This paper is in the following e-collection/theme issue:

Reviews in Medical Informatics (93) Health Informatics Education and Training (53) Methods and Instruments in Medical Informatics (126)

Published on in Vol 10, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33842, first published .
Global Scientific Research Landscape on Medical Informatics From 2011 to 2020: Bibliometric Analysis

Global Scientific Research Landscape on Medical Informatics From 2011 to 2020: Bibliometric Analysis

Global Scientific Research Landscape on Medical Informatics From 2011 to 2020: Bibliometric Analysis

Authors of this article:

Xuefei He1 Author Orcid Image ;   Cheng Peng2 Author Orcid Image ;   Yingxin Xu3 Author Orcid Image ;   Ye Zhang3 Author Orcid Image ;   Zhongqing Wang3 Author Orcid Image
Article Authors Cited by (5) Tweetations (2) Metrics

    Original Paper

    1Department of Ophthalmology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China

    2Department of Ophthalmology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China

    3Information Center, The First Hospital of China Medical University, Shenyang, China

    Corresponding Author:

    Zhongqing Wang, PhD

    Information Center

    The First Hospital of China Medical University

    155 Nanjingbei Street

    Shenyang, 110001

    China

    Phone: 86 15940082159

    Email: wangzhongqing@cmu.edu.cn


    Background: With the emerging information and communication technology, the field of medical informatics has dramatically evolved in health care and medicine. Thus, it is crucial to explore the global scientific research landscape on medical informatics.

    Objective: This study aims to present a visual form to clarify the overall scientific research trends of medical informatics in the past decade.

    Methods: A bibliometric analysis of data retrieved and extracted from the Web of Science Core Collection (WoSCC) database was performed to analyze global scientific research trends on medical informatics, including publication year, journals, authors, institutions, countries/regions, references, and keywords, from January 1, 2011, to December 31, 2020.

    Results: The data set recorded 34,742 articles related to medical informatics from WoSCC between 2011 and 2020. The annual global publications increased by 193.86% from 1987 in 2011 to 5839 in 2020. Journal of Medical Internet Research (3600 publications and 63,932 citations) was the most productive and most highly cited journal in the field of medical informatics. David W Bates (99 publications), Harvard University (1161 publications), and the United States (12,927 publications) were the most productive author, institution, and country, respectively. The co-occurrence cluster analysis of high-frequency author keywords formed 4 clusters: (1) artificial intelligence in health care and medicine; (2) mobile health; (3) implementation and evaluation of electronic health records; (4) medical informatics technology application in public health. COVID-19, which ranked third in 2020, was the emerging theme of medical informatics.

    Conclusions: We summarize the recent advances in medical informatics in the past decade and shed light on their publication trends, influential journals, global collaboration patterns, basic knowledge, research hotspots, and theme evolution through bibliometric analysis and visualization maps. These findings will accurately and quickly grasp the research trends and provide valuable guidance for future medical informatics research.

    JMIR Med Inform 2022;10(4):e33842

    doi:10.2196/33842

    Keywords

    medical informatics; bibliometrics; VOSviewer; data visualization 


    Background

    The field of medical informatics is dedicated to systematically processing data, information, and knowledge in medicine and health care [1]. In the 1950s, Robert S Ledley and Lee Browning Lusted first performed the complicated reasoning processes inherent in medical diagnosis using electronic computers to minimize medical errors primarily [2]. Given that the emerging information and communication technology is being continuously applied in the medical field, medical informatics, as a discipline, has dramatically evolved over the past 70 years and brought about significant changes to human social needs [2,3]. Recent advances in health care information technology, electronic health records (EHRs), health data standards, and health information exchange have become the major focus of scientific research [4]. Therefore, health informatics, which represents the development of systems and methods for acquisition, processing, handling, communication, storage, retrieval, management, discovery, analyzing, and synthesizing patient information to improve health and health care, is more often used in the literature [5-8]. The number of publications and journals focusing on medical informatics/health informatics has multiplied in recent years. Therefore, it is essential to explore the global scientific research landscape in this discipline.

    Bibliometrics is defined as scientific and quantitative research of publications, which describes the research trends of a certain research field using statistical methods to analyze a large number of publications [9]. In 2007, Bansard et al [10] first presented a bibliometric study on medical informatics and bioinformatics, which mainly identified the present links and potential synergies between the bioinformatics and medical informatics research areas. Subsequently, bibliometric analyses on specific medical informatics technology have been performed, such as those on mobile health research [11], shared decision making [12], telemedicine [13-15], computer-aided diagnosis [16], natural language processing [17], artificial intelligence (AI) in health care [18], digital health [19,20], among others.

    Objective

    This study aims to analyze medical informatics as a discipline (a catalog from the Web of Science Core Collection [WoSCC]) and demonstrate the longitudinal trends from the global perspective. Thus, we performed bibliometric analysis and prepared visualization maps to identify and present the publication trends, global collaboration patterns, basic knowledge, research hotspots, and emerging hotspots in medical informatics.


