Investigating the use of the HIS-based BOPPPS teaching model in medical imaging experimental course instruction

Investigating the use of the HIS-based BOPPPS teaching model in medical imaging experimental course instruction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating the use of the HIS-based BOPPPS teaching model in medical imaging experimental course instruction Ziqing Yang, Siyu Zhen, Ben Pan, Hanyu Wei, Qiang Li, Junyan Yue, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4882435/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in BMC Medical Education → Version 1 posted 4 You are reading this latest preprint version Abstract Background The efficacy of traditional teaching medical imaging experimental courses is not optimal due to a number of flaws. This study's main goal was to find out how well the Bridge-In, Outcomes, Pre-Assessment, Participatory Learning, Post-Assessment, and Summary (BOPPPS) teaching model, which is based on the Hospital Information System (HIS), works when teaching medical imaging experimental courses to undergraduate students pursuing five years of medical imaging education. Methods 117 medical imaging students who were interning at the First Affiliated Hospital of Xinxiang Medical University in the academic year 2021–2022 made up the research subjects. During the first semester, the first group was instructed using the BOPPPS teaching model based on HIS, while the second group was instructed using the standard teaching model. The two student groups swapped instructional models in the second semester. After the course, questionnaire surveys and closed-book exams were used to evaluate the effectiveness of the instruction. Results Compared to the group using the traditional teaching model, the BOPPPS teaching model group scored significantly higher on case reading and overall final test outcomes, and this difference was statistically significant (In the first semester, the scores of case reading questions were 39.27 ± 3.39 VS 35.31 ± 2.77,P < 0.001; the total scores were 77.47 ± 6.61 VS 74.33 ± 4.17,P = 0.003. In the second semester, the scores of case reading questions were 39.79 ± 3.45 VS 35.47 ± 3.15,P < 0.001; the total scores were 78.36 ± 5.11 VS 74.53 ± 5.68, P < 0.001). On multiple-choice questions, however, there was no statistically significant difference in the scores between the standard teaching model group and the BOPPPS teaching model group. Over 80% of the students rated questions 1–9 with a score of 4 or 5, indicating that students' evaluations of the BOPPPS teaching model in terms of learning efficiency, interest, clinical reasoning ability, and course satisfaction were all consistently positive. Conclusion The BOPPPS teaching model based on HIS system is a supplement, perfection and optimization of traditional medical imaging experimental courses teaching, and is helpful to improve the effectiveness and satisfaction of medical imaging experimental courses teaching. BOPPPS medical imaging HIS traditional teaching teaching model Figures Figure 1 Introduction The field of medical imaging makes use of imaging technology to identify diseases and direct their care. Medical imaging has evolved from low-resolution to high-resolution, from two-dimensional to three-dimensional, and from morphological to functional and metabolic imaging as a result of scientific and technological advancements. These images are now essential for early disease diagnosis, treatment response evaluation, and post-treatment follow-up [1]. The main objectives of medical imaging education are to improve students' diagnostic skills and provide the clinic with qualified imaging personnel. Under this approach, teaching medical imaging experimental courses is essential to helping students review and strengthen their theoretical understanding as well as develop their ability to read practical case studies. It plays a crucial role in helping medical imaging students enhance their clinical skills by serving as a bridge that tightly integrates academic knowledge with clinical case studies. Nonetheless, lectures and the distribution of theoretical knowledge are the main components of the traditional teaching model for medical imaging experimental courses [2]. The following three problems are raised by this model: Firstly, students adopt a passive part in their education. Under the traditional model, students merely receive information passively from teachers who take the lead in imparting theoretical knowledge. Students' interest and motivation decrease when the instructional material is frequently dull and the approach is inflexible and repetitive [3]. Secondly, there is a lack of close alignment between the integration of real clinical work and instruction. Real clinical context is sometimes absent from traditional education, which merely presents single pictures of common symptoms in common instances without adding any extra medical information. This constraint impedes the successful development of students' clinical reasoning abilities by restricting their diagnostic thinking and making it more difficult for them to comprehend the in-depth manifestations of disease imaging. Thirdly, it is unable to verify the caliber of imaging resources used in instruction. Presently, the majority of medical imaging courses still use the antiquated slide-based method of instruction, which comes with a danger of information loss and sluggish upgrades to the teaching materials. Students are consequently unable to witness entire medical image processing processes, including window width modification, window level adjustment, and three-dimensional reconstruction. This has an immediate effect on the image's clarity and ability to exhibit fine structures, which impedes students' ability to comprehend diseases in their whole and has a major negative impact on the standard of medical imaging course education. Hospital Information Systems (HIS) development has flourished with the advent of the big data era and the expansion of contemporary information technology. HIS serves as a vital resource in the medical field [4], providing essential support for various hospital operations. For medical research and teaching staff, the HIS system offers an extensive array of medical records, medical images, and the latest clinical data, thus significantly expanding research and teaching resources [5]. To increase students' interest in and effectiveness from their education, educators have been constantly innovating teaching models in the last several years, including Problem-Based Learning (PBL) [6], Case-Based Learning (CBL) [7], and Multi-Disciplinary Team (MDT) collaboration [8-11]. When compared to conventional teaching techniques, these forms of instruction have specific benefits. These days, active student participation is emphasized more and more in instructional practices [12, 13]. The Instruction Skills Workshop International Advisory Committee in North America recommended the BOPPPS teaching model, which was developed in Canada in the late 1970s and is an efficient model for course design. This model breaks down a whole teaching process into six interconnected components: Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment and Summary. It emphasizes a student-centered, teacher-guided teaching philosophy. The BOPPPS model offers a workable approach to educational reform by giving a precise framework and reliable assurance for accomplishing learning objectives. It has been effectively used in the practical teaching of several disciplines, including the education of thoracic surgery education [14], surgical nursing education [15], and physiology education [16], yielding positive teaching outcomes. Nonetheless, there aren't many reports—in China or elsewhere—of the use of the BOPPPS model in medical imaging experimental course instruction for undergraduate students pursuing a five-year degree in medical imaging. This study aims to evaluate the feasibility and effectiveness of the HIS-based BOPPPS model in the teaching of the medical imaging experimental course for the 117 five-year medical imaging undergraduate students undergoing internships at our university. Materials and Methods General Information 117 medical imaging interns from Xinxiang Medical University's First Affiliated Hospital who were enrolled in the 2021–2022 academic year were chosen. The students were divided into two groups: the first group was made up of students from classes 1 and 2, while the second group was made up of students from classes 3 and 4. Information was collected about the students' gender, age, prior semester academic performance, and professional interests. The textbook "Medical Imaging Diagnosis" (Han Ping, Yu Chunshui, People's Medical Publishing House, 4th edition) served as the foundation for the medical imaging experimental course. For both student groups, the same chapters were taught by the same professors, all of whom had at least five years of teaching experience.The research design was approved by the Ethics Committee of the First Affiliated Hospital of Xinxiang Medical University (approval number: EC-024-408). Informed consent was waived by the Institutional Review Board. Methods The first semester comprised 60 hours each of theory and practical classes, while the second semester comprised 52 hours each of theory and practical classes. In the first semester, the first group of students received instruction using the BOPPPS teaching model based on the Hospital Information System (HIS), while the second group received instruction using the traditional teaching model. In the second semester, the teaching models for the two groups were switched. This prospective study aimed to analyze the feasibility and effectiveness of the BOPPPS teaching model based on the HIS system in the teaching of medical imaging experimental courses. Traditional Teaching Model As part of this teacher-centered approach, students are encouraged to pieview pertinent reference materials or textbooks before class. Using slides, the instructor explains in class the common imaging manifestations and differential diagnosis of various diseases according to the curriculum's criteria. In addition to taking notes and participating in a post-class review of pertinent material, students pay attention to the lecture. The BOPPPS teaching model The teacher of the medical imaging experimental course creates learning objectives based on the curriculum's requirements and students' needs. Then, the teacher arranges the related teaching materials in accordance with the six BOPPPS model components. The following are the specific methods of implementation: Introduction: The objective is to draw students’ attention, stimulate their interest in the upcoming core teaching material, and stimulate their curiosity and thirst for knowledge. This can be achieved by using visual aids such as charts, movies, or real-world statistics to highlight the importance of the course subject in a brief introduction. Alternatively, it could involve going over previously taught material with students or posing stimulating questions to ease them into the main teaching content. Creating links between the new content and the students' past knowledge is another aspect of it. Learning Objectives: Creating learning objectives necessitates carefully weighing the qualities of the material being taught, as well as the requirements and skills of the students. This gives teachers the freedom to create teaching plans and use efficient teaching techniques, all the while allowing students to know exactly where their learning is going. The degree to which specified learning objectives are met can also be used to evaluate the effectiveness of learning. Pre-assessment: Teachers can better synchronize the depth and progression of their teaching content by having a better awareness of the fundamental knowledge levels of their students. Reviewing preliminary knowledge aids students in achieving a favorable learning environment more rapidly. Pre-assessment can be carried out using a variety of methods, including questioning, discussions, questionnaires, and pre-reading achievement displays, depending on the particular teaching subject. Participatory Learning: This is the foundation of the BOPPPS model, which emphasizes student-centered learning with teachers acting as mentors. To encourage students to actively participate in class activities, teachers are encouraged to use a variety of flexible teaching methods and tools, such as CBL, PBL, TBL, flipped classrooms, etc., depending on the learning objectives and material. Students are led to participate in group discussions and idea sharing when teaching medical imaging experiments, culminating in a comprehensive analysis of a typical imaging case. Post-assessment: Using targeted assessments, the teacher determines at the end of the class whether or not the students have mastered the information points and have met the predetermined learning goals. Depending on