f1 of the improved model are 96.01%, 93.3%, 98.8%, and 96.0%, respectively. The improvements were 5.7%, 1.6%, 13.1%, and 7.4%, respectively, compared with the baseline classification model.

The Expanding Role of Designated Institutional Officials in Graduate Medical Education

Abstract The Accreditation Council for Graduate Medical Education (ACGME) plays a pivotal role in ensuring the quality of graduate medical education (GME) training across the United States. Central to the success of this mission are designated institutional officials (DIOs), who usually serve as chief GME officers within the ACGME-accredited sponsoring institutions (SIs). Despite the critical role of DIOs, the qualifications, level of administrative support, and responsibilities of DIOs are not defined and vary significantly among SIs. Although responsibilities mandated by ACGME Institutional Requirements provide a framework, the actual scope of work of DIOs often extends far beyond what is outlined in the ACGME Institutional Requirements, involving collaboration with a wide variety of institutional stakeholders and harnessing the GME enterprise to achieve institutional goals. Nearly all DIOs face a multitude of challenges, including adapting to new ACGME requirements and initiatives, addressing resident and fellow wellness concerns, promoting health equity, nurturing scholarly endeavors, and advocating for their residents and fellows. Additionally, emerging technologies and innovations as well as the changing financial climate for SIs and the health care facilities that host GME learners can present both educational opportunities and new challenges for DIOs in ensuring trainees are prepared for their roles as the next generation of physicians. As the health care landscape evolves, DIOs will continue to play a vital role in shaping the educational experiences of physicians in training while balancing institutional priorities and ensuring high-quality patient care.

Exploring the attitudes and experiences of Hungarian primary care physicians on the utilisation of digital health solutions

BackgroundDuring the COVID-19 pandemic, digital health solutions ensured the continuity of care especially in primary healthcare practices. COVID-19 accelerated the adoption of digital health solutions. The aim of this study is to describe the digital health-related attitudes and experiences of Hungarian Primary Care Physicians (PCPs) and also analyse the socio-demographic effects on digital health attitudes among PCPs.MethodsThis study used a quantitative and qualitative mixed methodological approach to examine the digital health-related attitudes and experience of Hungarian PCPs. As part of the "E-physicians and E-patients in Hungary" survey, we conducted an online survey among medical doctors working in Hungary between July 2021 to May 2022. A total of 1,774 questionnaires were received, consisting of 1,576 medical doctors and 198 dentists. Among the medical doctors there were 415 primary care physicians (PCPs). In addition to the online questionnaire survey, qualitative research in the form of semi-structured interviews with doctors was also conducted. These interviews took place between October 2021 and June 2022. A total of 62 interviews were conducted,19 with PCPs.ResultsPrimary care physicians are more open to technologies that facilitate communication and collaboration with patients, while showing less interest in technologies that support clinical work. Of the demographic variables (age, gender, type of settlement), age was found to have the most significant effect on digital health knowledge, use and intended use. Both the interviews and the multivariate analysis indicate that individuals with greater knowledge, expertise, and experience are more likely to perceive the advantages of digital solutions. This highlights the importance of training, especially given the significant aging population among Hungarian primary care physicians, who may not be accustomed to using these tools naturally. The way PCPs perceive patient expectations regarding the use of digital health tools has a significant impact on the PCPs’ use and intended use of digital tools. When looking at perceived patient needs among PCPs the effect of age and municipality type of PCPs is significant. As age increases, the perception of patient needs decreases (from 5.02 to 4.47), and by municipality type, the average number of perceived needs decreases as one moves from larger cities to smaller municipalities (from 4.85 to 4.14).ConclusionsDigital health solutions have the potential to enhance the work of PCPs, but successful implementation requires addressing specific needs, demographic differences, and challenges faced by PCPs. Development of infrastructure, education, and institutional support is necessary to ensure more efficient and higher-quality healthcare delivery through the use of digital technologies.

