User-Centered Design of an Electronic Dashboard for Monitoring Facility-Level Basic Emergency Obstetric Care Readiness in Amhara, Ethiopia: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Maternal mortality remains a persistent public health concern in sub-Saharan African countries such as Ethiopia. Health information technology solutions are a flexible and low-cost method for improving health outcomes with proven benefits in low- to middle-income countries' health systems. OBJECTIVE: This study aimed to develop and assess the usability of an electronic dashboard to monitor facility-level readiness to manage basic emergency obstetric care (BEmOC) in Amhara, Ethiopia. METHODS: The study used three methods to iteratively refine the dashboard: (1) user-centered design sessions with individuals who interact with the BEmOC supply chain, (2) review and feedback from domain and information visualization subject matter experts (SMEs) to refine the dashboard, and (3) usability heuristic evaluation with human-computer interaction (HCI) SMEs. RESULTS: User-centered design sessions resulted in a preliminary version of the dashboard informed by end-user preferences and perceptions, with recommendations focusing on aesthetic design, filtering and sorting, and matching with the real world. An example of an end-user recommendation included increasing font sizes on the dashboard and using a red, yellow, and green color-coding scheme. Next, domain and visualization SMEs continued the dashboard's iterative refinement, focusing on aesthetic design and navigation, by confirming design choices incorporated from the user-centered design sessions and recommending changes to enhance user experience moving through the dashboard, such as adding more filtering options. HCI SMEs rated the dashboard as highly usable (0.82 on a scale of 0-4, with 0 being no usability concern and 4 being a catastrophic usability concern). The principle with the highest usability severity scores was a match between the system and the real world with a score of 1.4. The HCI SMEs also rated the information visualization aspects of the dashboard favorably with 2 usability principles, spatial organization and information coding, scoring 0. CONCLUSIONS: Dashboards are a novel method for promoting and tracking facility capacity to manage BEmOC. By including targeted end users and SMEs in the design process, the team was able to tailor the dashboard to meet user needs, fit it into the existing government health systems, and ensure that the dashboard follows design best practices. Collectively, the novel, customized BEmOC dashboard can be used to track and improve facility-level readiness in Amhara, Ethiopia, and similar global BEmOC facilities.
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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 | 0.037 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".