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Mobile Apps for People With Rare Diseases: Review and Quality Assessment Using Mobile App Rating Scale

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Mobile apps are becoming increasingly popular, with 5.70 million apps available in early 2021. Smartphones can provide portable and convenient access to health apps. Here, we consider apps for people with one of the estimated 7000 rare conditions, which…

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Sign up | Miro | The Visual Workspace for Innovation

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Miro is a visual workspace for innovation where teams manage projects, design products, and build the future together. Join 60M+ users from around the world.

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The UOC spin-off company Open Evidence has studied the implementation and interoperability of this technology in the EU countries, Norway and the United Kingdom

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The European Health Data Space is a health specific ecosystem comprised of rules, common standards and practices, infrastructures and a governance framework that aims at:

1. Empowering individuals through increased digital access to and control of their electronic personal health data, at national level and EU-wide.

2. Fostering a single market for electronic health record systems, relevant medical devices and high risk AI systems.

3. Providing a trustworthy and efficient set-up for the use of health data for research, innovation, policy-making and regulatory activities (secondary use of data).

The European Health Data Space is a key pillar of the European Health Union. It builds further on the General Data Protection Regulation (GDPR), and the NIS 2 Directive.

The European Union is building a strong European Health Union, in which all EU countries prepare and respond to health crises, have available, affordable, innovative and adequate medical supplies, and member countries work together to improve prevention, treatment and aftercare for diseases.

The COVID-19 pandemic shows the importance of coordination among European countries to protect health, both during a crisis and in normal times. The European Health Union improves EU-level protection, prevention, preparedness and response against human health hazards.
15 March 2024 - political agreement reached between the European Parliament and the Council of the EU on the European Health Data Space (EHDS), one of the central building blocks of a strong European Health Union.
Next step: The European Parliament and the Council will formally adopt the new Regulation.

The agreement reached by the co-legislators establishes clear rules for the use of health data for better healthcare delivery, research, innovation, and policymaking.

The new rules will harness the potential offered by the safe and secure exchange, use, and re-use of health data, while ensuring full compliance with the EU's high data protection standards.

Under the new rules, citizens will have immediate and easy access to their digital health data wherever they are in the EU. Health professionals will be able to access the medical records of a patient when required for treatment in a different Member State, allowing for evidence-based decision making, in full compliance with EU data protection rules.

The EHDS also creates a strong legal framework for the re-use of health data for research, innovation, and public health purposes. The data will help develop life-saving treatments and personalised medicines, but also improve crisis preparedness, under strict data security and access conditions, and respecting fundamental rights.
6 December 2023 - Agreement on the Council's proposal for the European Health Data Space (EHDS)
After this agreement, the EU Council presidency begins negotiations with the European Parliament, with a view to reaching a provisional agreement on the proposed regulation.

The Council's mandate provides clarity on issues such as the scope of the regulation, alignment with the General Data Protection Regulation (GDPR) and the criteria for providing access to electronic health data.

The Council proposes the creation of two steering groups, made up of member-state representatives, to manage MyHealth@EU and HealthData@EU; other stakeholders may be invited as observers to discuss relevant issues.

The Council's mandate expands the role of EU member states in the proposed EHDS governing board, and requires national digital health authorities to publish an activity report every two years.

Under the Council's mandate, member states will have the discretion to allow patients to opt out of the new data-sharing system.
3 May 2022 - The European Commission launched the European Health Data Space (EHDS)
1. Thanks to the EHDS, people will have immediate, and easy access to the data in electronic form, free of charge. They can easily share these data with other health professionals in and across Member States to improve health care delivery. Citizens will be in full control of their data and will be able to add information, rectify wrong data, restrict access to others and obtain information on how their data are used and for which purpose.

2. Member States will ensure that patient summaries, ePrescriptions, images and image reports, laboratory results, discharge reports are issued and accepted in a common European format.

3. Interoperability and security will become mandatory requirements. Manufacturers of electronic health record systems will need to certify compliance with these standards.

