Methodological challenges in the evidence synthesis of health outcomes of digital health technologies [védés előtt]

Medical devices and pharmaceuticals are worlds apart, but healthcare would be impossible without them. Digital biomarkers are the subject of this thesis defined as objective, measurable, physiological, and behavioural parameters collected using wearable, portable, implantable, or digestible digital devices. Since the 1970s, systematic reviews and meta-analyses have dominated medical evidence synthesis. They provide medical decision-making evidence. To avoid biases and maintain methodological quality, the Cochrane Handbook recommends systematic reviews follow certain procedures during study stages. This thesis comprises six hypotheses related to digital biomarkers. The first hypothesis aimed to evaluate the suitability of using tools provided by the World Health Organization (WHO), including ICD-11 (International Classification of Diseases, 11th Revision), ICHI (International Classification of Health Interventions), and ICF (International Classification of Functioning, Disability and Health), for categorizing populations, interventions, outcomes, and behavioral/physiological data in studies involving digital biomarkers. The results indicated that these tools were not applicable for categorizing digital biomarker studies as a whole. However, further analysis revealed that these tools were suitable for categorizing digital biomarker studies involving non-general populations or populations with specific diseases. The second hypothesis focused on comparing the statistical power of direct and indirect digital biomarkers. The results indicated that there was no significant difference in power between these two types of digital biomarkers (p-value > 0.05). The next three hypotheses compared the characteristics of systematic reviews and meta-analyses of digital biomarker-based interventions with those of non-digital biomarkers or pharmaceuticals. The comparisons were made in terms of methodological quality, quality of evidence, and publication bias. Although all these hypotheses revealed non-significant differences between the two groups (p-values > 0.05), the results showed that both digital biomarkers and non-digital biomarkers or pharmaceuticals systematic reviews did not exhibit high methodological quality or quality of evidence. The Medical Device Regulation (MDR) has significantly improved European medical device regulatory standards, addressing the above concerns and improving clinical evidence. Despite MDR implementation delays, digital health technology evidence requirements are rising. Companies that achieve these higher clinical requirements will survive and obtain access to large interconnected markets, while those that fail may lose their market authorisation. Thus, medical technology enterprises may gain a competitive edge by strategically planning and executing extensive clinical investigations to provide high-quality clinical data. Developing these essential skills needs immediate attention and effort. Digital health investors should actively monitor industry players' evidence quality and clinical trial competence, since these characteristics may significantly increase company risk

Towards Open Smart Services Platform

The landscape of services in the enterprise has changed significantly for both service providers and service clients over the last few years. In the IT services domain, the mega IT outsourcing service deals with a sole provider are diminishing fast. A typical service client is now consuming multiple IT services, from specialized providers, and services contracts has become smaller in size and duration. More importantly the line of business, not the IT, owns the decisions and the relationship for consuming services. This has also shifted the service consumption input from IT requirements into the business requirements. This new world is posing a new and unique set of opportunities and challenges for service providers in offering services, which include third party providers, to their clients, and for service clients to consume services from multiple providers. To facilitate offering and consuming such multi-vendor services, in this paper, we present a conceptual architecture for an open services platform which enables a given server provider (a service integrator) to offer services to its clients that are a mixture of its own and other services from third party providers. It also enables service clients to look for and choose services from multiple vendors in a seamless, integrated and consistent manner

The Features of Cardiovascular Papers and Impact on Citations

Introduction: The number of citations is a factor in evaluating the quality of scientific articles. The present study aims to examine the factors affecting the citation rate of cardiovascular articles. Methods: In this scientometrics study, the research population is all cardiovascular articles in 2014 in Web of Science (WoS), including a sample of 381 articles studied. Pearson correlation coefficient, Mann–Whitney, Kruskal–Wallis, and Bonferroni tests were used to examine the impact of article features on citations. Results: The results indicated that all quantitative variables (title length, number of authors, author's H-index, journal IF, number of pages, number of author's keywords, number of keywords-plus, number of references)had a significant relationship with the number of citations (P-value<0.001), except for the number of article keywords.  All of the qualitative variables (title length, number of authors, author's H-index, journal IF, number of pages, number of author's keywords, number of keywords-plus, number of references) also affect the number of citations (P-value<0.001). Open access articles, articles with the first author from Australia and North America, articles with international collaboration, and meta-analysis articles have received a more citation rate. Conclusion: Paying attention to the factors affecting the citation rate of cardiovascular articles can be of help to cardiovascular centers for policy-making and researchers in determining the research approach. In this way, they can improve the citation of their works

Fallstudie Swiss Re : «Case Management in P&C Reinsurance – Wissensarbeit im Fokus»

Die vorliegende Fallstudie von Swiss Re zeigt die komplexen Anforderungen, die Systeme zur Unterstützung von wissens- und dokumentenzentrierten Prozessen in einem kollaborativen Umfeld zu erfüllen haben. Dabei geht es in erster Linie darum, das optimale Mass an Standardisierung und Flexibilisierung zu finden, um Wissensarbeiter von Routinearbeiten zu entlasten und in ihren Entscheidungen zu unterstützen. Auf der herausfordernden Reise zur Lösung näherte sich Swiss Re schrittweise und mit einem klaren Richtungswechsel den Bedürfnissen ihrer Wissensarbeitenden

