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BACKGROUND: The COVID-19 pandemic sparked a surge of research publications spanning epidemiology, basic science, and clinical science. Thanks to the digital revolution, large data sets are now accessible, which also enables real-time epidemic tracking. However, despite this, academic faculty and their trainees have been struggling to access comprehensive clinical data. To tackle this issue, we have devised a clinical data repository that streamlines research processes and promotes interdisciplinary collaboration. OBJECTIVE: This study aimed to present an easily accessible up-to-date database that promotes access to local COVID-19 clinical data, thereby increasing efficiency, streamlining, and democratizing the research enterprise. By providing a robust database, a broad range of researchers (faculty and trainees) and clinicians from different areas of medicine are encouraged to explore and collaborate on novel clinically relevant research questions. METHODS: A research platform, called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), was constructed to house cleaned, highly granular, deidentified, and continually updated data from over 18,000 patients hospitalized with COVID-19 from January 2020 to January 2023, across the Yale New Haven Health System. Data across several key domains were extracted including demographics, past medical history, laboratory values during hospitalization, vital signs, medications, imaging, procedures, and outcomes. Given the time-varying nature of several data domains, summary statistics were constructed to limit the computational size of the database and provide a reasonable data file that the broader research community could use for basic statistical analyses. The initiative also included a front-end user interface, the DOM-CovX Explorer, for simple data visualization of aggregate data. The detailed clinical data sets were made available for researchers after a review board process. RESULTS: As of January 2023, the DOM-CovX Explorer has received 38 requests from different groups of scientists at Yale and the repository has expanded research capability to a diverse group of stakeholders including clinical and research-based faculty and trainees within 15 different surgical and nonsurgical specialties. A dedicated DOM-CovX team guides access and use of the database, which has enhanced interdepartmental collaborations, resulting in the publication of 16 peer-reviewed papers, 2 projects available in preprint servers, and 8 presentations in scientific conferences. Currently, the DOM-CovX Explorer continues to expand and improve its interface. The repository includes up to 3997 variables across 7 different clinical domains, with continued growth in response to researchers' requests and data availability. CONCLUSIONS: The DOM-CovX Data Explorer and Repository is a user-friendly tool for analyzing data and accessing a consistently updated, standardized, and large-scale database. Its innovative approach fosters collaboration, diversity of scholarly pursuits, and expands medical education. In addition, it can be applied to other diseases beyond COVID-19.

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BACKGROUND: Cross-institutional interoperability between health care providers remains a recurring challenge worldwide. The German Medical Informatics Initiative, a collaboration of 37 university hospitals in Germany, aims to enable interoperability between partner sites by defining Fast Healthcare Interoperability Resources (FHIR) profiles for the cross-institutional exchange of health care data, the Core Data Set (CDS). The current CDS and its extension modules define elements representing patients' health care records. All university hospitals in Germany have made significant progress in providing routine data in a standardized format based on the CDS. In addition, the central research platform for health, the German Portal for Medical Research Data feasibility tool, allows medical researchers to query the available CDS data items across many participating hospitals. OBJECTIVE: In this study, we aimed to evaluate a novel approach of combining the current top-down generated FHIR profiles with the bottom-up generated knowledge gained by the analysis of respective instance data. This allowed us to derive options for iteratively refining FHIR profiles using the information obtained from a discrepancy analysis. METHODS: We developed an FHIR validation pipeline and opted to derive more restrictive profiles from the original CDS profiles. This decision was driven by the need to align more closely with the specific assumptions and requirements of the central feasibility platform's search ontology. While the original CDS profiles offer a generic framework adaptable for a broad spectrum of medical informatics use cases, they lack the specificity to model the nuanced criteria essential for medical researchers. A key example of this is the necessity to represent specific laboratory codings and values interdependencies accurately. The validation results allow us to identify discrepancies between the instance data at the clinical sites and the profiles specified by the feasibility platform and addressed in the future. RESULTS: A total of 20 university hospitals participated in this study. Historical factors, lack of harmonization, a wide range of source systems, and case sensitivity of coding are some of the causes for the discrepancies identified. While in our case study, Conditions, Procedures, and Medications have a high degree of uniformity in the coding of instance data due to legislative requirements for billing in Germany, we found that laboratory values pose a significant data harmonization challenge due to their interdependency between coding and value. CONCLUSIONS: While the CDS achieves interoperability, different challenges for federated data access arise, requiring more specificity in the profiles to make assumptions on the instance data. We further argue that further harmonization of the instance data can significantly lower required retrospective harmonization efforts. We recognize that discrepancies cannot be resolved solely at the clinical site; therefore, our findings have a wide range of implications and will require action on multiple levels and by various stakeholders.