4. To ensure that citizens' rights are safeguarded, all Member States have to appoint digital health authorities. These authorities will participate in the cross-border digital infrastructure (MyHealth@EU) that will support patients to share their data across borders.

5. The EHDS creates a strong legal framework for the use of health data for research, innovation, public health, policy-making and regulatory purposes. Under strict conditions, researchers, innovators, public institutions or industry will have access to large amounts of high-quality health data, crucial to develop life-saving treatments, vaccines or medical devices and ensuring better access to healthcare and more resilient health systems.

6. The access to such data by researchers, companies or institutions will require a permit from a health data access body, to be set up in all Member States. Access will only be granted if the requested data is used for specific purposes, in closed, secure environments and without revealing the identity of the individual. It is also strictly prohibited to use the data for decisions, which are detrimental to citizens such as designing harmful products or services or increasing an insurance premium.

7. The health data access bodies will be connected to the new decentralised EU-infrastructure for secondary use (HealthData@EU) which will be set up to support cross-border projects.
Understanding the European Health Data Space (EHDS)
Digitalisation is essential for the future of healthcare. The digital transformation is crucial to provide better healthcare to citizens, to build stronger and more resilient health systems, to support long-term competitiveness and innovation in the EU's medical industry, and to help the EU recover from the pandemic.

Data is an indispensable part of today's world. When used responsibly and in full respect of fundamental rights, it can bring incredible benefits to every aspect of our everyday lives, including our health. Member States' health systems already generate, process and store a vast amount of data. Yet it often remains difficult for citizens to access their health data electronically and for researchers to use it to improve diagnosis and treatments.

A vast amount of health data is generated every second, providing healthcare services and researchers with potential valuable insights. Health data reuse is estimated to be worth around EUR 25-30 billion annually. That figure is expected to reach around EUR 50 billion within 10 years.

However, the complexity and divergence of rules, structures and processes within and across Member States makes it difficult to easily access and share health data. This creates barriers to healthcare delivery and innovation, leaving patients unable to benefit from its potential.

Moreover, health systems are becoming the target of cyberattacks. Therefore, the healthcare sector and relevant cyber security authorities need to consider cybersecurity as a key factor for ensuring the resilience and availability of key healthcare services.

In essence, the EU health sector is rich in data, but poor in making it work for people and science. The EU needs to tap into this huge potential to turn the wealth of health data across Europe into knowledge at the service of citizens, and to better prevent, diagnose and treat diseases.

Health data can help achieve more efficient, higher-quality, safer and more personalised care, and help improve healthcare delivery. Health data3 and data science could dramatically transform public health and revolutionise healthcare systems, enabling lifesaving healthcare improvements. Health data can also play a crucial role in speeding up the development of new medical products and treatments for patients who need them most.

The COVID-19 pandemic has clearly demonstrated the importance of digital services in the health domain. It has shown that up-to-date, reliable and FAIR health data is key in providing an efficient public health response to crisis and in developing effective treatments and vaccines. It has also significantly accelerated the uptake of digital tools, such as electronic health records (personal medical records or similar documents in digital form), e-prescriptions and digital health applications, as well as the sharing of research data. Digital health products and services, including telehealth, are no longer novelties. They are becoming a part of everyday care delivery.

Harnessing the power of health data through the digital transformation is especially relevant when patients move within or to other EU countries; and when researchers, innovators, policy-makers or regulators need critical data that can enable the power of science to help patients. Similarly, sharing health data in border regions where individuals access healthcare services across the border much more frequently will be far easier.
Current challenges in using health data.
People cannot always easily access their health data electronically, and if they want to consult doctors in more than one hospital or medical centre, they often cannot share the data with other health professionals. Today, a patient's health data is often still recorded on paper, untraceable and scattered across various places (hospitals, general practitioners' venues, medical centres, etc.).

