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Existing health informatics curriculum requirements mostly use a competency-based approach rather than a skill-based one.
The main objective of this study was to assess the current skills training requirements in graduate health informatics curricula to evaluate graduate students’ confidence in specific health informatics skills.
A quantitative cross-sectional observational study was developed to evaluate published health informatics curriculum requirements and to determine the comprehensive health informatics skill sets required in a research university in New York, United States. In addition, a questionnaire to assess students’ confidence about specific health informatics skills was developed and sent to all enrolled and graduated Master of Science students in a health informatics program.
The evaluation was performed in a graduate health informatics program, and analysis of the students’ self-assessments questionnaire showed that 79.4% (81/102) of participants were not confident (not at all confident or slightly confident) about developing an artificial intelligence app, 58.8% (60/102) were not confident about designing and developing databases, and 54.9% (56/102) were not confident about evaluating privacy and security infrastructure. Less than one-third of students (24/105, 23.5%) were confident (extremely confident and very confident) that they could evaluate the use of data capture technologies and develop mobile health informatics apps (10/102, 9.8%).
Health informatics programs should consider specialized tracks that include specific skills to meet the complex health care delivery and market demand, and specific training components should be defined for different specialties. There is a need to determine new competencies and skill sets that promote inductive and deductive reasoning from diverse and various data platforms and to develop a comprehensive curriculum framework for health informatics skills training.
The National Center for Education Statistics defines competency as a combination of skills, abilities, and knowledge needed to perform a specific task [
The first international recommendations to develop health informatics educational activities were published by the International Medical Informatics Association (IMIA) in 2000 [
The evolution of curriculum requirements for health informatics programs.
Domain (International Medical Informatics Association 2000; International) | Facet (CAHIIMa 2012; National) | Foundational domains (CAHIIM/American Medical Informatics Association 2017; National) |
Biomedical and health informatics core knowledge and skills | I. Information systems—concerned with such issues as information systems analysis, design, implementation, and management | F1. Health |
Medicine, health and biosciences, health system organization | II. Informatics—concerned with such issues as the structure, function and transfer of information, sociotechnical aspects of health computing, and human-computer interaction. | F2. Information science and technology |
Informatics/computer science, mathematics, biometry | III. Information technology—concerned with such issues as computer networks, database and systems administration, security, and programming | F3. Social and behavioral science |
Optional modules |
IV. Additional desired course content: epidemiology; quantitative, qualitative, and mixed methods; and biomedical sciences. |
F4. Health information science and technology |
aCAHIIM: Commission on Accreditation for Health Informatics and Information Management Education.
With the adoption of digital technologies around the world, several countries have focused on education initiatives to improve populations’ 21st century digital skills [
As there are several international and national initiatives to develop health informatics competencies, determining the standard comprehensive health information skill sets has always been a challenging task because of the continually evolving technology. National health informatics organizations and accreditation agencies have been specifying their own standards because of the lack of universal standards. For example, Canada’s Health Informatics Association (Digital Health Canada, known as COACH before 2017) developed 51 competencies about information management, information technology, clinical/health services, Canadian Health System, organizational and behavioral management, project management, and analysis and evaluation in 2009 [
Another notable development was the formation of the eHealth Collaboration Workforce Development Workgroup in 2013. A Web-based database about health information technology competencies to identify the gaps between information and communication technology (ICT) competency and knowledge deficiencies was built by the United States and the European Union (EU) [
The accreditation process evaluates an academic institution’s effectiveness in achieving its stated mission, and the graduate education programs that participate in the voluntary accreditation process should comply with the regional, national, or independent accrediting agencies’ core curriculum requirements. This is an important process to ensure the accountability of academic training programs and is widely considered as the de facto standard for quality assessment and continuous improvement.
In the United States, although the CAHIIM accredits undergraduate and graduate health informatics programs, the American Health Information Management Association (AHIMA) and the Commission on Certification for Health Informatics and Information Management certify individuals. In 2012, CAHIIM published the curriculum requirements for a master’s in health informatics degree and 3 mandatory facets about (1) the design, analysis, implementation, and management of information systems; (2) sociotechnical aspects; human-computer interaction; and structure, function, and transfer of information; and (3) computer networks, security, programming, database, and systems administration were determined [
In 2017, CAHIIM revised the accreditation standards for master’s degree programs in health informatics and published a revised version of core competencies that consists of the following foundational domains: (1) health; (2) information science and technology; (3) social and behavioral science; (4) health information science and technology; (5) human factors and sociotechnical systems; (6) social and behavioral aspects of health; (7) social, behavioral, and information science and technology applied to health; (8) professionalism; (9) interprofessional collaborative practice; and (10) leadership (
After the discussion about the need to explore the description of
New job opportunities for health informatics professionals require specific skill sets to utilize new cutting-edge, patient-focused delivery tools. Eligibility requirements for an advanced health informatics certification were proposed in 2016 [
The Commission on Accreditation for Health Informatics and Information Management Education’s revised health informatics skills according to the American Medical Informatics Association 2017 core competencies.