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Recent advancements in protein structure determination and especially in protein structure prediction techniques have led to the availability of vast amounts of macromolecular structures. However, the accessibility and integration of these structures into scientific workflows are hindered by the lack of standardization among publicly available data resources. To address this issue, we introduced the 3D-Beacons Network, a unified platform that aims to establish a standardized framework for accessing and displaying protein structure data. In this article, we highlight the importance of standardized approaches for accessing protein structure data and showcase the capabilities of 3D-Beacons. We describe four protocols for finding and accessing macromolecular structures from various specialist data resources via 3D-Beacons. First, we describe three scenarios for programmatically accessing and retrieving data using the 3D-Beacons API. Next, we show how to perform sequence-based searches to find structures from model providers. Then, we demonstrate how to search for structures and fetch them directly into a workflow using JalView. Finally, we outline the process of facilitating access to data from providers interested in contributing their structures to the 3D-Beacons Network. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Programmatic access to the 3D-Beacons API Basic Protocol 2: Sequence-based search using the 3D-Beacons API Basic Protocol 3: Accessing macromolecules from 3D-Beacons with JalView Basic Protocol 4: Enhancing data accessibility through 3D-Beacons.

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Data access committees (DAC) gatekeep access to secured genomic and related health datasets yet are challenged to keep pace with the rising volume and complexity of data generation. Automated decision support (ADS) systems have been shown to support consistency, compliance, and coordination of data access review decisions. However, we lack understanding of how DAC members perceive the value add of ADS, if any, on the quality and effectiveness of their reviews. In this qualitative study, we report findings from 13 semi-structured interviews with DAC members from around the world to identify relevant barriers and facilitators to implementing ADS for genomic data access management. Participants generally supported pilot studies that test ADS performance, for example in cataloging data types, verifying user credentials and tagging datasets for use terms. Concerns related to over-automation, lack of human oversight, low prioritization, and misalignment with institutional missions tempered enthusiasm for ADS among the DAC members we engaged. Tensions for change in institutional settings within which DACs operated was a powerful motivator for why DAC members considered the implementation of ADS into their access workflows, as well as perceptions of the relative advantage of ADS over the status quo. Future research is needed to build the evidence base around the comparative effectiveness and decisional outcomes of institutions that do/not use ADS into their workflows.

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Public health institutions rely on the access to social media data to better understand the dynamics and impact of infodemics - an overabundance of information during a disease outbreak, potentially including mis-and disinformation. The scope of the COVID-19 infodemic has led to growing concern in the public health community. The spread of harmful information or information voids may negatively impact public health. In this context, social media are of particular relevance as an integral part of our society, where much information is consumed. In this perspective paper, we discuss the current state of (in)accessibility of social media data of the main platforms in the European Union. The European Union's relatively new Digital Services Act introduces the obligation for platforms to provide data access to a wide range of researchers, likely including researchers at public health institutions without formal academic affiliation. We examined eight platforms (Facebook, Instagram, LinkedIn, Pinterest, Snapchat, TikTok, X, YouTube) affected by the new legislation in regard to data accessibility. We found that all platforms apart from TikTok offer data access through the Digital Services Act. Potentially, this presents a fundamentally new situation for research, as before the Digital Services Act, few platforms granted data access or only to very selective groups of researchers. The access regime under the Digital Services Act is, however, still evolving. Specifics such as the application procedure for researcher access are still being worked out and results can be expected in spring 2024. The impact of the Digital Services Act on research will therefore only become fully apparent in the future.

