Development and pre-evaluation of a "DiagNurse" mobile app to support nurses in clinical diagnosis using the ADDIE...

Healthcare workers are increasingly utilising cutting-edge technology, including mobile apps, to enhance patient health care and ensure efficient professional performance. The aim of this study was to design, develop and evaluate an educational mobile app dedicated towards being employed by nursing students and practicing nurses to support the clinical assessment of a patient's health condition in nursing care. In order to develop the mobile app, the Analysis, Design, Development, Implementation and Evaluation (ADDIE) model was employed. Between 2022 and 2023, a "Diagnostic Nurse" mobile app was developed in the "Android Application Package (APK)." The app's usability was tested in the laboratory by 20 participants. Three methods were employed in the study, that is, an eye-tracking technique, a qualitative evaluation and a quantitative evaluation. According to the System Usability Scale (SUS), the app test score for the nursing student group was assessed as 83.3 ± 8.9, and for the practicing nursing group, this was 84 ± 12.7. These results indicate that the mobile app's is highly usable. The app received high ratings in the "user-friendliness", "ease-of-use", and "user satisfaction" categories. The "DiagNurse" app makes it easier to learn how to conduct a clinical assessment of a patient's condition in nursing care, resulting in better information acquisition, assessment accuracy and speed. Given the low cost of the app development and the ADDIE model on which it is based, the app may be beneficial to nursing students, practicing nurses and other health-care professionals and students.