Context: High-quality software is essential to the progressing digitalisation of the Swedish healthcare sector. Developing software with the desired high quality is far from trivial due to the sophisticated requirements of the domain.

Problem: Studies on healthcare digitalisation challenges in Sweden and other countries, however, largely focus on the perceptions of healthcare professionals and patients and less on opinions of IT professionals.

Method: In this exploratory study, we conducted semi-structured interviews with nine IT professionals about observed challenges in developing software for the Swedish healthcare sector. A qualitative analysis was performed to identify common themes.

Results: We identified the prevalent challenges to be related to data integrity, privacy and security, rules and regulations, engineering usability, and software testing.

Conclusion: The results suggest that further research is required regarding agile methods, efficient requirement engineering, and testing in eHealth as well as in privacy and usability engineering. 

Context: Digital Health (DH) is widely considered essential for sustainable future healthcare systems. Software quality, particularly usability, is crucial for the success and adoption of most DH products. However, concerns about the effectiveness and efficiency of usability evaluation of DH products have been raised. Objective: This article aims to analyse the prevalence and application contexts of usability evaluation methods in DH and to highlight potential issues related to their effectiveness and efficiency. Method: A systematic literature review of usability evaluation studies, published by (academic) practitioners between 2016 and April 2023, was conducted. 610 primary articles were identified and analysed, utilising five major scientific databases. Results: Our findings show a preference for inquiry (85%) and testing (63%) methods, with inspection used less frequently (17%). The published studies employed methods like questionnaires (75%); notably the SUS (49%), semi-structured interviews (25%), and heuristic evaluations (73%), with percentages based on their group. Data collection mainly involved the use of participant feedback (45%), audio/video recordings (44%), and system logs (20%), with both qualitative and quantitative data analyses prevalent in studies. However, several usability characteristics such as accessibility, memorability, and operability were found to be largely overlooked, and automation tools or platforms were not widely used. Among the systems evaluated were mHealth applications (70%), telehealth platforms (36%), health information technology (HIT) solutions (29%), personalized medicine (Per. Med.) (17%), wearable devices (12%), and digital therapeutics (DTx) interventions (6%), with the participation of general users, patients, healthcare providers, and informal caregivers varying based on the health condition studied. Furthermore, insights and experiences gathered from 24 articles underscored the importance of a mixed-method approach in usability evaluations, the limitations of traditional methods, the necessity for sector-specific customisation, and the potential benefits of remote usability studies. Moreover, while eye-tracking emerged as a promising evaluation technique, careful execution and interpretation are crucial to avoid data misinterpretation. Conclusion: The study’s findings showed that employing a combination of inquiry and testing-based methods is prevalent for evaluating DH platforms. Despite an array of DH systems, method distribution remained consistent across platforms and targeted user groups. The study also underlines the importance of involving target user groups in the process. Potentially affected cognitive abilities of participants and potential user groups of interest have to be taken into account when choosing evaluation methods, and methods might therefore need to be tailored. Complementary inspection methods might be particularly useful when recruiting representative participants is difficult. Several potential paths for future research are outlined, such as exploring novel technologies like artificial intelligence, for improved automation tool support in the usability evaluation process. 

Context: Usability is a major factor in the acceptance of digital health (DH) solutions. Problem: Despite its importance, usability experts have expressed concerns about the insufficient attention given to usability evaluation in practice, indicating potential efficiency problems of common evaluation methods in the healthcare domain. Objectives: This research paper aimed to analyse industrial usability evaluation practices in digital health to identify potential threats to the efficiency of their application. Method: To this end, we conducted an online survey of 144 usability experts experienced in usability evaluations for digital health applications. The survey questions aimed to explore the prevalence of techniques applied, and the participants’ familiarity and perceptions regarding tools and techniques. Results: The prevalently applied techniques might impose efficiency problems in common scenarios in digital health. Participant recruitment is considered timeconsuming and selecting the most appropriate evaluation method for a given context is perceived difficult. The results highlight a lack of utilisation of tools automating aspects of usability evaluation. Conclusions: A more widespread adoption of tools for automating usability evaluation activities seems desirable as well as guidelines for selecting evaluation techniques in a given context. We furthermore recommend to explore AI-based solutions to address the problem of involving targeted user groups that are difficult to access for usability evaluations.

