Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection.

PURPOSE: Diagnosing pulmonary embolism (PE) is still challenging due to other conditions that can mimic its appearance, leading to incomplete or delayed management and several inter-observer variabilities. This study evaluated the performance and clinical utility of an artificial intelligence (AI)-based application designed to assist clinicians in the detection of PE on CT pulmonary angiography (CTPA). PATIENTS AND METHODS: CTPAs from 230 US cities acquired on 57 scanner models from 6 different vendors were retrospectively collected. Three US board certified expert radiologists defined the ground truth by majority agreement. The same cases were analyzed by CINA-PE, an AI-driven algorithm capable of detecting and highlighting suspected PE locations. The algorithm's performance at a per-case and per-finding level was evaluated. Furthermore, cases with PE not mentioned in the clinical report but correctly detected by the algorithm were analyzed. RESULTS: A total of 1204 CTPAs (mean age 62.1 years ± 16.6[SD], 44.4 % female, 14.9 % positive) were included in the study. Per-case sensitivity and specificity were 93.9 % (95%CI: 89.3 %-96.9 %) and 94.8 % (95%CI: 93.3 %-96.1 %), respectively. Per-finding positive predictive value was 89.5 % (95%CI: 86.7 %-91.9 %). Among the 196 positive cases, 29 (15.6 %) were not mentioned in the clinical report. The algorithm detected 22/29 (76 %) of these cases, leading to a reduction in the miss rate from 15.6 % to 3.8 % (7/186). CONCLUSIONS: The AI-based application may improve diagnostic accuracy in detecting PE and enhance patient outcomes through timely intervention. Integrating AI tools in clinical workflows can reduce missed or delayed diagnoses, and positively impact healthcare delivery and patient care.

Single-center outcomes of artificial intelligence in management of pulmonary embolism and pulmonary embolism response team activation.

Multidisciplinary pulmonary embolism response teams (PERTs) have shown that timely triage expedites treatment. The use of artificial intelligence (AI) may help improve pulmonary embolism (PE) management with early CT pulmonary angiogram (CTPA) screening and accelerate PERT coordination. This study aimed to test the clinical validity of an FDA-approved PE AI algorithm. CTPA scan data of 200 patients referred due to automated AI detection of suspected PE were retrospectively reviewed. In our institution, all patients suspected of PE received a CTPA. The AI app was then used to analyze CTPA for the presence of PE and calculate the right-ventricle/left-ventricle (RV/LV) ratio. We compared the AI's output with the radiologists' report. Inclusion criteria included segmental PE with and without RV dysfunction and high-risk PE. The primary endpoint was false positive rate. Secondary end points included clinical outcomes according to the therapy selected, including catheter-directed interventions, systemic thrombolytics, and anticoagulation. Fifty-seven of 200 exams (28.5%) were correctly identified as positive for PE by the algorithm. A total of 143 exams (71.5%) were incorrectly reported as positive. In 8% of cases, PERT was consulted. Four patients (7%) received systemic thrombolytics without any complications. There were six patients (10.5%) who developed high-risk PE and underwent thrombectomy, one of whom died. Among 46 patients with acute PE without right heart strain, 44 (95%) survived. The false positive rate of our AI algorithm was 71.5%, higher than what was reported in the AI's prior clinical validity study (91% sensitivity, 100% specificity). A high rate of discordant AI auto-detection of suspected PE raises concerns about its diagnostic accuracy. This can lead to increased workloads for PERT consultants, alarm/notification fatigue, and automation bias. The AI direct notification process to the PERT team did not improve PERT triage efficacy.

Study of the impact of introducing a multimedia learning tool in podiatric medical courses.

