Leveraging Health Information System Maturity Assessments to Guide Strategic Priorities: Perspectives from African Leaders
Bibliographic record
Abstract
Abstract Introduction Central to a functional public health system is a strong health information ecosystem and robust data use. Many low-and-middle-income countries (LMICs) face the task of digitizing their health information systems (HIS). For health leaders, deciding what to prioritize when investing in HIS strengthening is central to this daunting challenge. Objectives The study explores how HIS maturity assessment contributes to HIS strengthening, describes the facilitators and barriers to HIS maturity assessments, and how health leaders can prioritize conducting maturity assessments. Methods This descriptive qualitative study employed key informant interviews (KIIs) with fourteen eHealth leaders at national and international levels working or supporting Ministries of Health’s national HIS in LMICs. Results were analyzed using Dedoose Version 9.0 to develop themes based on the health systems’ building blocks as a framework for identifying facilitators and barriers to conducting HIS maturity assessment. Results Participants identified maturity assessments as a critical beginning step to HIS strengthening, showing the system’s performance, and building a baseline response to systematic data quality challenges. Barriers to conducting HIS maturity assessment include lacking collaborators’ buy-in, fragmented vision, low financial/human resources, and overdependence on donor priorities. Non- supportive policies, a lack of execution champions, and an inadequately skilled workforce in conducting maturity assessments or negotiating for their prioritization hinder maturity assessment implementation. Frequently identified facilitators to promoting HIS maturity assessment include multi-stakeholder engagement, understanding the country’s HIS ecosystem, and priorities to appropriately integrate 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 HIS maturity assessments. Conclusion Promoting HIS maturity assessments can help leaders prioritize areas to improve in the HIS ecosystem, making appropriate decisions that steward HIS maturity advancement. Addressing challenges that hinder HIS assessment implementation holds promise to identify a pathway to a strengthened health system. Author Summary Our manuscript specifically spotlights the perspectives of African eHealth leaders, centering voices on the barriers and facilitators to planning and implementing HIS maturity assessments. We demonstrate their perspective on how conducting maturity assessments can inform understanding of gaps to address in the HIS and strategic direction. We detail the leaders’ recommendations for using HIS maturity assessments in strengthening HIS governance and overall health systems for better population health outcomes in LMIC settings.
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 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 | 0.049 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".