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Citizens juries (CJ) are a method of deliberative action research that have been utilized in countries with well-funded health care systems to address questions about access to health data. Uganda is classified as a low-income country and utilizes a predominantly paper-based health record system. The burgeoning electronic health record in the central area represents an opportunity to collect and analyze longitudinal data on patients living with long term HIV infection and multiple diseases, a hitherto unexplored disease mapping exercise We set out to understand the public perception towards the use of data for research purposes such as this among Ugandans utilizing an adapted strategy sensitive to the local culture. The jury were unanimous that electronic data should be used for research provided certain safeguards are adhered to and most importantly, that consent to do so is obtained on the basis of a clear rationale for the project.

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Data access committees (DACs) are critical players in the data sharing ecosystem. DACs review requests for access to data held in one or more repositories and where specific constraints determine how the data may be used and by whom. Our team surveyed DAC members affiliated with genomic data repositories worldwide to understand standard processes and procedures, operational metrics, bottlenecks, and efficiencies, as well as their perspectives on possible improvements to quality review. We found that DAC operations and systemic issues were common across repositories globally. In general, DAC members endeavored to achieve an appropriate balance of review efficiency, quality, and compliance. Our results suggest a similarly proportionate path forward that helps DACs pursue mutual improvements to efficiency and compliance without sacrificing review quality.

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BACKGROUND: Despite some emerging lessons learned from the COVID-19 pandemic, evidence suggests the world remains largely underprepared for-and vulnerable to-similar threats in the future. METHODS: In 2022, researchers at the US National Institute for Occupational Safety and Health (NIOSH) led a team of volunteers to explore how future disruptions, such as pandemics, might impact work and the practice of occupational safety and health (OSH). This qualitative inquiry was framed as a strategic foresight project and included a series of activities designed to help better understand, prepare for, and influence the future. RESULTS: Findings from a thorough search for indicators of change were synthesized into nine critical uncertainties and four plausible future scenarios. Analysis of these outputs elucidated three key challenges that may impact OSH research, policy, and practice during future disruptions: (1) data access, (2) direct-to-worker communications, and (3) mis- and dis-information management. CONCLUSIONS: A robust strategic response is offered to address these challenges, and next steps are proposed to enhance OSH preparedness and institutionalize strategic foresight across the OSH community.

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The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published data products. We argue that any proposal for quantifying disclosure risk should be based on prespecified, objective criteria. We illustrate this approach to evaluate the absolute disclosure risk framework, the counterfactual framework underlying differential privacy, and prior-to-posterior comparisons. We conclude that satisfying all the desiderata is impossible, but counterfactual comparisons satisfy the most while absolute disclosure risk satisfies the fewest. Furthermore, we explain that many of the criticisms levied against differential privacy would be levied against any technology that is not equivalent to direct, unrestricted access to confidential data. More research is needed, but in the near term, the counterfactual approach appears best-suited for privacy versus utility analysis.