BACKGROUND: Medical students face the challenge of learning vast amounts of complex information. Existing research suggests improved learning outcomes using multimedia resources but reports on their impact on podiatric education are scarce. To explore the potential of multimedia-based learning tools in enriching medical education, this study examined the impact of Osmosis, a platform featuring interactive videos, flashcards, and self-assessment quizzes on podiatric medical student outcomes. METHODS: This quasi-experimental study examined the impact of Osmosis, a multimedia learning platform with videos, flashcards, and quizzes, on podiatric medical students' learning outcomes. Two cohorts (T = Osmosis access, N = 86; C = no access, N = 87) took Pharmacology and Podiatric Medicine courses consecutively. Final exam scores, final course grades, platform usage metrics (median weekly videos watched, flashcards, and quizzes), and student experience surveys were analyzed. RESULTS: Analyses revealed no statistically significant differences in final exam scores between the groups in Pharmacology and Podiatric Medicine. While the treatment group exhibited a slight upward trend, further research is required for conclusive evidence. Student perceptions of Osmosis were overwhelmingly positive, with 90.2% of students agreeing that it facilitated concept learning and understanding compared to 54.9% for the textbook. Similarly, 80.4% of the treatment group felt that Osmosis enhanced their test performance, exceeding the 54.9% recorded for the textbook. Correlation analysis indicates a plausible connection between platform usage and academic success, as reflected by moderate positive correlations (r = [0.14, 0.28]) with final grades. Logistic regression analysis revealed that students with Osmosis access were 2.88 times more likely to score 90% or higher on the Pharmacology final exam (p < 0.05) and exhibited increased odds of achieving high (90%+) final course grades in Podiatric Medicine (OR = 2.71). CONCLUSIONS: These findings suggest that Osmosis holds promise as a tool to support podiatric medical student learning. While the lack of statistically significant differences in final exam scores warrants further investigation, the positive student perceptions, high engagement rates, and increased odds of high scores in specific areas indicate the potential for Osmosis to positively impact academic outcomes. Therefore, a multimedia-based resource like Osmosis appears to show promise as a tool to support podiatric medical education. The limitations inherent in the quasi-experimental design necessitate further studies to confirm its effectiveness and long-term impact on podiatric medical education.

A machine learning approach for predicting treatment response of hyponatremia.

Hyponatremia leads to severe central nervous system disorders and requires immediate treatment in some cases. However, a rapid increase in serum sodium (s-Na) concentration could cause osmotic demyelination syndrome. To achieve a safety hyponatremia treatment, we develop a prediction model of s-Na concentration using a machine learning. Among the 341 and 47 patients admitted to two tertiary hospitals for hyponatremia treatment (s-Na <130 mEq/L), those who were admitted to the general unit with urine sodium <20 mEq/L or treated with desmopressin were excluded. Ultimately, 74 and 15 patients (342 and 146 6-hourly datasets) were included in the learning and validation data, respectively. We trained the prediction model using three regression algorithms for shallow machine learning to predict s-Na every 6 h during treatment with the data of patients with hyponatremia (median s-Na: 112.5 mEq/L; range: 110.0-116.8 mEq/L) from one hospital. The model was validated externally using the data of patients with hyponatremia (median s-Na: 117.0 mEq/L; range: 112.9-120.0 mEq/L) from another hospital. Using 5-7 predictors (water intake, sodium intake, potassium intake, urine volume, s-Na concentration, serum potassium concentration, serum chloride concentration), the support vector regression model showed the best performance overall (root mean square error = 0.05396; R2 = 0.92), followed by the linear regression and regression tree models. The predicted s-Na levels, using explainable machine learning algorithms and clinically accessible parameters, correlated well with the actual levels. Thus, our model could be applied to the treatment of hyponatremia in clinical practice.

Evaluating the Effectiveness of Videos for Teaching Pharmaceutical Calculations to Pharmacy Students.

