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BACKGROUND: The importance of real-world evidence is widely recognized in observational oncology studies. However, the lack of interoperable data quality standards in the fragmented health information technology landscape represents an important challenge. Therefore, adopting validated systematic methods for evaluating data quality is important for oncology outcomes research leveraging real-world data (RWD). OBJECTIVE: This study aims to implement real-world time to treatment discontinuation (rwTTD) for a systemic anticancer therapy (SACT) as a new use case for the Use Case Specific Relevance and Quality Assessment, a framework linking data quality and relevance in fit-for-purpose RWD assessment. METHODS: To define the rwTTD use case, we mapped the operational definition of rwTTD to RWD elements commonly available from oncology electronic health record-derived data sets. We identified 20 tasks to check the completeness and plausibility of data elements concerning SACT use, line of therapy (LOT), death date, and length of follow-up. Using descriptive statistics, we illustrated how to implement the Use Case Specific Relevance and Quality Assessment on 2 oncology databases (Data sets A and B) to estimate the rwTTD of an SACT drug (target SACT) for patients with advanced head and neck cancer diagnosed on or after January 1, 2015. RESULTS: A total of 1200 (24.96%) of 4808 patients in Data set A and 237 (5.92%) of 4003 patients in Data set B received the target SACT, suggesting better relevance of the former in estimating the rwTTD of the target SACT. The 2 data sets differed with regard to the terminology used for SACT drugs, LOT format, and target SACT LOT distribution over time. Data set B appeared to have less complete SACT records, longer lags in incorporating the latest data, and incomplete mortality data, suggesting a lack of fitness for estimating rwTTD. CONCLUSIONS: The fit-for-purpose data quality assessment demonstrated substantial variability in the quality of the 2 real-world data sets. The data quality specifications applied for rwTTD estimation can be expanded to support a broad spectrum of oncology use cases.

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SUMMARYThe emergence and worldwide dissemination of SARS-CoV-2 required both urgent development of new diagnostic tests and expansion of diagnostic testing capacity on an unprecedented scale. The rapid evolution of technologies that allowed testing to move out of traditional laboratories and into point-of-care testing centers and the home transformed the diagnostic landscape. Four years later, with the end of the formal public health emergency but continued global circulation of the virus, it is important to take a fresh look at available SARS-CoV-2 testing technologies and consider how they should be used going forward. This review considers current use case scenarios for SARS-CoV-2 antigen, nucleic acid amplification, and immunologic tests, incorporating the latest evidence for analytical/clinical performance characteristics and advantages/limitations for each test type to inform current debates about how tests should or should not be used.

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Lassa fever (LF) is a zoonotic viral hemorrhagic disease endemic to several West African countries. Approximately 300-500,000 cases occur annually across all ages with 10-20% case fatality rates. A LF vaccine is a recognized public health priority, with several candidates entering clinical trials. However, the perspectives of regional experts regarding critical vaccine properties, ideal delivery methods, and priority target populations remain unclear. Using a mixed methods approach with a standardized questionnaire, we individually interviewed 8 West African stakeholders, each with extensive knowledge and experience of LF. They strongly favored the use of a mass, proactive campaign strategy to immunize a wide age range of people in high-risk areas, including pregnant women and health care workers. We estimated that these and other plausible delivery scenarios could result in an initial demand of anywhere from 1 to 100 million doses, with most demand coming from Nigeria. These findings may help inform LF vaccine development and deployment efforts.

