Making sense of big data in health research: Towards an EU action plan

New Tools for Data Harmonization and Their Potential Applications in Organ Transplantation

In organ transplantation, accurate analysis of clinical outcomes requires large, high-quality data sets. Not only are outcomes influenced by a multitude of factors such as donor, recipient, and transplant characteristics and posttransplant events but they may also change over time. Although large data sets already exist and are continually expanding in transplant registries and health institutions, these data are rarely combined for analysis because of a lack of harmonization. Promoted by the digitalization of the healthcare sector, effective data harmonization tools became available, with potential applications also for organ transplantation. We discuss herein the present problems in the harmonization of organ transplant data and offer solutions to enhance its accuracy through the use of emerging new tools. To overcome the problem of inadequate representation of transplantation-specific terms, ontologies and common data models particular to this field could be created and supported by a consortium of related stakeholders to ensure their broad acceptance. Adopting clear data-sharing policies can diminish administrative barriers that impede collaboration between organizations. Secure multiparty computation frameworks and the artificial intelligence (AI) approach federated learning can facilitate decentralized and harmonized analysis of data sets, without sharing sensitive data and compromising patient privacy. A common image data model built upon a standardized format would be beneficial to AI-based analysis of pathology images. Implementation of these promising new tools and measures, ideally with the involvement and support of transplant societies, is expected to produce improved integration and harmonization of transplant data and greater accuracy in clinical decision-making, enabling improved patient outcomes.

Machine learning-based clinical decision support using laboratory data

Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.

A machine learning model to predict heart failure readmission: toward optimal feature set

Background

Hospital readmissions for heart failure patients remain high despite efforts to reduce them. Predictive modeling using big data provides opportunities to identify high-risk patients and inform care management. However, large datasets can constrain performance.

Objective

This study aimed to develop a machine learning based prediction model leveraging a nationwide hospitalization database to predict 30-day heart failure readmissions. Another objective of this study is to find the optimal feature set that leads to the highest AUC value in the prediction model.

Material and methods

Heart failure patient data was extracted from the 2020 Nationwide Readmissions Database. A heuristic feature selection process incrementally incorporated predictors into logistic regression and random forest models, which yields a maximum increase in the AUC metric. Discrimination was evaluated through accuracy, sensitivity, specificity and AUC.

Results

A total of 566,019 discharges with heart failure diagnosis were recognized. Readmission rate was 8.9% for same-cause and 20.6% for all-cause diagnoses. Random forest outperformed logistic regression, achieving AUCs of 0.607 and 0.576 for same-cause and all-cause readmissions respectively. Heuristic feature selection resulted in the identification of optimal feature sets including 20 and 22 variables from a pool of 30 and 31 features for the same-cause and all-cause datasets. Key predictors included age, payment method, chronic kidney disease, disposition status, number of ICD-10-CM diagnoses, and post-care encounters.

Conclusion

The proposed model attained discrimination comparable to prior analyses that used smaller datasets. However, reducing the sample enhanced performance, indicating big data complexity. Improved techniques like heuristic feature selection enabled effective leveraging of the nationwide data. This study provides meaningful insights into predictive modeling methodologies and influential features for forecasting heart failure readmissions.

A Sustainable Model for Healthcare Systems: The Innovative Approach of ESG and Digital Transformation

In recent years, the globe has faced a series of topics of growing concern, such as the COVID-19 pandemic, the international financial crisis, rising socio-economic inequalities, the negative outcomes of greenhouse gas emissions, which resulted in climate change, and many others. Organizations worldwide have confronted these new challenges of sustainable finance by incorporating environmental, social, and corporate governance (ESG) factors and digital transformation (DT) in their innovation business strategies. The healthcare sector represents a large share of the global economy (about 10% of global economic output), employs a large number of workers, and needs to rely more on an open innovation model where interested parties, especially patients, are going to have a say in their own well-being. Thus, it is imperative that healthcare providers be efficient, effective, resilient, and sustainable in the face of significant challenges and risks. At the same time, they must offer sustainable development goals and digital transformation to healthcare users through limited governmental resources. This study investigates the role, importance, and correlation of ESG factors and digital transformation to the sustainable finance of healthcare systems through an innovative model. The main purpose of the paper is to present the already implemented ESG and DT factors in the healthcare sector and to propose a mutual and combined implementation strategy based on common evaluation tools, methods, and actions. A set of proposed actions and strategies are presented for the sustainability and resilience of the healthcare sector.

From randomised controlled trials to real-world data: Clinical evidence to guide management of diabetic macular oedema

Randomised clinical trials (RCTs) are generally considered the gold-standard for providing scientific evidence for treatments' effectiveness and safety but their findings may not always be generalisable to the broader population treated in routine clinical practice. RCTs include highly selected patient populations that fit specific inclusion and exclusion criteria. Although they may have a lower level of certainty than RCTs on the evidence hierarchy, real-world data (RWD), such as observational studies, registries and databases, provide real-world evidence (RWE) that can complement RCTs. For example, RWE may help satisfy requirements for a new indication of an already approved drug and help us better understand long-term treatment effectiveness, safety and patterns of use in clinical practice. Many countries have set up registries, observational studies and databases containing information on patients with retinal diseases, such as diabetic macular oedema (DMO). These DMO RWD have produced significant clinical evidence in the past decade that has changed the management of DMO. RWD and medico-administrative databases are a useful resource to identify low frequency safety signals. They often have long-term follow-up with a large number of patients and minimal exclusion criteria. We will discuss improvements in healthcare information exchange technologies, such as blockchain technology and FHIR (Fast Healthcare Interoperability Resources), which will connect and extend databases already available. These registries can be linked with existing or emerging retinal imaging modalities using artificial intelligence to aid diagnosis, treatment decisions and provide prognostic information. The results of RCTs and RWE are combined to provide evidence-based guidelines.

Toward a global harmonization of service infrastructure in academic clinical trial units: an international survey

Background

Clinicians around the world perform clinical research in addition to their high workload. To meet the demands of high quality Investigator Initiated Trials (IITs), Clinical Trial Units (CTUs) (as part of Academic Research Institutions) are implemented worldwide. CTUs increasingly hold a key position in facilitating the international mutual acceptance of clinical research data by promoting clinical research practices and infrastructure according to international standards.

