Extensive data is collected from university official websites, CNKI, Web of Science databases, libraries, and other resources on the topic of talent training in medical product management, both domestically and internationally. This includes documents, papers, monographs, and conference materials, which are analyzed for core perspectives and research outcomes concerning theories related to talent training in medical product management. Data is gathered comprehensively from three macro dimensions—professional demeanor, knowledge reserves, and comprehensive abilities—and three micro dimensions—theoretical teaching, practical teaching, and extracurricular activities—to identify potential indicators for the talent capability requirement system and the medical product management professional training scheme system [7,11–13,21–25].

Final indicators are determined through surveys and expert interviews to ensure the accuracy, representativeness, and authority. Survey and interview content are designed based on findings from the literature review, and each indicator's importance is rated on a five-point scale; higher scores indicate greater importance and satisfaction [22]. Interviews and surveys involve regulatory departments for medical device products (regulatory departments), employees of enterprises employing medical product management professionals (enterprises), and students in the medical product management field (students). This process establishes the final evaluation indicator system and determines the weighting coefficients based on each indicator's scores. University heads of medical product management are surveyed to determine the correlation coefficients for capability requirements and training schemes. A preliminary survey sample is first tested, and based on the feedback, modifications are made to the questionnaire to minimize research errors caused by understanding discrepancies and question ambiguities. The scales of surveys are then analyzed for reliability and validity to ensure the effectiveness and reliability of the research.

The study employed an online survey methodology and strictly adhered to ethical guidelines outlined by the relevant institutional review board. The survey did not involve minors, and all data handling was conducted anonymously to ensure participant privacy. Prior to initiating the survey, clear and detailed explanations of the objectives were provided electronically, and digital written informed consent was obtained from all participants (Consent was obtained through WeChat, a widely used digital communication platform). Furthermore, the survey reiterated the purposes of the study and the usage of data to ensure participants were fully informed, thus safeguarding the scientific integrity, reasonableness, and ethical considerations of our research approach. The ethics committee approved our comprehensive consent process.

This paper employs the QFD method to develop a model for enhancing the quality of medical product management professional cultivation, starting from societal needs for capabilities in medical product management professionals. This demand is translated into the training scheme for medical product management professionals, with the basic structure illustrated in Fig 1. The model studies the interconnections among various components, considers the logical relationships between subsystems and the elements, and establishes a mapping between capability requirements and the training scheme. This process identifies key aspects and elements for enhancing the quality of medical product management professional training, achieving the training objectives. Firstly, the study starts with the societal demand for capabilities in medical product management professionals. It employs a variety of methods including literature review, expert interviews, and surveys to comprehensively and deeply understand these needs, which encompass professional ethics, knowledge reserves, and comprehensive abilities. Subsequently, the study delves into the intrinsic connections between the various components of the model, considering the logical associations between each subsystem and its elements through comprehensive expert surveys. For each dimension of talent capability required, the study meticulously analyzes the corresponding relationships with various elements of the training program. Based on this analysis, a mapping relationship between the talent capabilities needed and the elements of the training program is established. This mapping is not a simple direct correspondence but presents a complex many-to-many association. Certain talent capabilities might closely relate to multiple elements of the training program, and some training elements may impact multiple talent needs. Finally, using the established mapping relationships, the study identifies key components and elements that enhance the quality of training in medical product management. For instance, if the demand analysis reveals a high requirement for risk management and assessment capabilities in professionals, the training program should focus on courses related to these skills. By precisely managing and optimizing these key components, the study transforms societal demands for professional capabilities into a viable talent training program, thus providing a scientific and objective basis for training high-quality, application-oriented professionals in medical product management.

The importance of each element's indicator is calculated using QFD theory through the following steps:

Based on the results of the literature review and survey analysis, the indicator system for the competency requirements of medical product management professionals and the initial weight coefficients (demand level) were established. The indicator system includes three primary indicators: professional integrity, knowledge reserves, and comprehensive capabilities. Under these primary indicators, there are a total of 20 secondary indicators. Specific results are presented in Table 3.