Foundational domains | Skills |
F4. Health information science and technology | Design a solution to a biomedical or health information problem by applying computational and systems thinking, information science, and technology. |
F5. Human factors and sociotechnical systems | Applying social behavioral theories and human factors engineering to the design and evaluation of information systems and technology. |
F6. Social and behavioral aspects of health | Apply a model, which may be dependent upon the application area of the training program, to address a social and behavioral problem related to the health of individuals, populations, and organizations. |
F7. Social, behavioral, and information science and technology applied to health | Integrate and apply the theories, models, and tools from social, business, human factors, behavioral, and information sciences and technologies to design, implement, and evaluate health informatics solutions. |
F8. Professionalism | Demonstrate professional practices that incorporate ethical principles and values of the discipline. |
F9. Interprofessional collaborative practice | Apply relationship-building skills and the principles of interprofessional communication in a responsive and responsible manner that supports a team approach to solve complex health and health information problems. |
F10. Leadership | Employ leadership and fellowship methods, concepts, and tools to motivate others toward accomplishing a health informatics vision. |
Academicians have also been discussing the integration of skills training into the health informatics curriculum for a long time. For example, new educational approaches related to emerging health information technologies were described, efforts to increase electronic health record (EHR) adoption were discussed, and hands-on exposure to health information systems during the graduate education was recommended to provide the necessary skills to solve interoperability issues [
In addition, the IMIA’s working group encouraged the international health informatics community to begin a discussion on various big data and data training skills [
A number of health informatics students acquire skills training on the job rather than during their formal education, and recent studies emphasize the need for new models for skills acquisition [
According to the American Society for Training and Development, skills gaps in the organizations have been growing [
Moreover, health informatics students need additional competencies to design and develop patient-centered health information systems, mine and analyze health care data, and use telemedicine and wireless remote monitoring systems. Evolving information technology and the growing number of medical devices and software apps for mobile devices require qualified workers with new skill sets, which were not included in the health informatics curriculum in the past. Overall, 5 employer-desired skill categories in bioinformatics—general, computational, biology, statistics and mathematics, and bioinformatics—were determined [
One of the major competency-based training initiatives was the Technology Informatics Guiding Education Reform (TIGER), which was established in 2006 to review informatics competencies for nursing students and practicing nurses. This initiative identified knowledge and skill set needs, which subsequently led to the development of an informatics competency framework for nurses that consists of basic computer skills, information literacy, and information management components. The TIGER Informatics Competencies Collaborative published their final report in 2009, and complex demands in health care led to the development of other national collaborative projects [
Despite several recommendations by professional organizations, a skills training framework for health informatics students is still not clearly defined. Existing skills training recommendations mostly focus on EHR training, and they generally do not include mHealth, home care, remote monitoring, AI, and data science training skills [
A study to determine students’ confidence in specific health informatics skills was conducted. For this purpose, published health informatics competencies were evaluated by two researchers independently, and a questionnaire to investigate skill sets of graduate Master of Science (MS) in Health Informatics students was developed by surveying core facility directors [
The questionnaire was divided into three parts. Part 1 consisted of demographic questions. Part 2 collected information about self-assessed skill sets using Likert scale questions, and 24 health informatics skills were determined for the second part of the questionnaire. Part 3 explored students’ suggestions for a new curriculum using open-ended questions.
A Web-based questionnaire was sent to a total of 223 enrolled and graduated students in the master’s degree program. Overall, 45.7% (102/223) of the participants completed the questionnaire within 2 months of the survey period, and all survey submissions were suitable for analysis.
Frequency and percentage of respondents classified by demographic details (N=102).