The situation becomes even more difficult when crossing national borders. If a patient visits a doctor in another country, their medical information (including diagnostic images) is often not accessible, which can lead to delays and errors in diagnosis or treatment. In most cases, doctors cannot see the patient's health data if they have undergone health interventions in another country. Continuity of care and rapid access to personal electronic health data is even more important for residents in border regions, crossing the border frequently to receive healthcare.

The open public consultation23 for the European Health Data Space proposal showed that 88% of respondents think it should promote citizens' control over their own health data, including access to health data and transmission of their health data in electronic format. 84% of respondents say that citizens should have the right to transmit one's health data in electronic format to another professional or entity of their choice and 82% feel that they should have the right to request public healthcare providers to share their health data electronically with other healthcare providers/entities of their choice. 83% of respondents say that the European Health Data Space should facilitate delivery of healthcare for citizens across borders.

Researchers and industry, along with policy-makers and innovators, face important obstacles in accessing the data they need to develop new products, to take informed decisions or to monitor the side effects of medicinal products over the long term, based on real-world evidence, with impact on patient safety. In many cases, consent is the only way to access data for research, policy-making and regulatory purposes. It is very costly and cumbersome for researchers to get consent from every patient to use the patient's data in their research.

Even when the patient consents, data holders are sometimes reluctant to provide data for reasons other than data protection and prefer to keep the health data for their activities. The current regulatory fragmentation between Member States hampers research and innovation by small players, as well as cross-border research.
Primary use of health data.
1. Patients will have their electronic health data available via access points established by Member States. A cross-border digital infrastructure for primary use will connect Member States and allow patients to share their health data.

2. Patients will be empowered to control and share their electronic health data with a healthcare provider of their choice.

3. Member States will be required to make priority categories of data available in a common European electronic health record exchange format, such as patient summaries, e-prescriptions, e-dispensations, medical images and image reports, laboratory results and discharge reports.

4. Where personal health data has not been collected electronically prior to the application of this Regulation, Member States may decide not to convert it into an electronic format.

5. Health professionals will be able to access electronic health records and should update the electronic health data of the patients they treat.

6. To support data being shared between healthcare providers, mandatory requirements for interoperability, security, safety and privacy will be introduced, as well as mandatory self-certification of electronic health records covering interoperability and security.

7. All Member States will be required to participate in cross-border digital infrastructure for the exchange of health data for healthcare delivery (MyHealth@EU).

8. A pilot project will support patients having access to their data on a mobile device in the language of the country of destination.

9. To facilitate the implementation, transitional periods will apply for certain obligations including the registration of health data, access of health professionals to data, making data available in the European format, the participation in the cross border digital infrastructure, self-certification of electronic health record systems, as well as the voluntary label for wellness applications.

10. Member States will have to set up a digital health authority to ensure that the additional rights for individuals are properly implemented.
Secondary use of health data.
1. The European Health Data Space sets out a common EU framework allowing for use of health data for research, innovation, public health, policy-making, regulatory activities and personalised medicine. It will draw on the creation of a new and decentralised EUinfrastructure for secondary use of health data (HealthData@EU) that will connect health data access bodies which should be set up in all Member States.

2. Those who wish to re-use health data will need to apply for a permit from a health data access body. The data permit sets out how the data may be used and for what purpose.

3. The data can only be accessed and processed in closed secure environments to be provided by the health data access bodies with clear standards for cyber security.

4. Only anonymous data can be extracted by the user who applied for the permit from the secure processing environment. Where researchers, companies or public institutions need access to personal electronic health data they can only access it in pseudonymised form, i.e. data offering information about the disease, symptoms and medication, without revealing to the user the identity of the individual. It is forbidden for the user to attempt to re-identify the data subjects.

5. It will be forbidden to use the data to take decisions detrimental to individuals, to increase insurance premiums, to market health products towards health professionals or patients or to design harmful products or services.

6. Health data access bodies will have to ensure transparency: information will be published about data access applications. In addition, data users must make public the results of their electronic health data uses and inform the health data access bodies of any significant findings relevant for the health of individuals.

7. For simple cases, users can directly request data from a single health data provider as long as the same safeguards for privacy and security are ensured.

