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Methods and Instruments in Medical Informatics (126) Consumer & Patient Education and Shared-Decision Making (679) Data Science (270)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47934, first published .
Data Analysis of Physician Competence Research Trend: Social Network Analysis and Topic Modeling Approach

Data Analysis of Physician Competence Research Trend: Social Network Analysis and Topic Modeling Approach

Data Analysis of Physician Competence Research Trend: Social Network Analysis and Topic Modeling Approach

Authors of this article:

So Jung Yune1 Author Orcid Image ;   Youngjon Kim2 Author Orcid Image ;   Jea Woog Lee3 Author Orcid Image
Article Authors Cited by Tweetations (2) Metrics

    Original Paper

    1Department of Medical Education, Pusan National University, Busan, Republic of Korea

    2Department of Medical Education, Wonkwang University School of Medicine, Iksan, Republic of Korea

    3Intelligence Informatics Processing Lab, Chung-Ang University, Seoul, Republic of Korea

    *all authors contributed equally

    Corresponding Author:

    Jea Woog Lee, PhD

    Intelligence Informatics Processing Lab

    Chung-Ang University

    84, Heukseok-ro, Dongjak-gu,

    Seoul, 06974

    Republic of Korea

    Phone: 82 10 5426 7318

    Email: yyizeuks@cau.ac.kr


    Related ArticleThis is a corrected version. See correction statement in: https://medinform.jmir.org/2023/1/e53484

    Background: Studies on competency in medical education often explore the acquisition, performance, and evaluation of particular skills, knowledge, or behaviors that constitute physician competency. As physician competency reflects social demands according to changes in the medical environment, analyzing the research trends of physician competency by period is necessary to derive major research topics for future studies. Therefore, a more macroscopic method is required to analyze the core competencies of physicians in this era.

    Objective: This study aimed to analyze research trends related to physicians’ competency in reflecting social needs according to changes in the medical environment.

    Methods: We used topic modeling to identify potential research topics by analyzing data from studies related to physician competency published between 2011 and 2020. We preprocessed 1354 articles and extracted 272 keywords.

    Results: The terms that appeared most frequently in the research related to physician competency since 2010 were knowledge, hospital, family, job, guidelines, management, and communication. The terms that appeared in most studies were education, model, knowledge, and hospital. Topic modeling revealed that the main topics about physician competency included Evidence-based clinical practice, Community-based healthcare, Patient care, Career and self-management, Continuous professional development, and Communication and cooperation. We divided the studies into 4 periods (2011-2013, 2014-2016, 2017-2019, and 2020-2021) and performed a linear regression analysis. The results showed a change in topics by period. The hot topics that have shown increased interest among scholars over time include Community-based healthcare, Career and self-management, and Continuous professional development.

    Conclusions: On the basis of the analysis of research trends, it is predicted that physician professionalism and community-based medicine will continue to be studied in future studies on physician competency.

    JMIR Med Inform 2023;11:e47934

    doi:10.2196/47934

    Keywords

    physician competency; research trend; competency-based education; professionalism; topic modeling; latent Dirichlet allocation; LDA algorithm; data science; social network analysis 


    Background

    Medical publications began defining competencies in the 1970s [1-3]. Physician competency refers to the essential qualities that a physician should possess. The search for physician competency begins with the question of what it means to be a physician. Competency connotes various ideas, such as which physician traits are desired by society and what supports and promotes this transition of identity. Competency entails the concept of the physician as a professional, what the physician can do, and how the physician approaches their practice [4]. In summary, competency is considered a complex set of behaviors built on the components of knowledge, skills, attitudes, and competence as a personal ability [5]. Competency in this study is the core competency required to successfully perform a physician’s job and includes knowledge, skills, and attitude, regardless of the specific major.

    In the medical profession, the competency theme began by pursuing ways for the medical circle to improve the performance of health care workers. The issue arose in response to the growing demands of medical consumers and society as a neoliberalistic ideology spread in the 1970s and consumers’ awareness of their rights increased. In 1972, the American Academy of Pediatrics discussed physician competency by publishing a foundation for evaluating pediatricians’ competency [3]. In 1978, the World Health Organization found the cause of declines in medical service quality to be inadequate education and attempted to improve health care providers’ competencies through Competency-Based Curriculum Development in Medical Education [6]. Since then, this approach has influenced medical education globally, starting with the Royal College of Physicians and Surgeons in Canada and the Accreditation Council for Graduate Medical Education in the United States. Similar programs have been established worldwide, influencing strategies for global human resources and international partnerships for medical training [7]. These include the Outcome Project of the US Accreditation Council for Graduate Medical Education, General Medical Council’s Tomorrow’s Doctors [8-10], Scottish Doctor [11], and Canadian CanMEDS framework [12].

    For physicians, competency varies depending on the clinical, cultural, and geographical context [13]. In medical practice, the perception of a medical professional’s competent role changes continuously over time. In the early 19th century, physicians applied ointments and drew blood but did not deliver babies. In the 21st century, physicians are required to use advanced technologies and artificial intelligence in medical surgeries [14,15]. The ability to use technologies required by future health care systems is a challenge for physicians. However, at the same time, communication with patients and colleagues and interprofessional teamwork are essential human skills, and personal traits, such as empathy, humility, compassion, emotional intelligence, and a passion for continuous learning are also emphasized. Varying levels of health care infrastructure over time [16]; social awareness of minority groups [17]; and occasional health care challenges, such as global pandemics [18], emphasize certain specific physician competencies. Physicians’ competency, initially discussed within the scope of their social accountability, includes changes in their roles from the perspective of patients, health care, and self-management [19] of their health and wellness [20]. Thus, physicians’ abilities reflect their social situation and demands. In addition, physicians’ core abilities are expected to change over time. Therefore, this study, which analyzed changes in physician competency by period, will help understand the social demands expected of physicians in each period and identify research topics that should be important in medical education in the future.

    To date, studies on physician competency have focused on literature review [21-23] or meta-analyses [24,25]. However, systematic literature reviews have limitations in deriving comprehensively synthesized results because their analyses focus on narrow areas such as subjects, variables, environment, and intervention. In medical education, studies on competency often explore the acquisition, performance, and evaluation of particular skills, knowledge, or behaviors that constitute physician competency. Some of these studies examined patient-physician communication [26], risk management for emergency physicians, technical skills in robotic surgery in urological practice [27], and the instruction of medical staff [28]. Nevertheless, such approaches fail to convey the trends in physician competency research because they explore the essential medical skills for a specific task in a certain context. In addition, people may have different thoughts about the core competencies that physicians should possess. For example, a patient’s expectations of a physician’s ability may differ from a senior physician’s expectation of a junior physician’s ability. Therefore, a more macroscopic method is required to analyze the core competencies of physicians in this era.

    Recently, new big data analysis techniques such as social network analysis and topic modeling have been used. These approaches have the advantage of organizing the knowledge structure context by forecasting the trajectory of change in research and future issues and revealing the correlation between concepts. Social network analysis involves detecting influential core keywords in a vast amount of text and showing the relationship between keywords, allowing researchers to comprehend the context intuitively [29,30]. Topic modeling detects hidden topics in text data, analyzes the association and distribution of each topic, and provides integrated information [31]. From a microscopic perspective, it identifies core topics and their relationships. From a macroscopic perspective, it identifies the flow and context of core topics and the trend of topics by period [32]. Text network analysis and topic modeling are ideal approaches for analyzing trends in research on physicians’ competencies and accomplishing research objectives.