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BACKGROUND: Geopolitical and economic crises force a growing number of people to leave their countries and search better employment opportunities abroad. Meanwhile, the highly competitive labor market provides opportunities for employees to change workplaces and job positions. Health assessment data collected during the occupational history is an essential resource for developing efficient occupational disease prevention strategies as well as for ensuring the physical and psychological well-being of newly appointed workers. The diversity in data representation is source for interoperability problems that are insufficiently explored in the existing literature. OBJECTIVES: This research aims to design a worker's occupational health assessment summary (OHAS) dataset that satisfies the requirements of an international standard for semantic interoperability in the use case for exchanging extracts of such data. The focus is on the need for a common OHAS standard at EU level allowing seamless exchange of OHAS at both cross-border and at the worker's country of origin level. RESULTS: This paper proposes a novelty systematic approach ensuring semantic interoperability in the exchange of OHAS. Two use cases are explored in terms of UML sequence diagram. The OHAS dataset reflects common data requirements established in the national legislation of EU countries. Finally, an EN 13606 archetype of OHAS is designed by satisfying the requirements for semantic interoperability in the exchange of clinical data. Semantic interoperability of OHAS is demonstrated with realistic use case data. CONCLUSIONS: The designed static, non-volatile and reusable information model of OHAS developed in this paper allows to create EN 13606 archetype instances that are valid with respect to the Reference model and the datatypes of this standard. Thus, basic activities in the OHAS use case can be implemented in software, for example, by means of a native XML database as well as integrated into existing information systems.

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The proliferation of fifth-generation (5G) networks has opened up new opportunities for the deployment of cellular vehicle-to-everything (C-V2X) systems. However, the large-scale implementation of 5G-based C-V2X poses critical challenges requiring thorough investigation and resolution for successful deployment. This paper aims to identify and analyze the key challenges associated with the large-scale deployment of 5G-based C-V2X systems. In addition, we address obstacles and possible contradictions in the C-V2X standards caused by the special requirements. Moreover, we have introduced some quite influential C-V2X projects, which have influenced the widespread adoption of C-V2X technology in recent years. As the primary goal, this survey aims to provide valuable insights and summarize the current state of the field for researchers, industry professionals, and policymakers involved in the advancement of C-V2X. Furthermore, this paper presents relevant standardization aspects and visions for advanced 5G and 6G approaches to address some of the upcoming issues in mid-term timelines.

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Machine learning (ML) models were applied to pharmacovigilance (PV) data in a two-component proof-of-concept study. PV data were partitioned into Training, Validation, and Holdout datasets for model training and selection. During the first component ML models were challenged to identify factors in individual case safety reports (ICSRs) involving spinosad and neurological and ocular clinical signs. The target feature for the models were these clinical signs that were disproportionately reported for spinosad. The endpoints were normalized coefficient values representing the relationship between the target feature and ICSR free text fields. The deployed model accurately identified the risk factors "demodectic," "demodicosis," and "ivomec." In the second component, the ML models were trained to identify high quality and complete ICSRs free of confounders. The deployed model was presented with an external Test dataset of six ICSRs, one that was complete, of high quality, and devoid of confounders, and five that were not. The endpoints were model-generated probabilities for the ICSRs. The deployed ML model accurately identified the ICSR of interest with a greater than 10-fold higher probability score. Although narrow in scope, the study supports further investigation and potential application of ML models to animal health PV data.

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BACKGROUND: The Systematized Medical Nomenclature for Medicine-Clinical Terminology (SNOMED CT) is a clinical terminology system that provides a standardized and scientifically validated way of representing clinical information captured by clinicians. It can be integrated into electronic health records (EHRs) to increase the possibilities for effective data use and ensure a better quality of documentation that supports continuity of care, thus enabling better quality in the care process. Even though SNOMED CT consists of extensively studied clinical terminology, previous research has repeatedly documented a lack of scientific evidence for SNOMED CT in the form of reported clinical use cases in electronic health record systems. OBJECTIVE: The aim of this study was to explore evidence in previous literature reviews of clinical use cases of SNOMED CT integrated into EHR systems or other clinical applications during the last 5 years of continued development. The study sought to identify the main clinical use purposes, use phases, and key clinical benefits documented in SNOMED CT use cases. METHODS: The Cochrane review protocol was applied for the study design. The application of the protocol was modified step-by-step to fit the research problem by first defining the search strategy, identifying the articles for the review by isolating the exclusion and inclusion criteria for assessing the search results, and lastly, evaluating and summarizing the review results. RESULTS: In total, 17 research articles illustrating SNOMED CT clinical use cases were reviewed. The use purpose of SNOMED CT was documented in all the articles, with the terminology as a standard in EHR being the most common (8/17). The clinical use phase was documented in all the articles. The most common category of use phases was SNOMED CT in development (6/17). Core benefits achieved by applying SNOMED CT in a clinical context were identified by the researchers. These were related to terminology use outcomes, that is, to data quality in general or to enabling a consistent way of indexing, storing, retrieving, and aggregating clinical data (8/17). Additional benefits were linked to the productivity of coding or to advances in the quality and continuity of care. CONCLUSIONS: While the SNOMED CT use categories were well supported by previous research, this review demonstrates that further systematic research on clinical use cases is needed to promote the scalability of the review results. To achieve the best out-of-use case reports, more emphasis is suggested on describing the contextual factors, such as the electronic health care system and the use of previous frameworks to enable comparability of results. A lesson to be drawn from our study is that SNOMED CT is essential for structuring clinical data; however, research is needed to gather more evidence of how SNOMED CT benefits clinical care and patient safety.