8. Researchers and innovators from third countries can access data for secondary use under the same conditions and requirements as those from inside the EU.

9. All Member States will be required to participate in the EU-infrastructure for secondary use (HealthData@EU) to facilitate cross-border studies. This infrastructure will be piloted in a EU4Health project starting in 2022.
EU governance mechanism.
1. A new European Health Data Space Board chaired by the Commission will be created, composed of the representatives of digital health authorities and health data access bodies from all the Member States, and observers, depending on area of work.

2. It will contribute to the consistent application of the Regulation throughout the EU, to coordinate and exchange best practices and will cooperate with other bodies at EU level.

3. Member States will cooperate at EU level to ensure the smooth functioning of the two cross-border digital infrastructures (primary and secondary).
The European Health Data Space, together with the GDPR, will give people the right to:
1. Access their health data in electronic form immediately, free of charge and in an easily readable, accessible and commonly used format. Data can be accessed using patient portals, on computers or smart phones, depending on how the Member States make available this information at national level. For people with disabilities to be able to enjoy their rights, the access must be accessible in line with the requirements of the European Accessibility Act (Directive 2019/882);

2. Share their data in electronic form with other health professionals when going to another hospital, without hindrance from previous healthcare providers or manufacturers;

3. Add data to their electronic health record for themselves or for people who trust them, such as their children;

4. Request changes to erroneous data online;

5. Restrict access to their electronic health data or part of the data; in cases of vital interest, where their life is at stake, such data may however be made available with additional restrictions;

6. Easily obtain information on which professional(s) accessed their data.

Member States are required to designate digital health authorities that will have a fundamental role in enforcing the above-mentioned rights.
Examples of how the EHDS will function
Example 1: A woman living in Portugal is going on holidays to France. Unfortunately, she gets sick in France and therefore needs to see a local general practitioner. Thanks to the EHDS and MyHealth@EU, a doctor in France will see on his/her computer the medical history of this patient in French. The doctor can prescribe the necessary medicine based on the medical history of the patient, avoiding for instance products to which the patient is allergic.

Example 2: A health tech company is developing a new AI-based medical decision support tool that assists doctors to make diagnostic and treatment decisions following a review of the patient's laboratory images. The AI compares the patient's images with those of many other previous patients. Through the EHDS, the company is able to have efficient and secure access to a large number of medical images to train the AI algorithm and optimise its accuracy and effectiveness before seeking market approval.

Example 3: A man has a medical image of his lungs, taken in the public hospital where he was brought in by the emergency team. Shortly after, he visits his regular pulmonologist in another hospital. Thanks to the EHDS, his pulmonologist can see the medical image performed in the other hospital, thus avoiding a new, unnecessary test.

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The European Health Data Space (EHDS), a key pillar of the European Health Union

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Topics in the interviews that match our needs

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Topics in the interviews that match our needs

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nathan.lea@i-hd.eu

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Interview with medical geneticist - relevant excerpts 

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Interview with medical geneticist - relevant excerpts 

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hello, please share all your ideas down below about possible stakeholders and questions that we would like to ask them

Are there any specific guidelines or regulations we need to adhere to when handling sensitive health information?

Are there any existing security measures or protocols that we should integrate into our data-sharing practices?

What existing services or platforms should we consider for seamless data sharing between different entities?

What incentives can be provided to encourage individuals and organizations to actively participate in data sharing for rare diseases?

How can we involve the rare diseases community in decision-making processes related to data sharing?

Are there specific community representatives or advocacy groups that should be consulted regularly?

Are there any legal or regulatory challenges associated with data sharing in the context of rare diseases that we should be aware of?