As shown in Table 3, the indicator system for the talent capability requirements of the Medical Product Management program covers three core dimensions: professional qualities, knowledge reserve, and comprehensive abilities. The 20 secondary indicators comprehensively encompass the key capabilities required by industry professionals. The weight assignments from the three parties show a consistent overall trend with no significant divergence. Among these, R₃₁ (the ability to self-learn and update knowledge) has the highest overall weight (0.054), while R₂₇ (understanding of foreign regulatory systems and policies for medical devices) has the lowest overall weight (0.046), with the remaining indicators showing a relatively balanced distribution of overall weights.

Using one-way ANOVA, differences in competency requirements for professionals in medical product management among different groups can be assessed. When the p-value of the variance test is less than 0.05, it indicates significant differences between groups. Specific results are shown in Table 4.

Table 4 indicates that significant discrepancies exist between stakeholders in ten areas: Professional Ethics (R₁₁), Sense of Responsibility (R₁₂), New Policies and Market Dynamics in Medical Devices (R₂₅), Design and Data Analysis of Clinical Trials for Medical Devices (R₂₆), Understanding of Foreign Regulatory Systems and Policies for Medical Devices (R₂₇), Self-learning and Knowledge Updating (R₃₁), Stress Resistance and Adaptability (R₃₂), Problem Analysis and Solving (R₃₃), Team Collaboration (R₃₆), and Risk Assessment and Management (R₃₈) (P < 0.05). For indicators showing significant discrepancies, differences between enterprises and students, enterprises, and regulatory departments, and between regulatory departments and students were analyzed using independent sample T-tests. If the p-value is less than 0.05, it indicates significant discrepancies in the needs for the same indicator between the two stakeholders. Specific results are presented in Fig 2.

Fig 2 reveals the significance of demand differences between pairs of groups. Here, E represents enterprises, S represents students, and R represents regulatory departments. Dark blue indicates significant differences, blue indicates no significant differences, and light blue represents comparisons within the same group. Fig 2 indicates that in terms of Professional Ethics (R₁₁) and Responsibility (R₁₂) (P < 0.05). The demand for these indicators is highest among enterprises, followed by regulatory departments, and lowest among students. Significant discrepancies also exist between enterprises and students in Problem Analysis and Solving (R₃₃), Team Collaboration (R₃₆), and Risk Assessment and Management (R₃₈) (P < 0.05), with enterprises having a higher demand than both regulatory departments and students. Significant discrepancies are observed between students and both enterprises and regulatory departments regarding New Policies and Market Dynamics in Medical Devices (R₂₅) and Understanding of Foreign Regulatory Systems and Policies for Medical Devices (R₂₇) (P < 0.05), with students having higher demands than enterprises and regulatory departments. In the area of Design and Data Analysis of Clinical Trials for Medical Devices (R₂₆), student demand is significantly higher than that of regulatory departments (P < 0.05). No significant discrepancies were found in the comparisons of other indicators.

Building on the competency requirement system for professionals in medical product management, this study uses survey data on the satisfaction levels of various social entities with the current training of professionals in medical products. The obstacle degree model is utilized to further analyze factors impeding satisfaction. Using the SPSS software, the obstacle degrees for each indicator of competency requirements for medical product management professionals are calculated according to the Obstacle degree model's computational method. Based on the base weight coefficients (demand level), the final weight coefficients (the coefficients of both demand level and obstacle level) are calculated. The specific results are shown in Table 5. To visually present the corresponding relationship between the demand level and obstacle level of each indicator, Fig 3 is plotted.

Table 5 clarifies the obstacle levels, initial weights, and final weights for each capability indicator. The core output of the final weight data provides the essential data foundation for the subsequent construction of the Quality Function Deployment (QFD) model.