the particular course material, a variety of techniques can be employed, including questioning, multiple-choice questions, true/false questions, and on-site demonstrations. Summary: The teacher can conclude by summarizing the most important and difficult aspects learned, giving students homework to complete after class to further solidify and broaden their understanding, or quickly assessing how well the learning objectives were met before establishing new guidelines and expectations for the following session. As an alternative, the synopsis can include a preview at the material covered in the upcoming class and a list of prerequisites. Along with contributing to the summary, students can also reflect on their learning and areas for growth, facilitate post-class review and consolidation, summarize and synthesize important and difficult concepts, and improve the effectiveness of their learning. As an example, the specific teaching content design for the section on pulmonary inflammation in this course is detailed in Table 1. Table 1 The Specific Operations of the BOPPPS Teaching Model Segment Teacher Activity Student Activity Approach Introduction By using HIS to dynamically show typical cases, instructors can help students think critically by asking them to choose the diagnosis based on the patient's clinical and imaging presentations. Students can think about the patient's imaging abnormalities, and learn with questions. Case presentation Learning Objectives Cognitive objectives: Gain an understanding of the clinical presentations, pathological features, and imaging manifestations of pulmonary inflammation. (Purpose and Focus) Skill Objectives: Familiarize with writing pulmonary inflammation imaging reports. Affective Objectives: Develop the ability of clinical reasoning and cooperative and united professional qualities. Students can clearly define the key and difficult points of the classroom content and learning objectives. Slides presentation Pre-test Question: what are the types of pulmonary inflammation? What are the common pathogens that cause pulmonary inflammation? What is the preferred imaging examination for diagnosing pulmonary inflammation? Interactive discussion, answering questions Q&A Participatory Learning Choosing typical cases from the HIS system in accordance with the teaching plans, assisting students in learning and discussing in groups, and monitoring and correcting their thinking during the class. Lastly, giving thorough explanations and case summaries; evaluating and standardizing the diagnostic report writing; and assessing the differential diagnosis approach. Integrating clinical manifestations and laboratory examinations to consider the cases: What are the typical imaging signs? What are the pathological reasons for these signs? Are these signs seen in other diseases, and how to differentiate them? Finally, sharing and discussing the results and diagnostic reports in groups, self-assessing as a group, and conducting intra-group and inter-group evaluations. Case presentation and group discussions Post-test Multiple choice questions: What is the most common type of pneumonia in bacterial pneumonia? Which of the following descriptions is a characteristic imaging feature of lobar pneumonia? The typical imaging manifestations of lobar pneumonia correspond to which pathological stage? The main X-ray manifestation of bronchopneumonia is? The best imaging examination for interstitial lung disease is? independent thinking and answering Questionnaire survey Summary Encouraging and guiding students to summarize and consolidate the knowledge learned in this class, and analyzing the achievement of the teaching objectives for this session. Analyzing the accomplishment of the session's learning objectives and supporting and assisting students in synthesizing and consolidating the material they have learned in this class. evaluating and consolidating after class, reflecting on one's own successes and failures in this class, and revisiting the material covered in class. Q&A Effectiveness Evaluation Exams and anonymous questionnaire surveys are used to evaluate the BOPPPS teaching model's efficacy and student satisfaction after the course. Grades are a significant and direct indicator of students' knowledge mastery as well as a significant parameter for gauging the quality of education [17]. They may serve as a gauge of students' degree of knowledge and accumulating capacity. A three-hour, closed-book exam is given at the conclusion of each semester; the exam questions are based on the teaching outline. One point is awarded for each multiple-choice question on the final test, which has a total score of 100 points, with 50 points allocated to multiple-choice questions. The purpose of the multiple-choice questions is to assess students' theoretical understanding of different imaging systems. Furthermore, case reading questions take up 50 points (5 points a question), which primarily evaluate students' clinical reasoning and image reading abilities. In addition to exam results, students' genuine attitudes regarding the learning process are seen to be essential for reflecting the caliber and efficacy of the teaching. Both student groups took part in an anonymous questionnaire survey at the conclusion of the academic year; the results are shown in Table 2. The questionnaire focused mainly on topics like learning efficiency (i.e., whether the introduction attracted attention and allowed for a swift entry into the learning state, whether the learning objectives were clear and concise, whether pre- and post-tests helped consolidate important knowledge, and whether participatory learning improved classroom learning efficiency), learning interest(i.e., whether participatory learning increased interest in learning), image reading ability(i.e., whether retrieving images from the HIS system improved image reading ability), clinical thinking ability(i.e., whether retrieving images from the HIS system enhanced clinical thinking ability), course satisfaction(i.e., whether the BOPPPS teaching model's effectiveness and the classroom environment were highly satisfactory), and learning burden(i.e., whether the BOPPPS teaching model increased the learning burden.) After 117 valid questionnaires were collected out of a total of 117 that were distributed, there was a 100% response rate. The valid questionnaires were then analyzed. (The Likert five-level rating method was used as the evaluation standard, with values ranging from 1 to 5, meaning "strongly disagree" to "strongly agree"). Table 2: Questionnaire Survey Statistical Analysis Statistical analysis was conducted using SPSS 27.0. For normally distributed quantitative data, the results are presented as mean ± standard deviation (x ± s) and analyzed using t-tests. For non-normally distributed data, median and quartiles [M(Q1, Q3)] were used, and Z-tests were conducted. Count data are presented as number ［n(%)］and compared using the chi-squared test, with a significance level set at P<0.05. Results Demographic Data A total of 117 senior medical imaging students were included in this study, comprising 80 female students and 37 male students. The students' level of interest in the medical imaging profession was categorized as strong, moderate, or lacking. The results are shown in Table 3. There were no statistically significant differences between the two groups of students in terms of gender, age, previous semester's professional course grades, and professional interests (P > 0.05). The baseline data of the two groups were comparable. Table 3: Comparison of General Data between the Two Groups Characteristics Group 1 Group 2 x2/t/z P Value Sex 0.68 0.794 Male, n(%) 18(30.5) 19(32.8) Female, n(%) 41(69.5) 39(67.2) Mean Age 22(22,23) 22(22,23) -0.197 0.844 Scores of professional courses of last semester 74(70,78) 74.50(72,77.50) 0.129 0.898 Interest in Specialty 0.362 0.835 Strong, n(%) 40(67.8) 38(65.5) Moderate , n(%) 16(57.1) 18(31) lacking, n(%) 3(5.1) 2(3.4) End-of-Term Examination Results The end-of-term exam results of two student groups from the same semester as well as the same group of students from other semesters were compared in order to assess the efficacy of the BOPPPS teaching model. Scores for case reading questions, multiple-choice questions, and the overall end-of-term exam were all included in the analysis. Significant differences were found between the two groups' case reading scores and overall scores in the first and second semesters (p<0.05), as indicated by the results displayed in Tables 4 and 5. Specifically, in the first semester, the first group of students received much higher scores overall in the case reading questions (case reading score 39.27± 3.39 vs. 35.31±2.77, total score 77.47± 6.61 vs. 74.33±4.17), whereas in the second semester, the second group of students performed better in the same aspects as the first group(case reading score 35.47±3.15 vs. 39.79±3.45, total score 74.53±5.68 vs. 78.36± 5.11). Furthermore, there were statistically significant differences in the average scores for case reading questions and total scores for the same group of students in different semesters (p<0.05). However, no statistically significant differences were identified in the comparison of multiple-choice question scores or theoretical scores between the groups. Table 4: Comparison of End-of-Term Scores between Different Groups in the Same Semester Category First Semester Second Semester Group 1 Group 2 t P Group 1 Group 2 t P Theoretical Scores 38.20±4.27 39.02±2.83 -1.214 0.227 39.05±3.68 38.57±3.08 0.767 0.445 Image Reading Scores 39.27±3.39 35.31±2.77 6.925 <0.001 35.47±3.15 39.79±3.45 -7.066 <0.001 Total Scores 77.47±6.61 74.33±4.17 3.087 0.003 74.53±5.68 78.36±5.11 -3.844 <0.001 Category Group 1 Group 2 First Semester Second Semester t P First Semester Second Semester t P Theoretical Scores 38.20±4.27 39.05±3.68 -1.156 0.250 39.02±2.83 38.57±3.08 0.815 0.417 Image Reading Scores 39.27±3.39 35.47±3.15 6.306 <0.001 35.31±2.77 39.79±3.45 -7.709 <0.001 Total Scores 77.47±6.61 74.53±5.68 2.600 0.011 74.33±4.17 78.36±5.11 -4.662 <0.001 Table 5: Comparison of End-of-Term Scores in Different Semesters within the Same Group Questionnaire Survey The results of the questionnaire survey are shown in Figure 1. Over 80% of the students rated questions 1-9 with a score of 4 or 5, indicating that students have a high evaluation of the BOPPPS teaching model in terms of learning efficiency, interest, clinical thinking ability, and course satisfaction. However, students also perceived some drawbacks of this new teaching model. For question 10, 48 students rated it with a score of 3-5 (41.0%), 51 students rated it with a score of 2 (43.6%), and 18 students rated it with a score of 1 (15.4%), indicating that the majority of students believe that the BOPPPS teaching model has increased the learning burden to some extent. Discussion Requirements for effective teaching include high-quality content and well-designed instruction, which have a direct impact on the effectiveness and efficiency of education. In the context of modern medical education, the traditional, single-experiment teaching model has become outdated and is ill-suited to the demands of the digital information age. To improve the overall quality of education, it is imperative to investigate teaching models that are in line with modern features and the practical aspects of medical imaging in order to improve the design of medical imaging experimental teaching. This study employs the BOPPPS teaching model to instruct intern students of the class of 2018 majoring in medical imaging at the First Affiliated Hospital of Xinxiang Medical University. It is based on the utilization of the Hospital Information System (HIS) to acquire typical clinical cases. According to our study's findings, the group that used the BOPPPS teaching model outperformed the group that used the traditional teaching model in terms of overall final exam results and case reading, with the difference being statistically significant (p < 0.05). This highlights the efficacy of the teaching model and is consistent with the findings of Hu et al.'s comparison study between the BOPPPS model and conventional teaching techniques in thoracic surgery education [ 14 ]. According to their study, the experimental group that used the BOPPPS teaching model scored much higher on exams than the control group, indicating the model's beneficial effects on clinical teaching in thoracic surgery. Additionally, it was noted that the BOPPPS teaching model group and the standard teaching model group did not differ statistically significantly in their answers to multiple-choice questions. Consequently, it can be deduced that the BOPPPS teaching model group's exceptional performance in obtaining higher scores was due to their mastery of case-reading, even though there was no appreciable improvement in their retention of theoretical professional knowledge. This observation