Skin Cancer Diagnosis by Lesion, Physician, and Examination Type

Skin cancer is the most common cancer in the US; accurate detection can minimize morbidity and mortality. To assess the accuracy of skin cancer diagnosis by lesion type, physician specialty and experience, and physical examination method. PubMed, Embase, and Web of Science. Cross-sectional and case-control studies, randomized clinical trials, and nonrandomized controlled trials that used dermatologists or primary care physicians (PCPs) to examine keratinocytic and/or melanocytic skin lesions were included. Search terms, study objectives, and protocol methods were defined before study initiation. Data extraction was performed by a reviewer, with verification by a second reviewer. A mixed-effects model was used in the data analysis. Data analyses were performed from May 2022 to December 2023. Meta-analysis of diagnostic accuracy comprised sensitivity and specificity by physician type (primary care physician or dermatologist; experienced or inexperienced) and examination method (in-person clinical examination and/or clinical images vs dermoscopy and/or dermoscopic images). In all, 100 studies were included in the analysis. With experienced dermatologists using clinical examination and clinical images, the sensitivity and specificity for diagnosing keratinocytic carcinomas were 79.0% and 89.1%, respectively; using dermoscopy and dermoscopic images, sensitivity and specificity were 83.7% and 87.4%, and for PCPs, 81.4% and 80.1%. Experienced dermatologists had 2.5-fold higher odds of accurate diagnosis of keratinocytic carcinomas using in-person dermoscopy and dermoscopic images compared with in-person clinical examination and images. When examining for melanoma using clinical examination and images, sensitivity and specificity were 76.9% and 89.1% for experienced dermatologists, 78.3% and 66.2% for inexperienced dermatologists, and 37.5% and 84.6% for PCPs, respectively; whereas when using dermoscopy and dermoscopic images, sensitivity and specificity were 85.7% and 81.3%, 78.0% and 69.5%, and 49.5% and 91.3%, respectively. Experienced dermatologists had 5.7-fold higher odds of accurate diagnosis of melanoma using dermoscopy compared with clinical examination. Compared with PCPs, experienced dermatologists had 13.3-fold higher odds of accurate diagnosis of melanoma using dermoscopic images. The findings of this systematic review and meta-analysis indicate that there are significant differences in diagnostic accuracy for skin cancer when comparing physician specialty and experience, and examination methods. These summary metrics of clinician diagnostic accuracy could be useful benchmarks for clinical trials, practitioner training, and the performance of emerging technologies.

Different Evaluation Strategies of Oncological CT Examinations with Regard to Professional Experience: A Clinical Study Using Eye-tracking.

Contrast-enhanced CT is the standard imaging technique in oncological objectives. Rates of missed pathologies depend on work experience of the respective radiologists. Thus the aim of this study is to analyze the eye movements of professionals while reading CT images in order to evaluate whether the eye-fixation patterns and search strategies of experienced radiologists could explain higher detection rates of pathologies and whether such patterns can be learned.Anonymized images of 10 patients were presented to three medical students and six radiologists with different levels of work experience. During image analysis, ocular fixation positions were recorded using an eye-tracking software tool. The CT scans were analyzed retrospectively, considering the individual course of disease with the issue of successful detection of all pathologies. Visual attention and dwell time of ocular fixation on clinically important abnormalities or areas with pathological findings, general search patterns, and time efficiency were assessed. For statistical analysis, interobserver variability and accuracy of lesion detection were evaluated taking into account individual experience.The results revealed that observer sensitivity depends on work experience due to a more systematic order of inspection and a well-known course of disease, e.g. in case of metastatic spread. The areas of missed pathologies mostly included secondary findings. Inexperienced readers changed the stratification considerably more often and required more time for reporting or detecting pathologies.Our results suggest that experienced radiological physicians reduce their amount of missed findings by looking more systematically at images and by applying a more targeted inspection of clinically important regions. · CT interpretation by radiology residents is faster and less error-prone compared to postgraduate residents. · systematic image analysis is trainable. · engrams tend to be acquired through experience. · Kluge S, Schülke C, Ites HC et al. Different Evaluation Strategies of Oncological CT Examinations with Regard to Professional Experience: A Clinical Study Using Eye-tracking. Fortschr Röntgenstr 2024; DOI 10.1055/a-2452-2180.

