Dataset Information

Expert-led and artificial intelligence (AI) system-assisted tutoring course increase confidence of Chinese medical interns on suturing and ligature skills: prospective pilot study.

ABSTRACT: PURPOSE:Lack of confidence in suturing/ligature skills due to insufficient practices and assessments was common among novice Chinese medical interns. This study aims to improve the skill acquisition of medical interns with new interventional program. METHODS:In addition to regular clinical training, additional expert-led or expert-led plus artificial intelligent (AI) system tutoring courses were implemented during the first 2 week of surgical block in our hospital. Interns can voluntarily join the regular (no additional tutoring), expert-led tutoring or expert-led+AI tutoring groups freely. In regular group, interns (n=25) did not join additional tutoring. The expert-led group received 3-hour expert-led tutoring and in-training formative assessment after twice practices. After similar expert-led course as expert-led group, the expert-led+AI group (n=23) practice and assess their skills on the AI system. Through the comparison with the internal standard, the system automatically recorded and evaluated every intern's suturing/ligature skills. Among expert-led+AI group, the performance and confidence were compared between interns whose doing one, two and three times of AI practices. RESULTS:The end-of-surgical block OSCE' performance and self-assessed confidence for the suturing/ligature skills were highest in expert-led+AI group. In comparison with expert-led group, with similar performance in in-training assessment, expert-led+AI group have higher degree of improvement in their performance in end-of surgical block OSCE. Especially, in expert-led+AI group, the best performance and highest post-OSCE confidence were noted among trice AI practicing group. CONCLUSION:This pilot study demonstrated the promising effects of incorporation of additional expert-led plus AI system-assisted tutoring course into regular surgical curriculum.

SUBMITTER: Yang YY

PROVIDER: S-EPMC6517322 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Expert-led and artificial intelligence (AI) system-assisted tutoring course increase confidence of Chinese medical interns on suturing and ligature skills: prospective pilot study.

Yang Ying-Ying YY Shulruf Boaz B

Journal of educational evaluation for health professions 20190410

<h4>Purpose</h4>Lack of confidence in suturing/ligature skills due to insufficient practices and assessments was common among novice Chinese medical interns. This study aims to improve the skill acquisition of medical interns with new interventional program.<h4>Methods</h4>In addition to regular clinical training, additional expert-led or expert-led plus artificial intelligent (AI) system tutoring courses were implemented during the first 2 week of surgical block in our hospital. Interns can vol ...[more]

PMID: 30986892

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Project description:ImportanceTo better understand the emerging role of artificial intelligence (AI) in surgical training, efficacy of AI tutoring systems, such as the Virtual Operative Assistant (VOA), must be tested and compared with conventional approaches.ObjectiveTo determine how VOA and remote expert instruction compare in learners' skill acquisition, affective, and cognitive outcomes during surgical simulation training.Design, setting, and participantsThis instructor-blinded randomized clinical trial included medical students (undergraduate years 0-2) from 4 institutions in Canada during a single simulation training at McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre, Montreal, Canada. Cross-sectional data were collected from January to April 2021. Analysis was conducted based on intention-to-treat. Data were analyzed from April to June 2021.InterventionsThe interventions included 5 feedback sessions, 5 minutes each, during a single 75-minute training, including 5 practice sessions followed by 1 realistic virtual reality brain tumor resection. The 3 intervention arms included 2 treatment groups, AI audiovisual metric-based feedback (VOA group) and synchronous verbal scripted debriefing and instruction from a remote expert (instructor group), and a control group that received no feedback.Main outcomes and measuresThe coprimary outcomes were change in procedural performance, quantified as Expertise Score by a validated assessment algorithm (Intelligent Continuous Expertise Monitoring System [ICEMS]; range, -1.00 to 1.00) for each practice resection, and learning and retention, measured from performance in realistic resections by ICEMS and blinded Objective Structured Assessment of Technical Skills (OSATS; range 1-7). Secondary outcomes included strength of emotions before, during, and after the intervention and cognitive load after intervention, measured in self-reports.ResultsA total of 70 medical students (41 [59%] women and 29 [41%] men; mean [SD] age, 21.8 [2.3] years) from 4 institutions were randomized, including 23 students in the VOA group, 24 students in the instructor group, and 23 students in the control group. All participants were included in the final analysis. ICEMS assessed 350 practice resections, and ICEMS and OSATS evaluated 70 realistic resections. VOA significantly improved practice Expertise Scores by 0.66 (95% CI, 0.55 to 0.77) points compared with the instructor group and by 0.65 (95% CI, 0.54 to 0.77) points compared with the control group (P < .001). Realistic Expertise Scores were significantly higher for the VOA group compared with instructor (mean difference, 0.53 [95% CI, 0.40 to 0.67] points; P < .001) and control (mean difference. 0.49 [95% CI, 0.34 to 0.61] points; P < .001) groups. Mean global OSATS ratings were not statistically significant among the VOA (4.63 [95% CI, 4.06 to 5.20] points), instructor (4.40 [95% CI, 3.88-4.91] points), and control (3.86 [95% CI, 3.44 to 4.27] points) groups. However, on the OSATS subscores, VOA significantly enhanced the mean OSATS overall subscore compared with the control group (mean difference, 1.04 [95% CI, 0.13 to 1.96] points; P = .02), whereas expert instruction significantly improved OSATS subscores for instrument handling vs control (mean difference, 1.18 [95% CI, 0.22 to 2.14]; P = .01). No significant differences in cognitive load, positive activating, and negative emotions were found.Conclusions and relevanceIn this randomized clinical trial, VOA feedback demonstrated superior performance outcome and skill transfer, with equivalent OSATS ratings and cognitive and emotional responses compared with remote expert instruction, indicating advantages for its use in simulation training.Trial registrationClinicalTrials.gov Identifier: NCT04700384.