can be connected to the traditional educational approaches' focus on memorizing of theoretical information. According to the survey results, students gave the BOPPPS teaching model high ratings for effectiveness in teaching, interest in learning, capacity for clinical reasoning, and course satisfaction. These results are in line with those of Li et al. [ 18 ], who carried out a meta-analysis of randomized controlled trials (RCT) related to the BOPPPS teaching model in "Fundamentals of Nursing" education and found that students were highly satisfied with the model. Students did, nevertheless, indicate that the BOPPPS teaching approach added to their workload. Chinese students are used to traditional "spoon-feeding" offline teaching methods, which merely require them to attend lectures and transfer relevant information to memory. As opposed to this, the BOPPPS model places greater pressure on them by requiring them to start with self-learning and providing more in-depth course material. However, in our view, pressure may not always end in an adverse outcome; in fact, it can be a motivating factor for improving college students' capacity for independent study. To eliminate "easy courses" and create "quality courses," higher education needs to increase academic challenges, the difficulty of course content, and the proper workload for students. Therefore, teaching students how to adapt to this pressure will be a challenge for teachers. At the same time, it also needs to strengthen the supervision and management of extra-curricular learning, and participatory learning is more time-consuming than traditional lecture-based learning. In order to ensure the teaching capacity and depth under the condition of limited class hours, online learning can be conducted before and after class to consolidate and review the basic knowledge learned in theoretical classes[ 19 ]. The ultimate objective of teaching is to help students develop their capacity to use basic theoretical knowledge to address actual clinical problems—far more significant than simply having them comprehend the material. Teachers use the Hospital Information System's (HIS) resources to help students use their theoretical knowledge to undertake in-depth case analyses. The acquisition of typical cases from the HIS system presents many benefits in the context of medical imaging experiments. First of all, this gives students the ability to dynamically and continuously display image data and carry out different post-processing tasks based on diagnostic requirements. They can, for instance, modify window level and breadth, measure lesion size, volume, and density with tools, perform two- and three-dimensional reconstructions, conduct follow-up comparison of lesions, and adjust image size for best display. In conclusion, students may more clearly perceive picture features, think more deeply, and improve the quality of imaging teaching materials owing to the HIS system. Second, the HIS system offers more comprehensive teaching resources for medical imaging experiment teaching. Students can access a variety of diagnostic and treatment documents, including electronic medical records, pathology reports, laboratory test results, and medical imaging, for the same patient through the HIS system[ 20 , 21 ]. Students can now more effectively combine pathological, clinical, and imaging data, bringing them closer to real-world clinical work. This develops students' capacity for clinical reasoning, broadens their comprehension of medical imaging, and enhances their ability to identify and diagnose diseases. Last but not least, the ability to export teaching examples as Word or Excel documents immediately improves the caliber and productivity of creating courseware. One of the main components of the BOPPPS teaching model is active learning. Conventional one-way course lectures might only allow for a shallow memory of the material. Prioritizing student participation is essential to achieving a better comprehension of the material since it gives them a sense of control over the classroom, makes deep engagement enjoyable, and improves their understanding of fundamental concepts. Although the BOPPPS approach greatly improves the quality of teaching in medical imaging studies, it is crucial to avoid formalizing teaching methods when implementing it into practice. While following the BOPPPS teaching model, teachers should incorporate their wealth of teaching expertise into their regular teaching activities. Based on the learning objectives of the course and the demands of the teaching content, they should be adaptable in their modifications to the teaching designs. This methodology will foster a dynamic and engaging learning environment, enable fruitful communication between teachers and students, and guarantee that students may fully engage in the educational process. Additionally, some studies have used a hybrid BOPPPS teaching strategy, which includes uploading teaching resources online and integrating online teaching platforms to make it easier to carry out in-class activities and post-test reviews [ 22 ]. With the advancement of science and technology, artificial intelligence has made a significant impact on various industries, including healthcare and education [ 23 – 25 ]. Smart visual aids and realistic scenario simulations could be used in the future to further improve the efficacy of teaching. The BOPPPS teaching model, based on the Hospital Information System (HIS), is designed to be student-centered, goal-oriented, technology-driven, and grounded in comprehensive clinical data. It emphasizes interactive teaching and reflection, guiding students to seek knowledge proactively and explore consciously, thereby enhancing their overall qualities and fostering modern, practical talents in the field of medical imaging. This model serves as a valuable complement and enhancement to traditional medical imaging experimental teaching, effectively addressing inherent issues in traditional teaching methods. Our study is not without limits, however. First, a small sample size and a single center were used for the study. Potential future research directions include large-scale, multicenter validation studies conducted in various institutions and areas to confirm the efficacy of this teaching model. Second, there are potential biases in learning performance because the current study only evaluated final exam results; pre- and post-class testing that measure learning outcomes were absent. Finally, no comparison of the BOPPPS teaching approach with other methods was made in this study. Subsequent investigations could create distinct studies to evaluate various teaching models. Conclusion In this study, we utilized the HIS-based BOPPPS teaching model to perform teaching practices for medical imaging experimental courses. We also summarized a standardized teaching approach for these courses utilizing the HIS system and the BOPPPS teaching model. The results of this study demonstrate how this teaching model has enhanced students' focus and efficiency during learning, providing an effective way for medical imaging students to further their professional development and clinical practice abilities. This teaching model has the potential to yield fresh perspectives and serve as a guide for instructing experimental medical imaging Acknowledgements The authors thank all the participating medical teachers and students. We would like to express our gratitude to them for their assistance. Declarations Ethics approval and consent to participate The research design was approved by the Ethics Committee of the First Affiliated Hospital of Xinxiang Medical University (approval number: EC-024-408). Informed consent was waived by the Institutional Review Board. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details The First Affiliated Hospital of Xinxiang Medical University, Wei Hui 453100. Funding This work was funded by the Medical Education Research Project of Henan Province wjlx202136. Author Contribution Z.Y. contributed to conceptualization, methodology, survey design, statistical analysis ,results and writing; S.Z. contributed to conceptualization, methodology, statistical analysis ,results and funding acquisition; B.P. and H.W. contributed to methodology, data collection, statistical analysis and results; Q.L. contributed to survey design, ethics application and data collection; J.Y. and Y.H. contributed to methodology, survey design and writing -reviewing; C.L. contributed to project administration, funding acquisition, writing -reviewing.All authors read and approved the final manuscript. Availability of data and materials The datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author upon reasonable request. Authors’contributions Z.Y. contributed to conceptualization, methodology, survey design, statistical analysis ,results and writing; S.Z. contributed to conceptualization, methodology, statistical analysis ,results and funding acquisition; B.P. and H.W. contributed to methodology, data collection, statistical analysis and results; Q.L. contributed to survey design, ethics application and data collection; J.Y. and Y.H. contributed to methodology, survey design and writing -reviewing; C.L. contributed to project administration, funding acquisition, writing -reviewing.All authors read and approved the final manuscript. References Giardino A, Gupta S, Olson E, Sepulveda K, Lenchik L, Ivanidze J, Rakow-Penner R, Patel MJ, Subramaniam RM, Ganeshan D. Role of Imaging in the Era of Precision Medicine. Acad Radiol. 2017;24(5):639–49. Gao J, Yang L, Zou J, Fan X. Comparison of the influence of massive open online courses and traditional teaching methods in medical education in China: A meta-analysis. Biochem Mol Biol Educ. 2021;49(4):639–51. Qi Y, Zhao P, Bai R, Shao Y, Pan S, Hou Y. Teaching diagnostic radiology to radiology majors: Implementation and evaluation of a flipped classroom model. Med Teach 2024:1–13. Behnam F, Khajouei R, Nabizadeh AH, Saedi S, Ghaemi MM. Usability evaluation of emergency information systems in educational hospitals in Kerman, Iran. BMC Med Inf Decis Mak. 2023;23(1):277. Zhang L, Wu J, Yang J, Chen SS, Liu JP, Zhang P, Chu J, Luo CL. Development and Application Evaluation of a Nursing Simulation Teaching Information System Based on Hospital Information Systems. Int J Clin Pract. 2023;2023:6334967. Yu H. Enhancing creative cognition through project-based learning: An in-depth scholarly exploration. Heliyon. 2024;10(6):e27706. Ferrer Valencia V, Rivera Gonzales H, Ortiz Llinás JR. Cardona Ortegón JD: Case-Based Learning: An Immersive and Effective Educational Approach. Acad Radiol. 2023;30(11):2797. Stoehr F, Yang Y, Müller L, Gerstenmeier P, Pinto Dos Santos D, Dietz P, Weimer A, Ludwig M, Kloeckner R, Weimer JM. A blended learning approach for teaching thoracic radiology to medical students: a proof-of-concept study. Front Med (Lausanne). 2023;10:1272893. Vavasseur A, Muscari F, Meyrignac O, Nodot M, Dedouit F, Revel-Mouroz P, Dercle L, Rozenblum L, Wang L, Maulat C, et al. Blended learning of radiology improves medical students' performance, satisfaction, and engagement. Insights Imaging. 2020;11(1):61. Zhao PF, Gao Y, Xie SH, Qiao PF. Effect of multi-disciplinary-cooperation-mode, case-based-study and teaching-picture-archiving-communicating-system multi-mode joint teaching mode in medical imaging teaching. Asian J Surg. 2021;44(6):930–1. Vallée A, Blacher J, Cariou A, Sorbets E. Blended Learning Compared to Traditional Learning in Medical Education: Systematic Review and Meta-Analysis. J Med Internet Res. 2020;22(8):e16504. Stoehr F, Müller L, Brady AP, Catalano C, Mildenberger P, Mähringer-Kunz A, Hahn F, Düber C, Celik E, Diehl E, et al. Online teaching in radiology as a pilot model for modernizing medical education: results of an international study in cooperation with the ESR. Insights Imaging. 2021;12(1):141. Ma X, Zeng D, Wang J, Xu K, Li L. Effectiveness of bridge-in, objective, pre-assessment, participatory learning, post-assessment, and summary teaching strategy in Chinese medical education: A systematic review and meta-analysis. Front Med (Lausanne). 2022;9:975229. Hu K, Ma RJ, Ma C, Zheng QK, Sun ZG. Comparison of the BOPPPS model and traditional instructional approaches in thoracic surgery education. BMC Med Educ. 2022;22(1):447. Li Z, Cai X, Zhou K, Qin J, Zhang J, Yang Q, Yan F. Effects of BOPPPS combined with TBL in surgical nursing for nursing undergraduates: a mixed-method study. BMC Nurs. 2023;22(1):133. Liu XY, Lu C, Zhu H, Wang X, Jia S, Zhang Y, Wen H, Wang YF. Assessment of the effectiveness of BOPPPS-based hybrid teaching model in physiology education. BMC Med Educ. 2022;22(1):217. Zeng HL, Chen DX, Li Q, Wang XY. Effects of seminar teaching method versus lecture-based learning in medical education: A meta-analysis of randomized controlled trials. Med Teach. 