    Data Collection

    WoSCC is the most widely used database in various scientific fields, including over 9000 high-level academic journals worldwide. The research area is generated by a set of classification methods for all databases under WoSCC. Therefore, documents of the same research area or discipline can be identified, retrieved, and analyzed from WoSCC for bibliometrics analysis [21]. Medical informatics is one of the 252 research areas of WoSCC. For search purposes, the retrieval research area was set as “medical informatics”, the period was set as “from 2011 to 2020”, the document type was set as “article”, and the language was set as “English”. We conducted our search strategy in WoSCC on June 1, 2021, at China Medical University.

    We identified and incorporated 34,742 studies on medical informatics from WoSCC. The full record and cited references of the retrieved publications were collected and saved in text formats (eg, .txt). The data used in this study are publicly available and associated with no protected health information.

    Ethics Consideration

    Publicly available data were searched and downloaded from WoSSC. The extraction of this data did not involve interaction with human subjects or animals. Thus, there were no ethical issues involving the use of these data, and no approval from an Ethics Committee was required.

    Analytical Tool and Visualization Maps

    The most commonly used bibliometric methods are co-authorship, co-citation, and co-occurrence. Co-authorship analysis reveals collaboration patterns among authors, institutions, and countries [22]. Co-citation analysis contributes to discovering and determining the knowledge base of one discipline [23]. Co-occurrence analysis uses the frequency of multiple words in the same article to identify how close they are, thereby helping researchers identify hot topics and trends in the discipline. VOSviewer [24] is an excellent bibliometric analysis software developed by van Eck and Waltman [25,26]. It calculates the similarity sij of 2 items i and j with the equation sij = cij/(wiwj), where cij denotes the number of co-occurrences of items i and j, and wi and wj denote the total number of occurrences of items i and j. Once the similarity matrix is created, VOSviewer maps all the items in a 2D map so that items with a high similarity will be located close to each other, while those with a low similarity will be located far from each other. In this study, we employed VOSviewer version 1.6.16 to extract bibliometric information such as publication year, journals, authors, institutions, countries/regions, references, and keywords. Besides, we employed VOSviewer to conduct co-authorship analysis, co-citation analysis, co-occurrence analysis, and then built visualization network maps.


    Global Publications on Medical Informatics

    A total of 34,742 articles on medical informatics were retrieved. The average annual number of publications was 3474 during the past decade. The annual global publications on entire life sciences and biomedical sciences are presented in Multimedia Appendix 1. The annual global publications on entire life sciences and biomedical sciences increased 55.73% from 573,981 to 893,887 from 2011 to 2020. The annual global publications on medical informatics increased 193.86% from 1987 to 5839 from 2011 to 2020, which was the highest increase rate in the life sciences and biomedical fields.

    The global distribution of countries/regions participating in medical informatics research is shown in Figure 1. A total of 161 countries/regions contributed to medical informatics from 2011 to 2020. The top 10 countries contributed 27,213 articles in medical informatics. The United States (12,927 publications) is the most productive country, followed by Germany (3336 publications), England (3269 publications), China (3157 publications), and Canada (2237 publications). Changes in the annual ranking of the top 10 most productive countries for medical informatics research are shown in Figure 2. The rankings of the top 10 countries changed every year from 2011 to 2020, but the United States consistently ranked first in publications.

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    Figure 1. The global distribution of countries/regions participating in medical informatics research from 2011 to 2020.
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    Figure 2. The annual ranking changes of the top 10 most productive countries regarding publication of articles on medical informatics from 2011 to 2020.
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    Contribution of Source Journals

    Based on the retrieved results, articles on medical informatics were distributed in 37 journals. The top 10 journals with the most publications in the medical informatics discipline are presented in Table 1. From 2011 to 2020, Journal of Medical Internet Research with 3600/34,742 (10.36%) publications was the top productive journal, followed by Statistics in Medicine (3282 publications) and Computer Methods and Programs in Biomedicine (2409 publications). Journal of Medical Internet Research with 63,932 citations was also the most highly cited journal, followed by Statistics in Medicine (45,042 citations) and Journal of the American Medical Informatics Association (36,874 citations).

    Table 1. The top 10 most productive journals in Medical Informatics from 2011 to 2020.
    RankJournalCountryPublication start yearTotal publicationsTotal citations
    1Journal of Medical Internet ResearchCanada1999360063,932
    2Statistics in MedicineEngland1982328245,042
    3Computer Methods and Programs in BiomedicineIreland1985240935,157
    4Biomedical Engineering-Biomedizinische TechnikGermany197121843541
    5Journal of Medical SystemsUnited States1977210428,747
    6Journal of The American Medical Informatics AssociationEngland1994174636,874
    7Journal of Evaluation in Clinical PracticeEngland1995167613,720
    8BMC Medical Informatics and Decision MakingEngland2001166017,956
    9IEEE Journal of Biomedical and Health InformaticsUnited States2013165627,850
    10JMIR mHealth And uHealthCanada2013154416,156

    Contributions of Authors and Institutes

    A total of 114,841 authors (175,530 frequency) contributed to medical informatics from 2011 to 2020. As shown in Table 2, David W Bates (99 publications) was the most productive author, followed by Hua Xu (73 publications), and George Hripcsak (61 publications). Ian R White (5928 citations) was the most cited author, followed by David W Bates (2019 citations) and Joshua C Denny (1924 citations).