Performing pharmaceutical calculations accurately is a fundamental aspect of the pharmacy profession, ensuring treatment efficacy and patient safety. Incorporating videos in teaching can enhance visualisation and reinforce learning. The current study utilised videos to teach calculations and assessed how these affected students' performance. Initially, pharmacy students were surveyed at one UK University to identify calculation topics they found most challenging, and then two prototype videos were created based on these topics. Feedback was gathered through a follow-up survey on these prototypes, leading to the development of five additional videos. To evaluate the impact of these videos, students were given quizzes before and after watching them. The data were analysed in Microsoft Excel and included paired t-tests to compare mean scores, with significance set at p < 0.05. The survey was completed by 98/130 (75% response rate), with 58% expressing average or low confidence in calculations. A majority (78%) believed that videos would aid their comprehension of calculation concepts. In the subsequent phase, most respondents (92%, 80/87) agreed that the prototype videos improved their understanding of the two topics, but this increase was not statistically significant. However, quiz performance evaluation revealed a significant increase in average scores. This study affirms that videos can boost students' performance in calculations by allowing them to visualise the relevant practical scenarios.

CO2 Storage Monitoring via Time-Lapse Full Waveform Inversion with Automatic Differentiation.

In the field of CO2 capture utilization and storage (CCUS), recent advancements in active-source monitoring have significantly enhanced the capabilities of time-lapse acoustical imaging, facilitating continuous capture of detailed physical parameter images from acoustic signals. Central to these advancements is time-lapse full waveform inversion (TLFWI), which is increasingly recognized for its ability to extract high-resolution images from active-source datasets. However, conventional TLFWI methodologies, which are reliant on gradient optimization, face a significant challenge due to the need for complex, explicit formulation of the physical model gradient relative to the misfit function between observed and predicted data over time. Addressing this limitation, our study introduces automatic differentiation (AD) into the TLFWI process, utilizing deep learning frameworks such as PyTorch to automate gradient calculation using the chain rule. This novel approach, AD-TLFWI, not only streamlines the inversion of time-lapse images for CO2 monitoring but also tackles the issue of local minima commonly encountered in deep learning optimizers. The effectiveness of AD-TLFWI was validated using a realistic model from the Frio-II CO2 injection site, where it successfully produced high-resolution images that demonstrate significant changes in velocity due to CO2 injection. This advancement in TLFWI methodology, underpinned by the integration of AD, represents a pivotal development in active-source monitoring systems, enhancing information extraction capabilities and providing potential solutions to complex multiphysics monitoring challenges.

Should we add patients in concordance of judgment learning tool panels? - An analysis between patients and primary care physicians.

INTRODUCTION: The Concordance of Judgment Learning Tool (CJLT) has been developed for distance asynchronous learning of professionalism in health sciences education. The learning of professionalism is induced by a student's comparison of their own responses with those of the panel members. Whilst CJLT programs typically include same profession experts in their panels, we believe that they could also include patients. Accordingly, we conducted a study aimed at comparing CJLT response patterns between two groups of primary care physicians (PCPs) and patients. METHODS: We conducted a mixed prospective study of responses to a CJLT program based on a group of PCPs and a group of patients: an analysis of the response patterns of the two groups and a qualitative analysis of justifications. RESULTS: A total of 110 participants were included in the study: 70 patients and 40 PCPs. We found a significant difference in response patterns between the PCP and patient groups for nine of the fifteen questions (60%). The qualitative analysis of justifications between groups allowed us to comprehend patients' views on the professionalism of PCPs. CONCLUSIONS: Including patients in CJLT panels can enrich the feedback offered to students in these online training programs.

The effect of a prior e-learning tool on genetic counseling outcomes in diverse ethnic couples with abnormal Down syndrome screening tests: A randomized controlled trial.

