How to create value with unobtrusive monitoring technology in home-based dementia care: a multimethod study among key stakeholders

BACKGROUND: There is a growing interest to support extended independent living of people with dementia (PwD) via unobtrusive monitoring (UM) technologies which allow caregivers to remotely monitor lifestyle, health, and safety of PwD. However, these solutions will only be viable if developers obtain a clear picture of how to create value for all relevant stakeholders involved and achieve successful implementation. The aim of this study was therefore to explore the value proposition of UM technology in home-based dementia care and preconditions for successful implementation from a multi-stakeholder perspective. METHODS: We conducted an expert-informed survey among potential stakeholders (n = 25) to identify key stakeholders for UM technology in home-based dementia care. Subsequently, focus groups and semi-structured interviews were conducted among 5 key stakeholder groups (n = 24) including informal caregivers (n = 5), home care professionals (n = 5), PwD (n = 4), directors and managers within home care (n = 4), and policy advisors within the aged care and health insurance sector (n = 6). The sessions addressed the value proposition- and business model canvas and were analyzed using thematic analysis. RESULTS: Stakeholders agreed that UM technology should provide gains such as objective surveillance, timely interventions, and prevention of unnecessary control visits, whereas pains mainly included information overload, unplannable care due to real-time monitoring, and less human interaction. The overall design-oriented need referred to clear situation classifications including urgent care (fall- and wandering detection), non-urgent care (deviations in eating, drinking, sleeping), and future care (risk predictions). Most important preconditions for successful implementation of UM technology included inter-organizational collaboration, a shared vision on re-shaping existing care processes, integrated care ICT infrastructures, clear eligibility criteria for end-users, and flexible care reimbursement systems. CONCLUSIONS: Our findings can guide the value-driven development and implementation of UM technology for home-based dementia care. Stakeholder values were mostly aligned, although stakeholders all had their own perspective on what UM technology should accomplish. Besides, our study highlights the complexity of implementing novel UM technology in home-based dementia care. To achieve successful implementation, organizational and financial preconditions, as well as digital data exchange between home care organizations, will be important. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-022-03550-1

Big Data for personalized healthcare

Big Data, often defined according to the 5V model (volume, velocity, variety, veracity and value), is seen as the key towards personalized healthcare. However, it also confronts us with new technological and ethical challenges that require more sophisticated data management tools and data analysis techniques. This vision paper aims to better understand the technological and ethical challenges we face when using and managing Big Data in healthcare as well as the way in which it impacts our way of working, our health, and our wellbeing. A mixed-methods approach (including a focus group, interviews, and an analysis of social media) was used to gain a broader picture about the pros and cons of using Big Data for personalized healthcare from three different perspectives: Big Data experts, healthcare workers, and the online public. All groups acknowledge the positive aspects of applying Big Data in healthcare, touching upon a wide array of issues, both scientifically and socially. By sharing health data, value can be created that goes beyond the individual patient. The Big Data revolution in healthcare is seen as a promising and innovative development. Yet potential facilitators and barriers need to be faced first to reach its full potential. Concerns were raised about privacy, trust, reliability, safety, purpose limitation, liability, profiling, data ownership, and loss of autonomy. Also, the importance of adding the people-centered view to the rather data-centered 5V model is stressed, in order to get a grip on the opportunities for using Big Data in personalized healthcare. People should be aware that the development of Big Data advancements is not self-evident

Factors influencing implementation of ehealth technologies to support informal dementia care:Umbrella review

Background: The worldwide increase in community-dwelling people with dementia underscores the need for innovative eHealth technologies that aim to provide support to both patients and their informal caregivers in the home setting. However, sustainable implementation of eHealth technologies within this target group can be difficult. Objective: The goal of this study was to gain a thorough understanding of why it is often difficult to implement eHealth technologies in practice, even though numerous technologies are designed to support people with dementia and their informal caregivers at home. In particular, our study aimed to (1) provide an overview of technologies that have been used and studied in the context of informal dementia care and (2) explore factors influencing the implementation of these technologies. Methods: Following an umbrella review design, five different databases were searched (PubMed, PsycINFO, Medline, Scopus, and Cochrane) for (systematic) reviews. Among 2205 reviews retrieved, 21 were included in our analysis based on our screening and selection procedure. A combination of deductive and inductive thematic analyses was performed, using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework for organizing the findings. Results: We identified technologies designed to be used “by informal caregivers,” “by people with dementia,” and “with people with dementia.” Within those groups, most of the represented technologies included, respectively: (i) devices for in-home monitoring of lifestyle, health, and safety; (ii) technologies for supporting memory, orientation, and day structure; and (iii) technologies to facilitate communication between the informal caregiver and person with dementia. Most of the identified factors influencing implementation related to the condition of dementia, characteristics of the technology, expected/perceived value of users, and characteristics of the informal caregiver. Considerably less information has been reported on factors related to the implementing organization and technology supplier, wider institutional and sociocultural context of policy and regulations, and continued adaptation of technology over time. Conclusions: Our study offers a comprehensive overview of eHealth technologies in the context of informal dementia care and contributes to gaining a better understanding of a broad range of factors influencing their implementation. Our results uncovered a knowledge gap regarding success factors for implementation related to the organizational and broader context and continuous adaptation over the long term. Although future research is needed, the current findings can help researchers and stakeholders in improving the development and implementation of eHealth technologies to support informal dementia care