    A total of 20,513 institutions contributed to the medical informatics field from 2011 to 2020. The number of institutions that issued more than 10 publications was 1385. Harvard University (1161 publications) was the most productive institution, followed by University of Toronto (503 publications), University of Washington (488 publications), and Columbia University (462 publications).

    Table 2. The top 10 productive journals, authors, and institutions of medical informatics.
    RankAuthorTotal publicationsTotal citationsInstitutionTotal publicationsTotal citations
    1David W Bates992019Harvard University116119,471
    2Hua Xu731691University Toronto50310,904
    3George Hripcsak611465University Washington4886994
    4Dean F Sittig611233Columbia University4627744
    5Adam Wright591102University Michigan4556737
    6Hongfang Liu58873Stanford University4227976
    7Joshua C Denny511924Vanderbilt University3898971
    8Chunhua Weng51818University Penn3574084
    9Xiaoqian Jiang48565Duke University3365375
    10Ian R White475928Mayo Clinic3265066

    Co-authorship Analysis of Authors, Institutions, and Countries

    Upon analyzing the 34,742 retrieved articles, there was an average of 5 co-authors for each article, revealing extensive co-authorships among authors in the field of medical informatics. We employed VOSviewer to analyze the co-authorship of authors, institutions, and countries/regions and then built the visualization network map.

    We found that 304 productive authors published more than 15 articles. As shown in Figure 3, the largest collaborative network of productive authors comprising 234 authors was divided into 11 clusters of different colors. Hua Xu was the most active co-author with a total link strength of 162. The largest cluster (in red) involved 43 co-authors centering on Hua Xu, Xiaoqian Jiang, and Cui Tao. Figure 4 shows the collaborative network of 133 productive institutions that published more than 100 articles by 8 clusters of different colors. Harvard University was the most active co-author institution with a total link strength of 1484 and in the center of the green cluster. Figure 5 shows the largest collaborative network of countries/regions comprising 158 countries/regions divided into 4 different colored clusters. The United States was the most co-author country with a total link strength of 5495.

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    Figure 3. The collaborative network of productive authors participating in medical informatics publications from 2011 to 2020.
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    Figure 4. The collaborative network of productive institutions participating in medical informatics publications from 2011 to 2020.
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    Figure 5. The collaborative network of countries/regions participating in medical informatics publications from 2011 to 2020.
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    Co-citation Analysis of References and Cited Journal

    A total of 34,742 retrieved articles cited 700,628 references from 157,424 journals. The article published by Breiman in 2001 entitled “random forests” was the most cited reference. This article was cited 567 times in the retrieved publications on medical informatics and 40,253 times in WoSCC. Furthermore, the analysis of the distribution of cited journals helps identify the knowledge base of a certain field. The co-citation network of 64 cited journals with a minimum of 2000 citations was divided into 4 clusters of different colors. As shown in Figure 6, the red cluster centered on IEEE Transactions on Biomedical Engineering, and IEEE Transactions on Medical Imaging, Computer Methods and Programs in Biomedicine; the green cluster centered on Journal of Medical Internet Research, Journal of the American Medical Informatics Association, and International Journal of Medical Informatics; the blue cluster centered on Statistics in Medicine, Biometrics, Journal of The American Statistical Association; and the yellow cluster centered on JAMA, New England Journal of Medicine, Lancet, and PLoS One.

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    Figure 6. The co-citation network of highly cited journals on medical informatics from 2011 to 2020.
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    Co-occurrence Analysis of Author Keywords

    The primary purpose of keywords is to provide fast access to scientific publications for researchers. In a bibliometric study, co-occurrence analysis of keywords effectively reflects the research hotspots in a scientific field [27,28]. This study analyzed the “author keywords” retrieved from WoSCC to represent the research hotspots. We employed VOSviewer to perform a co-occurrence analysis of the 143 high-frequency author keywords, which appeared more than 100 times from 2011 to 2020. The co-occurrence network map of high-frequency author keywords on medical informatics is shown in Figure 7. The most high-frequency author keyword was EHRs (with 1591 occurrences), followed by mHealth (n=1331), machine learning (n=994), internet (n=827), and eHealth (n=824). The 143 high-frequency author keywords formed 4 clusters: red, green, blue, and yellow. The red cluster is the largest one with 43 keywords regarding the research hotspots of AI in health care and medicine. The green cluster mainly focused on the research hotspots of mobile health; the blue cluster represented the research hotspots of implementation and evaluation of EHRs; the yellow cluster demonstrated the research hotspots of medical informatics technology application in public health.

