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Advancing Sleep Research through Dynamic Consent and Trustee-Based Medical Data Processing
Abstract
Medical data obtained from individual sleep studies are of great value for scientific research. Yet in Germany, their use is often hindered by legal restrictions, problems with heterogeneous data landscape, and lack of standardized data formats and quality criteria. In this paper, we propose a pioneering architecture to remove these barriers. Our distributed setup ensures that sensitive data remains within the secure boundaries of the originating institutions while patients have control over the subsequent use of their anonymized data. At the heart of our approach is the concept of a data trustee, providing easy-to-use interfaces for the key stakeholders: data producers (sleep clinics), data recipients (researchers), and data providers (patients). We use the innovative concept of dynamic consent to update usage rights and conditions. By using containerized data processing and automated de-authentication, data usage requests are filtered through standardized metadata criteria across all connected data producers, ensuring both privacy protection and streamlined data selection. In addition, our system features tamper-proof logging to ensure transparency and traceability across all transactions. With this integrated approach, we aim to realize the full potential of sleep research while adhering to strict privacy standards and enabling seamless collaboration between stakeholders.
... Also, for current and new research projects, obtaining the data subject's informed consent is necessary, which is the reason for large efforts to develop standardised consent instruments. In Germany, the 'Broad Consent' was developed but criticised for being too general, making it difficult for data subjects to know who has their data and for what purpose; this is why web-based consent management and information hubs are being developed (Forschen fuer Gesundheit 2023; Burmeister et al. 2024). ...
The revolution of artificial intelligence (AI) methods in the scope of the last years has inspired a deluge of use cases but has also caused uncertainty about the actual utility and boundaries of these methods. In this overview, we briefly introduce their main characteristics before focusing on use cases in sleep medicine, discriminating four main areas: Measuring sleep state, advancing diagnostics, advancing research and general advances. We then outline the current European legal framework on AI and the related topic of data sharing.
Background:
Secondary use of health data has reached unequaled potential to improve health systems governance, knowledge, and clinical care. Transparency regarding this secondary use is frequently cited as necessary to address deficits in trust and conditional support and to increase patient awareness.
Objective:
We aimed to review the current published literature to identify different stakeholders’ perspectives and recommendations on what information patients and members of the public want to learn about the secondary use of health data for research purposes and how and in which situations.
Methods:
Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we conducted a scoping review using Medline, CINAHL, PsycINFO, Scopus, Cochrane Library, and PubMed databases to locate a broad range of studies published in English or French until November 2022. We included articles reporting a stakeholder’s perspective or recommendations of what information patients and members of the public want to learn about the secondary use of health data for research purposes and how or in which situations. Data were collected and analyzed with an iterative thematic approach using NVivo.
Results:
Overall, 178 articles were included in this scoping review. The type of information can be divided into generic and specific content. Generic content includes information on governance and regulatory frameworks, technical aspects, and scientific aims. Specific content includes updates on the use of one’s data, return of results from individual tests, information on global results, information on data sharing, and how to access one’s data. Recommendations on how to communicate the information focused on frequency, use of various supports, formats, and wording. Methods for communication generally favored broad approaches such as nationwide publicity campaigns, mainstream and social media for generic content, and mixed approaches for specific content including websites, patient portals, and face-to-face encounters. Content should be tailored to the individual as much as possible with regard to length, avoidance of technical terms, cultural competence, and level of detail. Finally, the review outlined 4 major situations where communication was deemed necessary: before a new use of data, when new test results became available, when global research results were released, and in the advent of a breach in confidentiality.
Conclusions:
This review highlights how different types of information and approaches to communication efforts may serve as the basis for achieving greater transparency. Governing bodies could use the results: to elaborate or evaluate strategies to educate on the potential benefits; to provide some knowledge and control over data use as a form of reciprocity; and as a condition to engage citizens and build and maintain trust. Future work is needed to assess which strategies achieve the greatest outreach while striking a balance between meeting information needs and use of resources.
Background:
An essential step in any medical research project after having identified the research question is to find out if there are enough patients available for a study and where to find them. Pursuing digital feasibility queries on available patient data registries has proven to be an excellent way of reusing existing real world data sources. To support multicentric research, these feasibility queries should be designed and implemented to run across multiple sites and securely access local data. Working across hospitals usually also means working with different data formats and vocabularies. Recently, the Fast Healthcare Interoperability Resources (FHIR) standard has been developed by HL7 to address this concern and describe patient data in a standardized format. The Medical Informatics Initiative (MII) in Germany has committed to this standard and created Data Integration Centers, which convert existing data into the FHIR format at each hospital. This partially solves the interoperability problem, however a distributed feasibility query platform for the FHIR standard is still missing.
Objective:
This study describes the design and implementation of the components involved in creating a cross-hospital feasibility query platform for researchers based on FHIR resources. This effort is part of a larger COVID-19 data exchange platform (CODEX) and is designed to be scalable for a broad range of patient data.
Methods:
We analyzed and designed the abstract components necessary for a distributed feasibility query. This included a user interface for creating the query, a backend with an ontology and terminology service, a middleware for query distribution and a FHIR feasibility query execution service.