Genetic counseling (GC) following abnormal Down syndrome (DS) screening tests aims to ensure learning of complex medical concepts and discussion of counselees' personal desires. Pre-GC use of electronic learning tools (e-learning tools) can facilitate GC sessions by allowing more time for dialogue rather than learning medical and genetic concepts, enabling greater focus on the counselee's decisional, psychological, and personal needs. Few studies have investigated such tools for DS screening tests and those who have focused on screening uptake rather than abnormal results and implications. This study evaluated prenatal GC outcomes following implementation of an e-learning tool utilizing an educational animated movie for couples of varied ethnic backgrounds in northern Israel, with abnormal DS screening tests. E-learning tool impact was assessed as knowledge level, informed choices, satisfaction with the intervention and GC process, the state of anxiety and duration of the GC meeting. The 321 study participants were randomized to three groups: animation movie, booklet, and control. All participants had been asked to complete pre- and post-counseling questionnaires. Outcome scores were compared between the research groups. Results showed increased knowledge level in general among participants in the animation group; among minority participants, the highest knowledge level was in the animation group. Anxiety levels and informed choices were not statistically different among the groups. However, watching the animation, Jewish ethnicity, good level of genetic literacy, and academic degree were significant predictors of informed choice, and those who watched the animation were three times more likely to make an informed choice than the control group. Our findings suggest that this e-learning tool is efficient and acceptable for the general population. Special attention is needed for minorities with lower genetic literacy and education.

Writing for reflection: Radiography students' experiences with reflective journaling.

INTRODUCTION: Reflective journaling is a widely recognised method of engaging in reflective practice, whereby individuals document their critical analysis of past experiences or actions, focusing on the implications and lessons learned for future application. In radiography and other healthcare professions, reflective practice is considered essential for professional development and should be cultivated during training. This study aimed to explore the experiences of senior radiography students in utilising the reflective journal as a clinical learning tool. METHODS: This study employed a phenomenological research design, with the study participants being third and fourth-year radiography students. Data collection took place after the introduction of reflective journaling, with nineteen participants having utilised the reflective journal daily, for a minimum of eight months. Individual interviews were conducted after obtaining the participants' consent. The collected data were analysed using Atlas. ti (version 9.3), guided by the content analysis framework. RESULTS: The study revealed that participants experienced adaptation, characterised by reflective adaptation and process adaptation when utilising the reflective journal. Participants also encountered reflective barriers, including personal expression bias, inconsistent feedback, rumination overload, and impulsive practice dominance. CONCLUSION: The findings demonstrate the benefits of reflective journaling in promoting critical thinking and reflective practice among radiography students. It is therefore recommended that this practice be fully embraced as a standard clinical component. Additionally, the frequency of reflections should be reviewed to ensure effectiveness and consistent feedback should be provided to enhance skills development. IMPLICATIONS FOR PRACTICE: The findings contribute further evidence to support the incorporation of reflective journaling in clinical training to promote critical thinking and reflective practice.

Is There a Case for Case-Based Learning in Pharmacology?

Background In a didactic lecture (DL), students listen, take notes, and passively accept the knowledge. Case-based learning (CBL) uses clinical cases for active learning and productive outcome. Although some studies have shown that DL is less effective than CBL, the results were inconclusive. Hence, this study aimed to evaluate the effectiveness of CBL in pharmacology. Methodology This study involved 80 second-year medical students divided into two groups. The results of post-test scores and retention test one month later were compared between the groups using multiple-choice questions. Results DL showed statistically significant better outcomes in immediate learning compared to CBL in both groups (p = 0.000 and 0.002). Although there were slightly better retention scores for CBL compared to DL in both groups, it was not statistically significant. Conclusions DL showed significantly better immediate learning outcomes compared to CBL, with no difference in long-term outcomes for both teaching-learning methods. Hence, DL continues to be the gold standard for teaching pharmacology.

Proposal of a Route Map for Cervical Spinal Ultrasonography: A Simple and Clear Learning Tool for Beginners.

Spinal ultrasonography has increasingly been used to image axial structures from the cervical to sacral region in recent years. In this work, we propose a comprehensive, systematic cervical scanning protocol that we refined as a route map, especially for beginners, to facilitate teaching, learning, diagnosis, and treatment in clinical pain practice. As a simple and clear tool to demonstrate the scanning protocol, the route map is delineated with rectangles, lines, and arrows. The rectangles represent the positions of the transducer, the arrows indicate route directions, and the lines show the routes of transducer movement. In this article, we describe cervical spinal ultrasonography to illustrate the route map interpretation, scanning protocol and target anatomical structures in detail. We believe that this comprehensive, clear, systematic, and portable route map will be beneficial for inexperienced pain clinicians and ultrasound beginners.