Aim

In this project, we aimed to identify services that established and internationally operating CTUs - members of the International Clinical Trial Center Network (ICN) - consider most important to ensure the smooth processing of a clinical trial while meeting international standards. We thereby aim to drive international harmonization by providing emerging and growing CTUs with a resource for informed service range set-up.

Methods

Following the AMEE Guide, we developed a questionnaire, addressing the perceived importance of different CTU services. Survey participants were senior representatives of CTUs and part of the ICN with long-term experience in their field and institution.

Results

Services concerning quality and coordination of a research project were considered to be most essential, i.e., Quality management, Monitoring and Project management, followed by Regulatory & Legal affairs, Education & Training, and Data management. Operative services for conducting a research project, i.e., Study Nurse with patient contact and Study Nurse without patient contact, were considered to be least important.

Conclusion

To balance the range of services offered while meeting high international standards of clinical research, emerging CTUs should focus on offering (quality) management services and expertise in regulatory and legal affairs. Additionally, education and training services are required to ensure clinicians are well trained on GCP and legislation. CTUs should evaluate whether the expertise and resources are available to offer operative services.

Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial

Deep learning-based clinical imaging analysis underlies diagnostic artificial intelligence (AI) models, which can match or even exceed the performance of clinical experts, having the potential to revolutionize clinical practice. A wide variety of automated machine learning (autoML) platforms lower the technical barrier to entry to deep learning, extending AI capabilities to clinicians with limited technical expertise, and even autonomous foundation models such as multimodal large language models. Here, we provide a technical overview of autoML with descriptions of how autoML may be applied in education, research, and clinical practice. Each stage of the process of conducting an autoML project is outlined, with an emphasis on ethical and technical best practices. Specifically, data acquisition, data partitioning, model training, model validation, analysis, and model deployment are considered. The strengths and limitations of available code-free, code-minimal, and code-intensive autoML platforms are considered. AutoML has great potential to democratize AI in medicine, improving AI literacy by enabling "hands-on" education. AutoML may serve as a useful adjunct in research by facilitating rapid testing and benchmarking before significant computational resources are committed. AutoML may also be applied in clinical contexts, provided regulatory requirements are met. The abstraction by autoML of arduous aspects of AI engineering promotes prioritization of data set curation, supporting the transition from conventional model-driven approaches to data-centric development. To fulfill its potential, clinicians must be educated on how to apply these technologies ethically, rigorously, and effectively; this tutorial represents a comprehensive summary of relevant considerations.

Omics and Multi-Omics in IBD: No Integration, No Breakthroughs

The recent advent of sophisticated technologies like sequencing and mass spectroscopy platforms combined with artificial intelligence-powered analytic tools has initiated a new era of "big data" research in various complex diseases of still-undetermined cause and mechanisms. The investigation of these diseases was, until recently, limited to traditional in vitro and in vivo biological experimentation, but a clear switch to in silico methodologies is now under way. This review tries to provide a comprehensive assessment of state-of-the-art knowledge on omes, omics and multi-omics in inflammatory bowel disease (IBD). The notion and importance of omes, omics and multi-omics in both health and complex diseases like IBD is introduced, followed by a discussion of the various omics believed to be relevant to IBD pathogenesis, and how multi-omics "big data" can generate new insights translatable into useful clinical tools in IBD such as biomarker identification, prediction of remission and relapse, response to therapy, and precision medicine. The pitfalls and limitations of current IBD multi-omics studies are critically analyzed, revealing that, regardless of the types of omes being analyzed, the majority of current reports are still based on simple associations of descriptive retrospective data from cross-sectional patient cohorts rather than more powerful longitudinally collected prospective datasets. Given this limitation, some suggestions are provided on how IBD multi-omics data may be optimized for greater clinical and therapeutic benefit. The review concludes by forecasting the upcoming incorporation of multi-omics analyses in the routine management of IBD.

Paucity of Health Data in Africa: An Obstacle to Digital Health Implementation and Evidence-Based Practice

Background: Among the numerous challenges that Africa faces in improving its healthcare systems, the paucity of health data stands out as paramount. This study aims to examine the challenges related to the paucity of health data in Africa and its impact on the implementation of digital health and evidence-based practice. The findings of the study reveal that health data availability in Africa is both limited and frequently of poor quality. Several factors contribute to this concerning situation, encompassing inadequate infrastructure, a shortage of resources, and cultural barriers. Furthermore, the available data, despite its limitations, is often underutilized due to a lack of capacity and expertise in data analysis and interpretation. Policy Options and Recommendations: To improve healthcare delivery in Africa, we recommend implementing novel strategies for data collection. It's important to recognize that effective information technology service is crucial for enhancing healthcare delivery, and a holistic approach is necessary to achieve this. Conclusion: This brief presents information to help policymakers develop long-term solutions to Africa's health data poverty. Taking action based on this evidence can assist in addressing the problem.

Use of electronic patient records and encrypted email patient communication among Swiss chiropractors: a population-based cross-sectional study

BACKGROUND

The implementation of electronic health information technologies is a key target for healthcare quality improvement. Among Swiss chiropractors, reliable data on the use of electronic heath information technologies and distribution of the health workforce was lacking.

OBJECTIVES

To estimate the prevalence of electronic patient record (EPR) and encrypted email communication use among Swiss chiropractors and describe the geographic distribution of chiropractors in Switzerland.

METHODS

Population-based cross-sectional study of all active practising members of the Swiss Chiropractic Association (ChiroSuisse) between 3 December 2019 and 31 January 2020. We asked about clinician and practice characteristics, EPR use for clinical record keeping, use of encrypted email for patient communication, and information on EPR and encrypted email communication products used. Multivariable logistic regression analyses assessed the associations between clinician and practice characteristics and (1) EPR use, and (2) encrypted email use.