Positive effects of audit and feedback on antimicrobial use in hospitalized patients limited to audited patients

Objective: Audit and feedback is an antimicrobial stewardship (AMS) strategy, with the potential to also optimize antimicrobial use in non-audited patients. This study aimed to determine whether audit and feedback reduce antimicrobial use in both audited and non-audited patients.Design: Before-after trial with a 1-year intervention period and 2.5-year historical cohort.Setting: 750-bed community hospital in the Netherlands.Patients: All patients admitted to the urology wards during the 3.5-year study period were observed. Patients were classified as using antimicrobials if any antimicrobial was used for therapeutic reasons. Patients using antimicrobials prophylactically were excluded from measurements.Intervention: The AMS team provided audit and feedback on antimicrobial use for patients using antimicrobials for 2 days. Retrospectively, antimicrobial use and length of stay (LOS) were compared with the historical cohort.Results: Audits modified antimicrobial treatment in 52.8% of the cases. De-escalating, stopping, and switching from intravenous to oral treatment accounted for 72% of these modifications. Compared to patients from the cohort, who also used antimicrobials for 2 days, antimicrobial use decreased from 14.21 DDD/patient (95% CI, 13.08–15.34) to 11.45 DDD/patient (95% CI, 8.26–14.64; P = .047) for audited patients. Furthermore, mean LOS decreased from 7.42 days (95% CI, 6.79–8.06) to 6.13 days (95% CI, 5.38–6.89; P = .031). However, looking at all patients admitted to the urology wards, the percentage of patients using antimicrobials and total antimicrobial use remained unchanged.Conclusions: Audit and feedback reduce antimicrobial use and LOS, but only for audited patients. Positive effects are not automatically transferred to patients for whom no audits have been performed

Psychosocial Effects and Use of Communication Technologies during Home Confinement in the First Wave of the COVID-19 Pandemic in Italy and The Netherlands

(1) Background: The COVID-19 pandemic forced people from all around the globe to strongly modify their daily routines, putting a significant strain on the social aspects of daily lives. While the first wave of the pandemic was a very challenging time in all countries, it is still uncertain whether various lockdown intensities and infection rates differed regarding their psychosocial impact. This work therefore aimed to investigate (i) the psychosocial effects of home confinement in two European countries that underwent different lockdown intensities: Italy and the Netherlands and (ii) the role of communication technology in relation to feelings of loneliness. (2) Methods: A cross-sectional online survey inquiring about different psychosocial variables and the use of and satisfaction towards communication technology was circulated among the general public during the first wave of the COVID-19 pandemic. In total, 629 participants (66% female, 68% from the Netherlands) answered each question twice, referring to “before” and “during” the pandemic. (3) Results: We found significant negative effects of COVID-19 home confinement on depressive feelings (p < 0.001, %∆ = +54%), loneliness (p < 0.001, %∆ = +37.3%), life satisfaction (p < 0.001, %∆ = −19.8%) and mental wellbeing (p < 0.001, %∆ = −10.6%) which were accompanied with a significantly increased need for psychosocial support (p < 0.001, %∆ = +17.3%). However, the magnitude of psychosocial impact did not significantly differ between residents undergoing a more intense (Italy) versus a less intense (Netherlands) lockdown, although the decrease in social participation was found to be significantly different for both countries (z = −7.714, p < 0.001). Furthermore, our findings demonstrate that the increase in loneliness was associated with the adoption of new digital communication tools (r = 0.21, p < 0.001), and significantly higher for individuals who started to adopt at least one new digital communication tool during confinement than for those who did not (z = −4.252, p < 0.001). (4) Conclusions: This study highlights that, although COVID-19 home confinement significantly impacted psychosocial wellbeing during the first wave of the pandemic, this impact did not differ based on lockdown intensity. Recognizing the increasing adoption of digital communication technology in an attempt to reduce lockdown loneliness, future studies should investigate what is needed from the technology to achieve this effect

Mapping twenty years of antimicrobial resistance research trends

OBJECTIVE: Antimicrobial resistance (AMR) is a global threat to health and healthcare. In response to the growing AMR burden, research funding also increased. However, a comprehensive overview of the research output, including conceptual, temporal, and geographical trends, is missing. Therefore, this study uses topic modelling, a machine learning approach, to reveal the scientific evolution of AMR research and its trends, and provides an interactive user interface for further analyses. METHODS: Structural topic modelling (STM) was applied on a text corpus resulting from a PubMed query comprising AMR articles (1999-2018). A topic network was established and topic trends were analysed by frequency, proportion, and importance over time and space. RESULTS: In total, 88 topics were identified in 158,616 articles from 166 countries. AMR publications increased by 450% between 1999 and 2018, emphasizing the vibrancy of the field. Prominent topics in 2018 were Strategies for emerging resistances and diseases, Nanoparticles, and Stewardship. Emerging topics included Water and environment, and Sequencing. Geographical trends showed prominence of Multidrug-resistant tuberculosis (MDR-TB) in the WHO African Region, corresponding with the MDR-TB burden. China and India were growing contributors in recent years, following the United States of America as overall lead contributor. CONCLUSION: This study provides a comprehensive overview of the AMR research output thereby revealing the AMR research response to the increased AMR burden. Both the results and the publicly available interactive database serve as a base to inform and optimise future research