General characteristics | Frequency, n (%) | |
|
||
|
Female | 71 (69.6) |
|
Male | 31 (30.4) |
|
||
|
<34 | 68 (66.7) |
|
35-44 | 19 (18.6) |
|
45-54 | 12 (11.8) |
|
>55 | 2 (2.0) |
|
||
|
Currently enrolled | 72 (70.6) |
|
Graduated | 29 (28.4) |
|
||
|
Information technology | 24 (23.5) |
|
Clinical | 29 (28.4) |
|
Health care medical services and products | 21 (20.6) |
|
Other | 16 (15.7) |
|
Not employed | 11 (10.8) |
The questionnaire was tested on a small sample of respondents to identify problems with the construction and potential problems with the unclear wording. Face validity was established by an expert faculty member. The questionnaire was assessed, and the feedback about the clarity, friendliness of questions, and consistency was provided. Cronbach alpha was used to assess internal consistency, and it ranged from .9947 to .9952 (N=102). The overall reliability demonstrated excellent internal consistency.
The inclusion criteria included the participants’ informed consent and being enrolled in or graduated from the MS in health informatics program at Adelphi University in Garden City, New York, United States. As skills training is not included in the curriculum, current students do not receive formal hands-on training. Therefore, all enrolled and graduated students were included in the study, and survey results were not divided.
An institutional review board–approved questionnaire was distributed to all graduated and enrolled students. The participants received the consent form and instructions to complete a Web-based questionnaire, and 4 reminder emails were sent at 1-week intervals. The survey was anonymous. Participation in the study was voluntary, and there was no grade or compensation.
Among the respondents, 30.4% (31/102) were males, and 70.0% (71/102) were females. The largest percentage of respondents was aged less than 34 years; nearly one-third (31/102, 30.4%) of the participants were aged 35 to 54 years, and only 2 participants were older than 55 years. The majority of the respondents were currently enrolled in the program (73/102, 71.6%). Most of the respondents had an information technology– or health care–related occupation (74/102, 72.5%), and only 10.8% (11/102) of participants were not employed (
Benner’s 5-level model of skill acquisition framework (novice, advanced beginner, competent, proficient, and expert) was applied to assess students’ level of confidence [
Respondents initially rated themselves higher on Microsoft Word essential skills. For skills to insert a table of contents, footnotes, endnotes, and cross-references, 84.3% (86/102) of respondents rated themselves as
Participants rated themselves as
Respondents rated themselves as
For AI app development skills, 6.9% (7/102) of the participants rated themselves as
Students’ responses regarding confidence with specific health informatics skills (N=102) (competency level according to Benner’s 5 levels of competencies: 0:00-1:00=novice; 1:0-2:00=advanced beginner; 2:0-3:00=competent; 3:01-4:00=proficient; 4:01-5:00=expert).
Survey item | Value, mean (SD) | Extremely confident/very confident, n (%) | Moderately confident, n (%) | Not at all confident/slightly confident, n (%) | Internal reliability Cronbach alpha | Interpretation |
Skills in evaluating health information systems and preparing recommendations to improve functionality | 3.14 (1.12) | 40 (39.2) | 31 (30.4) | 31 (30.4) | .9947 | Proficient |
Skills in developing machine learning apps for personalized health monitoring | 2.30 (1.07) | 13 (12.7) | 31 (30.4) | 58 (56.8) | .9948 | Competent |
Skills in building interfaces and developing and programming mobile health informatics apps | 2.24 (1.06) | 10 (9.8) | 31 (30.4) | 61 (59.8) | .9948 | Competent |
Skills in setting up new businesses and entrepreneurship | 2.61 (1.21) | 25 (24.5) | 21 (20.6) | 56 (54.9) | .9948 | Competent |
Skills in training staff on system use and troubleshooting software and hardware issues | 3.28 (1.27) | 47 (46.0) | 25 (24.5) | 30 (29.4) | .9947 | Proficient |
Skills in mining and analyzing data | 3.00 (1.08) | 34 (33.3) | 31 (30.4) | 37 (36.3) | .9948 | Competent |
Skills in interpreting inferential statistics | 2.63 (1.04) | 20 (19.6) | 29 (28.4) | 52 (51.0) | .9948 | Competent |
Skills in developing software to collect, organize, analyze, and interface with data | 2.35 (1.10) | 15 (14.7) | 29 (28.4) | 58 (56.8) | .9948 | Competent |
Skills in developing artificial intelligence apps | 1.80 (1.02) | 7 (6.9) | 14 (13.7) | 81 (79.4) | .9951 | Advanced beginner |