RESULTS

Among 286 eligible Swiss chiropractors (193 [68%] men; mean age, 51.4 [SD, 11.2] years), 217 (76%) completed the survey (140 [65%] men; mean age 50.7 [11.2] years). Among respondents, 47% (95% confidence interval [CI], 40-54%) reported using an EPR in their practice, while 60% (95% CI, 54-67%) endorsed using encrypted email technology. Chiropractors aged ≥ 60 (versus those ≤ 39) years were 74% less likely to use an EPR system (OR 0.26, 95% CI 0.08 to 0.77), while clinicians from practices with 4 or more chiropractors (versus those from solo practices) were over 5 times more likely to report EPR use (OR 5.6, 2.1 to 16.5). Findings for factors associated with encrypted email use were similar. The density of chiropractors in Switzerland was 3.3 per 100,000 inhabitants.

CONCLUSIONS

As of January 2020, 286 duly licensed chiropractors were available to provide musculoskeletal healthcare in Switzerland - just under 50% of responding Swiss chiropractors used an EPR system in clinical practice, while 60% used encrypted email technology. Better implementation of EPR and electronic health information technologies in Swiss chiropractic practice is possible and encouraged for the purpose of musculoskeletal healthcare quality improvement.

Design and Implementation of a Collaborative Clinical Practice and Research Documentation System Using SNOMED-CT and HL7-CDA in the Context of a Pediatric Neurodevelopmental Unit

This paper introduces a prototype for clinical research documentation using the structured information model HL7 CDA and clinical terminology (SNOMED CT). The proposed solution was integrated with the current electronic health record system (EHR-S) and aimed to implement interoperability and structure information, and to create a collaborative platform between clinical and research teams. The framework also aims to overcome the limitations imposed by classical documentation strategies in real-time healthcare encounters that may require fast access to complex information. The solution was developed in the pediatric hospital (HP) of the University Hospital Center of Coimbra (CHUC), a national reference for neurodevelopmental disorders, particularly for autism spectrum disorder (ASD), which is very demanding in terms of longitudinal and cross-sectional data throughput. The platform uses a three-layer approach to reduce components’ dependencies and facilitate maintenance, scalability, and security. The system was validated in a real-life context of the neurodevelopmental and autism unit (UNDA) in the HP and assessed based on the functionalities model of EHR-S (EHR-S FM) regarding their successful implementation and comparison with state-of-the-art alternative platforms. A global approach to the clinical history of neurodevelopmental disorders was worked out, providing transparent healthcare data coding and structuring while preserving information quality. Thus, the platform enabled the development of user-defined structured templates and the creation of structured documents with standardized clinical terminology that can be used in many healthcare contexts. Moreover, storing structured data associated with healthcare encounters supports a longitudinal view of the patient’s healthcare data and health status over time, which is critical in routine and pediatric research contexts. Additionally, it enables queries on population statistics that are key to supporting the definition of local and global policies, whose importance was recently emphasized by the COVID pandemic.

Stage III Non-Small-Cell Lung Cancer: An Overview of Treatment Options

Lung cancer is the second-most commonly diagnosed cancer and the leading cause of cancer death worldwide. The most common histological type is non-small-cell lung cancer, accounting for 85% of all lung cancer cases. About one out of three new cases of non-small-cell lung cancer are diagnosed at a locally advanced stage—mainly stage III—consisting of a widely heterogeneous group of patients presenting significant differences in terms of tumor volume, local diffusion, and lymph nodal involvement. Stage III NSCLC therapy is based on the pivotal role of multimodal treatment, including surgery, radiotherapy, and a wide-ranging option of systemic treatments. Radical surgery is indicated in the case of hilar lymphnodal involvement or single station mediastinal ipsilateral involvement, possibly after neoadjuvant chemotherapy; the best appropriate treatment for multistation mediastinal lymph node involvement still represents a matter of debate. Although the main scope of treatments in this setting is potentially curative, the overall survival rates are still poor, ranging from 36% to 26% and 13% in stages IIIA, IIIB, and IIIC, respectively. The aim of this article is to provide an up-to-date, comprehensive overview of the state-of-the-art treatments for stage III non-small-cell lung cancer.

Cardio oncology: Digital innovations, precision medicine and health equity

The rapid emergence of cardio-oncology has resulted in a rapid growth of cardio-oncology programs, dedicated professional societies sections and committees, and multiple collaborative networks that emerged to amplify the access to care in this new subspecialty. However, most existing data, position statements and guidelines are limited by the lack of availability of large clinical trials to support these recommendations. Furthermore, there are significant challenges regarding proper access to cardio-oncology care and treatment, particularly in marginalized and minority populations. The emergence and evolution of personalized medicine, artificial intelligence (AI), and machine learning in medicine and in cardio-oncology provides an opportunity for a more targeted, personalized approach to cardiovascular complications of cancer treatment. The proper implementation of these new modalities may facilitate a more equitable approach to adequate and universal access to cardio-oncology care, improve health related outcomes, and enable health care systems to eliminate the digital divide. This article reviews and analyzes the current status on these important issues.

Observing, spanning and shifting boundaries: working with data in non-clinical practice

Purpose

Effective use of data across public health organisations (PHOs) is essential for the provision of health services. While health technology and data use in clinical practice have been investigated, interactions with data in non-clinical practice have been largely neglected. The purpose of this paper is to consider what constitutes data, and how people in non-clinical roles in a PHO interact with data in their practice.

Design/methodology/approach

This mixed methods study involved a qualitative exploration of how employees of a large PHO interact with data in their non-clinical work roles. A quantitative survey was administered to complement insights gained through qualitative investigation.

Findings

Organisational boundaries emerged as a defining issue in interactions with data. The results explain how data work happens through observing, spanning and shifting of boundaries. The paper identifies five key issues that shape data work in relation to boundaries. Boundary objects and processes are considered, as well as the roles of boundary spanners and shifters.

Research limitations/implications

The study was conducted in a large Australian PHO, which is not completely representative of the unique contexts of similar organisations. The study has implications for research in information and organisational studies, opening fields of inquiry for further investigation.

Practical implications

Effective systems-wide data use can improve health service efficiencies and outcomes. There are also implications for the provision of services by other health and public sectors.

Originality/value

The study contributes to closing a significant research gap in understanding interactions with data in the workplace, particularly in non-clinical roles in health. Research analysis connects concepts of knowledge boundaries, boundary spanning and boundary objects with insights into information behaviours in the health workplace. Boundary processes emerge as an important concept to understand interactions with data. The result is a novel typology of interactions with data in relation to organisational boundaries.