    We analyzed the theme evolution of the annual top 10 author keywords from 2011 to 2020, as shown in Figure 8. From 2011 to 2020, 28 author keywords entered the annual top 10 author keywords. The annual top 10 author keywords were constantly changing. EHRs was the only author keyword that has been in the annual top 10 for 10 consecutive years. COVID-19, which was ranked third in 2020, was the emerging theme of Medical Informatics.

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    Figure 7. The co-occurrence network of high-frequency author keywords on medical informatics publications from 2011 to 2020.
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    Figure 8. Theme evolution of the annual top 10 author keywords on medical informatics publications from 2011 to 2020.
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    The Global Publication Trends of Medical Informatics

    As shown in Multimedia Appendix 1, the global output of the entire life sciences and biomedical field shows a rapid growth trend from 2011 to 2020, except for 4 research areas. From 2011 to 2020, the increase rate of popularity of medical informatics far exceeded the growth rate of other research areas in life sciences and biomedical sciences, and it was almost 4 times the average increase rate, indicating the vast potential of medical informatics research in the future.

    Figure 1 shows that most countries/regions have contributed to medical informatics research. Figure 2 shows that the United States was continuously ranked as the top productive country, indicating its outstanding contribution to the field of medical informatics. As the only developing and Asian country among the top 10 productive countries, China has leaped to second place in the top 10 productive countries in 2018, 2019, and 2020. The number of articles from China increased from 87 in 2011 to 946 in 2020, with a growth rate of 987.36%. China’s growth rate was much higher than the average growth rate (193.86%, from 1987 in 2011 to 5839 in 2020), enabling it to improve its ranking rapidly. The rapid development of medical informatics in China may be attributed to the fact that China’s medical reform in 2009 focused on the application of medical informatics technology. The number of articles from Germany increased from 124 in 2011 to 295 in 2020, with a growth rate of 137.90%. The growth rate of Germany was lower than the average growth rate (193.86%, from 1987 in 2011 to 5839 in 2020), which might be the main factor for its decline in ranking.

    As shown in Table 1, the top 10 productive journals published 21,861 articles, accounting for 62.92% (21,861/34,742) of all medical informatics articles in the past 10 years. These journals have made substantial contributions to the development of medical informatics. From 2011 to 2020, the annual number of articles published in Statistics in Medicine was relatively stable, with minor fluctuations between 263 and 403. However, the ranking of Statistics in Medicine dropped from the first in 2011 to sixth in 2020, and its proportion dropped from 13.24% in 2011 to 5.53% in 2020. Except for 2014, the annual number of articles published by Journal of Medical Internet Research has continued to increase from 2011 to 2020. In particular, the annual number of articles published by Journal of Medical Internet Research has increased by 8.96 times, from 111 in 2011 to 1106 in 2020. Journal of Medical Internet Research was the most influential journal in medical informatics from 2011 to 2020 regardless of publications and citations. IEEE Journal of Biomedical and Health Informatics and JMIR mHealth and uHealth, published since 2013, were the fastest-growing journals in this field in the past 10 years.

    The Global Collaboration Patterns of Medical Informatics

    The international collaboration of authorship is attributed to a fast-growing increase in the number of outputs in a certain field [29]. In the past decade, an increasing number of authors, institutions, and countries/regions contributed to the productivity of medical informatics. From 2011 to 2020, more than 110,000 authors and 20,000 institutions from 161 countries/regions published medical informatics research. Additionally, to further demonstrate the dynamic changes of the authors in the field of medical informatics, we analyzed the authors who published medical informatics articles by categorizing the last decade as 2011-2015 and 2016-2020. Among the 79,829 authors who published medical informatics articles between 2016 and 2020, only 10,051 (12.59%) authors had previously published in this area. However, 69,778 (87.41%) authors published their first medical informatics papers during this period. Thus, many new researchers have flooded into medical informatics research in the past 5 years.

    The top productive author David W Bates enjoyed an international reputation in medical informatics research, focusing on medical information technology to improve the safety and quality of medical care [30,31]. As shown in Figure 3, 70 of the 304 productive authors were not in the co-authorship network, indicating that the collaboration between productive authors still had certain limitations. Harvard University was always the top productive institution and at the center of the co-authorship network of institutions, indicating its substantial academic influences in medical informatics research. As shown in Figure 4, all the 133 productive institutions were in the co-authorship network, showing extensive collaborations between institutions worldwide. The United States continuously remained the top productive country and at the center of the co-authorship network of countries/regions, indicating its substantial academic influences in medical informatics research. As shown in Figure 5, 158 of the 161 countries/regions were in the co-authorship network, showing extensive collaborations between different countries.

