Evolution of the clinical simulation approach to assess digital health technologies.

Coronavirus 2019 (COVID-19) has catalysed digital transformation in the health space. However, it remains a challenge to generate timely and cost-effective evidence for digital health technologies (DHTs) to ensure their safety and efficacy. Traditional methods, such as randomised controlled trials (RCTs), are ill-suited for assessing DHTs for reasons of speed, agility, cost and context. Clinical simulation using high-fidelity synthetic patient cases is emerging as a promising yet underexplored method to evaluate DHTs. It offers several advantages, including conducting remote multi-site testing at low cost, inclusion of high-risk patient profiles that are usually excluded from RCTs and adaptability to different local clinical settings. This article shares some of the insights from studies using clinical simulation conducted at the Institute of Global Health Innovation (IGHI) at Imperial College London and describes the evolution of this approach as well as future opportunities.

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.

Inefficiency, dignity and patient experience: is it time for separate outpatient diagnostics?

There is international and national variation in the location of diagnostic imaging centres relative to hospitals. Diagnostic cross-sectional imaging has traditionally been performed within a hospital, catering for both inpatient and outpatients. The resulting two-tiered system caters for emergent and complex inpatients, in addition to typically ambulatory outpatients. These outpatients are less complex, and often attend an acute hospital for the specific purpose of diagnostic imaging. In both the UK and the Republic of Ireland, outpatient radiology is often provided on-campus in state-funded hospitals, reflecting the allocation of resources nationally. In many other countries, hospitals provide acute and high-level care, with community centres addressing outpatients' clinical and diagnostic needs. Mixing inpatients and outpatients introduces variability into the scanning process, and many institutions struggle to provide for the very different needs of outpatients. Current strategies of mixing these two fundamentally different groups should be reassessed, and either in-hospital segregation or dedicated outpatient diagnostic imaging centres merit serious consideration in any future healthcare planning.