Rebuilding Stakeholder Confidence in Health-Relevant Big Data Applications: A Social Representations Perspective

Big data applications are at the epicentre of recent breakthroughs in digital health. However, controversies over privacy, security, ethics, accountability, and data governance have tarnished stakeholder trust, leaving health-relevant big data projects under threat, delayed, or abandoned. Taking the notion of big data as social construction, this work explores the social representations of the big data concept from the perspective of stakeholders in Kenya’s digital health environment. Through analysing the similarities and differences in the way health professionals and information technology (IT) practitioners comprehend the idea of big data, we draw strategic implications for restoring confidence in big data initiatives. Respondents associated big data with a multiplicity of concepts and were conflicted in how they represented big data’s benefits and challenges. On this point, we argue that peculiarities and nuances in how diverse players view big data contribute to the erosion of trust and the need to revamp stakeholder engagement practices. Specifically, decision makers should complement generalised informational campaigns with targeted, differentiated messages designed to address data responsibility, access, control, security, or other issues relevant to a specialised but influential community.

Real World-Big Data Analytics in Healthcare

The term Big Data is used to describe extremely large datasets that are complex, multi-dimensional, unstructured, and heterogeneous and that are accumulating rapidly and may be analyzed with appropriate informatic and statistical methodologies to reveal patterns, trends, and associations [...].

MMKP: A mind mapping knowledgebase prototyping tool for precision medicine

Background

With significant advancements in the area of precision medicine, the breadth and complexity of the relevant knowledge in the field has increased significantly. However, the difficulty associated with dynamic modelling and the disorganization of such knowledge hinders its rapid development potential.

Results

To overcome the difficulty in using the relational database model for dynamic modelling, and to aid in the organization of precision medicine knowledge, we developed the Mind Mapping Knowledgebase Prototyping (MMKP) tool. The MMKP implements a novel design that we call a “polymorphic foreign key”, which allows the establishment of a logical linkage between a single table field and a record from any table. This design has advantages in supporting dynamic changes to the structural relationships in precision medicine knowledge. Knowledge stored in MMKP is presented as a mind map to facilitate human interaction. When using this tool, medical experts may curate the structure and content of the precision knowledge in a flow that is similar to the human thinking process.

Conclusions

The design of polymorphic foreign keys natively supports knowledge modelling in the form of mind mapping, which avoids the hard-coding of medical logic into a rigid database schema and significantly reduces the workload that is required for adapting a relational data model to future changes to the medical logic. The MMKP tool provides a graphical user interface for both data management and knowledgebase prototyping. It supports the flexible customization of the data field constraints and annotations. MMKP is available as open-source code on GitHub: https://github.com/ZjuLiangsl/mmkp.

A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research

Improvements in next-generation sequencing (NGS) technology and computer systems have enabled personalized therapies based on genomic information. Recently, health management strategies using genomics and big data have been developed for application in medicine and public health science. In this review, I first discuss the development of a genomic information management system (GIMS) to maintain a highly detailed health record and detect diseases by collecting the genomic information of one individual over time. Maintaining a health record and detecting abnormal genomic states are important; thus, the development of a GIMS is necessary. Based on the current research status, open public data, and databases, I discuss the possibility of a GIMS for clinical use. I also discuss how the analysis of genomic information as big data can be applied for clinical and research purposes. Tremendous volumes of genomic information are being generated, and the development of methods for the collection, cleansing, storing, indexing, and serving must progress under legal regulation. Genetic information is a type of personal information and is covered under privacy protection; here, I examine the regulations on the use of genetic information in different countries. This review provides useful insights for scientists and clinicians who wish to use genomic information for healthy aging and personalized medicine.

Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review

Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.

Considerations for an integrated population health databank in Africa: lessons from global best practices

Background: The rising digitisation and proliferation of data sources and repositories cannot be ignored. This trend expands opportunities to integrate and share population health data. Such platforms have many benefits, including the potential to efficiently translate information arising from such data to evidence needed to address complex global health challenges. There are pockets of quality data on the continent that may benefit from greater integration. Integration of data sources is however under-explored in Africa. The aim of this article is to identify the requirements and provide practical recommendations for developing a multi-consortia public and population health data-sharing framework for Africa.

Methods: We conducted a narrative review of global best practices and policies on data sharing and its optimisation. We searched eight databases for publications and undertook an iterative snowballing search of articles cited in the identified publications. The Leximancer software © enabled content analysis and selection of a sample of the most relevant articles for detailed review. Themes were developed through immersion in the extracts of selected articles using inductive thematic analysis. We also performed interviews with public and population health stakeholders in Africa to gather their experiences, perceptions, and expectations of data sharing.

Results: Our findings described global stakeholder experiences on research data sharing. We identified some challenges and measures to harness available resources and incentivise data sharing.  We further highlight progress made by the different groups in Africa and identified the infrastructural requirements and considerations when implementing data sharing platforms. Furthermore, the review suggests key reforms required, particularly in the areas of consenting, privacy protection, data ownership, governance, and data access.

Conclusions: The findings underscore the critical role of inclusion, social justice, public good, data security, accountability, legislation, reciprocity, and mutual respect in developing a responsive, ethical, durable, and integrated research data sharing ecosystem.

The application of artificial intelligence in hepatology: A systematic review

The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21^st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.

“Where There Is Light, There Is Also Darkness”: Discussing Young Adults’ Willingness to Disclose Data to Use Wearables and Health Applications—Results from a Focus Group Study

In recent years, the Internet of Medical Things (IoMT) has gained momentum. This development has only been intensified by the current COVID-19 crisis, which promotes the development of applications that can help stop the virus from spreading by monitoring people’s movements and their social contacts. At the same time, it has become increasingly difficult for individuals to control the use of their private data by commercial companies. While Internet users claim to be highly interested in protecting their privacy, their behaviors indicate otherwise. This phenomenon is discussed in literature as the so-called privacy paradox. The existence of the privacy paradox has also been confirmed by previous studies, which found individuals’ claims and actions to contradict one another. The present study investigates the following research questions: (1) What significance do individuals attribute to protecting their privacy, with a special focus on the health sector? (2) To what extent are they willing to grant commercial parties access to their data in order to use applications in general and health applications in particular? Results from seven focus groups with 40 respondents aged 20–30 years were conducted in an urban setting in Austria in late 2019. The respondents’ inputs are meant to provide answers to these questions. The results indicate that, overall, the young generation is well-informed about the growing data collection and is quite critical of it. As such, their willingness to share information in the health context is only moderately pronounced. Thus, only a moderately pronounced privacy paradox can be detected for the health sector when compared to other sectors. In conclusion, implications and directions for further research are addressed.