    The Basic Knowledge of Medical Informatics

    Co-citation analysis can comprehensively demonstrate the knowledge base of a certain discipline [32]. As shown in Figure 6, the red cluster represents journals on computer science; the blue cluster represented journals on statistics science; the green cluster represented journals on medical informatics; the yellow cluster represented journals on general medicine and science and technology. In this study, co-citation analysis of cited journals showed that the knowledge base of medical informatics comes from medical informatics itself and disciplines such as computer science, general medicine, statistics, science and technology, and others.

    The Research Hotspots of Medical Informatics

    The co-occurrence analysis of high-frequency author keywords clarified the leading hotspots of medical informatics research. As shown in Figure 7, there are 4 main research hotspots on medical informatics from 2011 to 2020.

    The red cluster focused on AI in health care and medicine. AI usually refers to computing technology that mimics or simulates the processes supported by human intelligence, which dramatically improves diagnosis and treatment accuracy and the entire clinical treatment process [33]. With the improvement in computer performance and the availability of big data from EHRs, the research and application of AI in health care and medicine have developed rapidly. With its advanced algorithms and learning capabilities, AI applications have helped medical professionals through symptom monitoring, predictive modeling, and decision support, especially in cancer and medical imaging [34,35].

    The green cluster focused on mobile health. With the popularity of the internet and the rapid development of mobile communication devices and wearable devices, mobile health has been widely used in developed and developing countries [36]. Mobile health improves the ability of health systems to provide high-quality health care, especially in chronic disease, mental health, physical activity, HIV, and smoking cessation [37-40].

    The blue cluster focused on the implementation and evaluation of EHRs. EHRs utilize information systems to store a digital format for patient and population health information [41]. Quantitative or qualitative methods were applied to evaluate the usability, interoperability, security, privacy, and other functions to improve EHRs continuously [42-44]. Health care professionals can access EHRs quickly and effectively to better serve patients and the population, and these have great potential in improving medical efficiency and quality [45]. Implementing EHR and decision support helps clinicians make precise decisions to improve health care, reduce medical errors, and ensure patient safety [46-48].

    The yellow cluster focused on medical informatics technology application in public health. The medical informatics technology represented by social media provides a series of possibilities for establishing multidirectional communication and interaction and quickly monitoring public emotions and activities. The application of new medical informatics technology can help increase the coverage and efficiency of public health services, especially in public communication, education, survey, engagement, and monitoring [49,50]. As our understanding of the most effective methods of using medical informatics technology to support public health research and practice matures, there will be more innovative applications of medical informatics technology in the field of public health, thereby making more remarkable contributions to improving population health.

    The Theme Evolution and Emerging Frontiers of Medical Informatics

    As shown in Figure 8, the content and ranking of the top 10 author keywords have evolved dramatically every year from 2011 to 2020. Only one of the top 10 author keywords in 2011 still appeared in the top 10 keywords in 2020. These were a microcosm of the rapid development of medical informatics and show that the theme of medical informatics research was significantly changing with the development of information technology.

    EHRs was the only author keyword that continuously ranked in the top 10 during the past 10 years, and it was the most high-frequency keyword from 2011 to 2020. EHR was the most significant research hotspot of medical informatics throughout the past decade.

    The internet consecutively ranked second from 2011 to 2015, but its ranking showed a gradual decline after 2016, showing that internet research based on traditional computers was the most concerned research theme in the early stages of medical informatics research in the past decade.

    mHealth first appeared in the top 10 author keywords in 2013, and its ranking increased every year. Since 2018, mHealth is ranked as the number 1 author keyword for 3 consecutive years. Moreover, the author keywords mobile applications ranked sixth in 2020 and mobile phones ranked ninth in 2020, which were closely related to mHealth, showing that mHealth based on mobile devices has become the undisputed most prominent emerging theme in medical informatics.

    Machine learning first appeared in the top 10 authors keywords in 2014 and has remained in the top 10 author keywords since then. The main methods of AI technology, machine learning and deep learning, were ranked second and fifth, respectively, in 2020, revealing that AI in health care was an emerging frontier of medical informatics. Especially, deep learning with the ability to mine a large amount of multimodal unstructured information and the ability to automate feature learning can promote the application of data-driven solutions in disease diagnosis and predicting prognosis [51,52].

    The keywords related to health care and disease such as cancer, diabetes, physical activity, and COVID-19 also appeared in the top 10 author keywords, indicating that the medical informatics technology has promising applications in treating, managing, monitoring, and preventing disease in the past decade. The outbreak of COVID-19 has had an unprecedented impact on global health, economy, and society. Various active response measures have been used to deal with the epidemic, and medical information also plays an important role, especially in coordinating medical resources, information dissemination, contact tracing, public education, and mental health intervention [53,54].

    Limitations

    Our research has some limitations. First, only English articles were retrieved in this study. Therefore, language bias may inevitably occur. Second, we did not evaluate the quality of publications, and some low-quality publications may have the same weight as high-quality publications. Finally, the data for this analysis were only extracted from WoSCC, excluding those from other databases such as Scopus, PubMed, or Google Scholar. Thus, publications appearing only through one of these databases may have been missed. Exploring ways to combine different data sources in future work is essential.