Results:
We implemented the components identified in the methods. The resulting solution was distributed to 33 German university hospitals. The functionality of the comprehensive network infrastructure was demonstrated using a test dataset based on the German Corona Consensus Dataset (GECCO). A performance test using specifically created synthetic data revealed the applicability of our solution to datasets containing millions of FHIR resources. The solution can be easily deployed across the hospitals and supports feasibility queries, combining multiple inclusion and exclusion criteria using standard Health Level Seven (HL7) query languages such as the Clinical Quality Language (CQL) and FHIR Search. Developing a platform based on multiple microservices allowed us to create an extendable platform and support multiple HL7 query languages and middleware components to allow integration with future directions of the Medical Informatics Initiative.
Conclusions:
We designed and implemented a feasibility platform for distributed feasibility queries, which works directly on FHIR formatted data and distributed it across 33 university hospitals in Germany. We showed that developing a feasibility platform directly on the FHIR standard is feasible.
Clinicaltrial:
Background
The number of mobile health apps (MHAs), which are developed to promote healthy behaviors, prevent disease onset, manage and cure diseases, or assist with rehabilitation measures, has exploded. App store star ratings and descriptions usually provide insufficient or even false information about app quality, although they are popular among end users. A rigorous systematic approach to establish and evaluate the quality of MHAs is urgently needed. The Mobile App Rating Scale (MARS) is an assessment tool that facilitates the objective and systematic evaluation of the quality of MHAs. However, a German MARS is currently not available.
Objective
The aim of this study was to translate and validate a German version of the MARS (MARS-G).
Methods
The original 19-item MARS was forward and backward translated twice, and the MARS-G was created. App description items were extended, and 104 MHAs were rated twice by eight independent bilingual researchers, using the MARS-G and MARS. The internal consistency, validity, and reliability of both scales were assessed. Mokken scale analysis was used to investigate the scalability of the overall scores.
ResultsThe retranslated scale showed excellent alignment with the original MARS. Additionally, the properties of the MARS-G were comparable to those of the original MARS. The internal consistency was good for all subscales (ie, omega ranged from 0.72 to 0.91). The correlation coefficients (r) between the dimensions of the MARS-G and MARS ranged from 0.93 to 0.98. The scalability of the MARS (H=0.50) and MARS-G (H=0.48) were good.
Conclusions
The MARS-G is a reliable and valid tool for experts and stakeholders to assess the quality of health apps in German-speaking populations. The overall score is a reliable quality indicator. However, further studies are needed to assess the factorial structure of the MARS and MARS-G.
Abstract We aimed to synthesise data on issues related to stakeholder perceptions of privacy, trust, and transparency in use of secondary data. A systematic literature review of healthcare consumer attitudes towards the secondary use and sharing of health administrative and clinical trial data was conducted. EMBASE/MEDLINE, Cochrane Library, PubMed, CINAHL, Informit Health Collection, PROSPERO Database of Systematic Reviews, PsycINFO, and ProQuest databases were searched. Eligible articles included those reporting qualitative or quantitative original research and published in English. No restrictions were placed on publication dates, study design or disease setting. One author screened articles for eligibility, and two authors were involved in the full text review process. Data was extracted using a pre-piloted data extraction template by one author and checked by another. Conflicts were resolved by consensus. Quality and bias were assessed using the QualSyst criteria for qualitative and quantitative studies. This paper focuses on a subset of 35 articles identified from the wider search which focus on issues of privacy, trust, and transparency. Studies included a total of 56,365 respondents. Results of this systematic literature review indicate that while respondents identified advantages in sharing health data, concerns relating to trust, transparency, and privacy remain. Organisations collecting health data and those who seek to share data or undertake secondary data analysis should continue to develop trust, transparency, and privacy with healthcare consumers through open dialogue and education. Consideration should be given to these issues at all stages of data collection including the conception, design, and implementation phases. While individuals understand the benefits of health data sharing for research purposes, ensuring a balance between public benefit and individual privacy is essential. Researchers and those undertaking secondary data analysis need to be cognisant of these key issues at all stages of their research. Systematic review registration: PROSPERO registration number CRD42018110559 (update June 2020).
New technologies, data, and algorithms impact nearly every aspect of daily life. Unfortunately, many of these algorithms operate like black boxes and cannot explain their results even to their programmers, let alone to end-users. As more and more tasks get delegated to such intelligent systems and the nature of user interactions with them becomes increasingly complex, it is important to understand the amount of trust that a user is willing to place on such systems. However, attempts at quantifying trust have either been limited in their scope or not empirically thorough. To address this, we build on prior work which empirically modelled trust in user-technology interactions and describe the development and evolution of a human computer trust scale. We present results of two studies (N=118 & N=183) which were undertaken to assess the reliability and validity of the proposed scale. Our study contributes to the literature by (a) developing a multi-dimensional scale to assess user trust in HCI and (b) being the first study to use the concept of design fiction and future scenarios to study trust.
