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The feasibility and effectiveness of a blended-learning course for detecting and avoiding bias in medical data: a pilot study.

ABSTRACT: BACKGROUND:Conflicts of interest (COIs), including those arising from interactions with pharmaceutical companies, may lead to bias in medical data. Although medical students are now requesting more education on COIs and bias, they are still not adequately taught during medical school, and few published courses on this topic exist. The objective of our study was therefore to evaluate the feasibility and effectiveness of a blended-learning course for detecting and avoiding bias in medical data, with a special focus on COIs. METHODS:We developed a blended learning course on bias detection, COIs, and risk communication. It was piloted in the Fall Semester of 2019/2020 using a pre/post-test design. The primary outcome was a gain in bias detection skills, tested by a novel key feature test. Secondary outcomes were (i) skepticism (tested using an attitude questionnaire), (ii) the intention to manage COIs in a professional way so as to avoid bias (tested using a situational judgment test) and (iii) the course evaluation by the students. RESULTS:Seventeen students participated in the study. The key feature test showed a significant improvement in bias detection skills at post-testing, with a difference in means of 3.1 points (95%-CI: 1.7-4.4, p-value:

SUBMITTER: Koch C

PROVIDER: S-EPMC7648418 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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The feasibility and effectiveness of a blended-learning course for detecting and avoiding bias in medical data: a pilot study.

Koch Cora C Brich Jochen J

BMC medical education 20201107 1

<h4>Background</h4>Conflicts of interest (COIs), including those arising from interactions with pharmaceutical companies, may lead to bias in medical data. Although medical students are now requesting more education on COIs and bias, they are still not adequately taught during medical school, and few published courses on this topic exist. The objective of our study was therefore to evaluate the feasibility and effectiveness of a blended-learning course for detecting and avoiding bias in medical ...[more]

PMID: 33160334

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Project description:Problem-based learning (PBL) allows students to learn medical statistics through problem solving experience. The aim of this study was to assess the efficiency of PBL modules implemented in the blended learning courses in medical statistics through knowledge outcomes and student satisfaction. The pilot study was designed as a randomized controlled trial that included 53 medical students who had completed all course activities. The students were randomized in two groups: the group with access to PBL modules within the blended learning course (hPBL group) and the group without access to PBL modules-only blended learning course (BL group). There were no significant differences between the groups concerning socio-demographic characteristics, previous academic success and modality of access to course materials. Students from hPBL group had a significantly higher problem solving score (p = 0.012; effect size 0.69) and the total medical statistics score (p = 0,046; effect size 0.57). Multivariate regression analysis with problem solving as an outcome variable showed that problem solving was associated with being in hPBL group (p = 0.010) and having higher grade point average (p = 0.037). Multivariate regression analysis with the medical statistics score as an outcome variable showed the association between a higher score on medical statistics with access to PBL modules (p = 0.045) and a higher grade point average (p = 0.021). All students in hPBL group (100.0%) considered PBL modules useful for learning medical statistics. PBL modules can be easily implemented in the existing courses within medical statistics using the Moodle platform, they have high applicability and can complement, but not replace other forms of teaching. These modules were shown to be efficient in learning, to be well accepted among students and to be a potential missing link between teaching and learning medical statistics. The authors of this study are planning to create PBL modules for advanced courses in medical statistics and to conduct this study on other universities with a more representative study sample, with the aim to overcome the limitations of the existing study and confirm its results.

Project description:BackgroundMost medical students lack confidence and are unable to accurately interpret ECGs. Thus, better methods of ECG instruction are being sought. Current literature indicates that the use of e-learning for ECG analysis and interpretation skills (ECG competence) is not superior to lecture-based teaching. We aimed to assess whether blended learning (lectures supplemented with the use of a web application) resulted in better acquisition and retention of ECG competence in medical students, compared to conventional teaching (lectures alone).MethodsTwo cohorts of fourth-year medical students were studied prospectively. The conventional teaching cohort (n = 67) attended 4 hours of interactive lectures, covering the basic principles of Electrocardiography, waveform abnormalities and arrhythmias. In addition to attending the same lectures, the blended learning cohort (n = 64) used a web application that facilitated deliberate practice of systematic ECG analysis and interpretation, with immediate feedback. All participants completed three tests: pre-intervention (assessing baseline ECG competence at start of clinical clerkship), immediate post-intervention (assessing acquisition of ECG competence at end of six-week clinical clerkship) and delayed post-intervention (assessing retention of ECG competence 6 months after clinical clerkship, without any further ECG training). Diagnostic accuracy and uncertainty were assessed in each test.ResultsThe pre-intervention test scores were similar for blended learning and conventional teaching cohorts (mean 31.02 ± 13.19% versus 31.23 ± 11.52% respectively, p = 0.917). While all students demonstrated meaningful improvement in ECG competence after teaching, blended learning was associated with significantly better scores, compared to conventional teaching, in immediate (75.27 ± 16.22% vs 50.27 ± 17.10%, p < 0.001; Cohen's d = 1.58), and delayed post-intervention tests (57.70 ± 18.54% vs 37.63 ± 16.35%, p < 0.001; Cohen's d = 1.25). Although diagnostic uncertainty decreased after ECG training in both cohorts, blended learning was associated with better confidence in ECG analysis and interpretation.ConclusionBlended learning achieved significantly better levels of ECG competence and confidence amongst medical students than conventional ECG teaching did. Although medical students underwent significant attrition of ECG competence without ongoing training, blended learning also resulted in better retention of ECG competence than conventional teaching. Web applications encouraging a stepwise approach to ECG analysis and enabling deliberate practice with feedback may, therefore, be a useful adjunct to lectures for teaching Electrocardiography.

