A multi-perspective approach to developing the Saudi Health Informatics Competency Framework
Bibliographic record
Abstract
BACKGROUND: Determining the key sets of competencies necessary for a Health Informatics (HI) professional to practice effectively either solo or as a member of a multidisciplinary team has been challenging for the regulator and registration body responsible for the healthcare workforce in Saudi Arabia, which is the Saudi Commission for Health Specialties (SCFHS). OBJECTIVE: The aim of this study was to develop a HI competency framework to guide SCFHS to introduce a HI certification program that meets local healthcare needs and is aligned with the national digital health transformation strategy. METHODOLOGY: A two-phase mixed methods approach was used in this study. For phase 1, a scoping review was conducted to identify HI competencies that have been published in the relevant literature. Out of a total 116 articles found relevant, 20 were included for further analysis. For phase 2, Saudi HI stakeholders (N = 24) that included HI professionals, administrators, academics, and healthcare professionals were identified and participated in an online survey, and asked to rank the importance of HI competencies distinguished in phase 1. To further validate and contextualize the competency framework, multiple focus groups and expert panel meetings were undertaken with the key stakeholders. RESULTS: For phase 1, about 1315 competencies were initially extracted from the included studies. After iterative reviews and refinements of codes and themes, 6 preliminary domains, 23 sub-domains and 152 competencies were identified. In phase 2, a total of 24 experts participated in the online surveys and ranked 58 out of 152 competencies as 'very important/required', each received 75 % or more of votes. The remaining competencies (N = 94) were included in a list for a further discussion in the focus groups. A Total of fourteen HI experts accepted and joined in the focus groups. The multiphase approach resulted in a competency framework that included 92 competencies, that were grouped into 6 domains and 22 subdomains. The six key domains are: Core Principles; Information and Communication Technology (ICT); Health Sciences; Health Data Analytics; Education and Research; Leadership and Management. CONCLUSION: The study developed the Saudi Health Informatics Competency Framework (SHICF) that is based on an iterative, evidence-based approach, with validation from key stakeholders. Future work should continue the validation, review, and development of the framework with continued collaboration from relevant stakeholders representing both the healthcare and educational communities. We anticipate that this work will be expanded and adopted by relative professional and scientific bodies in the Gulf Cooperation Council (GCC) region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".