2020;42(12):1343–9. Li Y, Li X, Liu Y, Li Y. Application effect of BOPPPS teaching model on fundamentals of nursing education: a meta-analysis of randomized controlled studies. Front Med (Lausanne). 2024;11:1319711. Li S, Liu Q, Guo S, Li Y, Chen F, Wang C, Wang M, Liu J, Liu X, Wang D, et al. Research on the application of the blended BOPPPS based on an online and offline mixed teaching model in the course of fermentation engineering in applied universities. Biochem Mol Biol Educ. 2023;51(3):244–53. Yusuf Mohamud MF, Mukhtar MS. Epidemiological characteristics, clinical relevance, and risk factors of thromboembolic complications among patients with COVID-19 pneumonia at A teaching hospital: Retrospective observational study. Ann Med Surg (Lond). 2022;77:103660. Wang Y, Li S, Zhou Q, Wang Y, Shi J. Vascular dementia has the highest hospitalisation rate in China: a nationwide hospital information system study. Stroke Vasc Neurol. 2023;8(1):59–68. Xu Z, Che X, Yang X, Wang X. Application of the hybrid BOPPPS teaching model in clinical internships in gynecology. BMC Med Educ. 2023;23(1):465. Boscardin CK, Gin B, Golde PB, Hauer KE. ChatGPT and Generative Artificial Intelligence for Medical Education: Potential Impact and Opportunity. Acad Med. 2024;99(1):22–7. Lee H. The rise of ChatGPT: Exploring its potential in medical education. Anat Sci Educ. 2024;17(5):926–31. Meng L, Liu X, Ni J, Shen P, Jiao F. An investigation for the efficacy of teaching model of combining virtual simulation and real experiment for clinical microbiology examination. Front Med (Lausanne). 2024;11:1255088. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 20 Aug, 2024 Editor assigned by journal 18 Aug, 2024 Submission checks completed at journal 16 Aug, 2024 First submitted to journal 08 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4882435","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342558095,"identity":"dab24d7c-fa1f-4c8f-b90f-ee1135d73428","order_by":0,"name":"Ziqing Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ziqing","middleName":"","lastName":"Yang","suffix":""},{"id":342558097,"identity":"ac1f2bd4-56cb-4d96-8781-70d714624c28","order_by":1,"name":"Siyu Zhen","email":"","orcid":"","institution":"The First Affiliated Hospital of Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Zhen","suffix":""},{"id":342558098,"identity":"153e4ab6-e994-4fd8-8459-e16ba5e49429","order_by":2,"name":"Ben Pan","email":"","orcid":"","institution":"The First Affiliated Hospital of Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Pan","suffix":""},{"id":342558099,"identity":"a2b72724-61ea-4dd7-baa8-456f24a91ca2","order_by":3,"name":"Hanyu Wei","email":"","orcid":"","institution":"The First Affiliated Hospital of Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hanyu","middleName":"","lastName":"Wei","suffix":""},{"id":342558101,"identity":"aad815e1-32dd-4baa-a111-664fc1fc03d4","order_by":4,"name":"Qiang Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Li","suffix":""},{"id":342558102,"identity":"acc3e19d-0535-476e-b3c9-01343d031df6","order_by":5,"name":"Junyan Yue","email":"","orcid":"","institution":"The First Affiliated Hospital of Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junyan","middleName":"","lastName":"Yue","suffix":""},{"id":342558103,"identity":"cbcc3e9a-95b1-49aa-952d-d50cf0144b77","order_by":6,"name":"Ying Hu","email":"","orcid":"","institution":"The First Affiliated Hospital of Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Hu","suffix":""},{"id":342558104,"identity":"ad4507e1-2b38-4d74-94b0-d0f5b8bf1b09","order_by":7,"name":"Changhua Liang","email":"data:image/png;base64,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","orcid":"","institution":"The First Affiliated Hospital of Xinxiang Medical University","correspondingAuthor":true,"prefix":"","firstName":"Changhua","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2024-08-08 16:24:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4882435/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4882435/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12909-025-07607-8","type":"published","date":"2025-07-02T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66544672,"identity":"93d75e62-ce97-4045-82b8-07ad34209b28","added_by":"auto","created_at":"2024-10-14 08:14:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59012,"visible":true,"origin":"","legend":"\u003cp\u003eResults of Questionnaire Survey: The Proportion of Students' Ratings for Each Question\u003c/p\u003e\n\u003cp\u003eNotes : The survey adopts Likert five-level scoring method (1, strongly disagree ; 2, disagree ; 3, undecided ; 4, agree ; 5, strongly agree .)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4882435/v1/99ecd06773f5d9ef38179d4d.png"},{"id":86179015,"identity":"d42ffca7-5f9e-4de6-b906-0c36d6d38838","added_by":"auto","created_at":"2025-07-07 16:14:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":755560,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4882435/v1/9ef428c2-9087-4bea-9f0e-538dc894771b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the use of the HIS-based BOPPPS teaching model in medical imaging experimental course instruction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe field of medical imaging makes use of imaging technology to identify diseases and direct their care. Medical imaging has evolved from low-resolution to high-resolution, from two-dimensional to three-dimensional, and from morphological to functional and metabolic imaging as a result of scientific and technological advancements. These images are now essential for early disease diagnosis, treatment response evaluation, and post-treatment follow-up [1]. The main objectives of medical imaging education are to improve students' diagnostic skills and provide the clinic with qualified imaging personnel. Under this approach, teaching medical imaging experimental courses is essential to helping students review and strengthen their theoretical understanding as well as develop their ability to read practical case studies. It plays a crucial role in helping medical imaging students enhance their clinical skills by serving as a bridge that tightly integrates academic knowledge with clinical case studies.\u003c/p\u003e\n\u003cp\u003eNonetheless, lectures and the distribution of theoretical knowledge are the main components of the traditional teaching model for medical imaging experimental courses [2]. The following three problems are raised by this model: Firstly, students adopt a passive part in their education. Under the traditional model, students merely receive information passively from teachers who take the lead in imparting theoretical knowledge. Students' interest and motivation decrease when the instructional material is frequently dull and the approach is inflexible and repetitive [3]. Secondly, there is a lack of close alignment between the integration of real clinical work and instruction. Real clinical context is sometimes absent from traditional education, which merely presents single pictures of common symptoms in common instances without adding any extra medical information. This constraint impedes the successful development of students' clinical reasoning abilities by restricting their diagnostic thinking and making it more difficult for them to comprehend the in-depth manifestations of disease imaging. Thirdly, it is unable to verify the caliber of imaging resources used in instruction. Presently, the majority of medical imaging courses still use the antiquated slide-based method of instruction, which comes with a danger of information loss and sluggish upgrades to the teaching materials. Students are consequently unable to witness entire medical image processing processes, including window width modification, window level adjustment, and three-dimensional reconstruction. This has an immediate effect on the image's clarity and ability to exhibit fine structures, which impedes students' ability to comprehend diseases in their whole and has a major negative impact on the standard of medical imaging course education.\u003c/p\u003e\n\u003cp\u003eHospital Information Systems (HIS) development has flourished with the advent of the big data era and the expansion of contemporary information technology. HIS serves as a vital resource in the medical field [4], providing essential support for various hospital operations. For medical research and teaching staff, the HIS system offers an extensive array of medical records, medical images, and the latest clinical data, thus significantly expanding research and teaching resources [5].\u003c/p\u003e\n\u003cp\u003eTo increase students' interest in and effectiveness from their education, educators have been constantly innovating teaching models in the last several years, including Problem-Based Learning (PBL) [6], Case-Based Learning (CBL) [7], and Multi-Disciplinary Team (MDT) collaboration [8-11]. When compared to conventional teaching techniques, these forms of instruction have specific benefits. These days, active student participation is emphasized more and more in instructional practices [12, 13]. The Instruction Skills Workshop International Advisory Committee in North America recommended the BOPPPS teaching model, which was developed in Canada in the late 1970s and is an efficient model for course design. This model breaks down a whole teaching process into six interconnected components: Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment and Summary. It emphasizes a student-centered, teacher-guided teaching philosophy. The BOPPPS model offers a workable approach to educational reform by giving a precise framework and reliable assurance for accomplishing learning objectives. It has been effectively used in the practical teaching of several disciplines, including the education of thoracic surgery education [14], surgical nursing education [15], and physiology education [16], yielding positive teaching outcomes. Nonetheless, there aren't many reports\u0026mdash;in China or elsewhere\u0026mdash;of the use of the BOPPPS model in medical imaging experimental course instruction for undergraduate students pursuing a five-year degree in medical imaging. This study aims to evaluate the feasibility and effectiveness of the HIS-based BOPPPS model in the teaching of the medical imaging experimental course for the 117 five-year medical imaging undergraduate students undergoing internships at our university.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eGeneral Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e117 medical imaging interns from Xinxiang Medical University\u0026apos;s First Affiliated Hospital who were enrolled in the 2021\u0026ndash;2022 academic year were chosen. The students were divided into two groups: the first group was made up of students from classes 1 and 2, while the second group was made up of students from classes 3 and 4. Information was collected about the students\u0026apos; gender, age, prior semester academic performance, and professional interests. The textbook \u0026quot;Medical Imaging Diagnosis\u0026quot; (Han Ping, Yu Chunshui, People\u0026apos;s Medical Publishing House, 4th edition) served as the foundation for the medical imaging experimental course. For both student groups, the same chapters were taught by the same professors, all of whom had at least five years of teaching experience.The research design was approved by the Ethics Committee of the First Affiliated Hospital of Xinxiang Medical University (approval number: EC-024-408). Informed consent was waived by the Institutional Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first semester comprised 60 hours each of theory and practical classes, while the second semester comprised 52 hours each of theory and practical classes. In the first semester, the first group of students received instruction using the BOPPPS teaching model based on the Hospital Information System (HIS), while the second group received instruction using the traditional teaching model. In the second semester, the teaching models for the two groups were switched. This prospective study aimed to analyze the feasibility and effectiveness of the BOPPPS teaching model based on the HIS system in the teaching of medical imaging experimental courses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTraditional Teaching Model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs part of this teacher-centered approach, students are encouraged to pieview pertinent reference materials or textbooks before class. Using slides, the instructor explains in class the common imaging manifestations and differential diagnosis of various diseases according to the curriculum\u0026apos;s criteria. In addition to taking notes and participating in a post-class review of pertinent material, students pay attention to the lecture.