Making tacit knowledge explicit through objects: a qualitative study of the translation of resilience into practice.

Introduction: It is common practice to use objects to bridge disciplines and develop shared understanding across knowledge boundaries. Objects for knowledge mediation provide a point of reference which allows for the translation of abstract concepts into more externalized representations. This study reports from an intervention that introduced an unfamiliar resilience perspective in healthcare, through the use of a resilience in healthcare (RiH) learning tool. The aim of this paper is to explore how a RiH learning tool may be used as an object for introduction and translation of a new perspective across different healthcare settings. Methods: This study is based on empirical observational data, collected throughout an intervention to test a RiH learning tool, developed as part of the Resilience in Healthcare (RiH) program. The intervention took place between September 2022 and January 2023. The intervention was tested in 20 different healthcare units, including hospitals, nursing homes and home care services. A total of 15 workshops were carried out, including 39-41 participants in each workshop round. Throughout the intervention, data was gathered in all 15 workshops at the different organizational sites. Observation notes from each workshop make up the data set for this study. The data was analyzed using an inductive thematic analysis approach. Results and conclusion: The RiH learning tool served as different forms of objects during the introduction of the unfamiliar resilience perspective for healthcare professionals. It provided a means to develop shared reflection, understanding, focus, and language for the different disciplines and settings involved. The resilience tool acted as a boundary object for the development of shared understanding and language, as an epistemic object for the development of shared focus and as an activity object within the shared reflection sessions. Enabling factors for the internalization of the unfamiliar resilience perspective were to provide active facilitation of the workshops, repeated explanation of unfamiliar concepts, provide relatedness to own context, and promote psychological safety in the workshops. Overall, observations from the testing of the RiH learning tool showed how these different objects were crucial in making tacit knowledge explicit, which is key to improve service quality and promote learning processes in healthcare.

Comparison of accuracy between augmented reality/mixed reality techniques and conventional techniques for epidural anesthesia using a practice phantom model kit.

BACKGROUND: This study used an epidural anesthesia practice kit (model) to evaluate the accuracy of epidural anesthesia using standard techniques (blind) and augmented/mixed reality technology and whether visualization using augmented/mixed reality technology would facilitate epidural anesthesia. METHODS: This study was conducted at the Yamagata University Hospital (Yamagata, Japan) between February and June 2022. Thirty medical students with no experience in epidural anesthesia were randomly divided into augmented reality (-), augmented reality (+), and semi-augmented reality groups, with 10 students in each group. Epidural anesthesia was performed using the paramedian approach with an epidural anesthesia practice kit. The augmented reality (-) group performed epidural anesthesia without HoloLens2Ⓡ and the augmented reality (+) group with HoloLens2Ⓡ. The semi-augmented reality group performed epidural anesthesia without HoloLens2Ⓡ after 30 s of image construction of the spine using HoloLens2Ⓡ. The epidural space puncture point distance between the ideal insertion needle and participant's insertion needle was compared. RESULTS: Four medical students in the augmented reality (-), zero in the augmented reality (+), and one in the semi-augmented reality groups failed to insert the needle into the epidural space. The epidural space puncture point distance for the augmented reality (-), augmented reality (+), and semi-augmented reality groups were 8.7 (5.7-14.3) mm, 3.5 (1.8-8.0) mm (P = 0.017), and 4.9 (3.2-5.9) mm (P = 0.027), respectively; a significant difference was observed between the two groups. CONCLUSIONS: Augmented/mixed reality technology has the potential to contribute significantly to the improvement of epidural anesthesia techniques.

