Patient and Staff Experience of Remote Patient Monitoring—What to Measure and How: Systematic Review (Preprint)

Abstract

UNSTRUCTURED Patient and staff experience are vital factors to consider in the evaluation of Remote Patient Monitoring (RPM) interventions. However, the current landscape of patient and staff experience measuring in RPM suffers from a lack of methodological standardization, affecting the quality of both primary and secondary research in this domain. In this research, we aim to obtain a comprehensive set of experience constructs and corresponding instruments used in contemporary RPM research and to propose an initial set of guidelines for improving methodological standardization in this domain. A systematic review is conducted on recent articles reporting instances of patient or staff experience measuring in the RPM domain. The obtained corpus of data is explored and structured through correspondence analysis, a multivariate statistical technique. The systematic review shows that the research landscape has seen sizeable growth in the past years, that it is affected by a relative lack of focus on the experience of staff, and that the overall corpus of collected measures can be organized into four main categories (service-system-related experience measures; care-related experience measures; usage and adherence-related experience measures; and health outcomes-related experience measures). In light of the collected findings, we provide a set of six actionable recommendations to RPM patient and staff experience evaluators, both in terms of what to measure and how to measure it. Overall, we suggest RPM researchers and practitioners develop integrated, interdisciplinary data strategies for continuous RPM evaluation.

From digital health to learning health systems: four approaches to using data for digital health design

“…In response to this need, continuous data collection from the context of use becomes necessary to inform processes other than design-related ones: in the healthcare domain, particularly, clinical evaluation, clinical research and auditing programs require dedicated data infrastructures supported by a network of compatible digital artefacts. In this context, a fourth kind of approach to using data for digital health design emerges, in which shared data strategies (Pannunzio et al, 2023b ) need to be employed both for design purposes and for other kinds of health-relevant data-driven processes, such as clinical evaluation, cost evaluation, policymaking, algorithmic auditing, or more. The establishment of these shared data strategies (intended as the embedding of data collection capabilities for a diverse set of purposes within digital health artefacts) is identified as a new layer of complexity on top of data-enabled design approaches, leading us to distinguish these as a new set of emerging new approaches.…”

“…The development of specialised knowledge dedicated to the disciplinary field of design undertaken across the past decades has detailed and expanded the formalisation of design practice, equipping professional designers with practical and theoretical resources to manage the design process without endangering the creativity of outputs from that process. Younger branches of design practice, such as data-enabled approaches, have more recently undertaken a process of formalisation noticeable in published literature (Bogers et al, 2016 , 2016 , 2018 , 2018 ; Gulotta et al, 2013 ; Jansen et al, 2020 ; Lovei et al, 2020 ; Van Kollenburg et al, 2018 ; Yang et al, 2018 ); while convergent approaches have only been conceptualised in the past few years (Pannunzio et al, 2019 ; 2023b ; Sharp et al, 2016 ).…”