Fig 3 visually presents the interrelationship between the demand level, satisfaction obstacle level, and final weight of the 20 capability indicators (R₁₁-R₃₈) in the Medical Product Management program. The blue bar chart represents the demand level (the higher the value, the more the capability is valued by the social groups); the red bar chart represents the obstacle level (the higher the value, the stronger the ability's constraint on market satisfaction); the line represents the integrated priority (calculated by calibrating the demand level and obstacle level, with higher values indicating that the corresponding training aspects need to be prioritized for optimization). The core value lies in precisely identifying the key improvement indicators of “high demand - high obstacle - high weight,” providing an intuitive and scientific decision-making basis for the targeted optimization of the training program. As shown in Fig 3, high obstacle indicators are concentrated in R₂₇ (understanding of foreign regulatory systems for medical devices), R₃₂ (stress resistance and adaptability), R₃₈ (risk assessment and management), R₂₉ (statistical methods and statistical tools in regulation), R₃₁ (self-learning and knowledge updating), and R₃₅ (data analysis and processing). These are the key areas that require breakthroughs in the current training program. Among them, R₃₈, R₃₁, and R₃₂ are core abilities highly valued by society and are also the main constraints on training satisfaction. The final weights for these indicators are ranked highly, making them the top priorities for improvement. R₁₁ (professional ethics) and R₁₂ (sense of responsibility) have lower obstacle levels, indicating that the satisfaction constraints on the foundational professional qualities in the current training are minimal and do not require significant adjustment.

A relationship matrix for the “Medical Product Management Professional Competency Requirements—Training Program” was established through the expert scoring method. The relationships are generally classified as strongly related, related, weakly related, and unrelated, with respective values of 5, 3, 1, and 0. The average of the expert scores is used as the correlation score. The importance and weight of each element in the training program are calculated using the independent scoring method, with results presented in Table 6. A heatmap of the quality house is shown in Fig 4. The darker the color and the larger the block, the greater the interrelationship on the horizontal axis.

As shown in Table 6 and Fig 4, there are differences in the importance of the elements of the training program. The top five elements in terms of importance are as follows: Professional Courses (P₁₃), Comprehensive Practical Teaching (P₂₃), Professional Practical Teaching (P₂₂), Personalized Training (P₁₄), and Basic Practical Teaching (P₂₁). Among these, three elements belong to the practical teaching category, and two elements belong to the theoretical teaching category. The elements in the extracurricular activities category (P₃₁–P₃₄) are not among the top five, with relatively lower importance.

The highest-priority training program element in the House of Quality results is professional courses (P₁₃). Within the theoretical teaching (P₁) category, general education courses (P₁₁) and foundational subject courses (P₁₂) are ranked 10th and 8th, respectively, while personalized training (P₁₄) is ranked 4th. This indicates that for the Medical Product Management program, the most important courses are professional courses. When designing the curriculum, it is essential to focus on personalized training, while general education and foundational subject courses should only meet the basic requirements of the discipline. More class hours should be allocated to developing students’ professional capabilities, enhancing their specialization. Based on student feedback such as “insufficient professional courses” and “lack of management-related knowledge,” along with the high weight of core competencies like R₃₈ (risk assessment and management ability), R₂₈ (medical device risk management and assessment), and R₂₉ (statistical methods and statistical tools in regulation), two aspects need optimization in the curriculum design: First, increase the class hours for core professional courses, with a focus on strengthening courses directly related to R₃₈, R₂₈, and R₂₉, such as medical device registration management, risk management, quality systems, and risk identification tools, addressing the “insufficient professional depth” issue. Second, supplement courses on public management, basic management theory, and other related subjects to solidify the foundation in public management and align with the management attributes of medical product regulation, thereby meeting the post-graduation job requirements of students.

In the House of Quality analysis results, three practical courses are ranked among the top five in terms of importance, indicating a high demand for practical courses in society. Additionally, an analysis of the open-ended questions in the student survey regarding issues in the curriculum revealed that students generally reported insufficient class hours for practical courses. As a result, they are unable to master the actual processes of the entire medical device lifecycle, such as registration, quality systems, and risk management, and are unable to apply the theoretical knowledge learned to practice. Medical Product Management students, after employment, need to manage the entire lifecycle of medical devices (registration, production, quality, distribution), and skills required for tasks such as preparing registration documents, risk identification, and quality system operation cannot be fully acquired through theoretical teaching alone [28].

Drawing on the “longitudinal practice” model from Harvard Medical School—Cambridge Integrated Internship [29,30], universities should: First, introduce targeted practical courses, such as medical device registration operations, quality system audit practice, and full-process risk management simulations, to cover core job skills. Second, optimize practical teaching faculty by hiring instructors with regulatory or industry experience, or inviting adjunct faculty from enterprises. These instructors can incorporate real industry cases into theoretical classrooms, and through practical courses, enable the conversion from “theory to hands-on practice,” directly enhancing core competencies such as R₃₈ and R₃₃, and addressing the “disconnect between practice and industry.”