Lived experiences of palliative care physicians on the impacts of language and cultural discordance on end-of-life care across Ontario, Canada: a qualitative study using the intersectionality-based policy framework

BackgroundLanguage and cultural discordance refer to when a physician and patient do not share the same language or culture. This can create barriers to providing high-quality care at the end-of-life (EoL). This study explores the intersections of language, culture, geography, and care model in EoL care from the perspectives of palliative care physicians.MethodsIn this exploratory-descriptive qualitative study, semi-structured interviews (1-h) were conducted virtually between July and November 2023. We interviewed 16 family physicians with experience providing linguistic and/or culturally discordant palliative/EoL care in various urban, suburban, and rural regions of Ontario, who practiced at community and hospital outpatient clinics, home-based care, or long-term care homes. We used reflexive thematic analysis to identify themes across the interviews guided by the intersectionality theoretical framework.ResultsWe identified three themes 1) Visible barriers to care access due to the inability to communicate accurate information and insufficient time spent during appointments with patients; 2) Invisible barriers to care access, shaped by the Eurocentric approach to palliative care and physicians’ lack of awareness on cultural discordance; 3) Workplace supports that currently exist and interventions that physicians would like to see. Community physicians following fee-for-service models were less likely to have access to professional interpreter services. Physicians in long-term care emphasized resource limitations to providing culturally-appropriate care environments.ConclusionCultural discordance required awareness of personal biases, while language discordance hindered basic communication. These findings will be useful in informing clinical practice guidelines and mobilizing policy-level change to improve palliative/EoL care for patients from linguistic and cultural minority groups.

Behavioural intentions towards use of digital video consultations in primary care: a survey study on physicians’, nurses’ and psychologists’ perceptions in Swedish primary care

ObjectivesThe study aimed to describe the experiences of physicians, nurses and psychologists employed in primary care in using digital video consultations. The second objective was to study the association between...

Utilization management and physician burnout.

This study was designed to assess physician experiences with utilization management and burnout and investigate whether they are linked. We conducted an electronic survey with items related to demographics, profession, utilization management, burnout, and potential policy solutions. The survey was sent to 7222 physicians working in outpatient settings who were recruited from a large, opt-in database. Outcome measures were responses to categorical and Likert-style survey items related to demographics, utilization management, burnout, and potential policy solutions. Of 7222 requests sent, 501 physicians completed the survey and were included in the final data set (77% men; mean [SD] age, 57 [9.8] years; mean [SD] years in practice, 24 [8.9]). Of these, 200 were general practitioners and 301 were nonhospital specialists. Physicians indicated that utilization management procedures for prior authorization (81%), step therapy (79%), and nonmedical switching (69%) were major or significant barriers to their clinical and patient care. More than half (52%) reported spending 6 to 21 or more hours per week on paperwork related to health insurance utilization management, 67% had experienced burnout at some point in their careers, and 64% indicated that utilization management had been a contributing factor to feelings of burnout, with an additional 8% citing it as the main factor. Physicians favored streamlining prior authorization practice (77%), requiring step therapy to be based on science (73%), and ensuring that peer-to-peer reviews are done by qualified medical experts (67%). These findings indicate that utilization management has a detrimental impact on physicians and patient care and contributes to physician burnout.

A machine learning framework to adjust for learning effects in medical device safety evaluation.

Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects. A gradient-boosted decision tree ML method was used to analyze synthetic datasets that replicate the complexity of clinical scenarios involving high-risk medical devices. We designed this process to detect learning effects using a risk-adjusted cumulative sum method, quantify the excess adverse event rate attributable to operator inexperience, and adjust for these alongside patient factors in evaluating device safety signals. To maintain integrity, we employed blinding between data generation and analysis teams. Synthetic data used underlying distributions and patient feature correlations based on clinical data from the Department of Veterans Affairs between 2005 and 2012. We generated 2494 synthetic datasets with widely varying characteristics including number of patient features, operators and institutions, and the operator learning form. Each dataset contained a hypothetical study device, Device B, and a reference device, Device A. We evaluated accuracy in identifying learning effects and identifying and estimating the strength of the device safety signal. Our approach also evaluated different clinically relevant thresholds for safety signal detection. Our framework accurately identified the presence or absence of learning effects in 93.6% of datasets and correctly determined device safety signals in 93.4% of cases. The estimated device odds ratios' 95% confidence intervals were accurately aligned with the specified ratios in 94.7% of datasets. In contrast, a comparative model excluding operator learning effects significantly underperformed in detecting device signals and in accuracy. Notably, our framework achieved 100% specificity for clinically relevant safety signal thresholds, although sensitivity varied with the threshold applied. A machine learning framework, tailored for the complexities of post-market device evaluation, may provide superior performance compared to standard parametric techniques when operator learning is present. Demonstrating the capacity of ML to overcome complex evaluative challenges, our framework addresses the limitations of traditional statistical methods in current post-market surveillance processes. By offering a reliable means to detect and adjust for learning effects, it may significantly improve medical device safety evaluation.