Project description:Background: Internationally, pediatric depression and suicide are significant issues. Additionally, in the context of the COVID-19 pandemic, pediatric mental health needs are rising astronomically. In light of Child & Adolescent Psychiatrist (CAP) subspecialist shortages in the United States (US), there is an increasing call for primary care physicians in Family Medicine and Pediatrics to address an increasingly broad variety of patient needs. Here we report on the development and preliminary evaluation of medical student and resident perceptions on the "INteractive Virtual Expert-led Skills Training" (INVEST) medical education curriculum, a virtual synchronous CAP curriculum employing active learning strategies, including expert-led discussion and video modeling, and discussion designed to meet those priorities. Methods: In a standardized 60-min training format, our curriculum leverages audience response system polling, video modeling of key clinical skills, and interactive discussion with an expert subspecialist, over a virtual video conferencing platform. The primary educational strategy relies on use of video modeling to demonstrate best practice with CAP led group discussion to solidify and explain important concepts. Five waves of medical students and residents (N = 149) participated in the INVEST curriculum and completed pre- and post-training surveys regarding knowledge and comfort in the management of pediatric patients with depression and suicidality. Results: Trainee participants reported significant positive gains in perceived likelihood of encountering pediatric suicidality as well as knowledge/comfort with depression screening and suicidality assessment in a primary care setting. Across some competency areas, there was an effect of medical learner level. Learners at lower levels generally reported the highest benefit. Medical students reported significant increases in their comfort interpreting and discussing positive depression screens and evidenced the greatest relative benefit in comfort with discussing suicidality. Conclusion: To our knowledge, INVEST is the first fully virtual, multimodal curriculum led by expert CAP subspecialists. Our findings suggest that INVEST shows promise for equipping medical learners with baseline knowledge for caring for patients with pediatric depression and suicidality. This synchronous, virtually delivered curriculum allows for critical training delivered to diverse medical learners regardless of geographic location, a particular benefit during the current COVID-19 pandemic.

Project description:Introduction: The use of artificial intelligence (AI) in medical imaging and radiotherapy has been met with both scepticism and excitement. However, clinical integration of AI is already well-underway. Many authors have recently reported on the AI knowledge and perceptions of radiologists/medical staff and students however there is a paucity of information regarding radiographers. Published literature agrees that AI is likely to have significant impact on radiology practice. As radiographers are at the forefront of radiology service delivery, an awareness of the current level of their perceived knowledge, skills, and confidence in AI is essential to identify any educational needs necessary for successful adoption into practice. Aim: The aim of this survey was to determine the perceived knowledge, skills, and confidence in AI amongst UK radiographers and highlight priorities for educational provisions to support a digital healthcare ecosystem. Methods: A survey was created on Qualtrics® and promoted via social media (Twitter®/LinkedIn®). This survey was open to all UK radiographers, including students and retired radiographers. Participants were recruited by convenience, snowball sampling. Demographic information was gathered as well as data on the perceived, self-reported, knowledge, skills, and confidence in AI of respondents. Insight into what the participants understand by the term "AI" was gained by means of a free text response. Quantitative analysis was performed using SPSS® and qualitative thematic analysis was performed on NVivo®. Results: Four hundred and eleven responses were collected (80% from diagnostic radiography and 20% from a radiotherapy background), broadly representative of the workforce distribution in the UK. Although many respondents stated that they understood the concept of AI in general (78.7% for diagnostic and 52.1% for therapeutic radiography respondents, respectively) there was a notable lack of sufficient knowledge of AI principles, understanding of AI terminology, skills, and confidence in the use of AI technology. Many participants, 57% of diagnostic and 49% radiotherapy respondents, do not feel adequately trained to implement AI in the clinical setting. Furthermore 52% and 64%, respectively, said they have not developed any skill in AI whilst 62% and 55%, respectively, stated that there is not enough AI training for radiographers. The majority of the respondents indicate that there is an urgent need for further education (77.4% of diagnostic and 73.9% of therapeutic radiographers feeling they have not had adequate training in AI), with many respondents stating that they had to educate themselves to gain some basic AI skills. Notable correlations between confidence in working with AI and gender, age, and highest qualification were reported. Conclusion: Knowledge of AI terminology, principles, and applications by healthcare practitioners is necessary for adoption and integration of AI applications. The results of this survey highlight the perceived lack of knowledge, skills, and confidence for radiographers in applying AI solutions but also underline the need for formalised education on AI to prepare the current and prospective workforce for the upcoming clinical integration of AI in healthcare, to safely and efficiently navigate a digital future. Focus should be given on different needs of learners depending on age, gender, and highest qualification to ensure optimal integration.