Methodological and Evidence Synthesis Quality Evaluation of MetaAnalyses Assessing the Effect of Antibacterial Envelopes to Reduce CIEDRelated Infections

Purpose: Antibacterial envelopes have been demonstrated to be therapeutically helpful in patients with Cardiac Implantable Electronic Devices (CIEDs). We examined the methodological and evidence synthesis quality of meta-analyses evaluating the effect of envelopes to reduce CIED infections. Methods: Full-text English systematic reviews published in peer-reviewed journals that described meta-analyses of the therapeutic efficacy of envelope on CIED-related infection were explored. A complete literature search was conducted from conception to September 27, 2021, using the electronic databases PubMed, Scopus, and Web of Science. On the 2nd of January 2022, the search was updated. Two reviewers independently screened the titles/abstracts and full-texts and extracted the data. The methodological quality of the included studies was assessed using the AMSTAR-2 tool. The GRADE technique was used to evaluate the quality of evidence synthesis. Results: Six reviews with a total of 15 outcomes were included. All of the reviews had a critically low methodological quality. Nine (60%) and six (40%) outcomes had low and moderate-quality evidence synthesis, respectively. Regarding the GRADE criteria, all outcomes were at risk of bias (n=15, 100%), followed by inconsistency (n=12, 80%), and publication bias (n=10, 67%). Researchers in the field should use the AMSTAR-2 scale and GRADE to perform high-quality studies in the future. Conclusion: To our knowledge, the current study is the first to analyze the methodological and evidence quality of systematic reviews providing meta-analyses on the effect of antibacterial envelopes on CIED-related infections. This is to help physicians, policymakers, and researchers to make better therapeutic decisions by revealing the methodological and evidence synthesis quality of systematic reviews

Assisted mashup development: On the discovery and recommendation of mashup composition knowledge

Over the past few years, mashup development has been made more accessible with tools such as Yahoo! Pipes that help in making the development task simpler through simplifying technologies. However, mashup development is still a difficult task that requires knowledge about the functionality of web APIs, parameter settings, data mappings, among other development efforts. In this work, we aim at assisting users in the mashup process by recommending development knowledge that comes in the form of reusable composition knowledge. This composition knowledge is harvested from a repository of existing mashup models by mining a set of composition patterns, which are then used for interactively providing composition recommendations while developing the mashup. When the user accepts a recommendation, it is automatically woven into the partial mashup model by applying modeling actions as if they were performed by the user. In order to demonstrate our approach we have implemented Baya, a Firefox plugin for Yahoo! Pipes that shows that it is indeed possible to harvest useful composition patterns from existing mashups, and that we are able to provide complex recommendations that can be automatically woven inside Yahoo! Pipes' web-based mashup editor

Health outcomes of Fitbit, Garmin or Apple Watch-based interventions

Introduction: A rising number of systematic reviews of smartwatch clinical effectiveness have been published; however, the findings are conflicting and need further investigation. Our purpose was to assess the methodological and evidence synthesis quality of meta-analyses of the therapeutic efficacy of Fitbit, Garmin, or Apple Watch-based interventions. Material and Methods: Systematic searches were carried out with three electronic databases from inception to October 2021. Full text systematic reviews originally published in English in peer-reviewed journals describing meta-analyses of all clinical outcomes of Fitbit-, Garmin-, or Apple Watch -based interventions were eligible for this study. The Amstar-2 scale and GRADE were used to assess the methodological and evidence synthesis quality, respectively. Results: One study with low methodological quality (overall score of 13.5) was identified, which contained five outcomes. None of the outcomes met the GRADE criteria for high-quality evidence (0%). Two outcomes (40%) had moderate-quality evidence synthesis, while three outcomes (60%) had low-quality evidence synthesis. The most prevalent downgrading GRADE factor was a risk of bias (100%), followed by imprecision (80%), and publication bias (60%). Conclusion: Despite the widespread adoption of smartwatches, there is a large gap in the literature as there is limited evidence. This review suggests that further clinical trials and meta- analyses are needed and it formulates research recommendations

Automatic service categorisation through machine learning in emergent middleware

The modern environment of mobile, pervasive, evolving services presents a great challenge to traditional solutions for enabling interoperability. Automated solutions appear to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to determine compatibility, as a precursor to interaction, come at a substantial computational cost, especially when checks are performed between systems in unrelated domains. To overcome this, we apply machine learning to extract high-level functionality information through text categorisation of a system's interface description. This categorisation allows us to restrict the scope of compatibility checks, giving an overall performance gain when conducting matchmaking between systems. We have evaluated our approach on a corpus of web service descriptions, where even with moderate categorisation accuracy, a substantial performance benefit can be found. This in turn improves the applicability of our overall approach for achieving interoperability in the Connect project