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Main points/buzzwords:

inclusivity

gaining support

overarching conceptual framings

capacity building

management

dynamic informed consent

data quality

data interoperability

data reuse

stakeholders

conceptual model of a project

addressing real-life problems

shared language

participation

examples of projects

social innovation

fundings

design and function of an application

social media

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vasculitisireland10@gmail.com

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O projeto inclui, em traços largos:

Pré-implementação: uma sessão de esclarecimentos acerca de todos os materiais referentes ao projeto, realizada pela RD-Portugal, dirigida a docentes que o vão implementar;

Implementação em sala de aula pelo/a docente, com os materiais fornecidos pela RD-Portugal específicos para a faixa etária:

apresentação dos materiais expositivos;

realização de uma atividade/desafio que envolve recortar/dobrar/pintar/desenhar/ordenar/colar/jogar, para aplicar e praticar os conceitos ouvidos e assim consolidar a parte expositiva

Pós-implementação:

preenchimento de questionários de avaliação e satisfação;

partilha dos materiais utilizados pelas/os discentes na atividade com familiares e pessoas amigas.

Encontram-se abertas as inscrições para:

escolas/turmas de Pré-escolar, 1º Ciclo e 2º Ciclo que queiram implementar o projeto (3-5 anos ao 6º ano)

escolas/turmas de 3º Ciclo que queiram integrar o piloto para o 7º, 8º e 9º anos

escolas/turmas de Secundário para serem contactados assim que haja data prevista para este ciclo ou integrar o pré-piloto

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'Relatório "Centros de Referência para as Doenças Raras e a relação de proximidade com as Redes Europeias de Referência" No âmbito do evento 2º Encontro do Conselho Científico realizado a 9 de novembro de 2023 no CUF Academic Center, do Hospital CUF Tejo, em Lisboa, reunimos cerca de 70 participantes especialistas na área das doenças [...]

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https://www.kliinikum.ee/harvikhaigused/

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https://globalbiodata.org

https://elixir-europe.org/platforms/data/core-data-resources

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660964/

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The case for open science: rare diseases

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The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into…

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Abstract

In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these 'big data' put together can be utilized to optimize treatments for each unique patient ('precision medicine'). For this to be possible, it is necessary that hospitals, academia and industry work together to bridge the 'valley of death' of translational medicine. However, hospitals and academia often are reluctant to share their data with other parties, even though the patient is actually the owner of his/her own health data. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. There are some publicly available datasets, but these are usually only shared after study (and publication) completion, which means a severe delay of months or even years before others can analyse the data. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here, we show an analysis of the current literature around data sharing, and we discuss five aspects of data sharing in the medical domain: publisher requirements, data ownership, growing support for data sharing, data sharing initiatives and how the use of federated data might be a solution. We also discuss some potential future developments around data sharing, such as medical crowdsourcing and data generalists.

Keywords: data sharing, data management, data science, big data, healthcare

1. Introduction

The past years have seen a steep rise in the amount of health data being generated. These data come not only from professional health systems (MRI scanners, pathology slides, DNA tests, etc.) but also from wearable devices. All these data combined form 'big data' that can be utilized to optimize treatments for each unique patient ('precision medicine') [ 1]. To achieve this precision medicine, it is necessary that hospitals, academia and industry work together to bridge the 'valley of death' of translational medicine [ 2]. However, hospitals and academia often have problems with sharing their data, even though the patient is actually the owner of his/her own health data, and data sharing is associated with increased citation rate [ 3, 4]. Academic hospitals usually want to be the first ones to publish papers on the data, because they spent a lot of time in setting up clinical trials and collecting the data. Society benefits the most if the patient's data are shared as soon as possible so that other researchers can work with it [ 5], but this idea has not settled in yet. Some datasets are publicly available (e.g., in prostate cancer [ 6]), but these are usually only shared after studies are finished and/or publications have been written based on the data, which means a severe delay of months or even years before others can use the data for analysis. One solution is to incentivize the hospitals [ 7, 8] to share their data with (other) academic institutes and the industry. Besides this academic reluctance, data is also being shared less because of stricter privacy laws such as the EU General Data Protection Regulation (GDPR) [ 9] and the California Consumer Privacy Act (CCPA) [ 10]. At the moment, only around 10% of the world's population has it personal information covered by the GDPR or similar laws, but Gartner Research predicts that this will be around 50% by 2022 [ 11]. There is an increasingly urgent need to balance the opportunity big data provides for improving healthcare, against the right of individuals to control their own data [ 1]. Scientists should maximize their efforts to improve healthcare, but they should also only use data with appropriate informed consent. This open science vs. privacy balance will remain an increasing challenge for the coming years.