Effective usability evaluation of user interface (UI) designs is essential. Particularly in digital healthcare, frequently involving relevant user groups in usability evaluations is not always possible or is ethically questionable. On the other hand, neglecting the perspectives of such groups can lead to UI designs that fail to be inclusive and adaptable. In this paper, we outline an initial idea to utilize artificial intelligence methods to simulate mobile user interface interactions of such user groups. The goal is to support software developers and designers with tools that show them how users of certain user groups might interact with a user interface under development and show potential issues before actual, more expensive usability evaluations are conducted. We present a study that employs synthetic representations of user interactions with UI elements based on a small sample of real interactions. This synthetic data was then used to train a classification model predicting whether real user interactions were from younger or elderly persons. The good performance of this model provides evidence that synthetic user interface interactions might be accurate enough to feed into imitation learning approaches, which, in turn, could be the foundation for the desired tool support.

Context: Ensuring smartphone interfaces are usable and accessible is essential for elderly users, particularly those with motor impairments, who face challenges with touchscreen interactions. Problem: Hand tremors and limited motor control can hinder touchscreen accuracy and efficiency. Meanwhile, recruiting elderly participants for usability studies can be challenging, often resulting in limited interaction data. Objectives: This study aimed to investigate elderly users’ smartphone interaction patterns, identify key challenges, and generate synthetic data to address data scarcity for usability research. Method: A custom-designed mobile app collected interaction data from 51 elderly participants performing tapping, dragging, and tracing tasks. Hand steadiness was assessed using accelerometer data. Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) models were used to generate synthetic datasets replicating user interaction patterns. Results: Users with shaky hands struggled with precision tasks, especially involving smaller GUI elements, while larger elements improved performance. Continuous control was also found to be challenging in tracing tasks. Synthetic datasets successfully replicated spatial, temporal, and distributional metrics, demonstrating potential utility in future usability evaluation research. Conclusions: Inclusive GUI designs and adaptive features can improve accessibility for the elderly with limited motor control. Synthetic data can offer a potential solution for further usability evaluation research in building AI-driven design evaluation tools, reducing reliance on resource-intensive participant recruitment in earlier prototypes. Future work should examine diverse tasks and scenarios and involve people with severe motor impairments. 

Context: Older adults (OA) increasingly rely on mobile services for managing health, finance, and daily activities, yet motor variability, such as hand shakiness, makes small targets, complex gestures, and dense layouts difficult to use. Recruiting older adults for usability testing is challenging, leaving software designers with limited insights into their interaction behaviors. 

Objective: We aim to develop and validate User Interface Interactions Imitation (UI³), a modeling framework that imitates the smartphone touch interaction behaviors of older adults with higher hand shakiness to support early-stage usability and accessibility evaluations. 

Method: The UI³ framework employed distributional modeling and imitation learning (behavior cloning) to reproduce older adults’ UI interactions from limited demonstration data. It is further integrated with a design prototyping tool, enabling interactive imitation and evaluation of user interfaces (UI). 

Results: Our findings showed that distributional modeling and imitation-learning models accurately captured spatial, temporal, and kinematic behaviors observed in OA demonstrations. Imitations on a login interface highlighted usability issues, such as frequent over-tapping on small buttons, offering actionable feedback that can guide redesign decisions, such as enlarging buttons for specific user profiles. 

Conclusion: The UI³ framework can enable early, data-driven usability and accessibility evaluations by imitating user interactions trained on limited data. While validated with older adults exhibiting higher hand shakiness, the framework can be adapted to other user groups or impairments through adapted feature representations and task designs, offering a reusable foundation for scalable, AI-assisted, ability-based design and evaluation.