Clinical research relies on high-quality patient data, however, obtaining big data sets is costly and access to existing data is often hindered by privacy and regulatory concerns. Synthetic data generation holds the promise of effectively bypassing these boundaries allowing for simplified data accessibility and the prospect of synthetic control cohorts. We employed two different methodologies of generative artificial intelligence – CTAB-GAN+ and normalizing flows (NFlow) – to synthesize patient data derived from 1606 patients with acute myeloid leukemia, a heterogeneous hematological malignancy, that were treated within four multicenter clinical trials. Both generative models accurately captured distributions of demographic, laboratory, molecular and cytogenetic variables, as well as patient outcomes yielding high performance scores regarding fidelity and usability of both synthetic cohorts (n = 1606 each). Survival analysis demonstrated close resemblance of survival curves between original and synthetic cohorts. Inter-variable relationships were preserved in univariable outcome analysis enabling explorative analysis in our synthetic data. Additionally, training sample privacy is safeguarded mitigating possible patient re-identification, which we quantified using Hamming distances. We provide not only a proof-of-concept for synthetic data generation in multimodal clinical data for rare diseases, but also full public access to synthetic data sets to foster further research.

The current generation of sequencing technologies has led to significant advances in identifying novel disease-associated mutations and generated large amounts of data in a high-throughput manner. Such data in conjunction with clinical routine data are proven to be highly useful in deriving population-level and patient-level predictions, especially in the field of cancer precision medicine. However, data harmonization across multiple national and international clinical sites is an essential step for the assessment of events and outcomes associated with patients, which is currently not adequately addressed. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an internationally established research data repository introduced by the Observational Health Data Science and Informatics (OHDSI) community to overcome this issue. To address the needs of cancer research, the genomic vocabulary extension was introduced in 2020 to support the standardization of subsequent data analysis. In this review, we evaluate the current potential of the OMOP CDM to be applicable in cancer prediction and how comprehensively the genomic vocabulary extension of the OMOP can serve current needs of AI-based predictions. For this, we systematically screened the literature for articles that use the OMOP CDM in predictive analyses in cancer and investigated the underlying predictive models/tools. Interestingly, we found 248 articles, of which most use the OMOP for harmonizing their data, but only 5 make use of predictive algorithms on OMOP-based data and fulfill our criteria. The studies present multicentric investigations, in which the OMOP played an essential role in discovering and optimizing machine learning (ML)-based models. Ultimately, the use of the OMOP CDM leads to standardized data-driven studies for multiple clinical sites and enables a more solid basis utilizing, e.g., ML models that can be reused and combined in early prediction, diagnosis, and improvement of personalized cancer care and biomarker discovery.

The European eHealthMonitor project (eHM) developed a user-sensitive and interactive Web portal for the dementia care setting called the eHM Dementia Portal (eHM-DP). It aims to provide targeted support for informal caregivers of persons with dementia and professionals. The objective of this study was to assess the usefulness and impact of the eHM-DP service in the dementia care setting from two user perspectives: informal caregivers and professionals. The evaluation study was conducted from June to September 2014 and followed a before-after, user-participatory, mixed-method design with questionnaires and interviews. The used intervention was the eHM-DP: an interactive Web portal for informal caregivers and professionals that was tested for a 12-week period. Primary outcomes for caregivers included empowerment, quality of life, caregiver burden, decision aid, as well as perceived usefulness and benefits of the eHM-DP. Primary outcomes for professionals involved decision aid, perceived usefulness, and benefits of the eHM-DP. A total of 25 informal caregivers and 6 professionals used the eHM-DP over the 12-week study period. Both professionals and informal caregivers indicated perceived benefits and support by the eHM-DP. In total, 65% (16/25) of informal caregivers would use the eHM-DP if they had access to it. Major perceived benefits were individualized information acquisition, improved interaction between informal caregivers and professionals, access to support from home, and empowerment in health-related decisions (PrepDM Score: 67.9). Professionals highlighted the improved treatment and care over the disease course (83%, 5/6) and improved health care access for people living in rural areas (67%, 4/6). However, there was no improvement in caregiver burden (Burden Scale for Family Caregivers) and quality of life (EuroQol-5D-5L) over the study period. Our study provides insight into the different user perspectives on an eHealth support service in the dementia treatment and care setting. These results are of importance for future developments and the uptake of eHealth solutions in the dementia domain and reinforce the importance of early user involvement. Turning to the primary target of the eHM-DP service, our findings suggest that the eHM-DP service proved to be a valuable post-diagnostic support service, in particular for the home-based care setting. Further research on a larger scale is needed to enhance the implementation in existing health care infrastructures.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Stressful situations during intraoperative emergencies have a negative impact on human cognitive functions. Consequently, task performance may decrease and patient safety may be compromised. Cognitive aids can counteract these effects and support anesthesiologists in their crisis management. The Professional Association of German Anesthesiologists (BDA) set up a project to develop a comprehensive set of digital cognitive aids for intraoperative emergencies. A parallel development for several software platforms as well as stationary and mobile devices will accommodate the inhomogeneity of the IT infrastructure within German anesthesia departments. Objective: This publication provides a detailed overview of how the task of developing a digital cognitive aid for intraoperative crisis management in anesthesia was addressed that meets user requirements, is highly user-friendly, and easy to navigate. Methods: A user-centered design (UCD) process was conducted to identify, specify, and supplement the requirements for a digital cognitive aid. The study covered the following four aspects: analysis of the context of use, specification of user requirements, development of design solutions, evaluation of design solutions. Three prototypes were developed and evaluated by end-users of the application. Following each evaluation, the new requirements were prioritized and used for redesign. For the first and third prototype the System Usability Scale (SUS) score was determined. The second prototype was evaluated with an extensive online-questionnaire. The evaluation of the third prototype included a think aloud protocol. Results: The chosen methods enabled successful and comprehensive collection of requirements and helped to improve the design of the application step by step. The first prototype achieved an average SUS score of 74 (SD=12), indicating good usability. The second prototype included the following main revisions: two-column layout, initial selection of patient type (infant, adult, parturient), four offered search options, option to check off completed action steps. Its evaluation identified the following major revision points: add quick selection for resuscitation checklists, design the top bar and tabs slightly larger, add more pictograms to the text. The third prototype achieved an average SUS score of 77 (SD=15). The evaluation of the think aloud protocol revealed a good intuitiveness of the application and identified a missing home button as the main issue. Conclusions: Anesthesiology – as an acute medical field – is particularly characterized by its high demands on decision-making and action in uncertain, dynamic, or time-critical situations. The integration of usability aspects is essential for everyday and emergency suitability. The UCD process allowed us to develop a prototypical digital cognitive aid, exhibiting high usability and user satisfaction in the demanding environment of anesthesiological emergencies. Both aspects are essential to increase the acceptance of the application in later stages. The study approach, combining many different methods for determining user requirements, may be useful for other implementation projects in a highly demanding environment.