Project description:PurposeThe application of point of care ultrasound (PoCUS) in medical education is a relatively new course. There are still great differences in the existence, quantity, provision, and depth of bedside ultrasound education. The left ventricular outflow tract velocity time integral (LVOT-VTI) has been successfully used in several studies as a parameter for hemodynamic management of critically ill patients, especially in the evaluation of fluid responsiveness. While LVOT-VTI has been broadly used, valuable applications using artificial intelligence (AI) in PoCUS is still limited. We aimed to identify the degree of correlation between auto LVOT-VTI and the manual LVOT-VTI acquired by PoCUS trained ICU doctors.MethodsAmong the 58 ICU doctors who attended PoCUS training from 1 September 2019 to 30 November 2020, 46 ICU doctors who trained for more than 3 months were enrolled. At the end of PoCUS training, each of the enrolled ICU doctors acquired echocardiography parameters of a new ICU patient in 2 h after new patient was admitted. One of the two bedside expert sonographers would take standard echocardiogram of new ICU patients within 24 h. For ICU doctors, manual LVOT-VTI was obtained for reference and auto LVOT-VTI was calculated instantly by using an AI software tool. Based on the image quality of the auto LVOT-VTI, ICU patients was separated into ideal group (n = 31) and average group (n = 15).ResultsLeft ventricular end-diastolic dimension (LVEDd, p = 0.1028), left ventricular ejection fraction (LVEF, p = 0.3251), left atrial dimension (LA-d, p = 0.0962), left ventricular E/A ratio (p = 0.160), left ventricular wall motion (p = 0.317) and pericardial effusion (p = 1) had no significant difference between trained ICU doctors and expert sonographer. ICU patients in average group had greater sequential organ failure assessment (SOFA) score (7.33 ± 1.58 vs. 4.09 ± 0.57, p = 0.022) and lactic acid (3.67 ± 0.86 mmol/L vs. 1.46 ± 0.12 mmol/L, p = 0.0009) with greater value of LVEDd (51.93 ± 1.07 vs. 47.57 ± 0.89, p = 0.0053), LA-d (39.06 ± 1.47 vs. 35.22 ± 0.98, p = 0.0334) and percentage of decreased wall motion (p = 0.0166) than ideal group. There were no significant differences of δLVOT-VTI (|manual LVOT-VTI - auto LVOT-VTI|/manual VTI*100%) between the two groups (8.8% ± 1.3% vs. 10% ± 2%, p = 0.6517). Statistically, significant correlations between manual LVOT-VTI and auto LVOT-VTI were present in the ideal group (R2 = 0.815, p = 0.00) and average group (R2 = 0.741, p = 0.00).ConclusionsICU doctors could achieve the satisfied level of expertise as expert sonographers after 3 months of PoCUS training. Nearly two thirds of the enrolled ICU doctors could obtain the ideal view and one third of them could acquire the average view. ICU patients with higher SOFA scores and lactic acid were less likely to acquire the ideal view. Manual and auto LVOT-VTI had statistically significant agreement in both ideal and average groups. Auto LVOT-VTI in ideal view was more relevant with the manual LVOT-VTI than the average view. AI might provide real-time guidance among novice operators who lack expertise to acquire the ideal standard view.

Dataset Information

Expert-led and artificial intelligence (AI) system-assisted tutoring course increase confidence of Chinese medical interns on suturing and ligature skills: prospective pilot study.

Publications

Expert-led and artificial intelligence (AI) system-assisted tutoring course increase confidence of Chinese medical interns on suturing and ligature skills: prospective pilot study.

Similar Datasets

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