Data protection, data management, and data sharing: Stakeholder perspectives on the protection of personal health information in South Africa

The Protection of Personal Information Act (POPIA) 2013 came into force in South Africa on 1 July 2020. It seeks to strengthen the processing of personal information, including health information. While POPIA is to be welcomed, there are concerns about the impact it will have on the processing of health information. To ensure that the National Health Laboratory Service [NHLS] is compliant with these new strict processing requirements and that compliance does not negatively impact upon its current screening, treatment, surveillance and research mandate, it was decided to consider the development of a NHLS POPIA Code of Conduct for Personal Health. As part of the process of developing such a Code and better understand the challenges faced in the processing of personal health information in South Africa, 19 semi-structured interviews with stakeholders were conducted between June and September 2020. Overall, respondents welcomed the introduction of POPIA. However, they felt that there are tensions between the strengthening of data protection and the use of personal information for individual patient care, treatment programmes, and research. Respondents reported a need to rethink the management of personal health information in South Africa and identified 5 issues needing to be addressed at a national and an institutional level: an understanding of the importance of personal information; an understanding of POPIA and data protection; improve data quality; improve transparency in data use; and improve accountability in data use. The application of POPIA to the processing of personal health information is challenging, complex, and likely costly. However, personal health information must be appropriately managed to ensure the privacy of the data subject is protected, but equally that it is used as a resource in the individual's and wider public interest.

Real-World Data for Healthcare Research in China: Call for Actions

OBJECTIVES

This study aimed to provide an overview of major data sources in China that can be potentially used for epidemiology, health economics, and outcomes research; compare them with similar data sources in other countries; and discuss future directions of healthcare data development in China.

METHODS

The study was conducted in 2 phases. First, various data sources were identified through a targeted literature review and recommendations by experts. Second, an in-depth assessment was conducted to evaluate the strengths and limitations of administrative claims and electronic health record data, which were further compared with similar data sources in developed countries.

RESULTS

Secondary databases, including administrative claims and electronic health records, are the major types of real-world data in China. There are substantial variations in available data elements even within the same type of databases. Compared with similar databases in developed countries, the secondary databases in China have some general limitations such as variations in data quality, unclear data usage mechanism, and lack of longitudinal follow-up data. In contrast, the large sample size and the potential to collect additional data based on research needs present opportunities to further improve real-world data in China.

CONCLUSIONS

Although healthcare data have expanded substantially in China, high-quality real-world evidence that can be used to facilitate decision making remains limited in China. To support the generation of real-world evidence, 2 fundamental issues in existing databases need to be addressed-data access/sharing and data quality.

Power analysis in health policy and systems research: a guide to research conceptualisation

Power is a growing area of study for researchers and practitioners working in the field of health policy and systems research (HPSR). Theoretical development and empirical research on power are crucial for providing deeper, more nuanced understandings of the mechanisms and structures leading to social inequities and health disparities; placing contemporary policy concerns in a wider historical, political and social context; and for contributing to the (re)design or reform of health systems to drive progress towards improved health outcomes. Nonetheless, explicit analyses of power in HPSR remain relatively infrequent, and there are no comprehensive resources that serve as theoretical and methodological starting points. This paper aims to fill this gap by providing a consolidated guide to researchers wishing to consider, design and conduct power analyses of health policies or systems. This practice article presents a synthesis of theoretical and conceptual understandings of power; describes methodologies and approaches for conducting power analyses; discusses how they might be appropriately combined; and throughout reflects on the importance of engaging with positionality through reflexive praxis. Expanding research on power in health policy and systems will generate key insights needed to address underlying drivers of health disparities and strengthen health systems for all.

National and international kidney failure registries: characteristics, commonalities, and contrasts

Registries are essential for health infrastructure planning, benchmarking, continuous quality improvement, hypothesis generation, and real-world trials. To date, data from these registries have predominantly been analyzed in isolated "silos," hampering efforts to analyze "big data" at the international level, an approach that provides wide-ranging benefits, including enhanced statistical power, an ability to conduct international comparisons, and greater capacity to study rare diseases. This review serves as a valuable resource to clinicians, researchers, and policymakers, by comprehensively describing kidney failure registries active in 2021, before proposing approaches for inter-registry research under current conditions, as well as solutions to enhance global capacity for data collaboration. We identified 79 kidney-failure registries spanning 77 countries worldwide. International Society of Nephrology exemplar initiatives, including the Global Kidney Health Atlas and Sharing Expertise to support the set-up of Renal Registries (SharE-RR), continue to raise awareness regarding international healthcare disparities and support the development of universal kidney-disease registries. Current barriers to inter-registry collaboration include underrepresentation of lower-income countries, poor syntactic and semantic interoperability, absence of clear consensus guidelines for healthcare data sharing, and limited researcher incentives. This review represents a call to action for international stakeholders to enact systemic change that will harmonize the current fragmented approaches to kidney-failure registry data collection and research.

Controlling my genome with my smartphone: first clinical experiences of the PROMISE system

Background

The development of Precision Medicine strategies requires high-dimensional phenotypic and genomic data, both of which are highly privacy-sensitive data types. Conventional data management systems lack the capabilities to sufficiently handle the expected large quantities of such sensitive data in a secure manner. PROMISE is a genetic data management concept that implements a highly secure platform for data exchange while preserving patient interests, privacy, and autonomy.

Methods

The concept of PROMISE to democratize genetic data was developed by an interdisciplinary team. It integrates a sophisticated cryptographic concept that allows only the patient to grant selective access to defined parts of his genetic information with single DNA base-pair resolution cryptography. The PROMISE system was developed for research purposes to evaluate the concept in a pilot study with nineteen cardiomyopathy patients undergoing genotyping, questionnaires, and longitudinal follow-up.

