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Automated framework for comprehensive usability analysis of healthcare websites using web parsing
Amandeep Kaur1, Jaswinder Singh2, Satinder Kaur3
1Department of Computer Science, Guru Nanak Dev University, Amritsar, Punjab, India. Amandeeprsh.cs@gndu.ac.in.
Scientific Reports
|July 2, 2025
Summary
A new automated tool, WUAHP, assesses hospital website usability using five criteria. Scores varied widely (12-97%), highlighting the need for improved healthcare website design and accessibility.
Area of Science:
- Health Informatics
- Human-Computer Interaction
- Web Science
Background:
- Hospital websites are critical for delivering healthcare information and services in the digital era.
- Assessing website usability is essential for effective patient engagement and information dissemination.
- Existing methods for website usability assessment can be time-consuming and subjective.
Purpose of the Study:
- To introduce WUAHP, an automated Python-based tool for assessing hospital website usability.
- To evaluate website structural and content components for enhanced user experience.
- To provide a scalable and objective method for healthcare website evaluation.
Main Methods:
- Developed WUAHP using Python and BeautifulSoup for HTML parsing.
- Assessed websites based on five criteria: navigational efficiency, operational efficiency, accessibility, responsiveness & compatibility, and security.
- Employed normalized modules (0-1 scale) and the entropy weighting method for objective scoring, validated with user feedback and Nielsen's heuristics.
Main Results:
- The study analyzed fifty healthcare websites using WUAHP.
- Usability scores demonstrated significant variability, ranging from 12% (HW39) to 97% (HW9).
- The findings underscore considerable disparities in the design efficacy of healthcare websites.
Conclusions:
- WUAHP offers an effective, automated solution for assessing and improving hospital website usability.
- The tool aids web developers and healthcare providers in enhancing user experience.
- This technology lays the groundwork for future machine learning applications in large-scale website assessment.
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