Abstract
To date, online appointment platforms (OAPs) have achieved widespread adoption within healthcare systems. While they offer significant convenience, they also present considerable challenges during use, particularly for inexperienced novice users or individuals who did not grow up in the internet era. Therefore, identifying usability issues within these online systems and analyzing their attributes and potential impact is essential. This study employed the Hierarchical Task Analysis (HTA) and Cognitive Walk through (CW) evaluation methods to analyze usability problems in an OAP. First, HTA was utilized to decompose the OAP process into a series of executable tasks. Subsequently, CW was applied to identify potential usability problems. Five human factors evaluators collectively identified 32 distinct usability problems. These problems were then categorized into relevant usability attributes. Additionally, the severity of each identified problem was rated independently by both the evaluators and five novice users. Results indicated that the average severity of all problems, as rated by the evaluators (Mean = 1.72) and the real users (Mean = 1.70), fell within the "minor problem" category. Task 1 contained the highest number of problems (15 problems, accounting for 46.9% of the total). The highest number of problems was associated with the Efficiency attribute (10 problems) and the Mem or ability attribute (8 problems). The highest average severity ratings were assigned by the evaluators (Mean = 3.0) to problems related to the Efficiency attribute and by the real users (Mean = 3.0) to problems related to the Errors attribute; both means correspond to the "major problem" category. In summary, Task 1 exhibited the highest number of usability problems and the longest execution time. The identified problems primarily pertained to the Efficiency and Mem or ability attributes. This analysis provides a foundation for subsequent usability improvements to the platform, aiming to reduce users' cognitive load and memory pressure while enhancing overall platform efficiency.
Keywords
Online appointment platform; Usability; Cognitive walkthrough; Hierarchical task analysis
DOI
https://doi.org/10.21606/iasdr.2025.559
Citation
Tan, Y., Hung, Y.,and Yau, C.(2025) Uncovering usability problems of a hospital online appointment platform: insights from hierarchical task analysis and cognitive walkthrough, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.559
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 9 - Healthcare Design
Uncovering usability problems of a hospital online appointment platform: insights from hierarchical task analysis and cognitive walkthrough
To date, online appointment platforms (OAPs) have achieved widespread adoption within healthcare systems. While they offer significant convenience, they also present considerable challenges during use, particularly for inexperienced novice users or individuals who did not grow up in the internet era. Therefore, identifying usability issues within these online systems and analyzing their attributes and potential impact is essential. This study employed the Hierarchical Task Analysis (HTA) and Cognitive Walk through (CW) evaluation methods to analyze usability problems in an OAP. First, HTA was utilized to decompose the OAP process into a series of executable tasks. Subsequently, CW was applied to identify potential usability problems. Five human factors evaluators collectively identified 32 distinct usability problems. These problems were then categorized into relevant usability attributes. Additionally, the severity of each identified problem was rated independently by both the evaluators and five novice users. Results indicated that the average severity of all problems, as rated by the evaluators (Mean = 1.72) and the real users (Mean = 1.70), fell within the "minor problem" category. Task 1 contained the highest number of problems (15 problems, accounting for 46.9% of the total). The highest number of problems was associated with the Efficiency attribute (10 problems) and the Mem or ability attribute (8 problems). The highest average severity ratings were assigned by the evaluators (Mean = 3.0) to problems related to the Efficiency attribute and by the real users (Mean = 3.0) to problems related to the Errors attribute; both means correspond to the "major problem" category. In summary, Task 1 exhibited the highest number of usability problems and the longest execution time. The identified problems primarily pertained to the Efficiency and Mem or ability attributes. This analysis provides a foundation for subsequent usability improvements to the platform, aiming to reduce users' cognitive load and memory pressure while enhancing overall platform efficiency.