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Advances in assistive exoskeleton technology, and a boom in related scientific literature, prompted a need to review the potential use of exoskeletons in defence and security. A systematic review examined the evidence for successful augmentation of human performance in activities deemed most relevant to military tasks. Categories of activities were determined a priori through literature scoping and Human Factors workshops with military stakeholders. Workshops identified promising opportunities and risks for integration of exoskeletons into military use cases. The review revealed promising evidence for exoskeletons' capacity to assist with load carriage, manual lifting, and working with tools. However, the review also revealed significant gaps in exoskeleton capabilities and likely performance levels required in the use case scenarios. Consequently, it was recommended that a future roadmap for introducing exoskeletons to military environments requires development of performance criteria for exoskeletons that can be used to implement a human-centred approach to research and development.

We assessed the state-of-the-art for the use of wearable assistive exoskeletons in UK defence and security use cases. A full systematic review of the literature was undertaken, informed by use cases developed in military stakeholder workshops. Clear gaps in exoskeleton capability and use case requirements were identified, leading to recommendations for future work.

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Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification.

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Although the power of genetic surveillance tools has been acknowledged widely, there is an urgent need in malaria endemic countries for feasible and cost-effective tools to implement in national malaria control programs (NMCPs) that can generate evidence to guide malaria control and elimination strategies, especially in the case of Plasmodium vivax. Several genetic surveillance applications ('use cases') have been identified to align research, technology development, and public health efforts, requiring different types of molecular markers. Here we present a new highly-multiplexed deep sequencing assay (Pv AmpliSeq). The assay targets the 33-SNP vivaxGEN-geo panel for country-level classification, and a newly designed 42-SNP within-country barcode for analysis of parasite dynamics in Vietnam and 11 putative drug resistance genes in a highly multiplexed NGS protocol with easy workflow, applicable for many different genetic surveillance use cases. The Pv AmpliSeq assay was validated using: 1) isolates from travelers and migrants in Belgium, and 2) routine collections of the national malaria control program at sentinel sites in Vietnam. The assay targets 229 amplicons and achieved a high depth of coverage (mean 595.7 ± 481) and high accuracy (mean error-rate of 0.013 ± 0.007). P. vivax parasites could be characterized from dried blood spots with a minimum of 5 parasites/µL and 10% of minority-clones. The assay achieved good spatial specificity for between-country prediction of origin using the 33-SNP vivaxGEN-geo panel that targets rare alleles specific for certain countries and regions. A high resolution for within-country diversity in Vietnam was achieved using the designed 42-SNP within-country barcode that targets common alleles (median MAF 0.34, range 0.01-0.49. Many variants were detected in (putative) drug resistance genes, with different predominant haplotypes in the pvmdr1 and pvcrt genes in different provinces in Vietnam. The capacity of the assay for high resolution identity-by-descent (IBD) analysis was demonstrated and identified a high rate of shared ancestry within Gia Lai Province in the Central Highlands of Vietnam, as well as between the coastal province of Binh Thuan and Lam Dong. Our approach performed well in geographically differentiating isolates at multiple spatial scales, detecting variants in putative resistance genes, and can be easily adjusted to suit the needs in other settings in a country or region. We prioritize making this tool available to researchers and NMCPs in endemic countries to increase ownership and ensure data usage for decision-making and malaria policy.