Based on student feedback from the survey (e.g., repetitive content in regulatory courses, layering of computer and regulatory courses) and the requirement in the QFD model results that “professional courses (P₁₃) should support multiple core competencies,” the curriculum design must be optimized in three ways to avoid inefficiency: First, addressing faculty shortages through cross-school collaboration: To address the weakness in faculty resources for regulatory science and management courses in medical institutions, we should adopt the “complementary advantages and resource sharing” model from the Nine-School Alliance [31]. This can be achieved by introducing high-quality management courses from other universities through cross-school electives and faculty sharing [32]. Industry experts should also be invited to participate in teaching, sharing real-world cases on compliance management, risk assessment, and other topics, thereby enhancing the professionalism of the courses and aligning them with core knowledge and capability requirements such as R₃₈ and R₂₄. Second, systematic integration to avoid content duplication: To avoid duplication in regulatory courses, communication between instructors teaching related subjects should be strengthened, and repeated content should be minimized during course preparation. The curriculum system should be more systematic, not limited to individual courses. Instead, it should adopt a comprehensive, multi-angle, and targeted approach to teaching medical device-related regulations. Drawing on the experience of pharmaceutical regulation teaching [33], the course system should be designed with “regulations for the entire lifecycle of medical devices” as the core. The content of regulations, such as the Medical Device Supervision and Administration Regulations, should be split across different courses based on stages such as “registration - production - distribution - use,” avoiding repeated teaching and improving teaching efficiency. Third, adaptation of regulatory tools through interdisciplinary integration: In line with the cultivation goal of “applying regulatory science methods and tools” and the high obstacle level of core competency R₃₅ (data analysis and processing), the issue of layering in computer and regulatory courses should be addressed. Drawing on interdisciplinary cultivation models such as Stanford University's Bio-X program [34–36], interdisciplinary courses such as “Regulatory Data Analysis” and “Medical Device Regulatory Information Tools” should be introduced. Regulatory cases (e.g., risk data visualization, compliance process digitalization) should be integrated into computer science teaching, while interdisciplinary faculty should be recruited to ensure students master the “regulation + technology” skills required, aligning with the demands of core competencies like R₃₅.

The House of Quality results (Table 6) indicate that personalized training (P₁₄) is a core optimization element, and Fig 2 shows significant demand differences between students, enterprises, and regulatory departments. Moreover, the demands of enterprises and regulatory departments differ as well. Students are more focused on R₂₅ (new policies and market dynamics for medical devices) and R₂₇ (understanding of foreign regulatory systems and policies for medical devices), while enterprises emphasize R₃₈ (risk assessment and management) and R₃₂ (stress resistance and adaptability), and regulatory departments prioritize R₂₄ (laws, regulations, and standards related to medical devices) and R₃₃ (problem analysis and solving). This not only reflects the diverse developmental needs of the student group but also demonstrates the differentiated demands of different employers. In light of this dual difference and the high priority of personalized training, differentiated cultivation and flexible elective course design should be implemented to achieve the training goal of “shared foundation, distinctive direction, and personalized adaptation,” while simultaneously aligning with the differing employment standards of enterprises and regulatory departments.

Differentiated cultivation can draw on the “shared foundational courses, distinctive core courses, and elective course selection” model from the pharmacy major [37], and incorporate the broad-based, interdisciplinary course design experience of the MIT EECS department [38]. This approach would involve constructing specialized direction modules, allowing students to select different course combinations based on their chosen direction. Looser elective course rules should be set to increase the proportion of optional courses, meeting students’ interest needs, fully motivating their learning engagement, and implementing a true credit system. This enables students to not only choose their learning direction but also cross-disciplinary electives, broadening their horizons and fostering the development of multidisciplinary talent.

Additionally, leveraging the traditional strengths of Shenyang Pharmaceutical University's pharmacy courses, the personalized elective course system should be optimized as follows: First, break the limits on the number of students per program and major, offering specialized courses such as “Foreign Regulations of Medical Devices” and “Advanced Regulatory Data Analysis” through online courses, video lessons, etc., to meet the diverse needs of students. Second, add elective modules related to the university's strengths (e.g., medical device research and development management in the context of pharmacy), utilizing the university's resource advantages and providing students with differentiated development paths. This ensures that personalized training is deeply aligned with core competency requirements and the university's distinctive characteristics.

