Usability and Acceptability of Electronic Immunization Registry Data Entry Workflows From the Health Care Worker Perspective in Siaya, Kenya (Part 3): Pre-Post Study
Bibliographic record
Abstract
BACKGROUND: Digital health tools such as electronic immunization registries (EIRs) have the potential to improve patient care and alleviate the challenges that arise from the use of paper-based clinic records for reporting. To address some of these challenges, the Kenya Ministry of Health and the International Training and Education Center for Health Kenya implemented an EIR system in 161 immunizing clinics in Siaya County between 2018 and 2019. The successful implementation of digital health tools depends on many factors, one of which is alignment between the technology and the context in which it is used. One important aspect of that implementation context is the perceptions of the health care workers (HCWs) using the EIR. OBJECTIVE: This study aimed to evaluate HCWs' perceptions of the usability and acceptability of multiple clinic workflows using the new EIR. METHODS: We performed a mixed methods pre-post study using semistructured interviews with HCWs at 6 facilities in Siaya County, Kenya. We interviewed HCWs at each facility 4 times: at baseline and once after the implementation of 3 different workflow modifications (n=24 interviews). The baseline state was dual data entry with paper records and the EIR. We then implemented 3 workflow modifications for 1 full day each: fully paperless data entry, preparation of an appointment diary before patient visits for the day, and a combination of the 2 workflows. We compared ratings and themes across interviews after each of the 4 workflows to understand the changes in the usability and acceptability of the EIR. RESULTS: HCWs considered the EIR clinic workflows to be usable and acceptable. Of the modified workflows, HCWs perceived the fully paperless workflow most favorably. In all workflows, HCWs' perceived benefits included ease of clinical decision-making using the EIR, reduced mental burden of data entry when using the EIR, and ease of identification of errors. Perceived barriers to the workflow included contextual challenges such as staffing shortages and lack of network connectivity, EIR platform challenges such as errors in saving records and missing fields, and workflow challenges such as the dual data entry burden of using paper and digital tools simultaneously. CONCLUSIONS: Fully paperless EIR implementation shows great promise from a workflow acceptability standpoint, contingent upon the presence of supporting contextual clinic factors and the resolution of system performance and design challenges. Rather than trying to identify a singular best workflow, future efforts should provide adequate flexibility for HCWs to implement the new system in their unique clinic context. Future EIR implementation stands to benefit from continued monitoring of EIR adoption acceptability during implementation, both for Siaya's program and for other efforts around the globe, as digital health interventions become more widely used.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".