Patient and Staff Experience of Remote Patient Monitoring-What to Measure and How: Systematic Review.

BACKGROUND: Patient and staff experience is a vital factor to consider in the evaluation of remote patient monitoring (RPM) interventions. However, no comprehensive overview of available RPM patient and staff experience-measuring methods and tools exists. OBJECTIVE: This review aimed at obtaining a comprehensive set of experience constructs and corresponding measuring instruments used in contemporary RPM research and at proposing an initial set of guidelines for improving methodological standardization in this domain. METHODS: Full-text papers reporting on instances of patient or staff experience measuring in RPM interventions, written in English, and published after January 1, 2011, were considered for eligibility. By "RPM interventions," we referred to interventions including sensor-based patient monitoring used for clinical decision-making; papers reporting on other kinds of interventions were therefore excluded. Papers describing primary care interventions, involving participants under 18 years of age, or focusing on attitudes or technologies rather than specific interventions were also excluded. We searched 2 electronic databases, Medline (PubMed) and EMBASE, on February 12, 2021.We explored and structured the obtained corpus of data through correspondence analysis, a multivariate statistical technique. RESULTS: In total, 158 papers were included, covering RPM interventions in a variety of domains. From these studies, we reported 546 experience-measuring instances in RPM, covering the use of 160 unique experience-measuring instruments to measure 120 unique experience constructs. We found that the research landscape has seen a sizeable growth in the past decade, that it is affected by a relative lack of focus on the experience of staff, and that the overall corpus of collected experience measures can be organized in 4 main categories (service system related, care related, usage and adherence related, and health outcome related). In the light of the collected findings, we provided a set of 6 actionable recommendations to RPM patient and staff experience evaluators, in terms of both what to measure and how to measure it. Overall, we suggested that RPM researchers and practitioners include experience measuring as part of integrated, interdisciplinary data strategies for continuous RPM evaluation. CONCLUSIONS: At present, there is a lack of consensus and standardization in the methods used to measure patient and staff experience in RPM, leading to a critical knowledge gap in our understanding of the impact of RPM interventions. This review offers targeted support for RPM experience evaluators by providing a structured, comprehensive overview of contemporary patient and staff experience measures and a set of practical guidelines for improving research quality and standardization in this domain.

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

Digital health technologies, powered by digital data, provide an opportunity to improve the efficacy and efficiency of health systems at large. However, little is known about different approaches to the use of data for digital health design, or about their possible relations to system-level dynamics. In this contribution, we identify four existing approaches to the use of data for digital health design, namely the silent, the overt, the data-enabled, and the convergent. After characterising the approaches, we provide real-life examples of each. Furthermore, we compare the approaches in terms of selected desirable characteristics of the design process, highlighting relative advantages and disadvantages. Finally, we reflect on the system-level relevance of the differentiation between the approaches and point towards future research directions. Overall, the contribution provides researchers and practitioners with a broad conceptual framework to examine data-related challenges and opportunities in digital health design.

The bigger picture of shared decision making: A service design perspective using the care path of locally advanced pancreatic cancer as a case.

PURPOSE: Solutions to improve the implementation of shared decision making (SDM) in oncology often focus on the consultation, with limited effects. In this study, we used a service design perspective on the care path of locally advanced pancreatic cancer (LAPC). We aimed to understand how experiences of patients, their significant others, and medical professionals over the entire care path accumulate to support their ability to participate in SDM. PARTICIPANTS AND METHODS: We used qualitative interviews including design research techniques with 13 patients, 13 significant others, and 11 healthcare professionals, involved in the diagnosis or treatment of LAPC. The topic list was based on the literature and an auto-ethnography of the illness trajectory by a caregiver who is also a service design researcher. We conducted a thematic content analysis to identify themes influencing the ability to participate in SDM. RESULTS: We found four interconnected themes: (1) Decision making is an ongoing and unpredictable process with many decision moments, often unannounced. The unpredictability of the disease course, tumor response to treatment, and consequences of choices on the quality of life complicate decision making; (2) Division of roles, tasks, and collaboration among professionals and between professionals and patients and/or their significant others is often unclear to patients and their significant others; (3) It involves "work" for patients and their significant others to obtain and understand information; (4) In "their disease journey," patients are confronted with unexpected energy drains and energy boosts, that influence their level of empowerment to participate in SDM. CONCLUSION: The service design perspective uncovered how the stage for SDM is often set outside the consultation, which might explain the limited effect currently seen of interventions focusing on consultation itself. Our findings serve as a starting point for (re)designing care paths to improve the implementation of SDM in oncology.

'Most advanced, yet acceptable': typicality and novelty as joint predictors of aesthetic preference in industrial design.

Typicality and novelty have often been shown to be related to aesthetic preference of human artefacts. Since a typical product is rarely new and, conversely, a novel product will not often be designated as typical, the positive effects of both features seem incompatible. In three studies it was shown that typicality (operationalized as 'goodness of example') and novelty are jointly and equally effective in explaining the aesthetic preference of consumer products, but that they suppress each other's effect. Direct correlations between both variables and aesthetic preference were not significant, but each relationship became highly significant when the influence of the other variable was partialed out. In Study 2, it was furthermore demonstrated that the expertise level of observers did not affect the relative contribution of novelty and typicality. It was finally shown (Study 3) that a more 'objective' measure of typicality, central tendency - operationalized as an exemplar's average similarity to all other members of the category - yielded the same effect of typicality on aesthetic preference. In sum, all three studies showed that people prefer novel designs as long as the novelty does not affect typicality, or, phrased differently, they prefer typicality given that this is not to the detriment of novelty. Preferred are products with an optimal combination of both aspects.