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Electronic Integrated Antenatal Care is a web-based application for antenatal care (ANC) data and information management for independent practice midwives. This study aimed to assess the end-user needs for e-iANC development. Their needs for data and information were represented by a use case diagram. Five key stakeholders were identified related to the development of an e-iANC. Interviewing representatives of each informant provided the functions, and information content and flows for each function. The Extended e-iANC needs and promoted insight among all of the actors' perspectives. The use-case scenario for extended e-iANC includes all of the elements of midwifery care include antenatal care, childbirth, puerperium, immunization, and family planning. The deployment of integrated information of extended e-iANC with the primary healthcare applications and the district health department is necessary for comprehensive information on maternal and child care innovation.

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BACKGROUND: Since the beginning of the COVID-19 outbreak, a significant number of mobile health apps have been created around the world and in Iran to help consequence reduction of this emerging pandemic. OBJECTIVES: This study aimed to review the characteristics of Persian Android and iOS apps related to COVID-19 and determine their use-cases based on a reference model. METHODS: This was a cross-sectional descriptive study conducted in three main steps. First, a systematic search was conducted via Iranian mobile apps' markets using the keywords related to COVID-19 in January 2021. Then, the retrieved apps were analyzed according to their characteristics. Finally, the use-cases of the given apps were determined and categorized based on a reference model. RESULTS: Based on our inclusion criteria, 122 apps were selected and evaluated. Most of these apps (87.7%) was free. Small proportions (5%) of reviewed apps have been developed with participation of clinical expert and half of the apps mentioned the references they used. Furthermore, about half of the apps (50.8%) were provided contact information of the developers. The studied apps were classified into four use-case major categories, including educational (98%), fulfilling a contextual need (18%), communicating, and/or sharing the information (0.83%), and health-related management (2%). CONCLUSION: The results showed that the Persian mobile apps for COVID-19 are not in a satisfying situation. Furthermore, although these apps are significant in quantity but in terms of use-cases, they are not widespread.

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BACKGROUND: Several tools have been developed for health care professionals to monitor the physical activity of their patients, but most of these tools have been considering only the needs of users in North American and European countries and applicable for only specific analytic tasks. To our knowledge, no research study has utilized the participatory design (PD) approach in the Middle East region to develop such tools, involving all the stakeholders in the product development phases, and no clear use cases have been derived from such studies that could serve future development in the field. OBJECTIVE: This study aims to develop an interactive visualization tool (ActiVis) to support local health care professionals in monitoring the physical activity of their patients measured through wearable sensors, with the overall objective of improving the health of the Qatari population. METHODS: We used PD and user-centered design methodologies to develop ActiVis, including persona development, brainwriting, and heuristic walkthrough as part of user evaluation workshops; and use cases, heuristic walkthrough, interface walkthrough, and survey as part of expert evaluation sessions. RESULTS: We derived and validated 6 data analysis use cases targeted at specific health care professionals from a collaborative design workshop and an expert user study. These use cases led to improving the design of the ActiVis tool to support the monitoring of patients' physical activity by nurses and family doctors. The ActiVis research prototype (RP) compared favorably with the Fitbit Dashboard, showing the importance of design tools specific to end users' needs rather than relying on repurposing existing tools designed for other types of users. The use cases we derived happen to be culturally agnostic, despite our assumption that the local Muslim and Arabic culture could impact the design of such visualization tools. At last, taking a step back, we reflect on running collaborative design sessions in a multicultural environment and oil-based economy. CONCLUSIONS: Beyond the development of the ActiVis tool, this study can serve other visualization and human-computer interaction designers in the region to prepare their design projects and encourage health care professionals to engage with designers and engineers to improve the tools they use for supporting their daily routine. The development of the ActiVis tool for nurses, and other visualization tools specific to family doctors and clinician researchers, is still ongoing and we plan to integrate them into an operational platform for health care professionals in Qatar in the near future.