    Objectives

    The research problems based on the abovementioned research necessity are as follows:

    1. Extract core keywords from physician competency studies and create a network
    2. Examine the structure and characteristics of the network created based on physician competency studies
    3. Examine the main topics through topic modeling in physician competency studies
    4. Examine the trend of physician competency studies by topic based on time flow

    Data Collection

    To examine the flow of academic research on physician competency, we selected related research articles as raw data, which were collected using NetMiner Biblio Data Collector (Cyram Inc). We selected 10 years (2011-2020) and collected 2164 articles on physician competency published in Springer using the following keywords: “(doctors or physicians) AND ((competence or competency or competencies) or (expertise or expert or proficiency) or (responsibility or accountability or liability or blameworthiness) or (profession or occupation or roles or duties or jobs) or (performance and practice) or professionalism)).” After eliminating 810 articles that overlapped or were unrelated, 1354 articles remained for our analysis. The research flow is illustrated in Figure 1.

    ‎
    Figure 1. Flow of research procedures. TF-IDF: term frequency–inverse document frequency.

    Data Preprocess

    Because we could not use the original text in the preselected articles in the analysis, we processed the sentences into separate words as units of analysis. The collected papers consisted of natural language sentences such as theories, knowledge, and opinions. However, the sentences cannot be used directly in the analysis. Therefore, steps must be taken to convert each sentence into an individual word that can be analyzed [33]. In this study, to extract nouns, adjectives, and verbs, we used the morpheme-refining function of the NetMiner program and extracted 5384 words from the titles, abstracts, and keywords of the research articles. Preprocessing was performed to convert these words into analyzable keyword data.

    Nouns that were unsuitable for analysis were eliminated. First, we removed words that appeared 5 times or keywords that did not appear in more than 20 of the 1354 articles. In the final stage, we eliminated infrequently appearing words (<5 times) and extremely common words that appeared frequently in all papers (term frequency–inverse document frequency<0.05) [34]. After preprocessing, 272 words were extracted.

    Social Network Analysis

    In this study, social network analysis was used to examine the knowledge structure and characteristics through keyword extraction and network generation from physician competency studies, designated as research problems 1 and 2. In addition, the roles of keywords in the network were determined by assessing their importance using social network analysis techniques such as degree centrality, closeness centrality, and betweenness centrality. Social network analysis determines core nodes based on degree centrality, closeness centrality, and betweenness centrality. The degree centrality of a node increases with the number of nodes directly connected to it. Thus, the degree centrality indicates the influence of a node (keyword) based on the number of connected nodes. The betweenness centrality indicates the centrality of a node (keyword) between 2 other nodes (keywords). Betweenness centrality increases when the number of times a node appears on the shortest path between 2 other nodes increases. Keywords with high betweenness centrality control the information flow and exert a substantial influence on the overall connectivity of the network. To visually understand the positions and relationships among keywords, we used spring mapping from NetMiner 4.0. Spring mapping maximizes the characteristics of branching out the graph by placing the connected nodes closer and the disconnected nodes farther, using a simulated annealing technique to balance the 2 forms of distance.

    Topic Modeling

    To address research problem 3, we conducted topic modeling to explore the topic areas of physician competency research. Topic modeling was performed using keywords extracted from social network analysis. NetMiner 4.0 was used as the analysis program. Topic modeling is used to predict latent topics based on the association of multiple words in a text. Topic modeling extracts topics from research papers through keyword exploration, which helps recognize knowledge structures and patterns [35]. Knowledge structures are defined as a visualization of keyword clustering and a network map of how concepts in domain knowledge are interrelated [36]. This pattern is defined as the process of change in the knowledge structure over a period [37]. Topic modeling, as a big data analysis technique, provides a quantitative approach for identifying previously undiscovered macrotopic areas in physician competency research. Topics were extracted by applying a latent Dirichlet allocation (LDA) algorithm. The LDA algorithm is a probability model for predicting hidden topics by analyzing the distribution of words observed in a document. It is useful for reducing the data size and producing consistent topics.

    Figure 2 shows a graphical model of LDA. The boxes in the figure are “plates,” which represent duplicates. The outer plate represents the document (M) and the inner plate represents repeatedly selected topics (z) and words (w) within the document (N). “ϴ” indicates the distribution of topics in the document. Both “α” and “β” are hyperparameters that indicate Dirichlet distribution. LDA cannot be directly used to determine the number of topics, so the third hyperparameter is the “number of topics” the algorithm will discover. The probability calculation formula is as follows [38]:

    ‎
    Figure 2. Illustration for the conceptual model of latent Dirichlet allocation. α: a parameter that represents the Dirichlet prior for the document topic distribution; β: a parameter that represents the Dirichlet for the word distribution; θ: a vector for topic distribution over a document d; z: a topic for a chosen word in a document; w: specific words in N; M: document length; N: number of words in the document.

    To increase the accuracy of the results, appropriate Cronbach α values, β values, number of topics, and keywords should be determined [39]. To evaluate how well each keyword described each topic, we used one of the topic consistency metrics, the silhouette coefficient. The silhouette coefficient is an indicator that evaluates how well keywords, which are components of a topic, are classified [40]. A value closer to 1 indicates that the keywords within each cluster are well formed. Moreover, good clustering means that similarity to other topics is low and keywords within the same cluster describe a topic well. For topic t characterized by a higher-order word set Wt (any word whose probability exceeds a predefined threshold or a fixed number of high-order words), the consistency formula is defined as follows [41]:

    In this study, we set the term frequency–inverse document frequency threshold value at 0.5 and word length at 2. We used a silhouette coefficient to calculate the optimal values for Cronbach α, β, and the number of topics. A silhouette coefficient (or score) closer to 1 had higher explanatory power, validating Cronbach α, validating β, and the number of topics and descriptions of the object in the topic model were well matched. We also used a silhouette-clustering configuration. To determine the optimal number of topics, we conducted a comprehensive analysis by varying the number of topics from 5 to 30 and exploring Cronbach α values ranging from .01 to .99 as well as β values ranging from .01 to .99. The silhouette coefficient was used as the evaluation criterion. Our findings revealed that the highest silhouette coefficient of 0.782 was achieved using a Cronbach α value of .89, a β value of .97, and 6 topics. Subsequently, we proceeded with topic modeling using the identified parameters.