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INTRODUCTION: There is increasing interest on re-use of outpatient healthcare data for research, as most medical diagnosis and treatment is provided in the ambulatory sector. One of the early projects to bring primary data from German ambulatory care into clinical research technically, organizationally and in compliance with legal demands has been the RADAR project, that is based on a broad consent and has used the then available practice information system's interfaces to extract and transfer data to a research repository. In course of the digital transformation of the German healthcare system, former standards are abandoned and new interoperability standards, interfaces and regulations on secondary use of patient data are defined, however with slow adoption by Health-IT systems. Therefore, it is of importance for all initiatives that aim at using ambulatory healthcare data for research, how to access this data in an efficient and effective way. METHODS: Currently defined healthcare standards are compared regarding coverage of data relevant for research as defined by the RADAR project. We compare four architectural options to access ambulatory health data through different components of healthcare and health research data infrastructures along the technical, organizational and regulatory conditions, the timetable of dissemination and the researcher's perspective. RESULTS: A high-level comparison showed a high degree of semantic overlap in the information models used. Electronic patient records and practice information systems are alternative data sources for ambulatory health data - but differ strongly in data richness and accessibility. CONCLUSION: Considering the compared dimensions of architectural routes to access health data for secondary research use we conclude that data extraction from practice information systems is currently the most promising way due to data availability on a mid-term perspective. Integration of routine data into the national research data infrastructures might be enforced by convergence of to date different information models.

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OBJECTIVES: Widespread use of electronic health records (EHRs) now makes it feasible to expand beyond health insurance claims data to include full EHR data for health economics and outcomes research (HEOR) studies. We seek to develop ways to maximize researcher access to such data while strongly protecting patients' privacy rights. METHODS: We analyzed alternative organizational structures and intellectual property rights assignments as they now exist and compared these with structures and intellectual property rights assignments that would maximize access to data for HEOR studies and minimize transactions costs. We analyzed data protection requirements and financial incentives at 3 levels: patient decision making, patients' data aggregators, and final aggregation across patients' data. RESULTS: Creating new HEOR data systems requires new organizations and funding, while also protecting patients' data privacy rights. The Cures Act enables a new market for trusted third parties (TTPs) to aggregate patients' data. New secondary data aggregators must combine individuals' aggregated EHRs into usable HEOR databases. Maximal patient participation requires complete health insurance coverage of costs that healthcare providers charge for transmitting patients' data to TTPs. The new secondary system to aggregate data from many TTPs into usable HEOR optimally has external funding. CONCLUSIONS: Important steps remain uncompleted to achieve maximally available HEOR data while protecting patients' privacy rights. HEOR information is a public good, so private incentives to support creation and operation of this new system remain incomplete. Public and private support can expand this system to optimally improve people's health.

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BACKGROUND: It has been argued that ethics review committees-e.g., Research Ethics Committees, Institutional Review Boards, etc.- have weaknesses in reviewing big data and artificial intelligence research. For instance, they may, due to the novelty of the area, lack the relevant expertise for judging collective risks and benefits of such research, or they may exempt it from review in instances involving de-identified data. MAIN BODY: Focusing on the example of medical research databases we highlight here ethical issues around de-identified data sharing which motivate the need for review where oversight by ethics committees is weak. Though some argue for ethics committee reform to overcome these weaknesses, it is unclear whether or when that will happen. Hence, we argue that ethical review can be done by data access committees, since they have de facto purview of big data and artificial intelligence projects, relevant technical expertise and governance knowledge, and already take on some functions of ethical review. That said, like ethics committees, they may have functional weaknesses in their review capabilities. To strengthen that function, data access committees must think clearly about the kinds of ethical expertise, both professional and lay, that they draw upon to support their work. CONCLUSION: Data access committees can undertake ethical review of medical research databases provided they enhance that review function through professional and lay ethical expertise.