Results

The safety of genetic data was very important to 79%, and patients generally regarded the data as highly sensitive. More than half the patients reported that their attitude towards the handling of genetic data has changed after using the PROMISE app for 4 months (median). The patients reported higher confidence in data security and willingness to share their data with commercial third parties, including pharmaceutical companies (increase from 5 to 32%).

Conclusion

PROMISE democratizes genomic data by a transparent, secure, and patient-centric approach. This clinical pilot study evaluating a genetic data infrastructure is unique and shows that patient’s acceptance of data sharing can be increased by patient-centric decision-making.

Graphic abstract

Molecular classification of blood and bleeding disorder genes

The advances and development of sequencing techniques and data analysis resulted in a pool of informative genetic data, that can be analyzed for informing decision making in designing national screening, prevention programs, and molecular diagnostic tests. The accumulation of molecular data from different populations widen the scope of utilization of this information. Bleeding disorders are a heterogeneous group of clinically overlapping disorders. We analyzed the targeted sequencing data from ~1285 Saudi individuals in 17 blood and bleeding disorders genes, to determine the frequency of mutations and variants. We used a replication set of ~5000 local exomes to validate pathogenicity and determine allele frequencies. We identified a total of 821 variants, of these 98 were listed in HGMD as disease related variants and 140 were novel variants. The majority of variants were present in VWF, followed by F5, F8, and G6PD genes, while FGG, FGB, and HBA1 had the lowest number of variants. Our analysis generated a priority list of genes, mutations and novel variants. This data will have an impact on informing decisions for screening and prevention programs and in management of vulnerable patients admitted to emergency, surgery, or interventions with bleeding side effects.

The Metallome as a Link Between the "Omes" in Autism Spectrum Disorders

Metal dyshomeostasis plays a significant role in various neurological diseases such as Alzheimer's disease, Parkinson's disease, Autism Spectrum Disorders (ASD), and many more. Like studies investigating the proteome, transcriptome, epigenome, microbiome, etc., for years, metallomics studies have focused on data from their domain, i.e., trace metal composition, only. Still, few have considered the links between other "omes," which may together result in an individual's specific pathologies. In particular, ASD have been reported to have multitudes of possible causal effects. Metallomics data focusing on metal deficiencies and dyshomeostasis can be linked to functions of metalloenzymes, metal transporters, and transcription factors, thus affecting the proteome and transcriptome. Furthermore, recent studies in ASD have emphasized the gut-brain axis, with alterations in the microbiome being linked to changes in the metabolome and inflammatory processes. However, the microbiome and other "omes" are heavily influenced by the metallome. Thus, here, we will summarize the known implications of a changed metallome for other "omes" in the body in the context of "omics" studies in ASD. We will highlight possible connections and propose a model that may explain the so far independently reported pathologies in ASD.

[Ethics of resuscitation and end of life decisions]

These European Resuscitation Council Ethics guidelines provide evidence-based recommendations for the ethical, routine practice of resuscitation and end-of-life care of adults and children. The guideline primarily focus on major ethical practice interventions (i.e. advance directives, advance care planning, and shared decision making), decision making regarding resuscitation, education, and research. These areas are tightly related to the application of the principles of bioethics in the practice of resuscitation and end-of-life care.

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

Big data analytics and artificial intelligence are revolutionizing the global healthcare industry. As the world accumulates unfathomable volumes of data and health technology grows more and more critical to the advancement of medicine, policymakers and regulators are faced with tough challenges around data security and data privacy. This paper reviews existing regulatory frameworks for artificial intelligence-based medical devices and health data privacy in Bangladesh. The study is legal research employing a comparative approach where data is collected from primary and secondary legal materials and filtered based on policies relating to medical data privacy and medical device regulation of Bangladesh. Such policies are then compared with benchmark policies of the European Union and the USA to test the adequacy of the present regulatory framework of Bangladesh and identify the gaps in the current regulation. The study highlights the gaps in policy and regulation in Bangladesh that are hampering the widespread adoption of big data analytics and artificial intelligence in the industry. Despite the vast benefits that big data would bring to Bangladesh's healthcare industry, it lacks the proper data governance and legal framework necessary to gain consumer trust and move forward. Policymakers and regulators must work collaboratively with clinicians, patients and industry to adopt a new regulatory framework that harnesses the potential of big data but ensures adequate privacy and security of personal data. The article opens valuable insight to regulators, academicians, researchers and legal practitioners regarding the present regulatory loopholes in Bangladesh involving exploiting the promise of big data in the medical field. The study concludes with the recommendation for future research into the area of privacy as it relates to artificial intelligence-based medical devices should consult the patients' perspective by employing quantitative analysis research methodology.

Informatics Management of Tumor Specimens in the Era of Big Data: Challenges and Solutions

Biomedical data bear the potential to facilitate personalized diagnosis and precision treatment. In the era of Big Data, high-quality annotation of human specimens has become the primary mission of biobankers, especially for tumor biobanks with large amounts of "omics" and clinical data. However, the lack of agreed-upon standardization and the gap among heterogeneous databases make information application and communication a major challenge. International efforts are underway to develop national projects on informatics management. The aim of this review is to provide references in specimen annotation to regulate and take full advantage of biological and biomedical information. First, critical data categories that are vital for specimen applications, including sample attributes, clinical data, preanalytical variations, and analytical records, are systematically listed for subsequent data mining. Second, current standards and guidelines related to biospecimen information are reviewed, and proper standards for tumor biobanks are recommended. In particular, commonly-used approaches and functionalities of data management are summarized and discussed. This review highlights the importance of informatics management of tumor specimens, defines critical data types, recommends data standards, and presents the methodologies of data harmonization for biobankers to reach high quality annotation of biospecimens.

IBD Systems Biology Is Here to Stay

BACKGROUND

Systems biology is a rapidly advancing field of science that allows us to look into disease mechanisms, patient diagnosis and stratification, and drug development in a completely new light. It is based on the utilization of unbiased computational systems free of the traditional experimental approaches based on personal choices of what is important and what select experiments should be performed to obtain the expected results.