    Analysis of Change in Topics by Period

    To address research problem 4—the change in topics in physician competency research over time—the analysis was divided into 4 periods: 2011-2013, 2014-2016, 2017-2019, and 2020-2021. We divided the period into before and after the COVID-19 outbreak, and the researchers discussed and classified them. Subsequently, we analyzed how the percentages of each topic changed. To categorize the topics by checking the pattern of increased or decreased topics by period, we performed a linear regression analysis using SPSS (version 23.0; IBM Corp). We used the categorized periods as independent variables, and the percentage of each topic as the dependent variable. Following the linear regression analysis, we classified the keywords into 4 types based on the regression coefficient sign (+ or −) and the significance probability (P value): hot, warm, cool, and cold. If the coefficient is positive and the significance probability is ≤.05, it is classified as a “hot topic” with increasing research interest. Conversely, if the coefficient is negative and the significance probability is ≤.05, it is classified as a “cold topic” with decreasing research interest. Meanwhile, the clusters that were either positive or negative with no statistical significance and with a significance probability of ≥.05 were classified as “warm” and “cool” topics, respectively [42].

    Ethics Approval

    This study was conducted after obtaining approval from the Medical Research Ethics Review Committee of Chungnam National University Hospital (CNUH 2021-02-025).


    Core Keywords From Physician Competency Studies

    Between 2011 and 2020, the words that appeared most frequently in physician competency studies were knowledge (604 times), hospital (598 times), family (597 times), job (573 times), guideline (491 times), management (482 times), and communication (443 times). The words that appeared in most studies were education (n=256), model (n=243), knowledge (n=238), and hospital (n=234). Table 1 presents the 25 words with the highest frequency and the number of articles in which they appeared.

    Table 1. High-ranking keywords by frequency in research.
    RankKeywordFrequency, nArticles in which it appears, n
    1Knowledge604238
    2Hospital598234
    3Family597161
    4Job573111
    5Guideline491164
    6Management482206
    7Communication443154
    8Education437256
    9Model425243
    10Assessment407150
    11Attitude388151
    12Information381213
    13Health care379196
    14Experience368224
    15Medication35664
    16Intervention332152
    17Cancer33089
    18Disease313150
    19Change276147
    20Need268196
    21Development255170
    22Behavior25387
    23Barrier23897
    24Evidence234158
    25Practice227199

    Social Network Analysis

    Table 2 presents keyword degree, closeness, and betweenness centrality from physician competency studies. Each keyword and its degree are as follows. The higher the degree of the keyword, the stronger the influence in the network. The keywords with the highest degree and closeness centrality were, in order, model (0.988 and 0.988, respectively), education (0.981 and 0.982), experience (0.973 and 0.975), health care (0.973 and 0.975), hospital (0.973 and 0.975), and information (0.973 and 0.975). The keywords with the highest betweenness centrality were, in order, model (0.007), education (0.006), knowledge (0.006), management (0.006), and education (0.006). In total, 29 words belonged to the top 30 keywords in all 3 centrality types. The keywords with a high degree centrality were also high in closeness centrality. However, the betweenness centrality of environment was higher than the degree and closeness centrality. Figure 3 shows the network map for centrality.

    Table 2. High-ranking keywords by degree, closeness, and betweenness centrality.
    RankDegree centralityCloseness centralityBetweenness centrality

    		KeywordDegreeKeywordDegreeKeywordDegree
    1Model0.98881Model0.98893Model0.00651
    2Education0.98134Education0.98169Education0.00630
    3Experience0.97388Experience0.97455Knowledge0.00616
    4Health care0.97388Health care0.97455Information0.00616
    5Hospital0.97388Hospital0.97455Management0.00614
    6Information0.97388Information0.97455Experience0.00614
    7Knowledge0.97388Knowledge0.97455Hospital0.00614
    8Management0.97388Management0.97455Need0.00610
    9Need0.97388Need0.97455Health care0.00607
    10Development0.96269Development0.96403Development0.00600
    11Evidence0.95522Evidence0.95714Evidence0.00571
    12Change0.95149Change0.95374Change0.00570
    13Intervention0.94776Intervention0.95036Intervention0.00551
    14Family0.93657Family0.94035Disease0.00543
    15Disease0.93284Disease0.93706Family0.00542
    16Guideline0.92910Guideline0.93380Guideline0.00512
    17Practice0.92164Practice0.92734Practice0.00509
    18Assessment0.91418Assessment0.92096Assessment0.00502
    19Challenge0.91418Challenge0.92096Communication0.00493
    20Attitude0.91045Attitude0.91781Challenge0.00492
    21Communication0.90672Communication0.91468Attitude0.00480
    22Evaluation0.90299Evaluation0.91157Evaluation0.00474
    23Concern0.88806Concern0.89933Concern0.00456
    24Influence0.88060Influence0.89333Influence0.00451
    25Activity0.86940Activity0.88449Improvement0.00442
    26Improvement0.86940Improvement0.88449Barrier0.00430
    27Community0.86194Community0.87869Activity0.00427
    28Barrier0.85821Barrier0.87582Environment0.00409
    29Implementation0.85821Implementation0.87582Implementation0.00408
    30Cost0.85448Cost0.87296Community0.00406
    ‎
    Figure 3. Centrality network by the 2D spring network map.

    Topic Modeling

    Regarding the number of topics from physician competency studies, we decided on 6 topics (silhouette=0.782) by considering the silhouette coefficient and the validity of interpretation. The core keywords by topic are listed in Table 3. The top keywords in topic 1 were management, intervention, disease, cost, and medication. The top keywords in topic 2 were family, health care, information, community, and need. The high-ranking keywords in topic 3 were knowledge, attitude, cancer, guidelines, and barriers. The high-ranking keywords in topic 4 were, in order, hospital, job, burnout, model, gender, and intention. The top keywords in topic 5 were, in order, assessment, education, competency, development, and graduation. Finally, the high-ranking keywords in topic 6 were, in order, communication, consultation, experience, emergency, and feedback.

    Topic groups were labeled based on high-ranking core keywords in terms of probability distribution: topic 1, Evidence-based clinical practice; topic 2, Community-based healthcare; topic 3, Patient care; topic 4, Career and self-management; topic 5, Continuous professional development; and topic 6, Communication and cooperation. On the basis of the nature of the topics, we divided the topic groups into 2 domains: those related to job competency and those related to personal competency. The job domain includes topics 1, 2, 3, and 6, and the personal domain includes topics 4 and 5. Figure 4 presents the results of visualizing the 7 networks of core keywords using a topic-keyword map.

    Table 3. Core keywords by topic.
    RankDomain and topic

    		JobPersonal

    		Evidence-based clinical practiceCommunity-based healthcarePatient careCommunication and cooperationCareer and self-managementContinuous professional development
    1ManagementFamilyKnowledgeCommunicationHospitalAssessment
    2InterventionHealth careAttitudeConsultationJobEducation
    3DiseaseInformationCancerExperienceBurnoutCompetency
    4CostCommunityGuidelineEmergencyModelDevelopment
    5MedicationNeedBarrierFeedbackGenderGraduate
    6DiagnosisChoicePCPaChildIntentionError
    7DiabetesClinicActivityChangeCareerLearning
    8AdherenceModelExperienceInteractionLifeExamination
    9GuidelineMentalConfidenceEvaluationEngagementModel
    10ChangeEducationCounselingModelManagerFramework

    aPCP: primary care provider.

    ‎
    Figure 4. Topic modeling network: topic-keyword map.