    Conclusions

    To our knowledge, this study provided the first comprehensive picture of global efforts on medical informatics in the past decade from a bibliometric analysis perspective. We summarize the recent advances in medical informatics in the past decade and shed light on their publication trends, influential journals, global collaboration patterns, basic knowledge, research hotspots, theme evolution, and emerging frontiers. These findings will accurately and quickly grasp the research trends and provide valuable guidance for future medical informatics research.

    Acknowledgments

    This work was funded by the Hwa Mei fund (Grant No. 2019HMKY23), Scientific Projects of the Education Department of Liaoning Province (Grant No. LJKR0273), Scientific Projects of the Education Department of Liaoning Province (Grant No. ZF2019026).

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    The global output of the entire life sciences and biomedical field.

    XLSX File (Microsoft Excel File), 38 KB
    1. Hasman A, Haux R, Albert A. A systematic view on medical informatics. Computer Methods and Programs in Biomedicine 1996 Nov;51(3):131-139. [CrossRef]
    2. Masic I. Five periods in development of medical informatics. Acta Inform Med 2014 Feb;22(1):44-48 [FREE Full text] [CrossRef] [Medline]
    3. Lorenzi NM, Gardner RM, Pryor TA, Stead WW. Medical informatics: the key to an organization's place in the new health care environment. J Am Med Inform Assoc 1995;2(6):391-392 [FREE Full text] [CrossRef] [Medline]
    4. Deng H, Wang J, Liu X, Liu B, Lei J. Evaluating the outcomes of medical informatics development as a discipline in China: A publication perspective. Comput Methods Programs Biomed 2018 Oct;164:75-85. [CrossRef] [Medline]
    5. Imhoff M, Webb A, Goldschmidt A, European Society of Intensive Care Medicine. ESCIM. Health informatics. Intensive Care Med 2001 Jan 19;27(1):179-186. [CrossRef] [Medline]
    6. Fridsma DB. Health informatics: a required skill for 21st century clinicians. BMJ 2018 Jul 12;362:k3043. [CrossRef] [Medline]
    7. Stead WW, Lorenzi NM. Health informatics: linking investment to value. J Am Med Inform Assoc 1999 Sep 01;6(5):341-348 [FREE Full text] [CrossRef] [Medline]
    8. Tremblay MC, Deckard GJ, Klein R. Health informatics and analytics - building a program to integrate business analytics across clinical and administrative disciplines. J Am Med Inform Assoc 2016 Jul;23(4):824-828. [CrossRef] [Medline]
    9. Young H, Belanger T. Young, T. In: The ALA Glossary of Library and Information Science. Chicago, IL: American Library Association; 1983.
    10. Bansard JY, Rebholz-Schuhmann D, Cameron G, Clark D, van Mulligen E, Beltrame E, et al. Medical informatics and bioinformatics: a bibliometric study. IEEE Trans Inf Technol Biomed 2007 May;11(3):237-243 [FREE Full text] [CrossRef] [Medline]
    11. Cao J, Lim Y, Sengoku S, Guo X, Kodama K. Exploring the Shift in International Trends in Mobile Health Research From 2000 to 2020: Bibliometric Analysis. JMIR Mhealth Uhealth 2021 Sep 08;9(9):e31097 [FREE Full text] [CrossRef] [Medline]
    12. Blanc X, Collet TH, Auer R, Fischer R, Locatelli I, Iriarte P, et al. Publication trends of shared decision making in 15 high impact medical journals: a full-text review with bibliometric analysis. BMC Med Inform Decis Mak 2014 Aug 09;14:71 [FREE Full text] [CrossRef] [Medline]
    13. Armfield NR, Edirippulige S, Caffery LJ, Bradford NK, Grey JW, Smith AC. Telemedicine--a bibliometric and content analysis of 17,932 publication records. Int J Med Inform 2014 Oct;83(10):715-725. [CrossRef] [Medline]
    14. Yang YT, Iqbal U, Ching JHY, Ting JBS, Chiu HT, Tamashiro H, et al. Trends in the growth of literature of telemedicine: A bibliometric analysis. Comput Methods Programs Biomed 2015 Dec;122(3):471-479. [CrossRef] [Medline]
    15. Waqas A, Teoh SH, Lapão LV, Messina LA, Correia JC. Harnessing Telemedicine for the Provision of Health Care: Bibliometric and Scientometric Analysis. J Med Internet Res 2020 Oct 02;22(10):e18835 [FREE Full text] [CrossRef] [Medline]
    16. Takahashi R, Kajikawa Y. Computer-aided diagnosis: A survey with bibliometric analysis. Int J Med Inform 2017 May;101:58-67. [CrossRef] [Medline]