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe BOPPPS teaching model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe teacher of the medical imaging experimental course creates learning objectives based on the curriculum\u0026apos;s requirements and students\u0026apos; needs. Then, the teacher arranges the related teaching materials in accordance with the six BOPPPS model components. The following are the specific methods of implementation:\u003c/p\u003e\n\u003cp\u003eIntroduction: The objective is to draw students\u0026rsquo;\u0026nbsp;attention, stimulate their interest in the upcoming core teaching material, and stimulate their curiosity and thirst for knowledge. This can be achieved by using visual aids such as charts, movies, or real-world statistics to highlight the importance of the course subject in a brief introduction. Alternatively, it could involve going over previously taught material with students or posing stimulating questions to ease them into the main teaching content. Creating links between the new content and the students\u0026apos; past knowledge is another aspect of it.\u003c/p\u003e\n\u003cp\u003eLearning Objectives: Creating learning objectives necessitates carefully weighing the qualities of the material being taught, as well as the requirements and skills of the students. This gives teachers the freedom to create teaching plans and use efficient teaching techniques, all the while allowing students to know exactly where their learning is going. The degree to which specified learning objectives are met can also be used to evaluate the effectiveness of learning.\u003c/p\u003e\n\u003cp\u003ePre-assessment: Teachers can better synchronize the depth and progression of their teaching content by having a better awareness of the fundamental knowledge levels of their students. Reviewing preliminary knowledge aids students in achieving a favorable learning environment more rapidly. Pre-assessment can be carried out using a variety of methods, including questioning, discussions, questionnaires, and pre-reading achievement displays, depending on the particular teaching subject.\u003c/p\u003e\n\u003cp\u003eParticipatory Learning: This is the foundation of the BOPPPS model, which emphasizes student-centered learning with teachers acting as mentors. To encourage students to actively participate in class activities, teachers are encouraged to use a variety of flexible teaching methods and tools, such as CBL, PBL, TBL, flipped classrooms, etc., depending on the learning objectives and material. Students are led to participate in group discussions and idea sharing when teaching medical imaging experiments, culminating in a comprehensive analysis of a typical imaging case.\u003c/p\u003e\n\u003cp\u003ePost-assessment: Using targeted assessments, the teacher determines at the end of the class whether or not the students have mastered the information points and have met the predetermined learning goals. Depending on the particular course material, a variety of techniques can be employed, including questioning, multiple-choice questions, true/false questions, and on-site demonstrations.\u003c/p\u003e\n\u003cp\u003eSummary: The teacher can conclude by summarizing the most important and difficult aspects learned, giving students homework to complete after class to further solidify and broaden their understanding, or quickly assessing how well the learning objectives were met before establishing new guidelines and expectations for the following session. As an alternative, the synopsis can include a preview at the material covered in the upcoming class and a list of prerequisites. Along with contributing to the summary, students can also reflect on their learning and areas for growth, facilitate post-class review and consolidation, summarize and synthesize important and difficult concepts, and improve the effectiveness of their learning.\u003c/p\u003e\n\u003cp\u003eAs an example, the specific teaching content design for the section on pulmonary inflammation in this course is detailed in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1 The Specific Operations of the BOPPPS Teaching Model\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"590\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.423728813559322%\" valign=\"top\"\u003e\n \u003cp\u003eSegment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.88135593220339%\" valign=\"top\"\u003e\n \u003cp\u003eTeacher Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.11864406779661%\" valign=\"top\"\u003e\n \u003cp\u003eStudent Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.576271186440678%\" valign=\"top\"\u003e\n \u003cp\u003eApproach\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.423728813559322%\" valign=\"top\"\u003e\n \u003cp\u003eIntroduction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.88135593220339%\" valign=\"top\"\u003e\n \u003cp\u003eBy using HIS to dynamically show typical cases, instructors can help students think critically by asking them to choose the diagnosis based on the patient\u0026apos;s clinical and imaging presentations.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.11864406779661%\" valign=\"top\"\u003e\n \u003cp\u003eStudents can think about the patient\u0026apos;s imaging abnormalities, and learn with questions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.576271186440678%\" valign=\"top\"\u003e\n \u003cp\u003eCase presentation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.423728813559322%\" valign=\"top\"\u003e\n \u003cp\u003eLearning Objectives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.88135593220339%\" valign=\"top\"\u003e\n \u003cp\u003eCognitive objectives: Gain an understanding of the clinical presentations, pathological features, and imaging manifestations of pulmonary inflammation. (Purpose and Focus)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSkill Objectives: Familiarize with writing pulmonary inflammation imaging reports.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAffective Objectives: Develop the ability of clinical reasoning and cooperative and united professional qualities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.11864406779661%\" valign=\"top\"\u003e\n \u003cp\u003eStudents can clearly define the key and difficult points of the classroom content and learning objectives.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.576271186440678%\" valign=\"top\"\u003e\n \u003cp\u003eSlides presentation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.423728813559322%\" valign=\"top\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.88135593220339%\" valign=\"top\"\u003e\n \u003cp\u003eQuestion: what are the types of pulmonary inflammation? What are the common pathogens that cause pulmonary inflammation? What is the preferred imaging examination for diagnosing pulmonary inflammation?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.11864406779661%\" valign=\"top\"\u003e\n \u003cp\u003eInteractive discussion, answering questions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.576271186440678%\" valign=\"top\"\u003e\n \u003cp\u003eQ\u0026amp;A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.423728813559322%\" valign=\"top\"\u003e\n \u003cp\u003eParticipatory Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.88135593220339%\" valign=\"top\"\u003e\n \u003cp\u003eChoosing typical cases from the HIS system in accordance with the teaching plans, assisting students in learning and discussing in groups, and monitoring and correcting their thinking during the class. Lastly, giving thorough explanations and case summaries; evaluating and standardizing the diagnostic report writing; and assessing the differential diagnosis approach.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.11864406779661%\" valign=\"top\"\u003e\n \u003cp\u003eIntegrating clinical manifestations and laboratory examinations to consider the cases: What are the typical imaging signs? What are the pathological reasons for these signs? Are these signs seen in other diseases, and how to differentiate them? Finally, sharing and discussing the results and diagnostic reports in groups, self-assessing as a group, and conducting intra-group and inter-group evaluations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.576271186440678%\" valign=\"top\"\u003e\n \u003cp\u003eCase presentation and group discussions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.423728813559322%\" valign=\"top\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.88135593220339%\" valign=\"top\"\u003e\n \u003cp\u003eMultiple choice questions:\u003c/p\u003e\n \u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eWhat is the most common type of pneumonia in bacterial pneumonia?\u003c/li\u003e\n \u003cli\u003eWhich of the following descriptions is a characteristic imaging feature of lobar pneumonia?\u003c/li\u003e\n \u003cli\u003eThe typical imaging manifestations of lobar pneumonia correspond to which pathological stage?\u003c/li\u003e\n \u003cli\u003eThe main X-ray manifestation of bronchopneumonia is?\u003c/li\u003e\n \u003cli\u003eThe best imaging examination for interstitial lung disease is?\u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.11864406779661%\" valign=\"top\"\u003e\n \u003cp\u003eindependent thinking and answering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.576271186440678%\" valign=\"top\"\u003e\n \u003cp\u003eQuestionnaire survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.423728813559322%\" valign=\"top\"\u003e\n \u003cp\u003eSummary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.88135593220339%\" valign=\"top\"\u003e\n \u003cp\u003eEncouraging and guiding students to summarize and consolidate the knowledge learned in this class, and analyzing the achievement of the teaching objectives for this session.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.11864406779661%\" valign=\"top\"\u003e\n \u003cp\u003eAnalyzing the accomplishment of the session\u0026apos;s learning objectives and supporting and assisting students in synthesizing and consolidating the material they have learned in this class. evaluating and consolidating after class, reflecting on one\u0026apos;s own successes and failures in this class, and revisiting the material covered in class.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.576271186440678%\" valign=\"top\"\u003e\n \u003cp\u003eQ\u0026amp;A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffectiveness Evaluation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExams and anonymous questionnaire surveys are used to evaluate the BOPPPS teaching model\u0026apos;s efficacy and student satisfaction after the course.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGrades are a significant and direct indicator of students\u0026apos; knowledge mastery as well as a significant parameter for gauging the quality of education\u0026nbsp;[17]. They may serve as a gauge of students\u0026apos; degree of knowledge and accumulating capacity. A three-hour, closed-book exam is given at the conclusion of each semester; the exam questions are based on the teaching outline. One point is awarded for each multiple-choice question on the final test, which has a total score of 100 points, with 50 points allocated to multiple-choice questions. The purpose of the multiple-choice questions is to assess students\u0026apos; theoretical understanding of different imaging systems. Furthermore, case reading questions take up 50 points (5 points a question), which primarily evaluate students\u0026apos; clinical reasoning and image reading abilities.\u003c/p\u003e\n\u003cp\u003eIn addition to exam results, students\u0026apos; genuine attitudes regarding the learning process are seen to be essential for reflecting the caliber and efficacy of the teaching. Both student groups took part in an anonymous questionnaire survey at the conclusion of the academic year; the results are shown in Table 2. The questionnaire focused mainly on topics like learning efficiency (i.e., whether the introduction attracted attention and allowed for a swift entry into the learning state, whether the learning objectives were clear and concise, whether pre- and post-tests helped consolidate important knowledge, and whether participatory learning improved classroom learning efficiency), learning interest(i.e., whether participatory learning increased interest in learning), image reading ability(i.e., whether retrieving images from the HIS system improved image reading ability), clinical thinking ability(i.e., whether retrieving images from the HIS system enhanced clinical thinking ability), course satisfaction(i.e., whether the BOPPPS teaching model\u0026apos;s effectiveness and the classroom environment were highly satisfactory), and learning burden(i.e., whether the BOPPPS teaching model increased the learning burden.)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter 117 valid questionnaires were collected out of a total of 117 that were distributed, there was a 100% response rate. The valid questionnaires were then analyzed. (The Likert five-level rating method was used as the evaluation standard, with values ranging from 1 to 5, meaning \u0026quot;strongly disagree\u0026quot; to \u0026quot;strongly agree\u0026quot;).