METHODS

Systems biology can be applied to inflammatory bowel disease (IBD) by learning basic concepts of omes and omics and how omics-derived "big data" can be integrated to discover the biological networks underlying highly complex diseases like IBD. Once these biological networks (interactomes) are identified, then the molecules controlling the disease network can be singled out and specific blockers developed.

RESULTS

The field of systems biology in IBD is just emerging, and there is still limited information on how to best utilize its power to advance our understanding of Crohn disease and ulcerative colitis to develop novel therapeutic strategies. Few centers have embraced systems biology in IBD, but the creation of international consortia and large biobanks will make biosamples available to basic and clinical IBD investigators for further research studies.

CONCLUSIONS

The implementation of systems biology is indispensable and unavoidable, and the patient and medical communities will both benefit immensely from what it will offer in the near future.

Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives

Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient's follow-up. Also, increasing evidence suggest that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, improvement of patients' quality of life, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer.

A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool

High blood pressure (BP) may lead to further health complications if not monitored and controlled, especially for critically ill patients. Particularly, there are two types of blood pressure monitoring, invasive measurement, whereby a central line is inserted into the patient’s body, which is associated with infection risks. The second measurement is cuff-based that monitors BP by detecting the blood volume change at the skin surface using a pulse oximeter or wearable devices such as a smartwatch. This paper aims to estimate the blood pressure using machine learning from photoplethysmogram (PPG) signals, which is obtained from cuff-based monitoring. To avoid the issues associated with machine learning such as improperly choosing the classifiers and/or not selecting the best features, this paper utilized the tree-based pipeline optimization tool (TPOT) to automate the machine learning pipeline to select the best regression models for estimating both systolic BP (SBP) and diastolic BP (DBP) separately. As a pre-processing stage, notch filter, band-pass filter, and zero phase filtering were applied by TPOT to eliminate any potential noise inherent in the signal. Then, the automated feature selection was performed to select the best features to estimate the BP, including SBP and DBP features, which are extracted using random forest (RF) and k-nearest neighbors (KNN), respectively. To train and test the model, the PhysioNet global dataset was used, which contains 32.061 million samples for 1000 subjects. Finally, the proposed approach was evaluated and validated using the mean absolute error (MAE). The results obtained were 6.52 mmHg for SBS and 4.19 mmHg for DBP, which show the superiority of the proposed model over the related works.

Economic evaluations of big data analytics for clinical decision-making: a scoping review

OBJECTIVE

Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making.

MATERIALS AND METHODS

We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed "big data analytics" based on a broad definition of this term.

RESULTS

The search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined "big data analytics" and only 7 reported both cost-savings and better outcomes.

DISCUSSION

The promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of "big data" limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.

European Resuscitation Council Guidelines 2021: Ethics of resuscitation and end of life decisions

These European Resuscitation Council Ethics guidelines provide evidence-based recommendations for the ethical, routine practice of resuscitation and end-of-life care of adults and children. The guideline primarily focus on major ethical practice interventions (i.e. advance directives, advance care planning, and shared decision making), decision making regarding resuscitation, education, and research. These areas are tightly related to the application of the principles of bioethics in the practice of resuscitation and end-of-life care.

Connecting and linking neurocognitive, digital phenotyping, physiologic, psychophysical, neuroimaging, genomic, & sensor data with survey data

Combining survey data with alternative data sources (e.g., wearable technology, apps, physiological, ecological monitoring, genomic, neurocognitive assessments, brain imaging, and psychophysical data) to paint a complete biobehavioral picture of trauma patients comes with many complex system challenges and solutions. Starting in emergency departments and incorporating these diverse, broad, and separate data streams presents technical, operational, and logistical challenges but allows for a greater scientific understanding of the long-term effects of trauma. Our manuscript describes incorporating and prospectively linking these multi-dimensional big data elements into a clinical, observational study at US emergency departments with the goal to understand, prevent, and predict adverse posttraumatic neuropsychiatric sequelae (APNS) that affects over 40 million Americans annually. We outline key data-driven system challenges and solutions and investigate eligibility considerations, compliance, and response rate outcomes incorporating these diverse "big data" measures using integrated data-driven cross-discipline system architecture.

Investigating the Adoption of Big Data Management in Healthcare in Jordan

Software developers and data scientists use and deal with big data to easily discover useful knowledge and find better solutions to improve healthcare services and patient safety. Big data analytics (BDA) is getting attention due to its role in decision-making across the healthcare field. Therefore, this article examines the adoption mechanism of big data analytics and management in healthcare organizations in Jordan. Additionally, it discusses health big data’s characteristics and the challenges, and limitations for health big data analytics and management in Jordan. This article proposes a conceptual framework that allows utilizing health big data. The proposed conceptual framework suggests a way to merge the existing health information system with the National Health Information Exchange (HIE), which might play a role in extracting insights from our massive datasets, increases the data availability and reduces waste in resources. When applying the framework, the collected data are processed to develop knowledge and support decision-making, which helps improve the health care quality for both the community and individuals by improving diagnosis, treatment, and other services.

Understanding the Uptake of Big Data in Health Care: Protocol for a Multinational Mixed-Methods Study

Background

Despite the high potential of big data, their applications in health care face many organizational, social, financial, and regulatory challenges. The societal dimensions of big data are underrepresented in much medical research. Little is known about integrating big data applications in the corporate routines of hospitals and other care providers. Equally little is understood about embedding big data applications in daily work practices and how they lead to actual improvements for health care actors, such as patients, care professionals, care providers, information technology companies, payers, and the society.

Objective

This planned study aims to provide an integrated analysis of big data applications, focusing on the interrelations among concrete big data experiments, organizational routines, and relevant systemic and societal dimensions. To understand the similarities and differences between interactions in various contexts, the study covers 12 big data pilot projects in eight European countries, each with its own health care system. Workshops will be held with stakeholders to discuss the findings, our recommendations, and the implementation. Dissemination is supported by visual representations developed to share the knowledge gained.

Methods

This study will utilize a mixed-methods approach that combines performance measurements, interviews, document analysis, and cocreation workshops. Analysis will be structured around the following four key dimensions: performance, embedding, legitimation, and value creation. Data and their interrelations across the dimensions will be synthesized per application and per country.