Project description:BackgroundPreparing today's undergraduate students from science, technology, engineering, and math (STEM) and related health professions to solve wide-sweeping healthcare challenges is critical. Moreover, it is imperative that educators help students develop the capabilities needed to meet those challenges, including problem solving, collaboration, and an ability to work with rapidly evolving technologies. We piloted a multidisciplinary education (ME) course aimed at filling this gap, and subsequently assessed whether or not students identified achieving the course objectives. In the course, undergraduate students from engineering, pre-nursing (students not yet admitted to the nursing program), and pre-professional health (e.g., pre-med and pre-physician's assistant) were grouped based on their diversity of background, major, and StrengthsFinder® proficiencies in a MakerSpace to create tangible solutions to health-related problems facing the community. We then used qualitative content analysis to assess the research question: what is the impact of undergraduate multidisciplinary education offered in a MakerSpace on student attitudes towards and perceptions of skills required in their own as well as others occupations?ResultsWe discovered these students were able to identify and learn capabilities that will be critical in their future work. For example, students appreciated the challenging problems they encountered and the ability to meet demands using cutting-edge technologies including 3D printers. Moreover, they learned the value of working in a multidisciplinary group. We expected some of these findings, such as an increased ability to work in teams. However, some themes were unexpected, including students explicitly appreciating the method of teaching that focused on experiential student learning through faculty mentoring.ConclusionsThese findings can be used to guide additional research. Moreover, offering a variety of these courses is a necessary step to prepare students for the current and future workforce. Finally, these classes should include a focus on intentional team creation with the goal of allowing students to solve challenging real-world problems through ethical reasoning and collaboration.

Project description:BackgroundBlended learning, which combines face-to-face learning and e-learning, has grown rapidly to be commonly used in education. Nevertheless, the effectiveness of this learning approach has not been completely quantitatively synthesized and evaluated using knowledge outcomes in health education.ObjectiveThe aim of this study was to assess the effectiveness of blended learning compared to that of traditional learning in health education.MethodsWe performed a systematic review of blended learning in health education in MEDLINE from January 1990 to July 2019. We independently selected studies, extracted data, assessed risk of bias, and compared overall blended learning versus traditional learning, offline blended learning versus traditional learning, online blended learning versus traditional learning, digital blended learning versus traditional learning, computer-aided instruction blended learning versus traditional learning, and virtual patient blended learning versus traditional learning. All pooled analyses were based on random-effect models, and the I2 statistic was used to quantify heterogeneity across studies.ResultsA total of 56 studies (N=9943 participants) assessing several types of learning support in blended learning met our inclusion criteria; 3 studies investigated offline support, 7 studies investigated digital support, 34 studies investigated online support, 8 studies investigated computer-assisted instruction support, and 5 studies used virtual patient support for blended learning. The pooled analysis comparing all blended learning to traditional learning showed significantly better knowledge outcomes for blended learning (standardized mean difference 1.07, 95% CI 0.85 to 1.28, I2=94.3%). Similar results were observed for online (standardized mean difference 0.73, 95% CI 0.60 to 0.86, I2=94.9%), computer-assisted instruction (standardized mean difference 1.13, 95% CI 0.47 to 1.79, I2=78.0%), and virtual patient (standardized mean difference 0.62, 95% CI 0.18 to 1.06, I2=78.4%) learning support, but results for offline learning support (standardized mean difference 0.08, 95% CI -0.63 to 0.79, I2=87.9%) and digital learning support (standardized mean difference 0.04, 95% CI -0.45 to 0.52, I2=93.4%) were not significant.ConclusionsFrom this review, blended learning demonstrated consistently better effects on knowledge outcomes when compared with traditional learning in health education. Further studies are needed to confirm these results and to explore the utility of different design variants of blended learning.

Dataset Information

The feasibility and effectiveness of a blended-learning course for detecting and avoiding bias in medical data: a pilot study.

Publications

The feasibility and effectiveness of a blended-learning course for detecting and avoiding bias in medical data: a pilot study.

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