\u003c/p\u003e\n\u003cp\u003eTable 2: Questionnaire Survey\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was conducted using SPSS 27.0. For normally distributed quantitative data, the results are presented as mean \u0026plusmn; standard deviation (x \u0026plusmn; s) and analyzed using t-tests. For non-normally distributed data, median and quartiles [M(Q1, Q3)] were used, and Z-tests were conducted. Count data are presented as number ［n(%)］and compared using the chi-squared test, with a significance level set at P\u0026lt;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic Data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 117 senior medical imaging students were included in this study, comprising 80 female students and 37 male students. The students\u0026apos; level of interest in the medical imaging profession was categorized as strong, moderate, or lacking. The results are shown in Table 3. There were no statistically significant differences between the two groups of students in terms of gender, age, previous semester\u0026apos;s professional course grades, and professional interests (P \u0026gt; 0.05). The baseline data of the two groups were comparable.\u003c/p\u003e\n\u003cp\u003eTable 3: Comparison of General Data between the Two Groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.254180602006688%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.066889632107024%\" valign=\"top\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.889632107023413%\" valign=\"top\"\u003e\n \u003cp\u003ex2/t/z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.254180602006688%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd width=\"20.066889632107024%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd width=\"16.889632107023413%\" valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.254180602006688%\" valign=\"top\"\u003e\n \u003cp\u003eMale, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003e18(30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.066889632107024%\" valign=\"top\"\u003e\n \u003cp\u003e19(32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.889632107023413%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.254180602006688%\" valign=\"top\"\u003e\n \u003cp\u003eFemale, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003e41(69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.066889632107024%\" valign=\"top\"\u003e\n \u003cp\u003e39(67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.889632107023413%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.254180602006688%\" valign=\"top\"\u003e\n \u003cp\u003eMean Age\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003e22(22,23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.066889632107024%\" valign=\"top\"\u003e\n \u003cp\u003e22(22,23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.889632107023413%\" valign=\"top\"\u003e\n \u003cp\u003e-0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.254180602006688%\" valign=\"top\"\u003e\n \u003cp\u003eScores of professional courses of last semester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003e74(70,78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.066889632107024%\" valign=\"top\"\u003e\n \u003cp\u003e74.50(72,77.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.889632107023413%\" valign=\"top\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.254180602006688%\" valign=\"top\"\u003e\n \u003cp\u003eInterest in Specialty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd width=\"20.066889632107024%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd width=\"16.889632107023413%\" valign=\"top\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.254180602006688%\" valign=\"top\"\u003e\n \u003cp\u003eStrong, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003e40(67.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.066889632107024%\" valign=\"top\"\u003e\n \u003cp\u003e38(65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.889632107023413%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.254180602006688%\" valign=\"top\"\u003e\n \u003cp\u003eModerate , n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003e16(57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.066889632107024%\" valign=\"top\"\u003e\n \u003cp\u003e18(31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.889632107023413%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.254180602006688%\" valign=\"top\"\u003e\n \u003cp\u003elacking, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\n \u003cp\u003e3(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.066889632107024%\" valign=\"top\"\u003e\n \u003cp\u003e2(3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.889632107023413%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd width=\"18.39464882943144%\" valign=\"top\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eEnd-of-Term Examination Results\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe end-of-term exam results of two student groups from the same semester as well as the same group of students from other semesters were compared in order to assess the efficacy of the BOPPPS teaching model. Scores for case reading questions, multiple-choice questions, and the overall end-of-term exam were all included in the analysis. Significant differences were found between the two groups\u0026apos; case reading scores and overall scores in the first and second semesters (p\u0026lt;0.05), as indicated by the results displayed in Tables 4 and 5. Specifically, in the first semester, the first group of students received much higher scores overall in the case reading questions (case reading score 39.27\u0026plusmn;\u0026nbsp;3.39 vs. 35.31\u0026plusmn;2.77, total score 77.47\u0026plusmn;\u0026nbsp;6.61 vs. 74.33\u0026plusmn;4.17), whereas in the second semester, the second group of students performed better in the same aspects as the first group(case reading score 35.47\u0026plusmn;3.15 vs. 39.79\u0026plusmn;3.45, total score 74.53\u0026plusmn;5.68 vs. 78.36\u0026plusmn;\u0026nbsp;5.11). Furthermore, there were statistically significant differences in the average scores for case reading questions and total scores for the same group of students in different semesters (p\u0026lt;0.05). However, no statistically significant differences were identified in the comparison of multiple-choice question scores or theoretical scores between the groups.\u003c/p\u003e\n\u003cp\u003eTable 4: Comparison of End-of-Term Scores between Different Groups in the Same Semester\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"759\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.631093544137023%\" rowspan=\"2\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.866930171278%\" colspan=\"4\"\u003e\n \u003cp\u003eFirst Semester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.503293807641634%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.998682476943344%\" colspan=\"5\"\u003e\n \u003cp\u003eSecond Semester\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.737704918032787%\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.934426229508198%\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.344262295081966%\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.344262295081966%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1147540983606556%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.573770491803279%\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.934426229508198%\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.344262295081966%\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.508196721311476%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.16393442622950818%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.631093544137023%\" valign=\"top\"\u003e\n \u003cp\u003eTheoretical Scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.648221343873518%\" valign=\"top\"\u003e\n \u003cp\u003e38.20\u0026plusmn;4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.198945981554678%\" valign=\"top\"\u003e\n \u003cp\u003e39.02\u0026plusmn;2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.509881422924901%\" valign=\"top\"\u003e\n \u003cp\u003e-1.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.509881422924901%\" valign=\"top\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.503293807641634%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.516469038208168%\" valign=\"top\"\u003e\n \u003cp\u003e39.05\u0026plusmn;3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.198945981554678%\" valign=\"top\"\u003e\n \u003cp\u003e38.57\u0026plusmn;3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.509881422924901%\" valign=\"top\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.64163372859025%\" valign=\"top\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.13175230566534915%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.631093544137023%\" valign=\"top\"\u003e\n \u003cp\u003eImage Reading Scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.648221343873518%\" valign=\"top\"\u003e\n \u003cp\u003e39.27\u0026plusmn;3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.198945981554678%\" valign=\"top\"\u003e\n \u003cp\u003e35.31\u0026plusmn;2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.509881422924901%\" valign=\"top\"\u003e\n \u003cp\u003e6.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.509881422924901%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.503293807641634%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.516469038208168%\" valign=\"top\"\u003e\n \u003cp\u003e35.47\u0026plusmn;3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.198945981554678%\" valign=\"top\"\u003e\n \u003cp\u003e39.79\u0026plusmn;3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.509881422924901%\" valign=\"top\"\u003e\n \u003cp\u003e-7.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.64163372859025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.13175230566534915%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.631093544137023%\" valign=\"top\"\u003e\n \u003cp\u003eTotal Scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.648221343873518%\" valign=\"top\"\u003e\n \u003cp\u003e77.47\u0026plusmn;6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.198945981554678%\" valign=\"top\"\u003e\n \u003cp\u003e74.33\u0026plusmn;4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.509881422924901%\" valign=\"top\"\u003e\n \u003cp\u003e3.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.509881422924901%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.503293807641634%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.516469038208168%\" valign=\"top\"\u003e\n \u003cp\u003e74.53\u0026plusmn;5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.198945981554678%\" valign=\"top\"\u003e\n \u003cp\u003e78.36\u0026plusmn;5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.509881422924901%\" valign=\"top\"\u003e\n \u003cp\u003e-3.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.64163372859025%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.13175230566534915%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"754\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.76127320954907%\" rowspan=\"2\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.06366047745358%\" colspan=\"4\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.519893899204244%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.6551724137931%\" colspan=\"4\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.016501650165017%\"\u003e\n \u003cp\u003eFirst Semester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.521452145214521%\"\u003e\n \u003cp\u003eSecond Semester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.58085808580858%\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.24092409240924%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.1353135313531353%\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.016501650165017%\"\u003e\n \u003cp\u003eFirst Semester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.676567656765677%\"\u003e\n \u003cp\u003eSecond Semester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.405940594059405%\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.405940594059405%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.735099337748345%\" valign=\"top\"\u003e\n \u003cp\u003eTheoretical Scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.052980132450331%\" valign=\"top\"\u003e\n \u003cp\u003e38.20\u0026plusmn;4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e39.05\u0026plusmn;3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.887417218543046%\" valign=\"top\"\u003e\n \u003cp\u003e-1.