Based on the “Demand-Obstacle” dual-dimensional priority comparison of key competency training (Fig 3), core competencies such as R₃₈ (risk assessment and management), R₃₁ (self-learning and knowledge updating), and R₃₂ (stress resistance and adaptability) are among the highest-priority areas for improvement. Enhancing these competencies requires practice in real-world scenarios. The development of such higher-order abilities cannot rely solely on traditional classroom teaching and must be achieved through diverse models such as university-enterprise cooperation, project-based practical training, and industry collaboration. The specific measures are as follows:

As Medical Product Management is an emerging field, some students enter the program with unclear perceptions and random choices, leading to low professional identity, confusion about future career planning, and lack of motivation to study [39]. This indirectly affects the development of R₃₁ (self-learning and knowledge updating). Therefore, guidance should focus on systematically enhancing students’professional awareness, professional identity, and sense of career belonging. Specifically, the following actions should be taken: First, increase awareness of the major: Efforts should be made to promote the major and raise its social recognition, encouraging more students to proactively choose this field rather than making random or uninformed choices. This can be achieved by advertising the demand for medical product management professionals in industries such as enterprises and regulatory departments.

Publicizing through internship opportunities in enterprises can increase the social recognition of the major and create more job opportunities. Second, implement professional awareness education: Introduce courses such as a compulsory introduction to the major for freshmen, professional awareness education activities, and integrate professional awareness into career planning courses [39]. This will allow students to understand the content of their chosen field and its future development, thus enhancing their professional awareness and identity. Drawing on the Oxford University mentorship system, which is a highly effective method for cultivating professional awareness and identity, each student is assigned a professional mentor who guides their academic research, as well as personal development and career planning [40]. Through in-depth communication and collaboration with their mentors, students can better understand the essence and value of their field, leading to a strong professional identity [41]. Universities may also consider introducing a similar mentorship system in the Medical Product Management program to enhance students’ professional awareness and identity.

Fig 2 shows that enterprises’ demand for R₁₁ (professional ethics) and R₃₆ (team collaboration) is significantly higher than that of students (P < 0.05). Additionally, as Medical Product Management involves human health, professional qualities (such as professional ethics and responsibility) form the foundation for core competencies like R₃₈ (risk assessment and management) and R₃₆ (team collaboration)—lacking professional integrity can directly affect the rigor of risk control and the coordination of teams. Therefore, the cultivation of professional qualities should closely align with industry demands and core competencies. Specifically, the following actions should be taken: First, strengthen professional ethics education: Drawing on Stanford University's interdisciplinary social ethics teaching model [42,43], offer courses in medical ethics. Through the analysis of real-world cases, such as illegal medical device production or compliance risk management, students should understand the crucial role that professional ethics play in job responsibilities and cultivate a respect for life and a commitment to compliance. Second, integrate ability cultivation into practical scenarios: Use methods such as “team collaboration on regulatory simulation projects” and “cross-group debates on compliance cases” to cultivate students’communication and coordination abilities (R₃₄) and problem analysis and solving abilities (R₃₃). Integrate professional quality requirements into course assessments. For example, in risk management courses, “compliance awareness” should be used as one of the evaluation criteria to ensure that professional qualities and core competencies are developed in parallel.

In summary, to enhance the quality of talent cultivation in the Medical Product Management program, the cultivation program should be proactively optimized across three key dimensions: optimizing course settings, enriching the training model, and strengthening student guidance. Optimizing course settings aims to equip students with necessary professional skills, evidenced by assessment scores and competition performances, which indicate the effectiveness of the curriculum. Enriching the training model through varied paths and industry collaborations enhances practical skills and industry knowledge, impacting competencies in teamwork and communication. Additionally, improving professional awareness and vocational skills prepares students for professional challenges and aligns with employer expectations concerning professional ethics and innovation.

Establishing a robust assessment system is essential for maintaining training quality. This system should evaluate the effectiveness of course settings, practical teaching, student competencies, and employer feedback. These assessments support the effective implementation and continual optimization of the training program, ultimately contributing to the medical device industry by providing well-prepared, application-oriented professionals.