Leveraging digital health systems maturity assessments to guide strategic priorities
Bibliographic record
Abstract
Background: Many low- and middle-income countries (LMICs) face the daunting task of digitising, maturing and deciding where to invest in digital health systems. Aim: Describing the facilitators and barriers to conducting digital health maturity assessments and how health leaders can prioritise the assessments. Setting: eHealth leaders from 10 African countries, working or supporting Ministries of Health's digital health and participating in the eHealth Leaders' Forum from July 2023 to September 2023. Methods: This qualitative, descriptive study utilised key informant interviews conducted via Zoom with 14 conveniently selected leaders. We used Dedoose Version 9.0 to develop themes based on the health system's building blocks. Results: Participants identified maturity assessments as a critical first step to digital health strengthening, showing the system's performance and building a baseline response to systematic data quality challenges. Barriers to conducting digital health maturity assessment include lacking collaborators' buy-in, fragmented vision, overdependence on donor priorities, non-supportive policies and an inadequately skilled workforce. Facilitators include multi-stakeholder engagement, understanding the country's digital health ecosystem and appropriately integrating maturity assessment objectives. Recommendations include capacity building in data use and conducting maturity assessments at all health system levels to grow the demand and value of digital health strengthening. Conclusion: Promoting digital health maturity assessments can help leaders to make appropriate decisions to prioritise areas of improvement and steward maturity advancement as a pathway to strengthening the health system. Contribution: We spotlight the perspectives of African eHealth leaders, centering voices on the barriers, facilitators to planning and recommendations for implementing digital health systems maturity assessments.
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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 | 0.086 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".