Identifying benefits and concerns with using digital health services during COVID-19: Evidence from a hospital-based patient survey.

Despite large-scale adoption during COVID-19, patient perceptions on the benefits and potential risks with receiving care through digital technologies have remained largely unexplored. A quantitative content analysis of responses to a questionnaire (N = 6766) conducted at a multi-site acute trust in London (UK), was adopted to identify commonly reported benefits and concerns. Patients reported a range of promising benefits beyond immediate usage during COVID-19, including ease of access; support for disease and care management; improved timeliness of access and treatment; and better prioritisation of healthcare resources. However, in addition to known risks such as data security and inequity in access, our findings also illuminate some less studied concerns, including perceptions of compromised safety; negative impacts on patient-clinician relationships; and difficulties in interpreting health information provided through electronic health records and mHealth apps. Implications for future research and practice are discussed.

Evaluation of prototype risk prediction tools for clinicians and people living with type 2 diabetes in North West London using the think aloud method.

The prevalence of type 2 diabetes in North West London (NWL) is relatively high compared to other parts of the United Kingdom with outcomes suboptimal. This presents a need for more effective strategies to identify people living with type 2 diabetes who need additional support. An emerging subset of web-based interventions for diabetes self-management and population management has used artificial intelligence and machine learning models to stratify the risk of complications from diabetes and identify patients in need of immediate support. In this study, two prototype risk prediction tools on the MyWay Diabetes and MyWay Clinical platforms were evaluated with six clinicians and six people living with type 2 diabetes in NWL using the think aloud method. The results of the sessions with people living with type 2 diabetes showed that the concept of the tool was intuitive, however, more instruction on how to correctly use the risk prediction tool would be valuable. The feedback from the sessions with clinicians was that the data presented in the tool aligned with the key diabetes targets in NWL, and that this would be useful for identifying and inviting patients to the practice who are overdue for tests and at risk of complications. The findings of the evaluation have been used to support the development of the prototype risk predictions tools. This study demonstrates the value of conducting usability testing on web-based interventions designed to support the targeted management of type 2 diabetes in local communities.

Evaluation of a clinical decision support tool for matching cancer patients to clinical trials using simulation-based research.

There is a growing need for alternative methodologies to evaluate digital health solutions in a short timeframe and at relatively low cost. Simulation-based research (SBR) methods have been proposed as an alternative methodology for evaluating digital health solutions; however, few studies have described the applicability of SBR methods to evaluate such solutions. This study used SBR to evaluate the feasibility and user experience of a clinical decision support (CDS) tool used for matching cancer patients to clinical trials. Twenty-five clinicians and research staff were recruited to match 10 synthetic patient cases to clinical trials using both the CDS tool and publicly available online trial databases. Participants were significantly more likely to report having sufficient time (p = 0.020) and to require less mental effort (p = 0.001) to complete trial matching with the CDS tool. Participants required less time for trial matching using the CDS tool, but the difference was not significant (p = 0.093). Most participants reported that they had sufficient guidance to participate in the simulations (96%). This study demonstrates the use of SBR methods is a feasible approach to evaluate digital health solutions and to collect valuable user feedback without the need for implementation in clinical practice. Further research is required to demonstrate the feasibility of using SBR to conduct remote evaluations of digital health solutions.

Challenges for the evaluation of digital health solutions-A call for innovative evidence generation approaches.

The field of digital health, and its meaning, has evolved rapidly over the last 20 years. For this article we followed the most recent definition provided by FDA in 2020. Emerging solutions offers tremendous potential to positively transform the healthcare sector. Despite the growing number of applications, however, the evolution of methodologies to perform timely, cost-effective and robust evaluations have not kept pace. It remains an industry-wide challenge to provide credible evidence, therefore, hindering wider adoption. Conventional methodologies, such as clinical trials, have seldom been applied and more pragmatic approaches are needed. In response, several academic centers such as researchers from the Institute of Global Health Innovation at Imperial College London have initiated a digital health clinical simulation test bed to explore new approaches for evidence gathering relevant to solution type and maturity. The aim of this article is to: (1) Review current research approaches and discuss their limitations; (2) Discuss challenges faced by different stakeholders in undertaking evaluations; and (3) Call for new approaches to facilitate the safe and responsible growth of the digital health sector.