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BackgroundHealthcare technologies are becoming more commonplace, however clinical and patient perspectives regarding the use of technology in the management of childhood asthma have yet to be investigated. Within a clinical trial of asthma management in children, we conducted a qualitative process evaluation that provided insights into the experiences and perspectives of healthcare staff and families on (i) the use of smart inhalers to monitor medication adherence and (ii) the use of algorithm generated treatment recommendations.MethodsWe interviewed trial staff (n = 15) and families (n = 6) who were involved in the trial to gauge perspectives around the use of smart inhalers to monitor adherence and the algorithm to guide clinical decision making.FindingsStaff and families indicated that there were technical issues associated with the smart inhalers. While staff suggested that the smart inhalers were good for monitoring adherence and enabling communication regarding medication use, parents and children indicated that smart inhaler use increased motivation to adhere to medication and provided the patient (child) with a sense of responsibility for the management of their asthma. Staff were open-minded about the use of the algorithm to guide treatment recommendations, but some were not familiar with its' use in clinical care. There were some concerns expressed regarding treatment step-down decisions generated by the algorithm, and some staff highlighted the importance of using clinical judgement. Families perceived the algorithm to be a useful technology, but indicated that they felt comforted by the clinicians' own judgements.ConclusionThe use of technology and individual data within appointments was considered useful to both staff and families: closer monitoring and the educational impacts were especially highlighted. Utilising an algorithm was broadly acceptable, with caveats around clinicians using the recommendations as a guide only and wariness around extreme step-ups/downs considering contextual factors not taken into account.

BackgroundImplementation of Robotic Assisted Surgery (RAS) is complex as it requires adjustments to associated physical infrastructure, but also changes to processes and behaviours. With the global objective of optimising and improving RAS implementation, this study aimed to: 1) Explore the barriers and enablers to RAS service adoption, incorporating an assessment of behavioural influences; 2) Provide an optimised plan for effective RAS implementation, with the incorporation of theory-informed implementation strategies that have been adapted to address the barriers/enablers that affect RAS service adoption.MethodsSemi-structured interviews were conducted with RAS personnel and stakeholders, including: surgeons, theatre staff, managers, industry representatives, and policy-makers/commissioners. The Theoretical Domains Framework (TDF) and the Consolidated Framework for Implementation Research (CFIR) was used to identify barriers and enablers that represent individual behaviours, capabilities, attitudes, beliefs, and external organisational factors that influence the implementation of RAS.ResultsFindings suggest that implementation planning has three separate phases-pre-, early, and late implementation. For pre-implementation, barriers and enablers identified included the cost of RAS equipment and issues of economic viability, weak outcome evidence for RAS, a preponderance of an eminence driven model, the clinician/manager relationship, and views around the uptake and expansion of RAS in the future. Early implementation findings revealed role changes for theatre personnel and an enhanced team approach, reliance on industry for training provision, and changes in skill sets and attentional processes. Late implementation factors included equipment maintenance costs, technological limitations, changes to cognition during RAS routine use, and benefits to institutions/healthcare professionals (such as ergonomic improvement).ConclusionTogether, findings suggest the factors that affect RAS implementation are multi-faceted and change across the life-cycle of intervention adoption. Theory-informed strategies are suggested which can optimise implementation of RAS. Optimisation strategies need planning from the outset.