The topic of data sharing has received more attention in recent years. In 1980, only 46 articles (0.0186% of the total) published in PubMed contained the keyword "data sharing", while in 2019 there were 5960 articles (0.4253% of the total) containing this keyword ( Figure 1 ). It is also interesting to see the sudden rise of interest in the subject since 2016, the year of the approval of the GDPR, and another peak in 2018, the year of its enforcement.

If we use PubMed to find terms related to "data sharing", there are some interesting observations ( Figure 2 ). Mostly used are obviously terms such as "patients", "health", "study" and "information", but closely behind these are "use" (or "used"/"using"), "treatment", "care" "analysis" and "rights". "Use" might point to the fact that data collection and sharing is closely connected to the usage of the data, i.e., in the consent form it should be mentioned in detail what the health data will be used for. "Treatment", "care" and "analysis" point to one of the main uses of the data: analysis in order to improve treatment and care, for example in clinical decision support (CDS) systems. "Rights" is probably related to the patients' privacy rights when it comes to data sharing, an issue that is discussed in detail in this manuscript.

There have been some studies on the conditions and challenges for sharing data. For example, for the BigData@Heart platform of the Innovative Medicines Initiative (IMI), a descriptive case study into the condition for data sharing was carried out [ 12]. Principle investigators of the participating databases were requested to send any kind of documentation that possibly specified the conditions for data sharing, which were then qualitatively reviewed for conditions related to data sharing and data access. This review revealed overlap on the conditions: (1) only to share health data for scientific research, (2) in anonymized/coded form, (3) after approval from a designated review committee, and while (4) observing all appropriate measures for data security and in compliance with the applicable laws and regulations. These challenges give thought to the design of an ethical governance framework for data sharing platforms. The conclusion of the case study was that current data sharing initiatives should concentrate on: (1) the scope of the research questions that may be addressed, (2) how to deal with varying levels of de-identification, (3) determining when and how review committees should come into play, (4) align what policies and regulations mean by "data sharing" and (5) how to deal with datasets that have no system in place for data sharing.

Sharing data should not just be a one-way street from the clinician to the researcher; ideally the clinician, the researcher and the patient (or patient organization) would work together on setting up the study, so that there is an agreement on data usage upfront, and expectations are managed. Sharing data will also increase confidence and trust in the conclusions drawn from clinical trials [ 14]. It will help to enable the independent confirmation of results (reproducibility), an essential part of the scientific process. It will foster the development and testing of new hypotheses. Sharing clinical trial data should also make progress more efficient by making the most of what may be learned from each trial and by avoiding unwarranted repetition. It will help to satisfy the moral obligation of researchers towards study participants, and it will benefit patients, investigators, sponsors, and society. In this review, we discuss several aspects of data sharing in the medical domain. The Section 2 is about publisher requirements, which shows what guidelines have been created by publishers and editors to promote the sharing of data. Since academics rely on publication of their data, these are important measures and a logical first topic to be discussed. The Section 3 shows that there is an ongoing discussion about data ownership, which influences the way that regulations are being implemented. The Section 4 shows the growing support for data sharing, making the link to open science and the reproducibility of results. The Section 5 shows data sharing initiatives that have been undertaken recently. The Section 6 and Section 7 discusses how the use of federated data might be a solution of the privacy and reproducibility issues mentioned in the Section 2, Section 3 and Section 4.

2. Publisher Requirements

Most publishers strongly recommend sharing research data. For this section, the publisher requirements of five major publishers are discussed, as well as the most widely used sets of guidelines from publishers and editors.