Social media as a learning tool in anatomy education from the perspective of medical and dental students.

The traditional approach to anatomy teaching is still the most common. Recently, there has been a demand for methods that make students more active and open to communication and cooperation, and are well integrated with technology. Our aim in this study was to determine the social media usage characteristics and anatomy learning experiences of medical and dentistry students, and their expectations about learning via social media. A questionnaire was prepared and distributed to faculties of Medicine and Dentistry in 10 different universities in Türkiye via Google forms. The questionnaire was voluntarily answered by 762 students. Frequency, exploratory factor and confirmatory factor analyses were applied to the data acquired, and a structural equation modeling (SEM) approach was used. The results showed that 54.3% of the students spent more than 90 min/day on social media; 96.5% of the participants preferred to follow anatomical content pages on social media (acpSM) administered by an academic. They stated that the instant responses from acpSM's admin had a positive effect on motivation to learning (4.08 ± 0.89, mean ± SD, on 5-point Likert-type scale). The SEM revealed a statistically significant correlation between the students' learning motivation and the sufficiency and suitability of acpSM content (p < 0.010). Thus, medical and dental students are eager to learn anatomy via social media. However, they found acpSM to be insufficient in quality and quantity and wanted future content to be administered by academics. An acpSM optimized for content sufficiency and suitability increased students' learning motivation.

Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms.

PURPOSE: Since the prompt recognition of acute pulmonary embolism (PE) and the immediate initiation of treatment can significantly reduce the risk of death, we developed a deep learning (DL)-based application aimed to automatically detect PEs on chest computed tomography angiograms (CTAs) and alert radiologists for an urgent interpretation. Convolutional neural networks (CNNs) were used to design the application. The associated algorithm used a hybrid 3D/2D UNet topology. The training phase was performed on datasets adequately distributed in terms of vendors, patient age, slice thickness, and kVp. The objective of this study was to validate the performance of the algorithm in detecting suspected PEs on CTAs. METHODS: The validation dataset included 387 anonymized real-world chest CTAs from multiple clinical sites (228 U.S. cities). The data were acquired on 41 different scanner models from five different scanner makers. The ground truth (presence or absence of PE on CTA images) was established by three independent U.S. board-certified radiologists. RESULTS: The algorithm correctly identified 170 of 186 exams positive for PE (sensitivity 91.4% [95% CI: 86.4-95.0%]) and 184 of 201 exams negative for PE (specificity 91.5% [95% CI: 86.8-95.0%]), leading to an accuracy of 91.5%. False negative cases were either chronic PEs or PEs at the limit of subsegmental arteries and close to partial volume effect artifacts. Most of the false positive findings were due to contrast agent-related fluid artifacts, pulmonary veins, and lymph nodes. CONCLUSIONS: The DL-based algorithm has a high degree of diagnostic accuracy with balanced sensitivity and specificity for the detection of PE on CTAs.

How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images.

Background: A CE- and FDA-approved cloud-based Deep learning (DL)-tool for automatic organs at risk (OARs) and clinical target volumes segmentation on computer tomography images is available. Before its implementation in the clinical practice, an independent external validation was conducted. Methods: At least a senior and two in training Radiation Oncologists (ROs) manually contoured the volumes of interest (VOIs) for 6 tumoral sites. The auto-segmented contours were retrieved from the DL-tool and, if needed, manually corrected by ROs. The level of ROs satisfaction and the duration of contouring were registered. Relative volume differences, similarity indices, satisfactory grades, and time saved were analyzed using a semi-automatic tool. Results: Seven thousand seven hundred sixty-five VOIs were delineated on the CT images of 111 representative patients. The median (range) time for manual VOIs delineation, DL-based segmentation, and subsequent manual corrections were 25.0 (8.0-115.0), 2.3 (1.2-8) and 10.0 minutes (0.3-46.3), respectively. The overall time for VOIs retrieving and modification was statistically significantly lower than for manual contouring (p<0.001). The DL-tool was generally appreciated by ROs, with 44% of vote 4 (well done) and 43% of vote 5 (very well done), correlated with the saved time (p<0.001). The relative volume differences and similarity indexes suggested a better inter-agreement of manually adjusted DL-based VOIs than manually segmented ones. Conclusions: The application of the DL-tool resulted satisfactory, especially in complex delineation cases, improving the ROs inter-agreement of delineated VOIs and saving time.