Background:
The Mobile Application Rating Scale (MARS) provides a reliable method to assess the quality of mobile health (mHealth) apps. However, training and expertise in mHealth and the relevant health field is required to administer it.
Objective:
This study describes the development and reliability testing of an end-user version of the MARS (uMARS).
Methods:
The MARS was simplified and piloted with 13 young people to create the uMARS. The internal consistency and test-retest reliability of the uMARS was then examined in a second sample of 164 young people participating in a randomized controlled trial of a mHealth app. App ratings were collected using the uMARS at 1-, 3,- and 6-month follow up.
Results:
The uMARS had excellent internal consistency (alpha = .90), with high individual alphas for all subscales. The total score and subscales had good test-retest reliability over both 1-2 months and 3 months.
Conclusions:
The uMARS is a simple tool that can be reliably used by end-users to assess the quality of mHealth apps.
With one million people treated every 36 hours, routinely collected UK National Health Service (NHS) health data has huge
potential for medical research. Advances in data acquisition from electronic patient records (EPRs) means such data are increasingly
digital and can be anonymised for research purposes. NHS England’s care.data initiative recently sought to increase the amount
and availability of such data. However, controversy and uncertainty following the care.data public awareness campaign led to a
delay in rollout, indicating that the success of EPR data for medical research may be threatened by a loss of patient and public
trust. The sharing of sensitive health care data can only be done through maintaining such trust in a constantly evolving ethicolegal
and political landscape. We propose that a dynamic consent model, whereby patients can electronically control consent through
time and receive information about the uses of their data, provides a transparent, flexible, and user-friendly means to maintain
public trust. This could leverage the huge potential of the EPR for medical research and, ultimately, patient and societal benefit.
In clinical scenarios, there is an increasing interest in complex computational experiments, as for example the training of Deep Learning models. Reproducibility is an essential property of such experiments, especially if the result contributes to a patient’s treatment. This paper introduces Curious Containers, a software framework for computational reproducibility that treats data, software and runtime environment as decentralized network resources. All experiment resources are described in a single file, using a new format that is compatible with a subset of the Common Workflow Language. Docker is used to deploy the experiment software in a container image, including arbitrary data transmission programs to connect with existing storage solutions. The framework supports Deep Learning applications, that have a high demand in storage and processing capabilities. Large datasets can be mounted inside containers via network filesystems like SSHFS based on the filesystem in user-space technology. The Nvidia-Container-Toolkit enables GPU usage. Curious Containers has been tested in two biomedical scenarios. The first use case is a Deep Learning application for tumor classification in images that requires a large dataset and a GPU. In this context, a prototypical integration of the framework with the existing Data Version Control system for exploratory Deep Learning modeling has been developed. The second use case extends an existing container image, including a scientific workflow for detection and comparison of human protein in mass spectrography data. The container image was originally developed for an archiving platform and could be extended to be compatible with both Curious Containers and cwltool, the Common Workflow Language reference implementation. The presented solution allows for consistent description and execution of computational experiments, while trying to be both flexible and interoperable with existing software and standards. Support for Deep Learning experiments is gaining importance as such systems are increasingly validated as medical decision support systems.
Professional sleep societies have identified a need for strategic research in multiple areas that may benefit from access to and aggregation of large, multidimensional datasets. Technological advances provide opportunities to extract and analyze physiological signals and other biomedical information from datasets of unprecedented size, heterogeneity, and complexity. The National Institutes of Health has implemented a Big Data to Knowledge (BD2K) initiative that aims to develop and disseminate state of the art big data access tools and analytical methods. The National Sleep Research Resource (NSRR) is a new National Heart, Lung, and Blood Institute resource designed to provide big data resources to the sleep research community. The NSRR is a web-based data portal that aggregates, harmonizes, and organizes sleep and clinical data from thousands of individuals studied as part of cohort studies or clinical trials and provides the user a suite of tools to facilitate data exploration and data visualization. Each deidentified study record minimally includes the summary results of an overnight sleep study; annotation files with scored events; the raw physiological signals from the sleep record; and available clinical and physiological data. NSRR is designed to be interoperable with other public data resources such as the Biologic Specimen and Data Repository Information Coordinating Center Demographics (BioLINCC) data and analyzed with methods provided by the Research Resource for Complex Physiological Signals (PhysioNet). This article reviews the key objectives, challenges and operational solutions to addressing big data opportunities for sleep research in the context of the national sleep research agenda. It provides information to facilitate further interactions of the user community with NSRR, a community resource.
Significance
Analysis of large volumes of data provide opportunities to shift research from a focus on predicting group averages to predicting individual outcomes, as is needed to support the goals of precision medicine. Aggregating polysomnography-derived signal data also provides opportunities to discover and replicate new physiological signatures for disease or disease risk. The National Sleep Research Resource in a National Heart Lung and Blood-funded resource established to enhance the use of research sleep data to promote discovery and generation of novel hypotheses, particularly leveraging information from overnight physiological signals. This article reviews how this Resource is structured, including approaches for annotating and visualizing data, for generating and sharing the results of quantitative signal analysis, and for promoting scientific collaboration.
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