Background: Common data models (CDMs) are essential tools for data harmonization, which can lead to significant improvements in the health domain. CDMs unite data from disparate sources and ease collaborations across institutions, resulting in the generation of large standardized data repositories across different entities. An overview of existing CDMs and methods used to develop these data sets may assist in the development process of future models for the health domain, such as for decision support systems. Objective: This scoping review investigates methods used in the development of CDMs for health data. We aim to provide a broad overview of approaches and guidelines that are used in the development of CDMs (ie, common data elements or common data sets) for different health domains on an international level. Methods: This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We conducted the literature search in prominent databases, namely, PubMed, Web of Science, Science Direct, and Scopus, starting from January 2000 until March 2022. We identified and screened 1309 articles. The included articles were evaluated based on the type of adopted method, which was used in the conception, users’ needs collection, implementation, and evaluation phases of CDMs, and whether stakeholders (such as medical experts, patients’ representatives, and IT staff) were involved during the process. Moreover, the models were grouped into iterative or linear types based on the imperativeness of the stages during development. Results: We finally identified 59 articles that fit our eligibility criteria. Of these articles, 45 specifically focused on common medical conditions, 10 focused on rare medical conditions, and the remaining 4 focused on both conditions. The development process usually involved stakeholders but in different ways (eg, working group meetings, Delphi approaches, interviews, and questionnaires). Twenty-two models followed an iterative process. Conclusions: The included articles showed the diversity of methods used to develop a CDM in different domains of health. We highlight the need for more specialized CDM development methods in the health domain and propose a suggestive development process that might ease the development of CDMs in the health domain in the future.

The healthcare sector is notable for its reliance on discrete, self-contained information systems, which are often characterised by the presence of disparate data silos. The growing demands for documentation, quality assurance, and secondary use of medical data for research purposes has underscored the necessity for solutions that are more flexible, straightforward to maintain and interoperable. In this context, modular systems have the potential to act as a catalyst for change, offering the capacity to encapsulate and combine functionalities in an adaptable manner. The objective of this scoping review is to determine the extent to which modular systems are employed in the medical field. The review will provide a detailed overview of the effectiveness of service-oriented or microservice architectures, the challenges that should be addressed during implementation, and the lessons that can be learned from countries with productive use of such modular architectures. The review shows a rise in the use of microservices, indicating a shift towards encapsulated autonomous functions. The implementation should use HL7 FHIR as communication standard, deploy RESTful interfaces and standard protocols for technical data exchange, and apply HIPAA security rule for security purposes. User involvement is essential, as is integrating services into existing workflows. Modular architectures can facilitate flexibility and scalability. However, there are well-documented performance issues associated with microservice architectures, namely a high communication demand. One potential solution to this problem may be to integrate modular architectures into a cloud computing environment, which would require further investigation.