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BACKGROUND: Deep learning is an important means to realize the automatic detection, segmentation, and classification of pulmonary nodules in computed tomography (CT) images. An entire CT scan cannot directly be used by deep learning models due to image size, image format, image dimensionality, and other factors. Between the acquisition of the CT scan and feeding the data into the deep learning model, there are several steps including data use permission, data access and download, data annotation, and data preprocessing. This paper aims to recommend a complete and detailed guide for researchers who want to engage in interdisciplinary lung nodule research of CT images and Artificial Intelligence (AI) engineering. METHODS: The data preparation pipeline used the following four popular large-scale datasets: LIDC-IDRI (Lung Image Database Consortium image collection), LUNA16 (Lung Nodule Analysis 2016), NLST (National Lung Screening Trial) and NELSON (The Dutch-Belgian Randomized Lung Cancer Screening Trial). The dataset preparation is presented in chronological order. FINDINGS: The different data preparation steps before deep learning were identified. These include both more generic steps and steps dedicated to lung nodule research. For each of these steps, the required process, necessity, and example code or tools for actual implementation are provided. DISCUSSION AND CONCLUSION: Depending on the specific research question, researchers should be aware of the various preparation steps required and carefully select datasets, data annotation methods, and image preprocessing methods. Moreover, it is vital to acknowledge that each auxiliary tool or code has its specific scope of use and limitations. This paper proposes a standardized data preparation process while clearly demonstrating the principles and sequence of different steps. A data preparation pipeline can be quickly realized by following these proposed steps and implementing the suggested example codes and tools.

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There is common consensus that data sharing accelerates science. Data sharing enhances the utility of data and promotes the creation and competition of scientific ideas. Within the Alzheimer's disease and related dementias (ADRD) community, data types and modalities are spread across many organizations, geographies, and governance structures. The ADRD community is not alone in facing these challenges, however, the problem is even more difficult because of the need to share complex biomarker data from centers around the world. Heavy-handed data sharing mandates have, to date, been met with limited success and often outright resistance. Interest in making data Findable, Accessible, Interoperable, and Reusable (FAIR) has often resulted in centralized platforms. However, when data governance and sovereignty structures do not allow the movement of data, other methods, such as federation, must be pursued. Implementation of fully federated data approaches are not without their challenges. The user experience may become more complicated, and federated analysis of unstructured data types remains challenging. Advancement in federated data sharing should be accompanied by improvement in federated learning methodologies so that federated data sharing becomes functionally equivalent to direct access to record level data. In this article, we discuss federated data sharing approaches implemented by three data platforms in the ADRD field: Dementia's Platform UK (DPUK) in 2014, the Global Alzheimer's Association Interactive Network (GAAIN) in 2012, and the Alzheimer's Disease Data Initiative (ADDI) in 2020. We conclude by addressing open questions that the research community needs to solve together.

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Background: Scientific research is becoming an increasingly collaborative and global venture. The Healthy Life Trajectories Initiative (HeLTI), for instance, is an international Developmental Origins of Health and Disease research collaboration developed to address the increasing burden of noncommunicable diseases around the world. It comprises four separate but harmonized cohort trials in Canada, China, India, and South Africa. These cohorts will generate rich data and biosample sets that can be shared both within the HeLTI Consortium and with other researchers from around the world. Methods: To ensure the coordination and operation of these types of collaborative research initiatives, a standardized and harmonized governance model is required to regulate the processes and interactions between all involved actors. To develop the governance models, frameworks and related policies from other longitudinal cohort studies and biobanks were used, as were guidance documents on biobank and database governance and relevant literature on data and biobank governance. Results: This article outlines the key components of the governance model for the HeLTI Consortium, including management of the cohorts' respective databases and biobanks, access to data and biosamples, and considerations related to intellectual property and publications. Conclusion: Governance within international collaborative research ventures is critical to ensure the operations and benefits of these types of research apparatuses. Although this article focuses on the HeLTI Consortium as a model, it may nonetheless serve as a model for both current and future collaborative consortium-based research initiatives. Clinical Trial Registration Numbers: Canada, ISRCTN13308752; China, ChiCTR1800017773; India, ISRCTN20161479; South Africa, PACTR201903750173871.