Nature states that data sharing makes new types of research possible [ 15], for example through the pooling of patient cohorts, and hints to future developments: sharing data is not only a way to improve the reproducibility and robustness of the science that is taking place today, but can drive new science for tomorrow. By browsing through existing datasets, new hypotheses can be formed, which can then be tested in new studies. Because nobody can predict how valuable a dataset will be in the future, data should be made available to future scientists whenever possible. The Science journals support the efforts of databases that aggregate published data for the use of the scientific community [ 16]. Therefore, before publication, large data sets must be deposited in an approved database and an accession number or a specific access address must be included in the published paper. The Science journals also encourage compliance with Minimum Information for Biological and Biomedical Investigations (MIBBI) guidelines [ 17]. British Medical Journal (BMJ) journals have three different data sharing policies ("tiers"), dependent of the journal [ 18]. They encourage researchers to make available as much of the underlying data from an article as possible (without compromising the privacy of the patients). The BMJ journals also consider reproducibility: all data that are needed to reproduce the results presented in the associated article should be made available. When submitting a manuscript to a publisher such as BioMed Central (BMC), the researcher even "agrees to make the raw data and materials described in your manuscript freely available to any scientist wishing to use them for non-commercial purposes, as long as this does not breach participant confidentiality" [ 19]. Public Library of Science (PLOS) journals require authors "to make all data necessary to replicate their study's findings publicly available without restriction at the time of publication. When specific legal or ethical restrictions prohibit public sharing of a data set, authors must indicate how others may obtain access to the data" [ 20]. Other publishers have similar guidelines in place, promoting data sharing on a global level.

In 2015, the Transparency and Openness Promotion (TOP) guidelines [ 21] were published. The guidelines were developed to translate scientific norms and values into concrete actions and change the current incentive structures to drive researchers' behavior toward more openness. The TOP guidelines have eight standards: (1) citation standards; (2) data transparency; (3) analytics methods (code) transparency; (4) research materials transparency; (5) design and analysis transparency; (6) preregistration of studies; (7) preregistration of analysis plans; and (8) replication. For each standard, there are three levels with increasing stringency. Currently, over 1000 scientific journals have implemented the TOP guidelines [ 22].

The International Committee of Medical Journal Editors (ICMJE) also recommends the sharing of data [ 14]. In 2016, they proposed to require authors to share with others the deidentified individual-patient data (IPD) underlying the results presented in the article no later than 6 months after publication. The data underlying the results are defined as "the IPD required to reproduce the article's findings, including necessary metadata". Since 2019, the ICMJE requires investigators to register a data-sharing plan when registering a trial as well. This plan must include where the researchers will house the data and, if not in a public repository, the mechanism by which they will provide others access to the data, whether data will be freely available to anyone upon request or only after application to and approval by a learned intermediary, whether a data use agreement will be required, etc. Declaring the plan for sharing data prior to their collection will further enhance transparency in the conduct and reporting of clinical trials by exposing when data availability following trial completion differs from prior commitments. However, ICMJE also stresses that the rights of investigators and trial sponsors must be protected. To achieve this, the following four rules apply: (1) editors will not consider the deposition of data in a registry to constitute prior publication; (2) authors of secondary analyses using these shared data must attest that their use was in accordance with the terms (if any) agreed to upon their receipt; (3) authors of secondary analyses must reference the source of the data using a unique identifier of a clinical trial's data set to provide appropriate credit to those who generated it and allow searching for the studies it has supported; (4) authors of secondary analyses must explain completely how theirs differ from previous analyses. In addition, those who generate and then share clinical trial data sets deserve substantial credit for their efforts. Those using data collected by others should seek collaboration with those who collected the data.

By providing the guidelines and rules set out above, the publishers and editors contribute to the acceptance of data sharing by researchers. Not only does it help solve their problem of a lack of reproducibility of the scientific results published in their journals, increasing confidence and trust in these results; it will also help the scientists in the generation of new hypotheses, and avoiding unnecessary repetition. In the end, publishers, as well as scientists, patients and societies will benefit from complying with these rules.