    Core Topics by Period

    Table 4 presents the changes in topics over time and Table 5 lists the rate of each topic during this period. Over the past 10 years, each topic has been studied with similar weights. Communication and cooperation accounted for 18.61% (252/1354) of the articles, followed by Evidence-based clinical practice at 17.73% (240/1354) and Continuous professional development at 16.77% (227/1354). Regarding the domains, studies related to physicians’ job competencies accounted for 61.21% (910/1354), whereas those related to physicians’ personal competencies accounted for 32.79% (444/1354).

    Table 4. Number of research on 6 topics by year (n=1354).
    Domain and topic2011 (n=82)2012 (n=107)2013 (n=98)2014 (n=152)2015 (n=134)2016 (n=120)2017 (n=125)2018 (n=141)2019 (n=129)2020 (n=167)2021 (n=99)Total (n=1354)
    Job, n (%)

    		Evidence-based clinical practice15 (6.3)24 (10)24 (10)27 (11.3)22 (9.2)21 (8.8)25 (10.4)26 (10.8)25 (10.4)21 (8.8)10 (4.2)240 (100)

    		Community-based healthcare11 (5.2)13 (6.1)13 (6.1)22 (10.3)26 (12.2)22 (10.3)17 (8)18 (8.5)20 (9.4)29 (13.6)22 (10.3)213 (100)

    		Patient care13 (6.3)22 (10.7)6 (2.9)29 (14.2)12 (5.9)15 (7.3)18 (8.8)23 (11.2)14 (6.8)36 (17.6)17 (17.6)205 (100)

    		Communication and cooperation20 (7.9)21 (8.3)22 (8.7)25 (9.9)30 (12)21 (8.3)22 (8.7)21 (8.3)25 (9.9)24 (9.5)21 (8.3)252 (100)
    Personal, n (%)

    		Career and self-management11 (5.1)13 (6)16 (7.4)25 (11.5)19 (8.8)22 (10.1)20 (9.2)24 (11.1)26 (12)30 (12)11 (13.8)217 (100)

    		Continuous professional development12 (5.3)14 (6.2)17 (7.5)24 (10.5)25 (11)19 (8.4)23 (10.1)29 (12.8)19 (8.4)27 (12)18 (7.9) 227 (100)
    Table 5. The topic frequency and possession during the period (n=1354).
    Domain and topicPeriod 1: 2011-2013 (n=287)Period 2: 2014-2016 (n=406)Period 3: 2017-2019 (n=395)Period 4: 2020-2021 (n=266)Total (n=1354)
    Job, n (%)

    		Evidence-based clinical practice63 (22)70 (17.2)76 (19.2)31 (11.6)240 (17.7)

    		Community-based healthcare37 (12.9)70 (17.2)55 (13.9)51 (19.2)213 (15.7)

    		Patient care41 (14.2)56 (13.8)55 (13.9)53 (19.9)205 (15.1)

    		Communication and cooperation63 (225)76 (18.7)68 (17.2)45 (16.9)252 (18.6)
    Personal, n (%)

    		Career and self-management40 (13.9)66 (16.3)70 (17.7)41 (15.4)217 (16)

    		Continuous professional development43 (15)68 (16.8)71 (18)45 (16.9) 227 (16.8)

    Dividing the period into 3-year groups, Evidence-based clinical practice (63/240, 26.3%) and Communication and cooperation (37/213, 12.9%) were studied most often during the first period (2011-2013), whereas research on Community-based healthcare (41/205, 14.3%) and Career and self-management (40/217, 13.9%) was conducted on a small scale. Communication and cooperation was studied most often during the second period (2014-2016), but the weight decreased compared with the first period (from 22% to 18.7%).

    The topics that increased in weight compared with the first period were Career and self-management (from 13.9% to 16.3%) and Continuous professional development (from 15% to16.8%). During the third period (2017-2019), Evidence-based clinical practice (76/240, 19.2%) was studied the most, followed by Continuous professional development (71/227, 18%) and Career and self-management (70/217, 17.7%). During the fourth period, which covers the COVID-19 pandemic (2020-2021), Patient care (53/205, 19.9%) and Community-based healthcare (51/213, 19.2%) were the most studied. Compared with the third period, Patient care increased from 13.9% to 19.9%, and Community-based healthcare increased from 13.9% to 19.2%. Conversely, there were decreases in Evidence-based clinical practice (from 19.2% to 11.6%), Career and self-management (from 17.7% to 15.4%), and Continuous professional development (from 18% to 16.9%).

    Regarding the overall possession during the first 3 periods before the COVID-19 pandemic, there was increased research interest in Career and self-management (from 13.9% to 16.3% to 17.7%) and Continuous professional development (from 15% to 16.8% to 18%) but decreased interest in Communication and cooperation (from 22% to 18.7% to 17.2%). In the fourth period, during the COVID-19 pandemic, there was increased research interest in Community-based healthcare (45/252, 16.9%) and Patient care (53/502, 19.9%) but decreased interest in Evidence-based clinical practice (31/240, 11.6%), Career and self-management (41/217, 15.4%), and Continuous professional development (45/227, 16.9%).

    In terms of domains, studies on physicians’ personal competencies increased from the first to the third period (from 28.9% to 33% to 35.7%, respectively). However, it decreased to 32.3% between 2020 and 2021 after the COVID-19 outbreak. Studies on physicians’ job competency gradually decreased (from 71.2% to 67.8% to 64.3%), before increasing to 67.8% during the fourth period.

    Topic Characteristics by Period

    Table 6 presents the topic characteristics for each period. We performed linear regression analysis to examine the characteristics of the 6 topics. Three topics were classified as hot topics with a positive regression coefficient and statistical significance: topic 2 (B=1.315; t9=2.621; P=.03), topic 4 (B=1.758; t9=5.414; P=.001), and topic 5 (B=1.339; t9=2.963; P=.02). We found no cool (negative regression coefficients and no statistical significance) or cold topics (negative regression coefficients and no statistical significance). Figure 5 shows the subject possession during this period.

    Table 6. Regression analysis results for each topic.
    Domain and topicBβt test (df)P valueDurbin-Watson statisticTopic type
    Job

    		Evidence-based clinical practice0.388.3391.019 (9).341.535—a

    		Community-based healthcare1.315.6802.621 (9).031.181Hot

    		Patient care1.248.4261.331 (9).222.960—

    		Communication and cooperation0.248.2510.733 (9).481.775—
    Personal

    		Career and self-management1.758.8865.414 (9).0012.013Hot

    		Continuous professional development1.339.7232.963 (9).022.157 Hot

    aNot available.

    ‎
    Figure 5. Topic trend during the period.

    Principal Findings

    In this study, we used social network analysis to examine keywords and their relationships in physician competency studies conducted over the past 10 years. Topic modeling identified the top 5 research topics, visualized the relationships among them, and described the research possession over time.

