Abstract
In the digital age, hospital websites are essential for providing healthcare information and services. This research introduces an automated tool, WUAHP, created in Python utilizing BeautifulSoup for HTML parsing. This facilitates the extraction of structural and content-based components essential for usability assessment. It assesses websites based on five principal criteria: Navigational efficiency, operational efficiency, accessibility, responsiveness & compatibility, and security-each subdivided into many sub-criteria. Each measure is evaluated on a scale from 0 (least desirable) to 1 (most ideal) utilizing normalized modules. The entropy weighting method is utilized to impartially allocate weights according to data variability. Usability scores are subsequently confirmed via user feedback and aligned with Nielsen's heuristic usability standards. The tool was utilized on fifty healthcare websites. The results indicated significant variability, with HW9 attaining the greatest usability score of 97% and HW39 the lowest at 12%. The ultimate usability scores varied from 12 to 97%, underscoring disparities in design efficacy. WUAHP provides web developers and healthcare providers with an effective method to assess and enhance website usability. The technology establishes a basis for future applications in training machine learning models for automated, large-scale website assessment.