Published in PLOS One on 2026-05-04, this study is presented as an attributed evidence briefing. The excerpts below are the original researchers’ words, reproduced under their Creative Commons Attribution licence. Health Signal has selected the abstract and, where needed, opening background paragraphs; the complete methods, results, limitations and references remain in the linked paper.
Original authors: Adamu Takele Jemere, Tesfahun Melese Yilma, Lemma Derseh Gezie, Shegaw Anagaw Mengiste, Monika Knudsen Gullslett, Jens Johan Kaasbøll, Binyam Tilahun
Source: PLOS One • DOI 10.1371/journal.pone.0348362
Read the complete primary study ↗ · Creative Commons Attribution licence
From the researchers
Abstract
Mobile health (mHealth) apps improve healthcare providers’ accurate disease classification in resource-limited settings. Ethiopia recently introduced the National Health Data Dictionary (NHDD) mobile app for disease classification; however, healthcare providers’ awareness of it remains unknown. This study aimed to assess awareness of mobile-based disease classification apps among healthcare providers working in public health facilities in northwest Ethiopia and to determine the factors associated with this awareness.
A facility-based cross-sectional study was conducted among 423 healthcare providers working at 19 public health facilities in northwest Ethiopia from October 1 to 25, 2023. Data were collected using a pre-tested self-administered questionnaire. Awareness was defined as being aware of the existence of a mobile app (NHDD) for disease classification. Multilevel logistic regression analysis was used to account for clustering at the health facility level. Adjusted Odds Ratio (AOR) with 95% confidence interval (CI) was used to identify associated factors.
Only 30.73% (95% CI: 26.30%−35.55%) of healthcare providers were aware of a mobile-based app for disease classification. Healthcare providers having social media accounts (AOR = 13.96; 95% CI: 2.33–83.64), ever visited the medical field by a mobile phone (AOR = 2.39; 95% CI: 1.03–5.51), digital literacy (AOR = 6.13; 95% CI: 1.50–25.01), awareness of the ESV-ICD-11 booklet on paper (AOR = 2.34; 95% CI: 1.06–5.18), and access to ESV-ICD-11 training or mentorship (AOR = 2.93; 95% CI: 1.25–6.87) were factors associated with awareness.
About one-third of healthcare providers are aware of the mobile-based disease classification app. Social media use, digital literacy, prior mobile use for the medical field, familiarity with the paper-based ESV-ICD-11 booklet, and ESV-ICD-11 training or mentorship were associated factors with awareness. Targeted awareness creation interventions could be considered to support the success of mobile-based app implementation in Ethiopia.
Introduction
In recent years, the integration of mobile health (mHealth) technology has transformed healthcare, mainly in countries with limited resources, by enhancing disease diagnosis and aiding physicians in accurate decisions [1]. Among these technologies, mobile healthcare apps potentially improve clinical documentation, standardize data, provide real-time decision support, and enhance disease classification accuracy, ultimately contributing to more accurate and efficient healthcare services [2 – 4]. Particularly, disease classification applications enable medical professionals by standardizing classification and coding procedures and enhancing the completeness and quality of clinical data [5 – 7].
Despite the mobile apps’ advantages, disparity in the use and awareness of these technologies persists. Research conducted in different regions indicates that greater use of mobile apps is correlated with healthcare providers’ awareness of apps [8, 9]. However, lack of knowledge and digital literacy are reported as barriers, which have been observed by 39.6% and 38% of healthcare personnel, respectively, in studies [10, 11]. Research from countries like Malaysia and Australia has shown that healthcare professionals who are aware of mHealth are more likely to incorporate these tools into their regular clinical practice, indicating that awareness and familiarity with these tools have a significant impact on implementation outcomes [12 – 16].
Reading this evidence
Use the full paper to check the study design, participant selection, comparator and outcome definitions before drawing a practical conclusion. An association does not by itself show cause and effect. A reported performance measure needs its denominator and uncertainty. A finding from a particular facility, sample or time period should retain that scope when it is discussed elsewhere.
This briefing makes the source accessible within the wider technology & industry desk. It does not assess an individual patient or certify a product. When considering relevance to another health system, examine the original authors’ account of context and limitations, then seek local implementation evidence. The related reporting below provides other studies to read alongside this one; it is a topic connection, rather than a claim that their results are directly comparable.
© 2026 Adamu Takele Jemere, Tesfahun Melese Yilma, Lemma Derseh Gezie, Shegaw Anagaw Mengiste, Monika Knudsen Gullslett, Jens Johan Kaasbøll, Binyam Tilahun. Excerpts reproduced under CC BY. Selection, formatting and contextual notes by Health Signal. The researchers are the authors of the source study, not members of Health Signal’s staff.
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- Evidence brief: Determinants of digital health literacy among health professionals: Evidence from public and private hospitals in Eastern Ethiopia
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