Evaluation of Healthcare Technology

Healthcare technology, especially its user-technology interface, needs to be usable and functional. There exist different approaches to evaluating such qualities, and one of them is the human factors method (Harte et al., 2017; Punchoojit & Hongwarittorrn, 2017). The present paper will use a specific example of a user-technology interface and apply the concept of human factors to it to determine its functionality. The results indicate that this approach shows flaws in application design and proposes improvements for enhancing healthcare technology.

Choosing Technology

The utilization of communication technologies for the management of different health concerns is called telehealth. This healthcare technology is a common solution to various health problems (Bernocchi et al., 2019; Milani, Lavie, Bober, Milani, & Ventura, 2017), including arthritis. Because of mobility problems, the importance of self-management, and the need for motivation and monitoring, telehealth is a good solution for this population (Dixon & Michaud, 2018; Geuens et al., 2016; Wang et al., 2018). A new mobile application described by Wang et al. (2018) was selected for analysis because the authors offer screenshots of the application’s interface but do not examine it. Thus, the present paper will be able to analyze the interface without summarizing the article.

Elements to be Evaluated and Evaluation Methods

The application designed by Wang et al. (2018) was developed to track the data about flares in people with rheumatoid arthritis. Patients are prompted to report symptoms every day in a survey tab; the application also offers patients’ history and contact tabs. This paper will consider specific elements of the interface from each of the three tabs. The chosen method of evaluation is the human factors approach. It can be defined as the usage of the knowledge about humans (including their capabilities, limitations, issues that they commonly experience, and human errors) to design technology that would be functional and usable (Harte et al., 2017). This method of assessment will help to assess the following elements; it applies to all of them.

  1. The first element is the survey tool that the patients use every day. Input controls are crucial for the user-technology interface (Harte et al., 2017; Punchoojit & Hongwarittorrn, 2017; Schnall et al., 2016). For this technology, information input is the justification for its introduction, which means that its functionality is crucial.
  2. The second element is the history graph which allows patients to track their information. It is an output element, which can also be very helpful (Punchoojit & Hongwarittorrn, 2017), especially for the awareness of the illness’s progression (Wang et al., 2018). Therefore, the analysis of the graph’s functionality is important.
  3. The third element is the contact information message; it will be analyzed from the perspective of text structuring and organization. The text-related choices are crucial in interface design, especially if the choices can hinder reading (Harte et al., 2017; Punchoojit & Hongwarittorrn, 2017; Schnall et al., 2016). Thus, this element’s inclusion is fully justified.

Assessment and Solutions

Survey

The survey element appears fairly functional: it is intuitive, and the response buttons are identified; clicking on any one of them results in a big tick mark. The individual survey items follow each other; the patient can scroll between them and is informed about their progress. Therefore, the task structure is straightforward and should be easy to understand and navigate (Harte et al., 2017). The size of the question text is rather big, which is also a positive factor (Harte et al., 2017; Punchoojit & Hongwarittorrn, 2017). However, the size of the response options is significantly smaller, and the question does not explain the scale that it uses. To make the survey more comprehensible and easier to use for the patient, the scale could be described using words, and the font size could be increased if reasonable.

Outputs

The graph that is employed as the output does not contain detailed information; it simply demonstrates the change in the patients’ responses over time. The design, especially colors, is minimalistic, which is why it is not particularly attractive, but the important information stands out in black and blue against the white background, which makes it functional (Harte et al., 2017; Punchoojit & Hongwarittorrn, 2017). However, the lack of details and the rather small size of the dates may cause difficulties in reading it (Harte et al., 2017). To improve the graph for the people who would like to get some more specific information, the options of including the data labels with the survey’s results and the possibility of displaying dates using bigger fonts are needed.

Contacts

The design choices of the text in the contact tab are not very helpful. This tab presents the information that can be very important, but it has little structure and is a wall of text, which can be a problem (Harte et al., 2017). To improve the situation, an outline at the beginning of the text should be included with the names of the services to be contacted. Also, it would make sense to incorporate the warning about contacting one’s healthcare professional at the very top of the page in sufficiently large letters (Harte et al., 2017; Punchoojit & Hongwarittorrn, 2017). The text size should be increased to at least the size of the survey’s fonts.

Conclusion

The analysis of the application with the help of the human factors methods presupposes considering the needs and abilities of the users. The application is fairly well-designed, but it lacks certain explanations and commonly uses rather small fonts. The contact tab appears to have particular problems. Improved text structure, font sizes, and extra options for the data output would improve the interface. Thus, issues were spotted through the analysis, which demonstrates the utility of the human factors approach.

References

Bernocchi, P., Giordano, A., Pintavalle, G., Galli, T., Ballini Spoglia, E., Baratti, D., & Scalvini, S. (2019). Feasibility and clinical efficacy of a multidisciplinary home-telehealth program to prevent falls in older adults: A randomized controlled trial. Journal of the American Medical Directors Association, 20(3), 340-346. Web.

Dixon, W., & Michaud, K. (2018). Using technology to support clinical care and research in rheumatoid arthritis. Current Opinion in Rheumatology, 30(3), 276-281. Web.

Geuens, J., Swinnen, T., Westhovens, R., de Vlam, K., Geurts, L., & Vanden Abeele, V. (2016). A review of persuasive principles in mobile apps for chronic arthritis patients: Opportunities for improvement. JMIR Mhealth and Uhealth, 4(4), e118. Web.

Harte, R., Glynn, L., Rodríguez-Molinero, A., Baker, P., Scharf, T., Quinlan, L., & ÓLaighin, G. (2017). A human-centered design methodology to enhance the usability, human factors, and user experience of connected health systems: A three-phase methodology. JMIR Human Factors, 4(1), e8. Web.

Milani, R., Lavie, C., Bober, R., Milani, A., & Ventura, H. (2017). Improving hypertension control and patient engagement using digital tools. The American Journal of Medicine, 130(1), 14-20. Web.

Punchoojit, L., & Hongwarittorrn, N. (2017). Usability studies on mobile user interface design patterns: A systematic literature review. Advances in Human-Computer Interaction, 2017, 1-22. Web.

Wang, P., Luo, D., Lu, F., Elias, J., Landman, A., Michaud, K., & Lee, Y. (2018). A novel mobile app and population management system to manage rheumatoid arthritis flares: Protocol for a randomized controlled trial. JMIR Research Protocols, 7(4), e84. Web.

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