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Is it really better to print everything, including software models, or is it better to view them on screen? With the ever increasing complexity of software systems, software modeling is integral to software development. Software models facilitate and automate many activities during development, such as code and test case generation. However, a core goal of software modeling is to communicate and collaborate. Software models are presented to team members on many mediums and two of the most common mediums are paper and computer screens. Reading from paper or screen is ostensibly considered to have the same effect on model comprehension. However, the literature on text reading has indicated that the reading experiences can be very different which in turn effects various metrics related to reader performance. This paper reports on an experiment that was conducted to investigate the effect of reading software models on paper in comparison with reading them on a computer screen with respect to cognitive effectiveness. Cognitive effectiveness here refers to the ease by which a model reader can read a model. The experiment used a total of 74 software engineering students as subjects. The experiment results provide strong evidence that displaying diagrams on a screen allows subjects to read them quicker. There is also evidence that indicates that on screen viewing induces fewer reading errors.

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Blockchain ecosystems are rapidly maturing and meeting the needs of business environments (e.g., industry, manufacturing, and robotics). The decentralized approaches in industries enable novel business concepts, such as machine autonomy and servitization of manufacturing environments. Introducing the distributed ledger technology principles into the machine sharing and servitization economy faces several challenges, and the integration opens new interesting research questions. Our research focuses on data and event models and secure upgradeable smart contract platforms for machine servitization. Our research indicates that with the proposed approaches, we can efficiently separate on- and off-chain data and assure scalability of the DApp without compromising the trust. We demonstrate that the secure upgradeable smart contract platform, which was adapted for machine servitization, supports the business workflow and, at the same time, assures common identification and authorization of all the participants in the system, including people, devices, and legal entities. We present a hybrid decentralized application (DApp) for the servitization of 3D printing. The solution can be used for or easily adapted to other manufacturing domains. It comprises a modular, upgradeable smart contract platform and off-chain machine, customer and web management, and monitoring interfaces. We pay special attention to the data and event models during the design, which are fundamental for the hybrid data storage and DApp architecture and the responsiveness of off-chain interfaces. The smart contract platform uses a proxy contract to control the access of smart contracts and role-based access control in function calls for blockchain users. We deploy and evaluate the DApp in a consortium blockchain network for performance and privacy. All the actors in the solution, including the machines, are identified by their blockchain accounts and are compeers. Our solution thus facilitates integration with the traditional information-communication systems in terms of the hybrid architectures and security standards for smart contract design comparable to those in traditional software engineering.

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In this review, we summarize the current challenges faced by cancer researchers and motivate the use of novel genomics solutions. We follow this up with a comprehensive overview of three recent genomics technologies: liquid biopsy, single-cell RNA sequencing and spatial transcriptomics. We discuss a few representative protocols/assays for each technology along with their strengths, weaknesses, optimal use-cases, and their current stage of clinical deployment by summarizing trial data. We focus on how these technologies help us develop a better understanding of cancer as a rapidly evolving heterogeneous genetic disease that modulates its immediate microenvironment leading to systemic macro-level changes in the patient body. We summarize the review with a flowchart that integrates these three technologies in the existing workflows of clinicians and researchers toward robust detection, accurate diagnosis, and precision oncology.

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BACKGROUND: The diagnosis of malaria, using microscopy or rapid diagnostic tests (RDTs), requires the collection of capillary blood. This procedure is relatively simple to perform but invasive and poses potential risks to patients and health workers, arising from the manipulation of potentially infectious bodily fluids. Less or non-invasive diagnostic tests, based on urine, saliva or requiring no sampling, have the potential to generate less discomfort for the patient and to offer simpler and less risky testing procedures that could be safely performed by untrained staff or even self-performed. To explore the potential acceptance and perceived value of such non-invasive tests, an online, international survey was conducted to gather feedback from National Malaria Control Programme (NMCP) representatives. METHODS: An online survey comprising nineteen questions, available in English, French or Spanish, was emailed to 300 individuals who work with NMCPs in malaria-endemic countries. Answers were collected between November and December 2017; responses were qualitatively analysed to identify key themes and trends and quantitatively analysed to determine average values stratified by region. RESULTS: Responses were received from 70 individuals, from 33 countries. Approximately half of the respondents (52 %) considered current blood-based tests for malaria to be minimally invasive and non-problematic in their setting. For these participants, non-invasive tests would only be of interest if they brought additional performance improvements, as compared with the performance of microscopy and RDTs. Most respondents were of the view that saliva-based (80 %) and urine-based (66 %) tests would be more readily acceptable among children than blood-based tests. Potential use-case scenarios of interest for both saliva- and urine-based tests were ease-of-testing by community health workers, additional surveillance, self-testing, and outbreak investigation. Many respondents (41 %) thought that if saliva-based tests retailed at <$0.50 per unit they could largely replace conventional RDTs, whereas only 25 % of respondents thought a similarly priced urine-based test would do so. CONCLUSIONS: Although limited to NMCP stakeholders, this survey indicated that current tests for malaria, based on capillary blood, are generally perceived to be minimally invasive and non-problematic. Non-invasive tests, especially if saliva-based, would be welcome if they could match or out-perform the price and performance of current blood-based tests.