Frequency and Clinical Utility of Alerts for Intra-Institutional Radiologist Discrepant Opinions.

OBJECTIVE: Determine the rate of documented notification, via an alert, for intra-institutional discrepant radiologist opinions and addended reports and resulting clinical management changes. METHODS: This institutional review board-exempt, retrospective study was performed at a large academic medical center. We defined an intra-institutional discrepant opinion as when a consultant radiologist provides a different interpretation from that formally rendered by a colleague at our institution. We implemented a discrepant opinion policy requiring closed-loop notification of the consulting radiologist's second opinion to the original radiologist, who must acknowledge this alert within 30 days. This study included all discrepant opinion alerts created December 1, 2019, to December 31, 2021, of which two radiologists and an internal medicine physician performed consensus review. Primary outcomes were degree of discrepancy and percent of discrepant opinions leading to change in clinical management. Secondary outcome was report addendum rate compared with an existing peer learning program using Fisher's exact test. RESULTS: Of 114 discrepant opinion alerts among 1,888,147 reports generated during the study period (0.006%), 58 alerts were categorized as major (50.9%), 41 as moderate (36.0%), and 15 as minor discrepancies (13.1%). Clinical management change occurred in 64 of 114 cases (56.1%). Report addendum rate for discrepant opinion alerts was 4-fold higher than for peer learning alerts at our institution (66 of 315 = 21% versus 432 of 8,273 =5.2%; P < .0001). DISCUSSION: Although discrepant intra-institutional radiologist second opinions were rare, they frequently led to changes in clinical management. Capturing these discrepancies by encouraging alert use may help optimize patient care and document what was communicated to the referring or consulting care team by consulting radiologists.

Habilidades procedimentales invariantes para el diagnóstico y el tratamiento de la peritonitis posoperatoria

Introducción: Las habilidades procedimentales invariantes en las peritonitis posoperatorias, les permite a los cirujanos el diagnóstico temprano y la reoperación abdominal urgente. Objetivo: Diseñar las habilidades procedimentales invariantes de atención diagnóstica y terapéutica a la peritonitis posoperatoria dirigida a los cirujanos. Método: Se realizó una investigación cualitativa en el Hospital Clínico Quirúrgico "Ambrosio Grillo" de Santiago de Cuba en el trimestre mayo-julio de 2022. La población de estudio estuvo constituida por 33 profesionales tratantes de la peritonitis posoperatoria: 22 médicos (12 cirujanos y 10 intensivistas) y 11 licenciadas en Enfermería. Se utilizó la sistematización como método teórico y como método empírico la revisión documental con la revisión de las historias clínicas y la observación con la práctica atencional-docente de la autora principal. Resultados: Se diseñaron las habilidades procedimentales invariantes para el diagnóstico temprano y la terapéutica de la peritonitis posoperatoria dirigida a los cirujanos con especial importancia en el método clínico. La autopreparación del tema en cuestión posibilita la complementación de conocimientos y habilidades aprendidas, así como la actualización de saberes que garantiza la atención clínica quirúrgica a este complejo enfermo. Conclusiones: El diseño de las habilidades procedimentales invariantes para el diagnóstico temprano y la terapéutica de la peritonitis posoperatoria en la necesaria superación permanente y continuada de los cirujanos representa una herramienta asistencial-docente que contribuye al mejoramiento del desempeño profesional ante este tipo de enfermo.