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The Global Burden of Animal Diseases (GBADs) programme will provide data-driven evidence that policy-makers can use to evaluate options, inform decisions, and measure the success of animal health and welfare interventions. The GBADs' Informatics team is developing a transparent process for identifying, analysing, visualising and sharing data to calculate livestock disease burdens and drive models and dashboards. These data can be combined with data on other global burdens (human health, crop loss, foodborne diseases) to provide a comprehensive range of information on One Health, required to address such issues as antimicrobial resistance and climate change. The programme began by gathering open data from international organisations (which are undergoing their own digital transformations). Efforts to achieve an accurate estimate of livestock numbers revealed problems in finding, accessing and reconciling data from different sources over time. Ontologies and graph databases are being developed to bridge data silos and improve the findability and interoperability of data. Dashboards, data stories, a documentation website and a Data Governance Handbook explain GBADs data, now available through an application programming interface. Sharing data quality assessments builds trust in such data, encouraging their application to livestock and One Health issues. Animal welfare data present a particular challenge, as much of this information is held privately and discussions continue regarding which data are the most relevant. Accurate livestock numbers are an essential input for calculating biomass, which subsequently feeds into calculations of antimicrobial use and climate change. The GBADs data are also essential to at least eight of the United Nations Sustainable Development Goals.

Le programme " Impact mondial des maladies animales " (GBADs) a pour but de réunir des éléments probants axés sur des données, qui soient exploitables par les décideurs politiques pour évaluer les solutions envisagées, fonder leurs décisions et mesurer le succès des interventions dans les domaines de la santé et du bien-être des animaux. L'équipe informatique du GBADs a conçu un processus transparent pour l'identification, l'analyse, la visualisation et le partage des données, grâce auquel il sera possible d'estimer l'impact des maladies du bétail et de réaliser des modèles et des tableaux de bord sur le sujet. Les données ainsi réunies peuvent être combinées avec celles couvrant d'autres problématiques ayant un impact mondial (santé humaine, pertes de récoltes, maladies d'origine alimentaire) afin de fournir l'éventail complet d'informations Une seule santé requis pour faire face à des enjeux tels que la résistance aux agents antimicrobiens ou le changement climatique. La première phase du programme a consisté à recueillir des données ouvertes auprès de diverses organisations internationales (qui procèdent également à leur propre transformation numérique). Les efforts déployés pour parvenir à une estimation précise des effectifs des cheptels ont mis en lumière les difficultés à trouver les données détenues par différentes sources, à y accéder et à les recouper au fil du temps. Des ontologies et des bases de données graphiques sont en cours d'élaboration pour résoudre le problème des silos de données et pour améliorer la facilité de recherche et l'interopérabilité des données. Les données du GBADs sont désormais expliquées sous forme de tableaux de bord, de récits construits à partir des données, ainsi que dans un site web documentaire et un Manuel de gouvernance des données, tous disponibles via une interface de programmation d'applications. Le partage des évaluations de la qualité des données renforce la confiance dans ces dernières et encourage à les appliquer pour traiter les problématiques affectant l'élevage ou relevant de l'approche Une seule santé. Les données relatives au bien-être animal présentent une difficulté particulière : elles sont, pour l'essentiel, détenues à titre privé et la question de savoir quelles sont les données les plus pertinentes est toujours en discussion. Les effectifs des cheptels doivent avoir été déterminés de manière précise afin de calculer la biomasse animale, élément qui entre par la suite dans le calcul des quantités d'agents antimicrobiens utilisés et des indicateurs du changement climatique. Les données du programme GBADs sont également essentielles au regard d'au moins huit des objectifs de développement durable des Nations Unies.