    17. Chen X, Xie H, Wang FL, Liu Z, Xu J, Hao T. A bibliometric analysis of natural language processing in medical research. BMC Med Inform Decis Mak 2018 Mar 22;18(Suppl 1):14 [FREE Full text] [CrossRef] [Medline]
    18. Guo Y, Hao Z, Zhao S, Gong J, Yang F. Artificial Intelligence in Health Care: Bibliometric Analysis. J Med Internet Res 2020 Jul 29;22(7):e18228 [FREE Full text] [CrossRef] [Medline]
    19. Taj F, Klein MCA, van Halteren A. Digital Health Behavior Change Technology: Bibliometric and Scoping Review of Two Decades of Research. JMIR Mhealth Uhealth 2019 Dec 13;7(12):e13311 [FREE Full text] [CrossRef] [Medline]
    20. Fornazin MP, Bruno EP, Costa de Castro L, de Castro Silva SLF. From medical informatics to digital health: a bibliometric analysis of the research field. In: AMCIS 2021 Proceedings. 2021 Presented at: AMCIS 2021; August 9-13, 2021; Virtual   URL: https://aisel.aisnet.org/amcis2021/healthcare_it/sig_health/18
    21. Agarwal A, Durairajanayagam D, Tatagari S, Esteves SC, Harlev A, Henkel R, et al. Bibliometrics: tracking research impact by selecting the appropriate metrics. Asian J Androl 2016;18(2):296-309 [FREE Full text] [CrossRef] [Medline]
    22. Newman MEJ. Coauthorship networks and patterns of scientific collaboration. Proc Natl Acad Sci U S A 2004 Apr 06;101 Suppl 1:5200-5205 [FREE Full text] [CrossRef] [Medline]
    23. Romero L, Portillo-Salido E. Trends in Sigma-1 Receptor Research: A 25-Year Bibliometric Analysis. Front Pharmacol 2019;10:564 [FREE Full text] [CrossRef] [Medline]
    24. VOSviewer.   URL: http://www.vosviewer.com [accessed 2022-03-29]
    25. van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010 Aug;84(2):523-538 [FREE Full text] [CrossRef] [Medline]
    26. Chen C. Searching for intellectual turning points: progressive knowledge domain visualization. Proc Natl Acad Sci U S A 2004 Apr 06;101 Suppl 1:5303-5310 [FREE Full text] [CrossRef] [Medline]
    27. Zhao D, Li J, Seehus C, Huang X, Zhao M, Zhang S, et al. Bibliometric analysis of recent sodium channel research. Channels (Austin) 2018;12(1):311-325 [FREE Full text] [CrossRef] [Medline]
    28. Zhou H, Tan W, Qiu Z, Song Y, Gao S. A bibliometric analysis in gene research of myocardial infarction from 2001 to 2015. PeerJ 2018;6:e4354 [FREE Full text] [CrossRef] [Medline]
    29. Corrales-Reyes I. Co-authorship and scientific collaboration networks in Medwave. Medwave 2017 Dec 28;17(09):e7103-e7103. [CrossRef]
    30. Nanji KC, Slight SP, Seger DL, Cho I, Fiskio JM, Redden LM, et al. Overrides of medication-related clinical decision support alerts in outpatients. J Am Med Inform Assoc 2014;21(3):487-491. [CrossRef] [Medline]
    31. Phansalkar S, van der Sijs H, Tucker AD, Desai AA, Bell DS, Teich JM, et al. Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc 2013 May 01;20(3):489-493 [FREE Full text] [CrossRef] [Medline]
    32. Zou LX, Sun L. Global diabetic kidney disease research from 2000 to 2017: A bibliometric analysis. Medicine (Baltimore) 2019 Feb;98(6):e14394 [FREE Full text] [CrossRef] [Medline]
    33. Tran BX, Vu GT, Ha GH, Vuong QH, Ho MT, Vuong TT, et al. Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study. J Clin Med 2019 Mar 14;8(3):360 [FREE Full text] [CrossRef] [Medline]
    34. Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 2018 Jul;21(4):653-660. [CrossRef] [Medline]
    35. Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers. Korean J Radiol 2019 Mar;20(3):405-410 [FREE Full text] [CrossRef] [Medline]
    36. Silva BMC, Rodrigues JJPC, de la Torre Díez I, López-Coronado M, Saleem K. Mobile-health: A review of current state in 2015. J Biomed Inform 2015 Aug;56:265-272 [FREE Full text] [CrossRef] [Medline]
    37. Miller AS, Cafazzo JA, Seto E. A game plan: Gamification design principles in mHealth applications for chronic disease management. Health Informatics J 2016 Jun;22(2):184-193 [FREE Full text] [CrossRef] [Medline]
    38. Berry K, Salter A, Morris R, James S, Bucci S. Assessing Therapeutic Alliance in the Context of mHealth Interventions for Mental Health Problems: Development of the Mobile Agnew Relationship Measure (mARM) Questionnaire. J Med Internet Res 2018 Apr 19;20(4):e90 [FREE Full text] [CrossRef] [Medline]