    Discussions on physicians’ competency arose in the 1990s because of the social atmosphere of consumerism, which demands accountability in all aspects of the profession. The medical and health care field emphasizes physicians’ roles and attitudes, reflecting the demands of medical consumers, such as citizens [43] and local communities [44]. Educational institutions have received demands to improve the curriculum considering educational outcomes [4,45]. Since the 1990s, some countries have begun to define the competence of their physicians and specify their components [46]. After the “Project on the future global role of the physician in healthcare” of the World Federation for Medical Education in 2012, it has become more active in many countries [7]. As a goal of the medical community, a competency model for desirable physicians was constructed in the 2000s, centered on Canada [46], the United States [47], the United Kingdom [48], and Scotland [11]. Since then, scholars have actively studied the development of the curriculum and revision of content, reflecting this competency model [8,49,50]. The physician competency studies conducted since 2010, which we analyzed, are on the continuum of physician competency studies carried out over the past 20 years in a large framework.

    The words that appeared most frequently in physician competency studies since 2010 are, in order, knowledge, hospital, family, job, guideline, management, and communication. The words that appeared in most studies were education, model, knowledge, and hospital.

    Previously, a physician’s professional competence was defined as the habitual and careful use of communication, knowledge, skills, clinical reasoning, emotions, values, and reflection in everyday care for the benefit of individuals and communities [51]. The keywords frequently used in this study had a broad coverage, including the knowledge and context necessary for physicians to perform medical practice, a standardized framework necessary to meet social needs, and traits pertaining to physician groups or individuals.

    In the network of top keywords, the keywords with high degree and closeness centrality values were model, education, experience, health care, and hospitals. Keywords with high betweenness centrality values were model, education, and knowledge. The keyword that emerged in the centrality analysis was change. Degree centrality indicates the number of times a node appears simultaneously with other nodes in a network. Closeness centrality connotes the distance between nodes within a network, and betweenness centrality connotes the role of a bridge between different nodes within a network. Network centrality analysis of keywords revealed that the 2 central nodes leading physician competency studies over the past 10 years were model and education. A model refers to a pattern, plan, or demonstration that illustrates the structure or work of an object, system, or concept. In competency studies, model began with a core competency model that explained what constitutes a physician. Later, scholars focused on different models essential for medical practice, such as specific clinical contexts, resident training, patient-physician communication, leadership [52], and health care management.

    Competency-based education for medical students and residents refers to an educational method that uses the content or criteria derived from the previous competency model. In other words, a professional’s competency should be built gradually based on scientific knowledge, basic clinical skills, and moral development. Education and experience are essential for acquiring or maintaining expertise in a professional. Song et al [53] conducted a bibliometric analysis of medical expertise from 2010 to 2019. According to these studies, academic journals on medical education primarily include studies on expertise. Likewise, studies on physician competency over the past 10 years have examined methods for developing, implementing, and evaluating competency through education.

    Through topic modeling, we identified 6 latent topics. Topic modeling is a researcher-centered content analysis method that identifies a specific pattern assumed to be latent in a document or text and derives a potentially meaningful topic. We also set topic names based on keywords derived from this study. On the basis of relevance of the topic, we divided it into 2 domains: job and personal competency. The job domain comprises Evidence-based clinical practice, Community-based healthcare, Patient care, and Communicationand cooperation, whereas the personal domain comprises Career and self-management and Continuous professional development. The topics derived from topic modeling in this study cover the criteria for physician competency suggested in many countries. In Canada, the Canadian Medical Education Directions for Specialists [46] organized physician roles into 7 competencies: medical experts, communicators, collaborators, leaders, health advocators, scholars, and professionals. In the United Kingdom, “Good Medical Practice 2020” [54] describes physician competency in terms of 4 core competencies: knowledge, skill, and performance; safety and quality; communication, partnership, and teamwork; and trust maintenance. In the United States, the Accreditation Council for Graduate Medical Education laid out 6 core competencies [46] focusing on the areas of physician activities: patient care, professionalism, interpersonal and communication skills, medical knowledge, systems-based practice, and practice-based learning and improvement.

    They also comprise the complex knowledge, skills, and attitudes that physicians must possess. Previous studies have emphasized the topics of physician competency research resulting from this study [5]. First, Evidence-based clinical practice is a core competency required for clinicians. It provides a framework for integrating research evidence into health care delivery [55], including patient history taking and analysis, physical examination, and diagnostic accuracy. The implementation of evidence-based practice principles has resulted in notable advances in improving the quality of delivered health care [56]. In addition, over the last 20 years, evidence-based practice has been increasingly integrated as a core component of undergraduate, postgraduate, and continuing education health programs worldwide [12]. Second, Community-based healthcare is increasingly emphasized in terms of the need for improvement from a broad cultural and institutional perspective to improve the quality of medical care [57]. Community-based healthcare has recently attracted more attention, as primary care is emphasized to solve social problems such as population aging and COVID-19 [58].

    Third, the topic of Patient care means patient-centered care (PCC). PCC enhances health outcomes, such as improved patient satisfaction, behavior change, trust, patient adherence, providers’ clinical accuracy, disease management plans, and active patient self-management [59]. Therefore, PCC is a crucial attribute of high-quality health care services [60]. Fourth, physicians’ interpersonal and communication skills have a significant impact on patient care. Furthermore, it is correlated with improved health outcomes and quality [61]. Ineffective communication skills are associated with malpractice claims and suits [62] and medication errors [63]. Communication is a core clinical skill that can be taught and learned [64]. Moreover, interprofessional communication skills are essential competencies for medical students to become physicians [65].

    Fifth, among the 2 topics belonging to the personal domain, Career and self-management is related to physicians’ burnout. Job satisfaction can affect physicians’ physical and mental illnesses, such as depression and burnout [66], and is related to patient safety and quality of care [67]. In addition, burnout syndrome is a major concern in occupational health [55]. Therefore, organizations should emphasize the importance of physicians’ self-care (rest, a healthy lifestyle, breaks, and sufficient sleep) and regular burnout screening [68]. Finally, Continuous professional development refers to the attitude of lifelong learning as a professional. Through professional development such as lifelong learning, medical specialists maintain their professional competence in addition to keeping track of and gaining advancing knowledge [69]. Furthermore, continuing professional development and lifelong learning are crucial for securing high-quality health care, patient safety, and societal trust in the health care system [70]. In other words, the subdomains of physician competency emphasized in many countries have been the main research topics over the past 10 years. Specifically, the main research topics include Evidence-based medicine (an explicit means for generating an important answerable question, interpreting new knowledge, and judging how to apply that knowledge in a clinical setting) [71]; Community-based healthcare (emphasizes social responsibility); Patient care (considering the patient’s condition and circumstances throughout the treatment process); Communication and cooperation with patients, families, and colleagues; Career and self-management; and Continuous professional development for maintaining competency as scholars and professionals.

    An examination of the changes in research topics over the past 10 years revealed that more studies have been conducted on job competencies than on physicians’ personal competencies. Personal domain studies gradually increased from the first to the third period; however, after the COVID-19 outbreak (2020-2021), the number of job domain studies increased.

    The most studied topic was Communication and cooperation. The topics that showed an increasing frequency and possession before the COVID-19 outbreak (first to third period) were Career and self-management and Continuous professional development; studies on Communication and cooperation showed a decreasing frequency and possession. Studies on Evidence-based clinical practice have also gradually decreased (first through second through fourth periods), except during the third period.