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Clinical processes need to be well understood before a new health IT tool can be introduced. Observations, interviews, surveys, or documentation analysis are carried out to systematically collect information to better understand a clinical process. To aggregate and visualize the collected information about a clinical process, use case diagrams can build a basis. Formal process models such as process chain diagrams or BPMN diagrams are well suited to model the process in detail. The objective of this chapter is to discuss these methods for analyzing and modeling clinical processes, as this is an important precondition for systematic process management in health care.

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Shodan is a search engine for exploring the Internet and thus finding connected devices. Its main use is to provide a tool for cybersecurity researchers and developers to detect vulnerable Internet-connected devices without scanning them directly. Due to its features, Shodan can be used for performing cybersecurity audits on Internet of Things (IoT) systems and devices used in applications that require to be connected to the Internet. The tool allows for detecting IoT device vulnerabilities that are related to two common cybersecurity problems in IoT: the implementation of weak security mechanisms and the lack of a proper security configuration. To tackle these issues, this article describes how Shodan can be used to perform audits and thus detect potential IoT-device vulnerabilities. For such a purpose, a use case-based methodology is proposed to teach students and users to carry out such audits and then make more secure the detected exploitable IoT devices. Moreover, this work details how to automate IoT-device vulnerability assessments through Shodan scripts. Thus, this article provides an introductory practical guide to IoT cybersecurity assessment and exploitation with Shodan.

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INTRODUCTION: The European medical information framework (EMIF) was an Innovative Medicines Initiative project jointly supported by the European Union and the European Federation of Pharmaceutical Industries and Associations, that generated a common technology and governance framework to identify, assess and (re)use healthcare data, to facilitate real-world data research. The objectives of EMIF included providing a unified platform to support a wide range of studies within two verification programmes-Alzheimer's disease (EMIF-AD), and metabolic consequences of obesity (EMIF-MET). METHODS: The EMIF platform was built around two main data-types: electronic health record data and research cohort data, and the platform architecture composed of a set of tools designed to enable data discovery and characterisation. This included the EMIF catalogue, which allowed users to find relevant data sources, including the data-types collected. Data harmonisation via a common data model were central to the project especially for population data sources. EMIF also developed an ethical code of practice to ensure data protection, patient confidentiality and compliance with the European Data Protection Directive, and GDPR. RESULTS: Currently 18 population-based disease agnostic and 60 cohort-based Alzheimer's data partners from across 14 countries are contained within the catalogue, and this will continue to expand. The work conducted in EMIF-AD and EMIF-MET includes standardizing cohorts, summarising baseline characteristics of patients, developing diagnostic algorithms, epidemiological studies, identifying and validating novel biomarkers and selecting potential patient samples for pharmacological intervention. CONCLUSIONS: EMIF was designed to provide a sustainable model as demonstrated by the sustainability plans for EMIF-AD. Although network-wide studies using EMIF were not conducted during this project to evaluate its sustainability, learning from EMIF will be used in the follow-on IMI-2 project, European Health Data and Evidence Network (EHDEN). Furthermore, EMIF has facilitated collaborations between partners and continues to promote a wider adoption of principles, technology and architecture through some of its continued work.