Skills in developing data visualization techniques | 2.42 (1.16) | 19 (18.6) | 25 (24.5) | 58 (56.8) | .9948 | Competent |
Skills in designing and leading health informatics projects | 2.84 (1.15) | 28 (27.5) | 36 (35.3) | 38 (37.3) | .9947 | Competent |
Skills in assessing data integrity and assessing data reliability | 2.94 (1.10) | 35 (34.3) | 29 (28.4) | 38 (37.3) | .9948 | Competent |
Skills in compiling data from secondary sources | 3.20 (1.09) | 43 (42.2) | 34 (33.3) | 25 (24.5) | .9948 | Proficient |
Skills in evaluating the use of data capture technologies | 2.82 (1.01) | 24 (23.5) | 37 (36.3) | 41 (40.2) | .9949 | Competent |
Skills in designing and developing databases | 2.34 (1.10) | 15 (14.7) | 27 (26.4) | 60 (58.8) | .9949 | Competent |
Skills in evaluating privacy and security infrastructure | 2.34 (1.11) | 18 (17.6) | 28 (27.5) | 56 (54.9) | .9948 | Competent |
Skills in using Microsoft Word to insert a table of contents, footnotes, endnotes, and cross-references | 4.21 (0.92) | 85 (83.3) | 10 (9.8) | 7 (6.9) | .9952 | Expert |
Skills in using Microsoft Word’s macro commands, creating dialogue boxes, and understanding the notions of Visual Basic Application programming | 2.62 (1.23) | 24 (23.5) | 32 (31.4) | 46 (45.1) | .9948 | Competent |
Skills in developing a database using Microsoft Access | 2.82 (1.09) | 31 (30.4) | 23 (22.5) | 48 (47.1) | .9948 | Competent |
Skills in performing math using Microsoft Excel and enter a calculation formula | 3.81 (1.03) | 62 (60.1) | 27 (26.5) | 13 (12.7) | .9949 | Proficient |
Skills in using Microsoft Excel for statistics such as calculating sample variance and standard deviation | 3.48 (1.10) | 46 (45.1) | 36 (35.3) | 20 (19.6) | .9948 | Proficient |
Skills in performing statistical tests using SPSS | 2.74 (1.21) | 28 (27.5) | 30 (29.4) | 44 (43.1) | .9947 | Competent |
Skills in choosing evidence-based resources | 3.75 (0.99) | 66 (64.7) | 24 (23.5) | 12 (11.8) | .9949 | Proficient |
Skills in using PICO to plan a search | 2.50 (1.19) | 21 (20.6) | 32 (31.4) | 49 (48.0) | .9948 | Competent |
The qualitative data analysis process to identify patterns and themes was inspired by Braun and Clark’s thematic analysis method [
Theme 1: EHR software training: Participants expressed an interest in hands-on training in EHR documentation and security (
Theme 2: Data science, visualization, and analytics: Respondents expressed a strong preference for hands-on experience with Structured Query Language (SQL), Tableau, Crystal Reports, and other database and data visualization products (
Theme 3: Software training and app development: Students emphasized the need for programming classes and coding skills and requested courses that focus on entry-level programming, HTML courses, and Microsoft Project (
Theme 4: Specialization courses: Participants acknowledged a desire to receive certifications and indicated the need for specific tracks depending on career plans (
“There could be more exposure to and training on information technology that we will come into contact with in the field like the EHR.”
“Maybe there can be a class on EMRs which can incorporate what is out right now and teach students about what makes a health care system successful and lasting.”
“If one class required the students to virtually build a system.”
“Hands-on working of top EMR like EPIC.”
“I think medical terminologies could be added to the curriculum. I also think the program could offer different tracks so we could choose.”
“Having access to an EMR system and being able to utilize it.”
“Perhaps purchasing a low-cost small practice EHR and over the course of the semester have students learn the backend; how to create users, manage security, edit forms, notes, and templates. Divide the class into groups assign a new functionality to be built within the system (anew note for example) task the team with building that item including everything from building a project plan to creating training materials the rest of the class on the new functionality.”
“Interactive training for VBA, Tableau, Python.”
“One change would be to definitely increase the actual use of apps such as the Microsoft suite (Excel, Word, Access, Project, Visio), as well as learning more about Structured Query Language (SQL). Database creation and querying are such important functions in IT.”
“More on reporting data.”
“Data modeling and visualization classes based on industry software.”
“A little more database work and knowledge could help.”
“More technical courses—data analytics, predictive models, cognitive computing, Crystal Reports.”
