Developing Health Management Competency for Digital Health Transformation: Protocol for a Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Globally, the health care system is experiencing a period of rapid and radical change. In response, innovative service models have been adopted for the delivery of high-quality care that require a health workforce with skills to support transformation and new ways of working. OBJECTIVE: The aim of this research protocol is to describe research that will contribute to developing the capability of health service managers in the digital health era and enabling digital transformation within the Australian health care environment. It also explains the process of preparing and finalizing the research design and methodologies by seeking answers to the following three research questions: (1) To what extent can the existing health service management and digital health competency frameworks guide the development of competence for health service managers in understanding and managing in the digital health space? (2) What are the competencies that are necessary for health service managers to acquire in order to effectively work with and manage in the digital health context? (3) What are the key factors that enable and inhibit health service managers to develop and demonstrate digital health competence in the workplace? METHODS: The study has adopted a qualitative approach, guided by the empirically validated management competency identification process, using four steps: (1) health management and digital health competency mapping, (2) scoping review of literature and policy analysis, (3) focus group discussions with health service managers, and (4) semistructured interviews with digital health leaders. The first 2 steps were to confirm the need for updating the current health service management curriculum to address changing competency requirements of health service managers in the digital health context. RESULTS: Two initial steps have been completed confirming the significance of the study and study design. Step 1, competency mapping, found that nearly half of the digital competencies were only partially or not addressed at all by the health management competency framework. The scoping review articulated the competencies health service managers need to effectively demonstrate digital health competence in the workplace. The findings effectively support the importance of the current research and also the appropriateness of the proposed steps 3 and 4 in answering the research questions and achieving the research aim. CONCLUSIONS: This study will provide insights into the health service management workforce performance and development needs for digital health and inform credentialing and professional development requirements. This will guide health service managers in leading and managing the adoption and implementation of digital health as a contemporary tool for health care delivery. The study will develop an in-depth understanding of Australian health service managers' experiences and views. This research process could be applied in other contexts, noting that the results need contextualization to individual country jurisdictions and environments. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51884.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.108 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.061 | 0.011 |
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 source (direct Gemma or distilled Codex), 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".