Results

The study was funded in August 2017. Data collection started in April 2018 and will continue until September 2021. The multidisciplinary focus of this study enables us to combine insights from several social sciences (health policy analysis, business administration, innovation studies, organization studies, ethics, and health services research) to advance a holistic understanding of big data value realization. The multinational character enables comparative analysis across the following eight European countries: Austria, France, Germany, Ireland, the Netherlands, Spain, Sweden, and the United Kingdom. Given that national and organizational contexts change over time, it will not be possible to isolate the factors and actors that explain the implementation of big data applications. The visual representations developed for dissemination purposes will help to reduce complexity and clarify the relations between the various dimensions.

Conclusions

This study will develop an integrated approach to big data applications that considers the interrelations among concrete big data experiments, organizational routines, and relevant systemic and societal dimensions.

International Registered Report Identifier (IRRID)

DERR1-10.2196/16779

Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.

The Need to Develop Standard Measures of Patient Adherence for Big Data: Viewpoint

Despite half a century of dedicated studies, medication adherence remains far from perfect, with many patients not taking their medications as prescribed. The magnitude of this problem is rising, jeopardizing the effectiveness of evidence-based therapies. An important reason for this is the unprecedented demographic change at the beginning of the 21st century. Aging leads to multimorbidity and complex therapeutic regimens that create a fertile ground for nonadherence. As this scenario is a global problem, it needs a worldwide answer. Could this answer be provided, given the new opportunities created by the digitization of health care? Daily, health-related information is being collected in electronic health records, pharmacy dispensing databases, health insurance systems, and national health system records. These big data repositories offer a unique chance to study adherence both retrospectively and prospectively at the population level, as well as its related factors. In order to make full use of this opportunity, there is a need to develop standardized measures of adherence, which can be applied globally to big data and will inform scientific research, clinical practice, and public health. These standardized measures may also enable a better understanding of the relationship between adherence and clinical outcomes, and allow for fair benchmarking of the effectiveness and cost-effectiveness of adherence-targeting interventions. Unfortunately, despite this obvious need, such standards are still lacking. Therefore, the aim of this paper is to call for a consensus on global standards for measuring adherence with big data. More specifically, sound standards of formatting and analyzing big data are needed in order to assess, uniformly present, and compare patterns of medication adherence across studies. Wide use of these standards may improve adherence and make health care systems more effective and sustainable.

Including Digital Sequence Data in the Nagoya Protocol Can Promote Data Sharing

The Nagoya Protocol (NP), a legal framework under the Convention on Biological Diversity (CBD), formalises fair and equitable sharing of benefits arising from biological diversity. It encompasses biological samples and associated indigenous knowledge, with equitable return of benefits to those providing samples. Recent proposals that the use of digital sequence information (DSI) derived from samples should also require benefit-sharing under the NP have raised concerns that this might hamper research progress. Here, we propose that formalised benefit-sharing for biological data use can increase willingness to participate in research and share data, by ensuring equitable collaboration between sample providers and researchers, and preventing exploitative practices. Three case studies demonstrate how equitable benefit-sharing agreements might build long-term collaborations, furthering research for global benefits.

Creation and application of virtual patient cohorts of heart models

Patient-specific cardiac models are now being used to guide therapies. The increased use of patient-specific cardiac simulations in clinical care will give rise to the development of virtual cohorts of cardiac models. These cohorts will allow cardiac simulations to capture and quantify inter-patient variability. However, the development of virtual cohorts of cardiac models will require the transformation of cardiac modelling from small numbers of bespoke models to robust and rapid workflows that can create large numbers of models. In this review, we describe the state of the art in virtual cohorts of cardiac models, the process of creating virtual cohorts of cardiac models, and how to generate the individual cohort member models, followed by a discussion of the potential and future applications of virtual cohorts of cardiac models. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Benefits and challenges of Big Data in healthcare: an overview of the European initiatives

Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen’s expectations. Improving health outcomes while containing costs acts as a stumbling block. In this context, Big Data can help healthcare providers meet these goals in unprecedented ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers. In several contexts, the use of Big Data in healthcare is already offering solutions for the improvement of patient care and the generation of value in healthcare organizations. This approach requires, however, that all the relevant stakeholders collaborate and adapt the design and performance of their systems. They must build the technological infrastructure to house and converge the massive volume of healthcare data, and to invest in the human capital to guide citizens into this new frontier of human health and well-being. The present work reports an overview of best practice initiatives in Europe related to Big Data analytics in public health and oncology sectors, aimed to generate new knowledge, improve clinical care and streamline public health surveillance.

Introducing PIONEER: a project to harness big data in prostate cancer research

Prostate Cancer Diagnosis and Treatment Enhancement Through the Power of Big Data in Europe (PIONEER) is a European network of excellence for big data in prostate cancer, consisting of 32 private and public stakeholders from 9 countries across Europe. Launched by the Innovative Medicines Initiative 2 and part of the Big Data for Better Outcomes Programme (BD4BO), the overarching goal of PIONEER is to provide high-quality evidence on prostate cancer management by unlocking the potential of big data. The project has identified critical evidence gaps in prostate cancer care, via a detailed prioritization exercise including all key stakeholders. By standardizing and integrating existing high-quality and multidisciplinary data sources from patients with prostate cancer across different stages of the disease, the resulting big data will be assembled into a single innovative data platform for research. Based on a unique set of methodologies, PIONEER aims to advance the field of prostate cancer care with a particular focus on improving prostate-cancer-related outcomes, health system efficiency by streamlining patient management, and the quality of health and social care delivered to all men with prostate cancer and their families worldwide.

When information is the treatment? Precision medicine in healthcare

Profoundly more data-intensive than conventional medicine, precision medicine's distinctive informational needs present new challenges for healthcare management. Data protection and privacy law are key determinants in precision medicine's future. This article examines legal and regulatory barriers to the incorporation of precision medicine into healthcare. Specific attention is paid to analyzing recent health privacy laws, court cases, and medical device regulations. Considering the challenges identified, recommendations and guidance are crafted for health leaders with reference to domestic and international initiatives.