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Unlocking the eHealth professionals’ career pathways: A case of Gulf Cooperation Council countries

2022· article· en· W4308799508 on OpenAlexaboutno aff
Nasriah Zakaria, Norhayati Zakaria, Omar Alnobani, Manal Almalki, Osama El-Hassan, Mohammed Alhefzi, Mowafa Househ, Amr Jamal

Bibliographic record

VenueInternational Journal of Medical Informatics · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersDeanship of Scientific Research, King Saud UniversityKing Saud University
KeywordseHealthCareer PathwaysHealth professionalsPublic relationsMedical educationMedicineBusinessKnowledge managementPolitical scienceHealth careComputer science

Abstract

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BACKGROUND: During the past two decades, various sectors and industries have undergone digital transformation. Healthcare is poised to make a full transformation in the near future. Although steps have been taken toward creating an infrastructure for digital health in the Middle East, as it stands, digital health is still an emerging field here. The current global health care crisis has underscoredthe need for digitization of the healthcare sector to provide high-value, high-quality care and knowledge generation. With the advent of digital transformation in countries around the globe, there is a rising demand for investment and innovation in health information technology. With the demand for health informatics (HI) graduates in different disciplines (e.g., healthcare professions, information technology, etc.), there is an urgent need to determine and regulate clear career pathways and the core competencies necessary for digital health professional to practice effectively and to allow technology to add value to the healthcare systems. Given the changing landscape of the profession, the Kingdom of Saudi Arabia (KSA) and the Gulf Cooperation Council (GCC) countries are experiencing a rising demand to produce digital health professionals who can meet the needs of all the stakeholders involved, including patients, healthcare professionals, managers, and policymakers. However, despite the number of region-wide initiatives in the form of training programs, there remains a knowledge-practice gap and unclear job roles within the HI community. In recent years, regional digital health workforce initiatives have been put forward, such as the GCC Taskforce on Workforce Development in Digital Healthcare. The taskforce initiated a survey and several workshops to identify and classify HI disciplines according to the needs of the job market and through comparisons with similar efforts developed across the globe, such as the TIGER project and the EU*US eHealth Work project. Digital health implementation has been flourishing in the Middle East for the past 15 years. During this period, while digital health professions have been thriving in the industry to deliver tools and technologies, academic institutions have offered some amount of training and education in digital health; however, the career pathway for digital health professionals is not clear due to mismatch about the qualifications, skills, competencies and experience needed by the healthcare industry. OBJECTIVES: Due to this discrepancy between the academic curriculum and the skills needed in the healthcare industry, the objectives of this study are to define the career pathway for eHealth professions and identify the challenges experienced by academic institutions and the industry in describing digital health professionals. METHODS: We elicited qualitative data by conducting six focus groups with individuals from different professional backgrounds, including healthcare workers, information managers, computer sciences professionals, and workers in the revenue cycle who participated in a workshop on November 2-3, 2019, in Dubai. All focus group sessions were audio-recorded and transcribed, and participants were de-identified before analysis. An exploratory method was used to identify themes and subthemes. Saturation was reached when similar responses were found during the analysis. In this study, we found that respondents clearly defined eHealth career pathways based on criteria that included qualifications, experience, job scope, and competency. We also explored the challenges that the respondents encountered, including differences in the required skill sets and training and the need to standardize the academic curriculum across the GCC region, to recognize the various career pathways, and to develop local training programs. Additionally, country-specific projects have been initiated, such as the competency-based Digital Health framework, which was developed by the Saudi Commission of Healthcare Specialties (SCFHS) in 2018. Competency-based digital health frameworks generally include relevant job definitions, roles, and recommended competencies. Both the GCC taskforce and the Saudi studies capitalized on previous efforts by professional organizations, including Canada's Digital Health formerly known as (COACH), the U.S. Office of the National Coordinator for Health Information Technology (ONC), the American Medical Informatics Association (AMIA), and the Health Information and Management Systems Society (HIMSS). RESULTS: In this study, we found that respondents defined eHealth career pathways based on different criteria such as: qualifications; various background of health and IT in the HI field; work experiences; job scope and competency. We also further explore the challenges that the respondents encountered which delineates four key aspects such as need of hybrid skills to manage the digital transformation, need of standardization of academic curriculum across GCC, recognition of the career pathways by the industry in order to open up career opportunity and career advancement, and availability of local training programs for up-skilling the current health workforce. CONCLUSION: We believe that successful health digital transformation is not limited to technology advancement but requires an adaptive change in: the related competency-based frameworks, the organisation of work and career paths for eHealth professionals, and the development of educational programmes and joint degrees to equip clinicians with understanding of technology, and informaticians with understanding of healthcare. We anticipate that this work will be expanded and adopted by relevant professional and scientific bodies in the GCC region.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.113
GPT teacher head0.432
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations16
Published2022
Admission routes1
Has abstractyes

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