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.417218543046357%\" valign=\"top\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5165562913907285%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.052980132450331%\" valign=\"top\"\u003e\n \u003cp\u003e39.02\u0026plusmn;2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\" valign=\"top\"\u003e\n \u003cp\u003e38.57\u0026plusmn;3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.549668874172186%\" valign=\"top\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.549668874172186%\" valign=\"top\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.735099337748345%\" valign=\"top\"\u003e\n \u003cp\u003eImage Reading Scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.052980132450331%\" valign=\"top\"\u003e\n \u003cp\u003e39.27\u0026plusmn;3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e35.47\u0026plusmn;3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.887417218543046%\" valign=\"top\"\u003e\n \u003cp\u003e6.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.417218543046357%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5165562913907285%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.052980132450331%\" valign=\"top\"\u003e\n \u003cp\u003e35.31\u0026plusmn;2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\" valign=\"top\"\u003e\n \u003cp\u003e39.79\u0026plusmn;3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.549668874172186%\" valign=\"top\"\u003e\n \u003cp\u003e-7.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.549668874172186%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.735099337748345%\" valign=\"top\"\u003e\n \u003cp\u003eTotal Scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.052980132450331%\" valign=\"top\"\u003e\n \u003cp\u003e77.47\u0026plusmn;6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e74.53\u0026plusmn;5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.887417218543046%\" valign=\"top\"\u003e\n \u003cp\u003e2.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.417218543046357%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5165562913907285%\" valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.052980132450331%\" valign=\"top\"\u003e\n \u003cp\u003e74.33\u0026plusmn;4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.582781456953642%\" valign=\"top\"\u003e\n \u003cp\u003e78.36\u0026plusmn;5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.549668874172186%\" valign=\"top\"\u003e\n \u003cp\u003e-4.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.549668874172186%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5: Comparison of End-of-Term Scores in Different Semesters within the Same Group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuestionnaire Survey\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the questionnaire survey are shown in Figure 1. Over 80% of the students rated questions 1-9 with a score of 4 or 5, indicating that students have a high evaluation of the BOPPPS teaching model in terms of learning efficiency, interest, clinical thinking ability, and course satisfaction. However, students also perceived some drawbacks of this new teaching model. For question 10, 48 students rated it with a score of 3-5 (41.0%), 51 students rated it with a score of 2 (43.6%), and 18 students rated it with a score of 1 (15.4%), indicating that the majority of students believe that the BOPPPS teaching model has increased the learning burden to some extent.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRequirements for effective teaching include high-quality content and well-designed instruction, which have a direct impact on the effectiveness and efficiency of education. In the context of modern medical education, the traditional, single-experiment teaching model has become outdated and is ill-suited to the demands of the digital information age. To improve the overall quality of education, it is imperative to investigate teaching models that are in line with modern features and the practical aspects of medical imaging in order to improve the design of medical imaging experimental teaching. This study employs the BOPPPS teaching model to instruct intern students of the class of 2018 majoring in medical imaging at the First Affiliated Hospital of Xinxiang Medical University. It is based on the utilization of the Hospital Information System (HIS) to acquire typical clinical cases.\u003c/p\u003e \u003cp\u003eAccording to our study's findings, the group that used the BOPPPS teaching model outperformed the group that used the traditional teaching model in terms of overall final exam results and case reading, with the difference being statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This highlights the efficacy of the teaching model and is consistent with the findings of Hu et al.'s comparison study between the BOPPPS model and conventional teaching techniques in thoracic surgery education [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. According to their study, the experimental group that used the BOPPPS teaching model scored much higher on exams than the control group, indicating the model's beneficial effects on clinical teaching in thoracic surgery. Additionally, it was noted that the BOPPPS teaching model group and the standard teaching model group did not differ statistically significantly in their answers to multiple-choice questions. Consequently, it can be deduced that the BOPPPS teaching model group's exceptional performance in obtaining higher scores was due to their mastery of case-reading, even though there was no appreciable improvement in their retention of theoretical professional knowledge. This observation can be connected to the traditional educational approaches' focus on memorizing of theoretical information.\u003c/p\u003e \u003cp\u003eAccording to the survey results, students gave the BOPPPS teaching model high ratings for effectiveness in teaching, interest in learning, capacity for clinical reasoning, and course satisfaction. These results are in line with those of Li et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], who carried out a meta-analysis of randomized controlled trials (RCT) related to the BOPPPS teaching model in \"Fundamentals of Nursing\" education and found that students were highly satisfied with the model. Students did, nevertheless, indicate that the BOPPPS teaching approach added to their workload. Chinese students are used to traditional \"spoon-feeding\" offline teaching methods, which merely require them to attend lectures and transfer relevant information to memory. As opposed to this, the BOPPPS model places greater pressure on them by requiring them to start with self-learning and providing more in-depth course material. However, in our view, pressure may not always end in an adverse outcome; in fact, it can be a motivating factor for improving college students' capacity for independent study. To eliminate \"easy courses\" and create \"quality courses,\" higher education needs to increase academic challenges, the difficulty of course content, and the proper workload for students.\u003c/p\u003e \u003cp\u003eTherefore, teaching students how to adapt to this pressure will be a challenge for teachers. At the same time, it also needs to strengthen the supervision and management of extra-curricular learning, and participatory learning is more time-consuming than traditional lecture-based learning. In order to ensure the teaching capacity and depth under the condition of limited class hours, online learning can be conducted before and after class to consolidate and review the basic knowledge learned in theoretical classes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe ultimate objective of teaching is to help students develop their capacity to use basic theoretical knowledge to address actual clinical problems\u0026mdash;far more significant than simply having them comprehend the material. Teachers use the Hospital Information System's (HIS) resources to help students use their theoretical knowledge to undertake in-depth case analyses. The acquisition of typical cases from the HIS system presents many benefits in the context of medical imaging experiments. First of all, this gives students the ability to dynamically and continuously display image data and carry out different post-processing tasks based on diagnostic requirements. They can, for instance, modify window level and breadth, measure lesion size, volume, and density with tools, perform two- and three-dimensional reconstructions, conduct follow-up comparison of lesions, and adjust image size for best display. In conclusion, students may more clearly perceive picture features, think more deeply, and improve the quality of imaging teaching materials owing to the HIS system. Second, the HIS system offers more comprehensive teaching resources for medical imaging experiment teaching. Students can access a variety of diagnostic and treatment documents, including electronic medical records, pathology reports, laboratory test results, and medical imaging, for the same patient through the HIS system[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Students can now more effectively combine pathological, clinical, and imaging data, bringing them closer to real-world clinical work. This develops students' capacity for clinical reasoning, broadens their comprehension of medical imaging, and enhances their ability to identify and diagnose diseases. Last but not least, the ability to export teaching examples as Word or Excel documents immediately improves the caliber and productivity of creating courseware.\u003c/p\u003e \u003cp\u003eOne of the main components of the BOPPPS teaching model is active learning. Conventional one-way course lectures might only allow for a shallow memory of the material. Prioritizing student participation is essential to achieving a better comprehension of the material since it gives them a sense of control over the classroom, makes deep engagement enjoyable, and improves their understanding of fundamental concepts. Although the BOPPPS approach greatly improves the quality of teaching in medical imaging studies, it is crucial to avoid formalizing teaching methods when implementing it into practice. While following the BOPPPS teaching model, teachers should incorporate their wealth of teaching expertise into their regular teaching activities. Based on the learning objectives of the course and the demands of the teaching content, they should be adaptable in their modifications to the teaching designs. This methodology will foster a dynamic and engaging learning environment, enable fruitful communication between teachers and students, and guarantee that students may fully engage in the educational process. Additionally, some studies have used a hybrid BOPPPS teaching strategy, which includes uploading teaching resources online and integrating online teaching platforms to make it easier to carry out in-class activities and post-test reviews [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. With the advancement of science and technology, artificial intelligence has made a significant impact on various industries, including healthcare and education [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Smart visual aids and realistic scenario simulations could be used in the future to further improve the efficacy of teaching.\u003c/p\u003e \u003cp\u003eThe BOPPPS teaching model, based on the Hospital Information System (HIS), is designed to be student-centered, goal-oriented, technology-driven, and grounded in comprehensive clinical data. It emphasizes interactive teaching and reflection, guiding students to seek knowledge proactively and explore consciously, thereby enhancing their overall qualities and fostering modern, practical talents in the field of medical imaging. This model serves as a valuable complement and enhancement to traditional medical imaging experimental teaching, effectively addressing inherent issues in traditional teaching methods. Our study is not without limits, however. First, a small sample size and a single center were used for the study. Potential future research directions include large-scale, multicenter validation studies conducted in various institutions and areas to confirm the efficacy of this teaching model. Second, there are potential biases in learning performance because the current study only evaluated final exam results; pre- and post-class testing that measure learning outcomes were absent. Finally, no comparison of the BOPPPS teaching approach with other methods was made in this study. Subsequent investigations could create distinct studies to evaluate various teaching models.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we utilized the HIS-based BOPPPS teaching model to perform teaching practices for medical imaging experimental courses. We also summarized a standardized teaching approach for these courses utilizing the HIS system and the BOPPPS teaching model. The results of this study demonstrate how this teaching model has enhanced students' focus and efficiency during learning, providing an effective way for medical imaging students to further their professional development and clinical practice abilities. This teaching model has the potential to yield fresh perspectives and serve as a guide for instructing experimental medical imaging \u003cb\u003eAcknowledgements\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe authors thank all the participating medical teachers and students. We would like to express our gratitude to them for their assistance.