Artificial Intelligence-Based Chronic Disease Detection Application Among Hypertension and Diabetes Mellitus Risk Group in Indonesian Primary Healthcare: A Usability and User Experience Evaluation
DOI:
https://doi.org/10.70135/seejph.vi.670Keywords:
Chronic Disease, Artificial Intelligence, Hypertension, Diabetes MellitusAbstract
This study aims to considering the assessment of the usability and user experience of applications based on artificial intelligence (AI) for early detection of chronic diseases. Health data is recorded and chronic disease risk is classified using an AI-based early detection of chronic disease application. The study was conducted in Semarang city/regency health service facilities, using quantitative research methodology. The study's inclusion criteria were individuals with a history of diabetes mellitus and hypertension. Using the System Usability Scale (SUS) and User Experience Question (UEQ) surveys, 131 respondents were studied in May–July 2023. The study's findings showed that respondents who were older than 60, female, had not completed their education, worked for a living or were self-employed, did not use a mobile phone, and had never used health applications scored poorly for usability and user experience. The system satisfation aspect receives the lowest grade in terms of system usability, while the memorability aspect has the best score. The efficiency aspect of the system receives the greatest score in terms of user experience, while the novelty aspect receives the lowest. It is known that the AI-based early detection of chronic disease application has a reasonably acceptable usability and user experience based on the findings of the SUS and UEQ questionnaires for patients at risk of hypertension and diabetes mellitus. It is necessary to design an AI-based chronic disease detection application that is easier to learn and more innovative so that it can be used by the wider community.
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