{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:49:05Z","timestamp":1789778945795,"version":"4.0.1"},"reference-count":59,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Hum Factors"],"abstract":"\n Background<\/jats:title>\n Neonatal mortality remains a leading contributor to under-5 deaths globally, particularly in low- and middle-income countries (LMICs). While machine learning (ML)\u2013based risk prediction models show promise for identifying high-risk neonates, published evidence describing the real-world usability and practical implementation of ML-derived neonatal risk prediction tools within routine clinical workflows in LMIC neonatal units remains limited.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to evaluate the usability, user experience, and perceived clinical utility of a paper-based neonatal risk predictor tool derived from an ML model and implemented across 3 Kenyan health facilities.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A postimplementation, cross-sectional usability evaluation was conducted following a 4-month implementation period from August through November 2025. The study was embedded within a longitudinal mixed methods project. Frontline neonatal health care workers (n=10) completed standardized usability instruments, including adapted global usability items (System Usability Scale [SUS]), selected Questionnaire for User Interaction Satisfaction (QUIS) domains, and the Post-Study System Usability Questionnaire (PSSUQ), alongside a project-specific Post-Study Neonatal Utility Questionnaire (PSNUQ). Descriptive statistics (medians, IQRs, and category percentages) were computed. A total of 3 purposively selected neonatal unit leaders participated in semistructured key informant interviews (KIIs), which were analyzed using thematic analysis. Quantitative and qualitative findings were triangulated to contextualize perceptions of usability.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Among participating frontline health care workers, overall perceptions of usability were generally favorable. Around 75% (6\/8) of respondents reported being willing to use the tool frequently, and 55% (5\/9) indicated confidence in using it independently. Around half (5\/10) disagreed that the tool was complex, while 22% (2\/9) agreed, indicating moderate polarization in perceived complexity. Median PSSUQ composite scores were below 3 across subscales, reflecting positive usability ratings. Overall, 8 of 10 (80%) respondents agreed that the tool supports early identification of high-risk neonates and improves care prioritization within the first 48 hours. However, workflow integration was workload-sensitive: 40% (4\/10) reported an increased documentation burden during periods of high patient volume. KIIs identified staffing shortages, parallel documentation systems, and the importance of administrative endorsement as key structural influences on adoption.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The neonatal risk predictor tool demonstrated acceptable usability, learnability, and perceived clinical relevance across 3 diverse Kenyan facilities. However, variability in perceived complexity and workload sensitivity highlights the importance of structured onboarding, workflow-aligned integration, and context-aware implementation planning. These findings underscore that translating ML-derived predictor models into clinical practice requires not only technical validity but also strong usability and system-level readiness within routine neonatal care settings.<\/jats:p>\n <\/jats:sec>\n \n International Registered Report Identifier (IRRID)<\/jats:title>\n RR2-10.2196\/81996<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/94828","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T18:10:06Z","timestamp":1786990206000},"page":"e94828","source":"Crossref","is-referenced-by-count":1,"title":["Usability and Workflow Integration of a Machine Learning\u2013Derived Neonatal Risk Predictor in Kenyan Neonatal Units: Multisite User-Centered Pilot Evaluation"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6251-7695","authenticated-orcid":false,"given":"Ronald Danny","family":"Nyatuka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3564-8873","authenticated-orcid":false,"given":"Paul","family":"Macharia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3145-6193","authenticated-orcid":false,"given":"Esther","family":"Kakhata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0608-0892","authenticated-orcid":false,"given":"Faith","family":"Siva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1393-2045","authenticated-orcid":false,"given":"Betsy","family":"Muriithi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0197-8716","authenticated-orcid":false,"given":"Md Shafiqur Rahman","family":"Jabin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref1","unstructured":"Newborn mortalityWorld Health Organization202403142026-02-21https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/newborn-mortality?"},{"key":"ref2","unstructured":"Levels and trends in child mortalityUNICEF20242026-08-31https:\/\/data.unicef.org\/resources\/levels-and-trends-in-child-mortality-2024\/"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1159\/000530496"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.26750"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1159\/000516891"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.2196\/55897"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2026.1786358"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2026.1785141"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.2196\/78950"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.2196\/77330"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00549-7"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.medj.2021.04.006"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1148\/rg.220105"},{"key":"ref14","unstructured":"Ergonomics of human-system interaction (ISO 9241-11:2018); Part 11: Usability: Definitions and conceptsISO20182026-02-21https:\/\/www.iso.org\/standard\/63500.html?"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-01764-2"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2026.1785086"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.9745\/GHSP-D-24-00164"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.85897"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.2196\/28102"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1186\/s43058-020-00102-9"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.2196\/81996"},{"key":"ref22","unstructured":"NA610-1990 - 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