A Phenomenological Qualitative Study: Family Physicians Experiences of Consulting Older Adults

Objective: Due to the aging population, older adult health became important. Family physicians play an important role in the health of older adults. This study aims to identify the challenges family physicians experience in counselling older adults. Method: A qualitative, phenomenological methodology was used. Authors conducted three semi-structured focus group interviews with 22 family physicians. The recorded interviews were transcribed, and thematic analysis was applied. Results: The mean age of the participants was 33.68 ± 8.37. The mean professional years of the participating physicians were 8.77±8.26 years. Seven of the participants were women and fifteen were men. Three themes emerged: (1) patient-related factors, (2) physician-related factors, and (3) physician remedies. Conclusion: There are positive and negative aspects to consulting older adults. Understanding the difficulties experienced can facilitate the solution. Physicians stated that there was no geriatrics training both in their medical school undergraduate and postgraduate curriculum. They also stated that they found themselves inadequate in this regard and that creates anxiety. Effective communication is important both physically and emotionally during the interview. In fact, communication and active listening skills can be taught and postgraduate training in geriatrics can be organized.

A Qualitative Study Evaluating the Challenges of Orthopedists and Internists in the Earthquake Zone

Abstract Background The earthquake centered in Kahramanmaras province, Turkey, on February 6, 2023, with a magnitude of 7.7, resulted in the loss of 53,537 lives and the injury of 107,213 people. This study was conducted to evaluate the challenges and experiences of physicians who provided post-disaster medical care. Methods We conducted a qualitative study with orthopedists and internists who had worked in the earthquake zone using in-depth interviews. Participants were selected through snowball sampling. Interviews explored challenges in the disaster zone, obstacles related to healthcare services, and disaster management. Thematic analysis was used. Results A total of 13 physicians participated in the study. Data revealed four main themes: disaster management, organizational deficiencies, challenges faced, and logistical issues. Participants indicated that the Ministry of Health didn’t have a priori operational emergency action plan, which led to problems in healthcare service delivery. Some physicians complained that they had to serve just as emergency consultants and were not able to carry out tasks compatible with their expertise. Some indicated that they organized the services rather than providing medical care. Almost all encountered a variety of interesting cases and evaluated their experience as significant. Logistical needs were met mostly with the support of non-governmental organizations. Conclusions We need to have a priori operational emergency action plan to ensure the effective delivery of healthcare services in the aftermath of a disaster. The Ministry of Health needs to allocate and coordinate healthcare personnel in line with their areas of expertise and prevent service disruptions. Key messages • There is a need to make an operational emergency action plan beforehand to deliver healthcare services effectively following a disaster. • Physicians should be allocated in line with their specialty of expertise to efficiently address the healthcare needs.