El programa sobre el Impacto Global de las Enfermedades Animales (GBADs) proporcionará información contrastada y basada en el uso de datos de la que luego puedan servirse los planificadores de políticas para valorar distintas opciones, decidir con conocimiento de causa y medir la eficacia de una u otra intervención en materia de sanidad y bienestar animales. El equipo informático encargado del GBADs está preparando un proceso transparente destinado a seleccionar, analizar, visualizar y poner en común datos que ayuden a calcular la carga de enfermedades del ganado y a guiar la elaboración de modelos y paneles de control. Estos datos pueden ser combinados con datos referidos a otros grandes problemas planetarios (salud humana, pérdida de cultivos, enfermedades de transmisión alimentaria) para obtener el repertorio completo de información en clave de Una sola salud que se necesita para abordar problemáticas como la resistencia a los antimicrobianos o el cambio climático. El programa empezó por reunir datos abiertos procedentes de organizaciones internacionales (inmersas, por otra parte, en su propio proceso de transformación digital). La labor emprendida para estimar con exactitud las cifras de ejemplares del mundo pecuario reveló ciertos problemas a la hora de encontrar, obtener y conciliar datos de distintas fuentes a lo largo del tiempo. Ahora se están elaborando ontologías y bases de datos gráficos para crear conexiones entre los "silos de datos" y lograr que los datos sean a la vez más compatibles entre sí y más fáciles de localizar. Paneles de control, interpretaciones narrativas de los datos ("data stories"), un sitio web de documentación y un manual de gestión de datos ayudan a explicar y aprehender los datos del GBADs, accesibles ahora por medio de una interfaz de programación de aplicaciones. El hecho de poner en común las evaluaciones de la calidad de los datos genera mayor confianza en esta información, promoviendo con ello su aplicación en temas de ganadería y de Una sola salud. Los datos de bienestar animal plantean una particular dificultad, pues gran parte de esta información está en manos privadas y todavía no está claro cuáles son los datos de mayor interés. Disponer de cifras exactas sobre el número de cabezas de ganado es fundamental para efectuar los cálculos de biomasa que después se utilizan para hacer otros cómputos referidos al uso de antimicrobianos y al cambio climático. Los datos del GBADs son asimismo esenciales para al menos ocho de los Objetivos de Desarrollo Sostenible de las Naciones Unidas.

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Since the European Commission has published a Proposal for a Regulation - The European Health Data Space, Croatia is actively working on the implementation. Public sector bodies including the Croatian Institute of Public Health, Ministry of Health and Croatian Health Insurance Fund play a key role in this process. The establishment of a Health Data Access Body is the main challenge in this effort. Potential challenges and obstacles in this process and projects that follow the efforts are described in this paper.

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BACKGROUND: Flexible Assertive Community Treatment (FACT) is a model of integrated care for patients with long-term serious mental illness. FACT teams deliver services using assertive outreach to treat patients who can be hard to reach by the health care service, and focus on both the patient's health and their social situation. However, in Norway, FACT team members have challenges with their information and communication (ICT) solutions. OBJECTIVE: The aim of this study was to explore Norwegian FACT teams' experiences and expectations of their ICT solutions, including electronic health records, electronic whiteboards, and calendars. METHODS: We gathered data in two phases. In the first phase, we conducted semistructured interviews with team leaders and team coordinators, and made observations in FACT teams targeting adults. In the second phase, we conducted semistructured group interviews in FACT teams targeting youth. We performed a thematic analysis of the data in a theoretical manner to address the specific objectives of the study. RESULTS: A total of 8 teams were included, with 5 targeting adults and 3 targeting youth. Due to the COVID-19 pandemic, we were not able to perform observations in 2 of the teams targeting adults. Team leaders and coordinators in all 5 teams targeting adults were interviewed, with a total of 7 team members participating in the teams targeting youth. We found various challenges with communication, documentation, and organization for FACT teams. The COVID-19 pandemic was challenging for the teams and changed the way they used ICT solutions. There were issues with some technical solutions used in the teams, including electronic health records, electronic whiteboards, and calendars. Lack of integration and access to data were some of the main issues identified. CONCLUSIONS: Despite the FACT model being successfully implemented in Norway, there are several issues regarding the ICT solutions they use, mainly related to access to data and integration. Further research is required to detail how improved ICT solutions should be designed. While FACT teams targeting adults and youth differ in some ways, their needs for ICT solutions are largely similar.