    Most studies conducted before the COVID-19 outbreak covered physicians’ individual professionalism [10,72]. However, during the COVID-19 pandemic (fourth period), studies on Community-based healthcare and Patient care increased. This can be explained as follows: physicians’ social responsibility and community-centered care began to be emphasized during the COVID-19 pandemic, and the importance of care centered on patients and communities has resurfaced. Particularly during the COVID-19 pandemic, many problems threatened patients’ health because of the gap in health and medical care, despite individual physicians’ expertise and commitment. These challenging social situations highlight the importance of Community-based healthcare [73,74].

    In times of crisis, the role of physicians can be broadened. For example, physicians have a duty not only to take care of their patients but also to protect them from infection, and thus take care of their families [18]. In addition, social interventions, such as school closures, affect the supply and demand for medical personnel. However, it is not easy to clarify whether a physician’s role in situations such as COVID-19 is regular duty. Nevertheless, COVID-19 has broadened the demand for physicians’ roles and competencies. This was also manifested in “hot topics” that have gradually increased over the past 10 years, such as Community-based healthcare, Career and self-management, and Continuous professional development. This indicates that topics related to Community-based healthcare are gradually becoming more important [73,74], as are topics related to the professionalism of individual physicians [10,51,53,72].

    On the basis of the results of this study, the keywords that many researchers were interested in over the past 10 years were model and education. Therefore, they developed competency-based education and training systems at the hospital, university, and national levels. Consequently, many countries and training institutions, such as hospitals and universities, have developed and educated physicians with competency-based curricula. This was effective in cultivating physicians’ competency in responding to social needs.

    The topics we should pay attention to are Community-based healthcare, Career and self-management, and Continuous professional development, which are research topics that have gradually increased with time. This indicates that the scope of physicians’ competencies has been studied more extensively and comprehensively than in the past. It is meaningful in that it defines physicians’ competency as the ability to develop into professional and social leaders, beyond just the ability necessary to perform a job.

    The general public’s and patients’ expectations and consciousness of medical care are changing, and the medical system pursued by society is also changing. Consequently, perceptions of the roles of medicine and physicians are rapidly evolving. In line with these changes, research on the core competencies of physicians must be conducted using more detailed competencies and major fields.

    Limitations

    This study had some limitations. First, it was difficult to repeat the keyword refining process in the keyword network analysis. To eliminate researcher subjectivity, we have described the analytical procedure in this study. Second, the study period was not equally divided. The last period is short, covering only 6 months, between 2020 and 2021. Because we forecasted that the effect of the COVID-19 pandemic since December 2019 would be reflected between 2020 and the first half of 2021, we set these periods separately. Considering the gap between the time of undertaking and publishing this study, it is unreasonable to argue that the last period accurately reflected the pandemic. However, we believe that it is worth examining the effect of the pandemic because of its global nature, which surpasses its regional and cultural characteristics.

    Third, similar to other studies, we explored articles published in English. Physician competency studies are influenced by cultural and social demands and changes in the context of medical services. Although we did not identify the regions in which the studies were published, similar studies on medical professionalism [52] and medical education [75] led us to believe that the English publications in this study were from North America, Europe, or parts of Asia. Future studies should analyze the differences in research trends in physician competency based on culture and region. Finally, this study investigated the core competencies for successfully performing a physician’s job, regardless of the specific major. Subsequently, we can research physician’s competencies for each major.

    Conclusions

    The top research topics on physician competency over the past 10 years are Evidence-based clinical practice, Community-based healthcare, Patient care, Career and self-management, Continuous professional development, and Communication and cooperation. The discussion of physician competency entails the establishment of a physician’s fundamental roles and competencies based on a constantly changing health care environment and the implementation of education from studying to competency acquisition. Studies on competency include discussions on the model physician desired by society, as well as the issue of wellness encompassing an individual physician’s job choice and quality of life.

    The hot topics in physician competency studies conducted within the past 10 years are Community-based healthcare in the job domain and Career and self-management and Continuous professional development in the physician’s personal domain. These 2 areas are hot topics that have gradually gained interest over time.

    Acknowledgments

    This paper was supported by Wonkwang University in 2022.

    Conflicts of Interest

    None declared.