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThe research design was approved by the Ethics Committee of the First Affiliated Hospital of Xinxiang Medical University (approval number: EC-024-408). Informed consent was waived by the Institutional Review Board.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthor details\u003c/h2\u003e \u003cp\u003eThe First Affiliated Hospital of Xinxiang Medical University, Wei Hui 453100.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was funded by the Medical Education Research Project of Henan Province wjlx202136.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.Y. contributed to conceptualization, methodology, survey design, statistical analysis ,results and writing; S.Z. contributed to conceptualization, methodology, statistical analysis ,results and funding acquisition; B.P. and H.W. contributed to methodology, data collection, statistical analysis and results; Q.L. contributed to survey design, ethics application and data collection; J.Y. and Y.H. contributed to methodology, survey design and writing -reviewing; C.L. contributed to project administration, funding acquisition, writing -reviewing.All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch3\u003eAuthors’contributions\u003c/h3\u003e\n\u003cp\u003eZ.Y. contributed to conceptualization, methodology, survey design, statistical analysis ,results and writing; S.Z. contributed to conceptualization, methodology, statistical analysis ,results and funding acquisition; B.P. and H.W. contributed to methodology, data collection, statistical analysis and results; Q.L. contributed to survey design, ethics application and data collection; J.Y. and Y.H. contributed to methodology, survey design and writing -reviewing; C.L. contributed to project administration, funding acquisition, writing -reviewing.All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGiardino A, Gupta S, Olson E, Sepulveda K, Lenchik L, Ivanidze J, Rakow-Penner R, Patel MJ, Subramaniam RM, Ganeshan D. Role of Imaging in the Era of Precision Medicine. Acad Radiol. 2017;24(5):639\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao J, Yang L, Zou J, Fan X. Comparison of the influence of massive open online courses and traditional teaching methods in medical education in China: A meta-analysis. Biochem Mol Biol Educ. 2021;49(4):639\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi Y, Zhao P, Bai R, Shao Y, Pan S, Hou Y. Teaching diagnostic radiology to radiology majors: Implementation and evaluation of a flipped classroom model. Med Teach 2024:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBehnam F, Khajouei R, Nabizadeh AH, Saedi S, Ghaemi MM. Usability evaluation of emergency information systems in educational hospitals in Kerman, Iran. BMC Med Inf Decis Mak. 2023;23(1):277.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Wu J, Yang J, Chen SS, Liu JP, Zhang P, Chu J, Luo CL. Development and Application Evaluation of a Nursing Simulation Teaching Information System Based on Hospital Information Systems. Int J Clin Pract. 2023;2023:6334967.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu H. Enhancing creative cognition through project-based learning: An in-depth scholarly exploration. Heliyon. 2024;10(6):e27706.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerrer Valencia V, Rivera Gonzales H, Ortiz Llin\u0026aacute;s JR. Cardona Orteg\u0026oacute;n JD: Case-Based Learning: An Immersive and Effective Educational Approach. Acad Radiol. 2023;30(11):2797.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoehr F, Yang Y, M\u0026uuml;ller L, Gerstenmeier P, Pinto Dos Santos D, Dietz P, Weimer A, Ludwig M, Kloeckner R, Weimer JM. A blended learning approach for teaching thoracic radiology to medical students: a proof-of-concept study. Front Med (Lausanne). 2023;10:1272893.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVavasseur A, Muscari F, Meyrignac O, Nodot M, Dedouit F, Revel-Mouroz P, Dercle L, Rozenblum L, Wang L, Maulat C, et al. Blended learning of radiology improves medical students' performance, satisfaction, and engagement. Insights Imaging. 2020;11(1):61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao PF, Gao Y, Xie SH, Qiao PF. Effect of multi-disciplinary-cooperation-mode, case-based-study and teaching-picture-archiving-communicating-system multi-mode joint teaching mode in medical imaging teaching. Asian J Surg. 2021;44(6):930\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVall\u0026eacute;e A, Blacher J, Cariou A, Sorbets E. Blended Learning Compared to Traditional Learning in Medical Education: Systematic Review and Meta-Analysis. J Med Internet Res. 2020;22(8):e16504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoehr F, M\u0026uuml;ller L, Brady AP, Catalano C, Mildenberger P, M\u0026auml;hringer-Kunz A, Hahn F, D\u0026uuml;ber C, Celik E, Diehl E, et al. Online teaching in radiology as a pilot model for modernizing medical education: results of an international study in cooperation with the ESR. Insights Imaging. 2021;12(1):141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa X, Zeng D, Wang J, Xu K, Li L. Effectiveness of bridge-in, objective, pre-assessment, participatory learning, post-assessment, and summary teaching strategy in Chinese medical education: A systematic review and meta-analysis. Front Med (Lausanne). 2022;9:975229.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu K, Ma RJ, Ma C, Zheng QK, Sun ZG. Comparison of the BOPPPS model and traditional instructional approaches in thoracic surgery education. BMC Med Educ. 2022;22(1):447.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z, Cai X, Zhou K, Qin J, Zhang J, Yang Q, Yan F. Effects of BOPPPS combined with TBL in surgical nursing for nursing undergraduates: a mixed-method study. BMC Nurs. 2023;22(1):133.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu XY, Lu C, Zhu H, Wang X, Jia S, Zhang Y, Wen H, Wang YF. Assessment of the effectiveness of BOPPPS-based hybrid teaching model in physiology education. BMC Med Educ. 2022;22(1):217.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng HL, Chen DX, Li Q, Wang XY. Effects of seminar teaching method versus lecture-based learning in medical education: A meta-analysis of randomized controlled trials. Med Teach. 2020;42(12):1343\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Li X, Liu Y, Li Y. Application effect of BOPPPS teaching model on fundamentals of nursing education: a meta-analysis of randomized controlled studies. Front Med (Lausanne). 2024;11:1319711.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Liu Q, Guo S, Li Y, Chen F, Wang C, Wang M, Liu J, Liu X, Wang D, et al. Research on the application of the blended BOPPPS based on an online and offline mixed teaching model in the course of fermentation engineering in applied universities. Biochem Mol Biol Educ. 2023;51(3):244\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYusuf Mohamud MF, Mukhtar MS. Epidemiological characteristics, clinical relevance, and risk factors of thromboembolic complications among patients with COVID-19 pneumonia at A teaching hospital: Retrospective observational study. Ann Med Surg (Lond). 2022;77:103660.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Li S, Zhou Q, Wang Y, Shi J. Vascular dementia has the highest hospitalisation rate in China: a nationwide hospital information system study. Stroke Vasc Neurol. 2023;8(1):59\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Z, Che X, Yang X, Wang X. Application of the hybrid BOPPPS teaching model in clinical internships in gynecology. BMC Med Educ. 2023;23(1):465.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoscardin CK, Gin B, Golde PB, Hauer KE. ChatGPT and Generative Artificial Intelligence for Medical Education: Potential Impact and Opportunity. Acad Med. 2024;99(1):22\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee H. The rise of ChatGPT: Exploring its potential in medical education. Anat Sci Educ. 2024;17(5):926\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng L, Liu X, Ni J, Shen P, Jiao F. An investigation for the efficacy of teaching model of combining virtual simulation and real experiment for clinical microbiology examination. Front Med (Lausanne). 2024;11:1255088.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"BOPPPS, medical imaging, HIS, traditional teaching, teaching model","lastPublishedDoi":"10.21203/rs.3.rs-4882435/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4882435/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe efficacy of traditional teaching medical imaging experimental courses is not optimal due to a number of flaws. This study's main goal was to find out how well the Bridge-In, Outcomes, Pre-Assessment, Participatory Learning, Post-Assessment, and Summary (BOPPPS) teaching model, which is based on the Hospital Information System (HIS), works when teaching medical imaging experimental courses to undergraduate students pursuing five years of medical imaging education.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e117 medical imaging students who were interning at the First Affiliated Hospital of Xinxiang Medical University in the academic year 2021\u0026ndash;2022 made up the research subjects. During the first semester, the first group was instructed using the BOPPPS teaching model based on HIS, while the second group was instructed using the standard teaching model. The two student groups swapped instructional models in the second semester. After the course, questionnaire surveys and closed-book exams were used to evaluate the effectiveness of the instruction.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared to the group using the traditional teaching model, the BOPPPS teaching model group scored significantly higher on case reading and overall final test outcomes, and this difference was statistically significant (In the first semester, the scores of case reading questions were 39.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39 VS 35.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; the total scores were 77.47\u0026thinsp;\u0026plusmn;\u0026thinsp;6.61 VS 74.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17,P\u0026thinsp;=\u0026thinsp;0.003. In the second semester, the scores of case reading questions were 39.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45 VS 35.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15,P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; the total scores were 78.36\u0026thinsp;\u0026plusmn;\u0026thinsp;5.11 VS 74.53\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). On multiple-choice questions, however, there was no statistically significant difference in the scores between the standard teaching model group and the BOPPPS teaching model group. Over 80% of the students rated questions 1\u0026ndash;9 with a score of 4 or 5, indicating that students' evaluations of the BOPPPS teaching model in terms of learning efficiency, interest, clinical reasoning ability, and course satisfaction were all consistently positive.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe BOPPPS teaching model based on HIS system is a supplement, perfection and optimization of traditional medical imaging experimental courses teaching, and is helpful to improve the effectiveness and satisfaction of medical imaging experimental courses teaching.\u003c/p\u003e","manuscriptTitle":"Investigating the use of the HIS-based BOPPPS teaching model in medical imaging experimental course instruction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 08:14:31","doi":"10.21203/rs.3.rs-4882435/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-20T08:54:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-19T01:53:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-16T14:36:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2024-08-08T16:23:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fffac4a6-5a0a-4d4c-be5f-e044053894a1","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:02:33+00:00","versionOfRecord":{"articleIdentity":"rs-4882435","link":"https://doi.org/10.1186/s12909-025-07607-8","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2025-07-02 15:57:36","publishedOnDateReadable":"July 2nd, 2025"},"versionCreatedAt":"2024-10-14 08:14:31","video":"","vorDoi":"10.1186/s12909-025-07607-8","vorDoiUrl":"https://doi.org/10.1186/s12909-025-07607-8","workflowStages":[]},"version":"v1","identity":"rs-4882435","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4882435","identity":"rs-4882435","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}