Splenic injury associated with colonoscopy in hospitalized patients: incidence, risk factors, management and outcome

Objectives: The frequency of splenic injuries due to coloscopy is largely unknown. Therefore, the aim of this study was to give an estimate on the risk for hospitalized patients. Methods: Using an administrative data base of a health insurance company with more than 10 million insured subjects, patients undergoing in-patient colonoscopy associated with a splenic injury within 2 weeks were retrieved from the administrative records based upon OPS (comparable to ICPM) and ICD-10 codes. In each case identified (n=141), the individual course of the hospital stay was reconstructed and analyzed by 2 experienced physicians. Patients with overt other causes of splenic injuries (e. g. abominal surgery, trauma etc.) were not further evaluated. Results: Among 190,927 total colonoscopies over a 5-year period, 27 splenic lesions were most likely solely due to colonoscopy. This accounts for 14.1 splenic injuries per 100,000 colonoscopies or 1 lesion in 7,071 patients. Afflicted patient were older and more likely to be female than male (risk in females: 1 in 5,324 colonoscopies). Surgical interventions were carried out in 17 cases, predominantly splenectomy (n=15) whereas 10 events were managed conservately. Transfusions and requirement of intensive care unit treatment were strong predictors for the need of surgery. Most lesions (74%) were diagnosed within 48 hours after colonoscopy. Death occurred in 2 patients, but less likely directly due to the splenic injury. Conclusion: Adminstrative data analysis suggests that splenic injuries due to colonoscopy are more frequent than previously estimated. Endoscopists should be aware of this rare life-threatening adverse event.

Fully automated artificial intelligence-based cine cardiac magnetic resonance image quantification

Abstract Background Analyzing cine cardiac magnetic resonance (CMR) images is a task that demands substantial experience and time, and it is subject to both inter- and intra-observer variability. Existing automated tools in the clinics for image analysis often necessitate substantial editing to enable accurate quantification. Objective To tackle this issue, in this study, we developed and validated a fully automated artificial intelligence (AI)-based system for the analysis of cine CMR images, aiming to address these challenges. Method In this study, 1,038 patients with CMR images were enrolled. Out of these, 350 CMR images, along with corresponding segmentations for the left ventricle myocardium (LVM) and the left- and right ventricles (LV and RV, respectively), sourced from open access data from 5 centers and databases (with different cardiac diseases), were utilized for model development. The remaining images from two local registries were used to evaluate the model. A 2D U-Net network with deep supervision, enhanced with various image augmentation techniques, was developed for automatic segmentation. The developed model was subsequently utilized to segment the test dataset of 4D (3D+time) short-axis cine CMR images. End-diastolic volumes (EDV) and end-systolic volumes (ESV) were automatically detected from these segmentations and evaluated against the ground truth calculations performed by two experienced physicians using commercially validated software. Results In the test set, Dice scores of 0.84, 0.90, and 0.93 were achieved for the RV, LV, and LVM, respectively. The mean percentage error for EDV was 0.6% for LV and -6.32% for RV. Regarding ESV, the mean percentage error was 2.95% for LV and -6.32% for RV. A -6.95% error was achieved for LV mass calculation. The stroke volumes for LV and RV were calculated with a mean percent error of -2.52% and -8.43%, respectively. We achieved a mean percent error of -3.22% for LV and -1.64% for RV ejection fraction. Conclusion The fully automated AI-based quantitative analysis of cine cardiac MR images developed in this study demonstrated high agreement with the ground truth across large datasets, including those from external centers. This method can significantly reduce the time required for image analysis and enhance the accuracy and consistency of cardiac quantitative assessments in clinical settings.

Comparison of Drug-Assisted Tracheal Intubation Using Benzodiazepines and Induction Agents for First-Pass Intubation Success in Emergency Airway Management: A Retrospective Cohort Study

Objective: To determine first-pass intubation success between benzodiazepines and induction agents in adult patients who underwent drug-assisted intubation in the emergency department (ED). Materials and Methods: A retrospective chart review observational study collected the data of patients who underwent drug-assisted intubation in the ED of a university teaching hospital between January 1, 2021 and August 31, 2022. Two hundred seven patients were enrolled in the present study. The benzodiazepine and induction agent groups were compared in terms of baseline characteristics, and clinical management. Each benzodiazepine and induction agent were compared in first-pass intubation success, time to intubation success, adverse events, and 24-hour mortality. Results: Of the 207 patients included in the present study, 35 patients used benzodiazepine, and 172 patients used induction agents. The two groups had no differences in baseline characteristics. The first-pass intubation success rate was not significantly different among the medications with 90% in nine diazepam patients, 92% in 23 midazolam patients, 89.8% in 79 etomidate patients, and 94% in 79 propofol patients (p=0.553). Complications associated with intubation included hypotension, desaturation, tachycardia, bradycardia, arrhythmia, cardiac arrest, and pneumonia. No differences in 24-hour survival were observed. Conclusion: The sedative medications used during drug-assisted intubation should be chosen based on patient comorbidities and characteristics, as well as physician preferences and experiences. There were no differences between the benzodiazepine and induction agent groups in terms of first-pass intubation success, adverse events, or 24-hour survival. The use of benzodiazepines in settings with limited resources may be considered.