    1. Brown TC, McCleary LE, Stenchever MA, Poulson Jr AM. A competency-based educational approach to reproductive biology. Am J Obstet Gynecol. Aug 01, 1973;116(7):1036-1042. [CrossRef] [Medline]
    2. Spady WG. Competency based education: a bandwagon in search of a definition. Educ Res. 1977;6(1):9-14. [FREE Full text] [CrossRef]
    3. Burg FD, Brownlee RC, Wright FH, Levine H, Daeschner CW, Vaughan 3rd VC, et al. A method for defining competency in pediatrics. J Med Educ. Oct 1976;51(10):824-828. [CrossRef] [Medline]
    4. Harden RM. AMEE guide no. 14: outcome-based education: part 1-an introduction to outcome-based education. Med Teach. 1999;21(1):7-14. [FREE Full text] [CrossRef]
    5. Carraccio C, Wolfsthal SD, Englander R, Ferentz K, Martin C. Shifting paradigms: from Flexner to competencies. Acad Med. May 2002;77(5):361-367. [CrossRef] [Medline]
    6. McGaghie WC, Miller GE, Sajid AW, Telder TV. Competency-based curriculum-development in medical-education: an introduction. World Health Organization. 1978. URL: https://apps.who.int/iris/bitstream/handle/10665/39703/WHO_PHP_68.pdf?sequence=1&isAllowed=y [accessed 2021-11-28]
    7. Stern DT, Friedman Ben-David M, Norcini J, Wojtczak A, Schwarz MR. Setting school-level outcome standards. Med Educ. Feb 2006;40(2):166-172. [FREE Full text] [CrossRef] [Medline]
    8. Tomorrow’s doctors: recommendations on undergraduate medical education issued by education committee of the general medical council in pursuance of section 5 of the medical act 1983. General Medical Council. 1983. URL: https://books.google.co.kr/books/about/Tomorrow_s_Doctors.html?id=BDm2xgEACAAJ&redir_esc=y [accessed 2023-06-26]
    9. Zaini RG, Bin Abdulrahman KA, Al-Khotani AA, Al-Hayani AM, Al-Alwan IA, Jastaniah SD. Saudi meds: a competence specification for Saudi medical graduates. Med Teach. 2011;33(7):582-584. [CrossRef] [Medline]
    10. Ahn D. The future roles of Korean doctors: cultivating well‐rounded doctors. Korean Med Educ Rev. Oct 2014;16(3):119-125. [FREE Full text] [CrossRef]
    11. Simpson JG, Furnace J, Crosby J, Cumming AD, Evans PA, Friedman Ben David M, et al. The Scottish doctor--learning outcomes for the medical undergraduate in Scotland: a foundation for competent and reflective practitioners. Med Teach. Mar 2002;24(2):136-143. [CrossRef] [Medline]
    12. Frank JR, Snell L, Sherbino J. CanMEDS 2015 Physician Competency Framework. Ottawa, Canada. Royal College of Physicians and Surgeons of Canada; 2015.
    13. Hodges BD, Lingard L. The Question of Competence. New York, NY, USA. Cornell University Press; 2012.
    14. Alrassi J, Katsufrakis PJ, Chandran L. Technology can augment, but not replace, critical human skills needed for patient care. Acad Med. Jan 01, 2021;96(1):37-43. [CrossRef] [Medline]
    15. Liu XX, Keane PA, Denniston AK. Time to regenerate: the doctor in the age of artificial intelligence. J R Soc Med. Apr 2018;111(4):113-116. [FREE Full text] [CrossRef] [Medline]
    16. Longenecker RL, Wendling A, Hollander-Rodriguez J, Bowling J, Schmitz D. Competence revisited in a rural context. Fam Med. Jan 2018;50(1):28-36. [FREE Full text] [CrossRef] [Medline]
    17. Margolies L, Brown CG. Increasing cultural competence with LGBTQ patients. Nursing. Jun 2019;49(6):34-40. [CrossRef] [Medline]
    18. Johnson SB, Butcher F. Doctors during the COVID-19 pandemic: what are their duties and what is owed to them? J Med Ethics. Jan 2021;47(1):12-15. [FREE Full text] [CrossRef] [Medline]
    19. Rotenstein LS, Huckman RS, Cassel CK. Making doctors effective managers and leaders: a matter of health and well-being. Acad Med. May 01, 2021;96(5):652-654. [CrossRef] [Medline]
    20. Stergiopoulos E, Hodges B, Martimianakis MA. Should wellness be a core competency for physicians? Acad Med. Sep 2020;95(9):1350-1353. [CrossRef] [Medline]
    21. Wiskar K. Physician health: a review of lifestyle behaviors and preventive health care among physicians. BC Med J. Oct 2012;54(8):419-423. [FREE Full text]
    22. Dellinger EP, Pellegrini CA, Gallagher TH. The aging physician and the medical profession: a review. JAMA Surg. Oct 01, 2017;152(10):967-971. [CrossRef] [Medline]
    23. Simons MR, Zurynski Y, Cullis J, Morgan MK, Davidson AS. Does evidence-based medicine training improve doctors' knowledge, practice and patient outcomes? A systematic review of the evidence. Med Teach. May 2019;41(5):532-538. [CrossRef] [Medline]
    24. Cook DA, Oh SY, Pusic MV. Accuracy of physicians' electrocardiogram interpretations: a systematic review and meta-analysis. JAMA Intern Med. Nov 01, 2020;180(11):1461-1471. [FREE Full text] [CrossRef] [Medline]
    25. Sibeoni J, Bellon-Champel L, Mousty A, Manolios E, Verneuil L, Revah-Levy A. Physicians' perspectives about burnout: a systematic review and metasynthesis. J Gen Intern Med. Aug 2019;34(8):1578-1590. [FREE Full text] [CrossRef] [Medline]
    26. Travaline JM, Ruchinskas R, D'Alonzo Jr GE. Patient-physician communication: why and how. J Am Osteopath Assoc. Jan 2005;105(1):13-18. [CrossRef] [Medline]
    27. Schommer E, Patel VR, Mouraviev V, Thomas C, Thiel DD. Diffusion of robotic technology into urologic practice has led to improved resident physician robotic skills. J Surg Educ. 2017;74(1):55-60. [CrossRef] [Medline]
    28. Milner RJ, Gusic ME, Thorndyke LE. Perspective: toward a competency framework for faculty. Acad Med. Oct 2011;86(10):1204-1210. [CrossRef] [Medline]
    29. Saheb T, Saheb M. Analyzing and visualizing knowledge structures of health informatics from 1974 to 2018: a bibliometric and social network analysis. Healthc Inform Res. Apr 2019;25(2):61-72. [FREE Full text] [CrossRef] [Medline]
    30. Onan A, Korukoglu S, Bulut H. LDA-based topic modelling in text sentiment classification: an empirical analysis. Int J Comput Linguistics Appl. 2016;7(1):101-119.
    31. Dieng AB, Ruiz FJ, Blei DM. Topic modeling in embedding spaces. Trans Assoc Comput Linguist. Dec 2020;8:439-453. [CrossRef]
    32. Jelodar H, Wang Y, Yuan C, Feng X, Jiang X, Li Y, et al. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl. 2019;78(11):15169-15211. [CrossRef]
    33. Zhao WX, Jiang J, Weng J, He J, Lim EP, Yan H, et al. Comparing twitter and traditional media using topic models. In: Proceedings of the 33rd European Conference on Advances in Information Retrieval. Presented at: ECIR '11; April 18-21, 2011, 2011;338-349; Dublin, Ireland. URL: https://link.springer.com/chapter/10.1007/978-3-642-20161-5_34 [CrossRef]
    34. Kim SW, Gil JM. Research paper classification systems based on TF-IDF and LDA schemes. Hum Centric Comput Inf Sci. Aug 26, 2019;9(1):30. [CrossRef]
    35. Kumari R, Jeong JY, Lee BH, Choi KN, Choi K. Topic modelling and social network analysis of publications and patents in humanoid robot technology. J Inf Sci. Oct 2021;47(5):658-676. [CrossRef]
    36. Jonassen DH, Wang S. Acquiring structural knowledge from semantically structured hypertext. J Comput Base Instr. 1993;20(1):1-8.
    37. Ley T. Knowledge structures for integrating working and learning: a reflection on a decade of learning technology research for workplace learning. Br J Educ Technol. Mar 2020;51(2):331-346. [CrossRef]
    38. Debnath R, Bardhan R. India nudges to contain COVID-19 pandemic: a reactive public policy analysis using machine-learning based topic modelling. PLoS One. Sep 11, 2020;15(9):e0238972. [FREE Full text] [CrossRef] [Medline]
    39. Song CW, Jung H, Chung K. Development of a medical big-data mining process using topic modeling. Cluster Comput. 2019;22(S1):1949-58. Retracted in: Cluster Comput. December 5, 2022. doi: 10.1007/s10586-022-03869-9. [CrossRef]