“More exposure to databases and SQL, or coding of some kind.”
“During my journey I have learned many technical staff such as database design and management, health care information management, security design and other similar subjects, but in my opinion the program should include more practical technical staff like teaching a programming language.”
“I wish there was more courses that was geared toward MS Excel and PowerPoint. Being sufficiently prepared with these apps can build confidence and adequately prepare an individual for employment.”
“Add technical skills; SQL, Java, ...”
“I wish we learned SQL.”
“SQL class.”
“I can’t stress enough the need for a programming class to be added to the curriculum. Since graduating, I have had to invest in this training as it is needed when building reports.”
“To add more classes that involve direct software learning as opposed to just researching and writing papers. It would have definitely helped me in the future.”
“Include more IT related classes (programming, software development) that will actually help in our career paths. Classes should be similar to what health Informaticians will face practically in the workforce rather than textbook-based.”
“One suggestion that I would give to add to the existing curriculum is incorporating more informatics and technology. Let’s say coding for example. Although we had the opportunity to understand how to analyze data I do feel like in terms of technology there are a lot more components to learn.”
“Teaching basic coding skills/HTML, allowing hands-on experience identifying system issues or software bugs, would have been very helpful to have more technical experience.”
“Possibly video lectures or some step by step instructions on developing software, databases, computer programming, etc.”
“Entry level programming.”
“Additional use of Microsoft Project.”
“I would suggest incorporating more skills-based courses in the program. Skills that frequently seen in the field of health care informatics. A lot of the students that I took the classes with did not have clinical backgrounds. As a clinical informaticist, it’s imperative to understand the clinical side of the health care industry. It would be very beneficial to future students to receive some type of course or resource that includes that.”
“I wish some of the classes included real-life systems and applications we could practice more with. It just seemed like a lot of material to cover in a short amount of time.”
“The MSc online program should shift focus from theory to more practice beyond preceptorship. Employers want staff who have hands-on experience in various software and systems.”
“You need to add all of the focused subject areas. Students should be able to specialize in the last few courses. Some of the classes are not useful depending on the field of interest.”
“I even wonder if adding specific tracks would be a good idea. While some students may want more project management courses, others may prefer database querying and reporting, or research, or even security. Perhaps specified “minors,” so to speak, could help students gain more knowledge in their areas of interest and make them more confident in applying for specific jobs after graduation.”
“I believe that it’s very important that the Practicum allows for hands-on experience with the actual hardware set-up so that the student will learn how to troubleshoot a technical issue. I have not yet found a job in the field, and one of my biggest fear is that I lack the technical experience. Besides the jobs I have seen, are seeking applicants with years of experience. If the Practicums entail just as much learning experience with the hardware as well as the software, that would certainly be advantageous.”
“Instead of a practicum, perhaps the program could offer other options that are often associated with different career paths that a degree in health care informatics could take. For instance, the program could offer the option of getting certified as a Project Management Professional (PMP), or certified in Data Analytics, etc. These options would boost a resume significantly and are directly related to potential career options within the realm of health care informatics. I understand job placement is a challenging undertaking for universities. However, this could be a great option to make sure your students have an advantage in the hiring process.”
Even though the demand for health informatics graduates has been changing, to the best of our knowledge, the number of studies that focus on hands-on health informatics skills training is limited. Recently, the Institute of Education Sciences developed a classification system called the Integrated Postsecondary Education Data System to track and report the fields of study [
In this study, we evaluated students’ perceived skills and self-confidence to develop health informatics apps. As professional and accrediting organizations have not determined distinct boundaries between competency and skill terms, we used these terms interchangeably. Currently, formal health informatics skills training with medical devices and apps are limited. Although students without any health informatics education might become an expert in programming, developing, and using an innovative health informatics app or system, others with formal education might not have any hands-on skills using the same apps. Hence, using competency and hands-on skills interchangeably in all cases is quite challenging. This research revealed that most students were employed (91/102, 89.2%), and presumably, they were knowledgeable about the required skills. Participants did not consider themselves experts in any skills, which indicates the need for the integration of skill-based training into the health informatics curriculum, except for skills in using Microsoft Word’s macro commands.
Furthermore, our research has several implications. First, this analysis identified a gap between existing competencies and in-demand skills. Developing innovative solutions to improve health care quality has become the major focus of leading health informatics companies, and recent publications emphasize that tomorrow’s workforce needs to design, develop, and implement innovative systems and work with new medical devices, patient monitoring apps, telemedicine, and smart home systems [
Second, this study revealed the need to determine new occupation-specific health informatics terms that will define different levels of practical know-how to generate disruptive ideas and design, develop, and implement sophisticated innovative technological solutions to health problems.