    39. Guo Y, Xu Z, Qiao J, Hong YA, Zhang H, Zeng C, et al. Development and Feasibility Testing of an mHealth (Text Message and WeChat) Intervention to Improve the Medication Adherence and Quality of Life of People Living with HIV in China: Pilot Randomized Controlled Trial. JMIR Mhealth Uhealth 2018 Sep 04;6(9):e10274 [FREE Full text] [CrossRef] [Medline]
    40. Bustamante LA, Gill Ménard C, Julien S, Romo L. Behavior Change Techniques in Popular Mobile Apps for Smoking Cessation in France: Content Analysis. JMIR Mhealth Uhealth 2021 May 13;9(5):e26082 [FREE Full text] [CrossRef] [Medline]
    41. Gunter TD, Terry NP. The emergence of national electronic health record architectures in the United States and Australia: models, costs, and questions. J Med Internet Res 2005 Mar 14;7(1):e3 [FREE Full text] [CrossRef] [Medline]
    42. Horsky J, McColgan K, Pang JE, Melnikas AJ, Linder JA, Schnipper JL, et al. Complementary methods of system usability evaluation: surveys and observations during software design and development cycles. J Biomed Inform 2010 Oct;43(5):782-790 [FREE Full text] [CrossRef] [Medline]
    43. Praveen D, Patel A, Raghu A, Clifford GD, Maulik PK, Mohammad Abdul A, et al. SMARTHealth India: Development and Field Evaluation of a Mobile Clinical Decision Support System for Cardiovascular Diseases in Rural India. JMIR Mhealth Uhealth 2014 Dec 08;2(4):e54 [FREE Full text] [CrossRef] [Medline]
    44. Fossum M, Ehnfors M, Fruhling A, Ehrenberg A. An evaluation of the usability of a computerized decision support system for nursing homes. Appl Clin Inform 2011;2(4):420-436 [FREE Full text] [CrossRef] [Medline]
    45. Opipari-Arrigan L, Dykes DMH, Saeed SA, Thakkar S, Burns L, Chini BA, et al. Technology-Enabled Health Care Collaboration in Pediatric Chronic Illness: Pre-Post Interventional Study for Feasibility, Acceptability, and Clinical Impact of an Electronic Health Record-Linked Platform for Patient-Clinician Partnership. JMIR Mhealth Uhealth 2020 Nov 26;8(11):e11968. [CrossRef] [Medline]
    46. Payne TH, Hines LE, Chan RC, Hartman S, Kapusnik-Uner J, Russ AL, et al. Recommendations to improve the usability of drug-drug interaction clinical decision support alerts. J Am Med Inform Assoc 2015 Nov;22(6):1243-1250. [CrossRef] [Medline]
    47. Galanter WL, Hier DB, Jao C, Sarne D. Computerized physician order entry of medications and clinical decision support can improve problem list documentation compliance. Int J Med Inform 2010 May;79(5):332-338. [CrossRef] [Medline]
    48. Miller A, Moon B, Anders S, Walden R, Brown S, Montella D. Integrating computerized clinical decision support systems into clinical work: A meta-synthesis of qualitative research. Int J Med Inform 2015 Dec;84(12):1009-1018. [CrossRef] [Medline]
    49. Capurro D, Cole K, Echavarría MI, Joe J, Neogi T, Turner AM. The use of social networking sites for public health practice and research: a systematic review. J Med Internet Res 2014 Mar 14;16(3):e79 [FREE Full text] [CrossRef] [Medline]
    50. Zhang Y, Cao B, Wang Y, Peng TQ, Wang X. When Public Health Research Meets Social Media: Knowledge Mapping From 2000 to 2018. J Med Internet Res 2020 Aug 13;22(8):e17582 [FREE Full text] [CrossRef] [Medline]
    51. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med 2019 Jan;25(1):24-29 [FREE Full text] [CrossRef] [Medline]
    52. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, et al. Deep Learning for Health Informatics. IEEE J Biomed Health Inform 2017 Jan;21(1):4-21. [CrossRef] [Medline]
    53. Bao H, Cao B, Xiong Y, Tang W. Digital Media's Role in the COVID-19 Pandemic. JMIR Mhealth Uhealth 2020 Sep 18;8(9):e20156 [FREE Full text] [CrossRef] [Medline]
    54. Crawford A, Serhal E. Digital Health Equity and COVID-19: The Innovation Curve Cannot Reinforce the Social Gradient of Health. J Med Internet Res 2020 Jun 02;22(6):e19361 [FREE Full text] [CrossRef] [Medline]

    ‎
    AI: artificial intelligence
    EHRs: electronic health records
    WoSCC: Web of Science Core Collection

    Edited by C Lovis; submitted 16.10.21; peer-reviewed by J Wang, JK Kumar; comments to author 10.11.21; revised version received 26.12.21; accepted 31.01.22; published 21.04.22

    Copyright

    ©Xuefei He, Cheng Peng, Yingxin Xu, Ye Zhang, Zhongqing Wang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 21.04.2022.

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