    40. O’Callaghan D, Greene D, Carthy J, Cunningham P. An analysis of the coherence of descriptors in topic modeling. Expert Syst Appl. Aug 2015;42(13):5645-5657. [CrossRef]
    41. Mimno D, Wallach H, Talley E, Leenders M, McCallum A. Optimizing semantic coherence in topic models. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Presented at: EMNLP '11; July 27-31, 2011, 2011;262-272; Edinburgh, UK.
    42. Griffiths TL, Steyvers M. Finding scientific topics. Proc Natl Acad Sci U S A. Apr 06, 2004;101(Suppl 1):5228-5235. [FREE Full text] [CrossRef] [Medline]
    43. Habbick BF, Leeder SR. Orienting medical education to community need: a review. Med Educ. May 1996;30(3):163-171. [CrossRef] [Medline]
    44. Hamad B. Community-oriented medical education: what is it? Med Educ. Jan 1991;25(1):16-22. [CrossRef] [Medline]
    45. Long DM. Competency-based residency training: the next advance in graduate medical education. Acad Med. Dec 2000;75(12):1178-1183. [CrossRef] [Medline]
    46. Frank JR, Danoff D. The CanMEDS initiative: implementing an outcomes-based framework of physician competencies. Med Teach. Sep 2007;29(7):642-647. [CrossRef] [Medline]
    47. Swing SR. The ACGME outcome project: retrospective and prospective. Med Teach. Sep 2007;29(7):648-654. [CrossRef] [Medline]
    48. Rubin P, Franchi-Christopher D. New edition of tomorrow's doctors. Med Teach. Jul 2002;24(4):368-369. [CrossRef] [Medline]
    49. Cumming A, Ross M. The Tuning Project for Medicine--learning outcomes for undergraduate medical education in Europe. Med Teach. Sep 2007;29(7):636-641. [CrossRef] [Medline]
    50. Newble D, Stark P, Bax N, Lawson M. Developing an outcome-focused core curriculum. Med Educ. Jul 2005;39(7):680-687. [CrossRef] [Medline]
    51. Epstein RM, Hundert EM. Defining and assessing professional competence. JAMA. Jan 09, 2002;287(2):226-235. [CrossRef] [Medline]
    52. Calhoun JG, Vincent ET, Baker GR, Butler PW, Sinioris ME, Chen SL. Competency identification and modeling in healthcare leadership. J Health Adm Educ. 2004;21(4):419-440. [Medline]
    53. Song X, Jiang N, Li H, Ding N, Wen D. Medical professionalism research characteristics and hotspots: a 10-year bibliometric analysis of publications from 2010 to 2019. Scientometrics. 2021;126(9):8009-8027. [FREE Full text] [CrossRef] [Medline]
    54. Good medical practice: protecting patients, guiding doctors. General Medical Council. 2001. URL: https://www.gmc-uk.org/-/media/documents/good-medical-practice-2001-55612679.pdf?la=en%22 [accessed 2023-06-26]
    55. De Hert S. Burnout in healthcare workers: prevalence, impact and preventative strategies. Local Reg Anesth. Oct 28, 2020;13:171-183. [FREE Full text] [CrossRef] [Medline]
    56. Albarqouni L, Hoffmann T, Straus S, Olsen NR, Young T, Ilic D, et al. Core competencies in evidence-based practice for health professionals: consensus statement based on a systematic review and Delphi survey. JAMA Netw Open. Jun 01, 2018;1(2):e180281. [FREE Full text] [CrossRef] [Medline]
    57. Aveling EL, McCulloch P, Dixon-Woods M. A qualitative study comparing experiences of the surgical safety checklist in hospitals in high-income and low-income countries. BMJ Open. Aug 15, 2013;3(8):e003039. [FREE Full text] [CrossRef] [Medline]
    58. Panigrahi SK, Majumdar S, Galhotra A, Kadle SC, John AS. Community based management of COVID-19 as a way forward for pandemic response. Front Public Health. Jan 13, 2021;8:589772. [FREE Full text] [CrossRef] [Medline]
    59. Sultan WI, Sultan MI, Crispim J. Palestinian doctors' views on patient-centered care in hospitals. BMC Health Serv Res. Oct 11, 2018;18(1):766. [FREE Full text] [CrossRef] [Medline]
    60. Singh S, Evans N, Williams M, Sezginis N, Baryeh NA. Influences of socio-demographic factors and health utilization factors on patient-centered provider communication. Health Commun. Jul 2018;33(7):917-923. [CrossRef] [Medline]
    61. Nobile C, Drotar D. Research on the quality of parent-provider communication in pediatric care: implications and recommendations. J Dev Behav Pediatr. Aug 2003;24(4):279-290. [CrossRef] [Medline]
    62. Levinson W, Roter DL, Mullooly JP, Dull VT, Frankel RM. Physician-patient communication. The relationship with malpractice claims among primary care physicians and surgeons. JAMA. Feb 19, 1997;277(7):553-559. [CrossRef] [Medline]
    63. Maurette P, Comité analyse et maîtrise du risque de la Sfar. To err is human: building a safer health system. Ann Fr Anesth Reanim. Jun 2002;21(6):453-454. [CrossRef] [Medline]
    64. Rider EA, Keefer CH. Communication skills competencies: definitions and a teaching toolbox. Med Educ. Jul 2006;40(7):624-629. [CrossRef] [Medline]
    65. Kim SJ, Kwon OD, Kim KH, Lee JE, Lee SH, Shin JS, et al. Investigating the effects of interprofessional communication education for medical students. Korean J Med Educ. Jun 2019;31(2):135-145. [FREE Full text] [CrossRef] [Medline]
    66. Kumar S, Fischer J, Robinson E, Hatcher S, Bhagat RN. Burnout and job satisfaction in New Zealand psychiatrists: a national study. Int J Soc Psychiatry. Jul 2007;53(4):306-316. [CrossRef] [Medline]
    67. Kravitz RL. Physician job satisfaction as a public health issue. Isr J Health Policy Res. Dec 14, 2012;1(1):51. [FREE Full text] [CrossRef] [Medline]
    68. Youssef D, Youssef J, Abou-Abbas L, Kawtharani M, Hassan H. Prevalence and correlates of burnout among physicians in a developing country facing multi-layered crises: a cross-sectional study. Sci Rep. Jul 23, 2022;12(1):12615. [FREE Full text] [CrossRef] [Medline]
    69. Lombarts KM, Plochg T, Thompson CA, Arah OA, DUQuE Project Consortium. Measuring professionalism in medicine and nursing: results of a European survey. PLoS One. May 21, 2014;9(5):e97069. [FREE Full text] [CrossRef] [Medline]
    70. van den Goor MM, Wagner CC, Lombarts KM. Poor physician performance in the Netherlands: characteristics, causes, and prevalence. J Patient Saf. Mar 2020;16(1):7-13. [CrossRef] [Medline]
    71. Haynes RB, Sackett DL, Richardson WS, Rosenberg W, Langley GR. How to practice and teach evidence-based medicine. Can Med Assoc J. 1997;157(6):788. [CrossRef]
    72. Smith R. Medical professionalism: out with the old and in with the new. J R Soc Med. Feb 2006;99(2):48-50. [FREE Full text] [CrossRef] [Medline]
    73. Lucey CR, Davis JA, Green MM. We have no choice but to transform: the future of medical education after the COVID-19 pandemic. Acad Med. Mar 01, 2022;97(3S):S71-S81. [FREE Full text] [CrossRef] [Medline]
    74. Ohta R, Ryu Y, Sano C. The uncertainty of COVID-19 inducing social fear and pressure on the continuity of rural, community-based medical education: a thematic analysis. Healthcare (Basel). Feb 17, 2021;9(2):223. [FREE Full text] [CrossRef] [Medline]
    75. Ji YA, Nam SJ, Kim HG, Lee J, Lee SK. Research topics and trends in medical education by social network analysis. BMC Med Educ. Sep 24, 2018;18(1):222. [FREE Full text] [CrossRef] [Medline]

    ‎
    LDA: latent Dirichlet allocation
    PCC: patient-centered care

    Edited by C Lovis; submitted 06.04.23; peer-reviewed by KH Park, H Hyeonmi; comments to author 02.05.23; revised version received 15.05.23; accepted 16.05.23; published 19.07.23.

    Copyright

    ©So Jung Yune, Youngjon Kim, Jea Woog Lee. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 19.07.2023.

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