Introduction: Invariant procedural skills in postoperative peritonitis allow surgeons an early diagnosis and the performance of urgent abdominal resurgery. Objective: To design invariant procedural skills to be implemented for surgeons on the diagnostic and therapeutic care of postoperative peritonitis. Method: A qualitative research was conducted at the Hospital Clínico Quirúrgico "Ambrosio Grillo" of Santiago de Cuba in the quarter period of May-July 2022. The study population consisted of 33 specialists on postoperative peritonitis: 22 physicians (12 surgeons and 10 intensivists) and 11 graduated bachelor in nursing. Systematization was used as the theoretical method and the empirical methods used were the document analysis supported with the review of clinical histories and the observation with the main author's attentional-teaching practice. Results: It was designed an invariant procedural skills for the early diagnosis and therapy of postoperative peritonitis with special emphasis on the clinical method. The self-preparation on the subject makes possible the complementation of both knowledge and skills learned, as well as an update of knowledge that guarantees the surgical clinical care of these complex patients. Conclusions: The design of invariant procedural skills for the early diagnosis and therapy of postoperative peritonitis in the necessary permanent and continuous knowledge improvement of surgeons represents a care-teaching tool that contributes to improve the professional performance at the time to attend to this type of patient.

Introdução: As habilidades processuais invariantes na peritonite pós-operatória permitem aos cirurgiões o diagnóstico precoce e a reoperação abdominal urgente. Objetivo: Projetar as habilidades processuais invariantes de atenção diagnóstica e terapêutica para peritonite pós-operatória destinadas a cirurgiões. Método: Uma pesquisa qualitativa foi realizada no Hospital Clínico Cirúrgico "Ambrosio Grillo" em Santiago de Cuba no trimestre maio-julho de 2022. A população do estudo consistiu em 33 profissionais que tratam de peritonite pós-operatória: 22 médicos (12 cirurgiões e 10 intensivistas) e 11 graduados em enfermagem. Utilizou-se como método teórico a sistematização e como métodos empíricos a revição documental com revisão de histórias clínicas e a observação com a prática docente-atencional da autora principal. Resultados: Desenharam-se competências processuais invariantes para o diagnóstico precoce e terapêutica da peritonite pós-operatória dirigidas a cirurgiões com especial importância no método clínico. O autopreparo do sujeito em questão possibilita a complementação dos conhecimentos e habilidades aprendidas, bem como a atualização de conhecimentos que garantem o cuidado clínico cirúrgico desta complexa doença. Conclusões: O desenho de habilidades processuais invariantes para o diagnóstico precoce e tratamento da peritonite pós-operatória no necessário aperfeiçoamento permanente e contínuo dos cirurgiões representa uma ferramenta cuidado-ensino que contribui para o aperfeiçoamento da atuação profissional frente a este tipo de paciente.

Developing and Integrating Asynchronous Web-Based Cases for Discussing and Learning Clinical Reasoning: Repeated Cross-sectional Study.

BACKGROUND: Trainees rely on clinical experience to learn clinical reasoning in pediatric emergency medicine (PEM). Outside of clinical experience, graduate medical education provides a handful of explicit activities focused on developing skills in clinical reasoning. OBJECTIVE: In this paper, we describe the development, use, and changing perceptions of a web-based asynchronous tool to facilitate clinical reasoning discussion for PEM providers. METHODS: We created a case-based web-based discussion tool for PEM clinicians and fellows to post and discuss cases. We examined website analytics for site use and collected user survey data over a 3-year period to assess the use and acceptability of the tool. RESULTS: The learning tool had more than 30,000 site visits and 172 case comments for the 55 published cases over 3 years. Self-reported engagement with the learning tool varied inversely with clinical experience in PEM. The tool was relevant to clinical practice and useful for learning PEM for most respondents. The most experienced clinicians were more likely than fellows to report posting commentary, although absolute rate of commentary was low. CONCLUSIONS: An asynchronous method of case presentation and web-based commentary may present an acceptable way to supplement clinical experience and traditional education methods for sharing clinical reasoning.