Comparing Traditional and Newer Glaucoma Procedures by Physician Experience and Practice Location in the United States.

Analysis of CMS Physician Payment data found that physician certification year, degree of urbanization, and estimated income by zip code were significant in predicting the proportion of traditional glaucoma procedures performed by each provider in the United States. The prevalence of microinvasive glaucoma surgery (MIGS) compared to traditional procedures has been increasing in glaucoma management. It is important to understand whether the uptake of new glaucoma procedures is associated with aspects of the physician practice. In this cross-sectional study, data on glaucoma procedure distribution, degree of urbanization and physician certification in the United States were extracted from the 2019 Centers for Medicare & Medicaid Services (CMS) Physician Payment and American Board of Ophthalmology databases. The distribution of traditional and MIGS procedure numbers for metropolitan versus non-metropolitan providers were visualized with boxplots and statistically compared using Mann-Whitney U tests. We used two-dimensional histograms and regression analyses to assess the relationship between certification year and volume of traditional versus MIGS procedures. A multivariable linear regression model was created using certification year, rural-urban commuting area (RUCA) code, and estimated income by practice zip code to predict the number and proportion of each type of glaucoma procedure. This study included 2625 providers from the CMS physician payment data who performed only traditional procedures (n=370), MIGS procedures (n=1727), or both procedure types (n=528) in 2019. The median number of MIGS procedures performed by each provider was greater in non-metropolitan areas (31 vs. 29, P=0.015) while the proportion of traditional procedures performed by each provider was greater in metropolitan areas (0.24 vs. 0.08, P<0.001). Regression analysis showed a positive relationship between certification year and the proportion of traditional procedures performed by each provider (P<0.001). Multivariable regression models found that certification year, RUCA code, and estimated income of practice location were all significant predictors (P<0.02) of the proportion of traditional procedures performed by each provider. In the United States, physicians in metropolitan areas and more recent certification years are more likely to perform a larger proportion of traditional procedures. This finding suggests that the distribution of glaucoma procedure types is related to physician factors such as degree of urbanization and duration of practice. More research is needed to better understand how such differences affect patient access and outcomes.

Mitigating Interobserver Variability in Radiomics with ComBat: A Feasibility Study.

This study investigates Intraobserver Features Variability (IFV) in radiomics studies and assesses the effectiveness of the ComBat harmonization method in mitigating these effects. Methods: This study utilizes data from the NSCLC-Radiomics-Interobserver1 dataset, comprising CT scans of 22 Non-Small Cell Lung Cancer (NSCLC) patients, with multiple Gross Tumor Volume (GTV) delineations performed by five radiation oncologists. Segmentation was completed manually ("vis") or by autosegmentation with manual editing ("auto"). A total of 1229 radiomic features were extracted from each GTV, segmentation method, and oncologist. Features extracted included first order, shape, GLCM, GLRLM, GLSZM, and GLDM from original, wavelet-filtered, and LoG-filtered images. Results: Before implementing ComBat harmonization, 83% of features exhibited p-values below 0.05 in the "vis" approach; this percentage decreased to 34% post-harmonization. Similarly, for the "auto" approach, 75% of features demonstrated statistical significance prior to ComBat, but this figure declined to 33% after its application. Among a subset of three expert radiation oncologists, percentages changed from 77% to 25% for "vis" contouring and from 64% to 23% for "auto" contouring. This study demonstrates that ComBat harmonization could effectively reduce IFV, enhancing the feasibility of multicenter radiomics studies. It also highlights the significant impact of physician experience on radiomics analysis outcomes.