Third, we also identified the need to develop a specific competency assessment framework. Currently, AMIA uses Miller’s competency framework, which was initially developed to assess the clinical competence of medical school graduates. The Miller’s pyramid consists of four levels of clinical competence: knowing signs and symptoms (knows), knowing how to utilize exam and laboratory test data to diagnose a disease (knows how), demonstrate clinical performance (shows how), and being able to apply knowledge into practice (does) [
We developed a new framework that will include different tiers for evolving hands-on health informatics competencies (
Proposed health informatics competency framework. AI: artificial intelligence.
Health informatics specialists should know how to analyze and interpret health care data and identify potential areas of applications of AI. Defective AI algorithms can cause severe and unforeseen health consequences. However, integration of experiential AI training in health informatics curriculum and determination of necessary skill sets for different specializations is quite a challenging task as AI technology has many components such as machine learning, deep learning, pattern recognition, real-time data analytics, model building, data collection, and data visualization. This research demonstrated that participants defined themselves as an advanced beginner for skills in developing AI apps. This definition could be considered as being insufficient; however, a master’s degree program should consider students’ career perspectives and provide individualized tracks in addition to meeting mandatory accreditation standards. We propose that specific health informatics skills training should be identified using the enhanced health informatics curriculum components described in
Including R, Python, inferential and descriptive statistics, machine learning, database systems, and SQL, data presentation and visual encoding courses in the curriculum without real-life medical apps might not be enough to provide the required skills as students need to learn how to operate sophisticated medical equipment and remote monitoring devices and solve interoperability challenges. For example, with the hands-on laboratory exercises, students will be able to develop clinical decision support apps that can collect data from a wireless blood pressure monitor, wireless blood glucose meters, digital weight scales, and write the program code to integrate these apps with other databases. Consequently, depending on the students’ career plans, they might need further specialization such as integrating machine learning code with sophisticated medical software. Recent studies demonstrate the effectiveness of hands-on health informatics skills exercises [
Enhanced health informatics curriculum components. CAHIIM: Commission on Accreditation for Health Informatics and Information Management Education; IMIA: International Medical Informatics Association.
Several limitations need to be acknowledged. This research was conducted in an academic institution, and the feedback was limited to the MS in health informatics students’ assessments. All students with different educational backgrounds were included in the study and analyzed together because skill-based training is not a part of the current curriculum. Thus, conducting regular national and international studies to analyze students’ confidence levels and course requests and comparing responses of students within the same educational backgrounds would be helpful.
The main objective of this study was to highlight evolving health informatics competencies rather than provide detailed information about country-level competencies. Owing to the universal nature of technology, health informaticians use the same data standards, methods, and algorithms to store, retrieve, and analyze the data around the world. Although national and international organizations have determined different foundational domains, professional and accrediting organizations have been updating their recommendations frequently and adopting similar measurable competencies (
Evolving health informatics curriculum competencies. AHIEC: Australian Health Informatics Education Council; CAHIIM: Commission on Accreditation for Health Informatics and Information Management Education; IMIA: International Medical Informatics Association.
Health informatics graduates need hands-on experience with various health informatics tools and apps to develop skills and the ability to apply this practical expertise to unfamiliar situations, serve as subject-matter experts, and lead and manage innovative projects. Regional, national, and international accreditation standards, and in-demand technical skills to use and develop patient-centered health informatics systems could be taken into consideration when determining health informatics curriculum components. It is also essential to capture students’ perspectives before developing skills training components and to develop an up-to-date health informatics skills training framework depending on different medical specialties and health care needs for physicians, nurses, pharmacists, and medical and laboratory technologists. Developing new terminologies that will clearly specify the difference between competency-based and skill-based approaches for each health informatics discipline might be useful. Further studies that evaluate employers’ feedback and students’ perceptions after they get hired are suggested to determine the potential gaps and needs in health informatics skills training.
American Health Information Management Association
artificial intelligence
American Medical Informatics Association
Commission on Accreditation for Health Informatics and Information Management Education
electronic health
electronic health record
European Union
information and communication technology
International Medical Informatics Association
mobile health
Master of Science
Office of the National Coordinator
Structured Query Language
Technology Informatics Guiding Education Reform
None declared.