3. Data Ownership

When discussing the sharing of data, it is important to realize that there is not much consensus on who is actually the owner of that data. This section briefly discusses this issue of data ownership in the light of recent privacy laws. These laws have a very large impact on the topic of data sharing.

Institutions tend to believe that they own the patient data, since they collected it. However, these institutions are in fact just "data custodians"; the data is the property of the patient and the access and use of that data outside of the clinical institute usually requires patient consent [ 1]. This limits the exploitation of the "big data" that are available in the clinical records, because the data should be destroyed (or sufficiently anonymized) after the end of the study. Big data techniques such as machine learning and deep learning use thousands to millions of data points, which may have required considerable processing. It would be a waste to lose such valuable data at the end of the project. Therefore, it is advised to ask the patient for consent to store and use their data for future scientific research. Although it is not possible to use the data from a large number of retrospective datasets in this manner, this will make sure that at least the prospectively collected data can be used in future studies. The dilemma of the use of patient data versus privacy rights has gotten much attention because of the implementation of the GDPR in 2018 (as well as the CCPA in 2020), initiating an international debate on the sharing of big data in the healthcare domain [ 23]. Earlier laws such as the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule [ 24] of the USA and the Personal Information Protection and Electronic Documents Act (PIPEDA) [ 25] of Canada already gave more rights to patients regarding their data, but the GDPR and CCPA have taken it to another level. However, GDPR and similar laws do not say much about data ownership. The GDPR's main entities are the data controller and the data processor [ 9]. "Data controller" means the natural or legal person, public authority, agency or other body which, alone or jointly with others, determines the purposes and means of the processing of personal data. "Data processor" means a natural or legal person, public authority, agency or other body which processes personal data on behalf of the controller. In countries outside of the European Union, where GDPR does not apply, there is also not much agreement on data ownership, making it even more justifiable to always ask for the consent of the patient.

4. Growing Support for Data Sharing

The idea that data should be shared as much as possible to enable scientific progress is gaining momentum, mostly because of the power of big data analyses, machine learning, deep learning, etc. In this section, some developments are discussed which show this growing support for data sharing. Some of them were already known to the author, whereas others were a result from the literature analysis mentioned in the introduction.

Science in Transition [ 26] claims that "science has become a self-referential system where quality is measured mostly in bibliometric parameters and where societal relevance is undervalued", emphasizing that researchers tend to care mostly about publications instead of using the data to solve real-life problems. It also gives attention to the reproducibility problem in science: more than 70% of researchers have tried and failed to reproduce another scientist's experiments, and more than half have even failed to reproduce their own experiments [ 27]. This problem is not only caused by a lack of data sharing, but also because researchers do not share methodologies used to combine and analyse datasets. In many projects, data from several sources (possibly collected using different protocols and standards) need to be combined before the data analysis can take place. If these methodologies, as well as the analysis scripts, are not shared, results cannot be reproduced even if the data is available. This reproducibility issue could be resolved by 'Open Science', which is defined as the practising of science in a sustainable manner which gives others the opportunity to work with, contribute to and make use of the scientific process. This allows users from outside science to influence the research world with questions and ideas and help gather research data [ 28]. The Open Science movement stimulates not only open access to data, but also open access publishing, open source scientific software and open educational resources [ 29].

The Mayo Clinic Platform [ 30] is a new cloud-based clinical data analytics platform, storing de-identified patient data, which providers, payers and pharmaceutical companies outside of Mayo can link up to via application programming interfaces (APIs), as well as establishing standard templates for compliance and legal agreements. The first partner of the Mayo Clinic Platform is Nference, a software startup that Mayo is an investor in. Nference develops analytics, machine learning and natural language processing tools that "augment" the work of data scientists, in order to help research organizations and pharmaceutical companies conduct "research at scale". Mayo Clinic hopes to work with pharma to commercialize new therapies. Mayo itself wouldn't commercialize those therapies, though the system could receive royalties from insights generated on the platform. These royalties would be re-invested into Mayo's clinical practice, research and education work.

Healthcare Business and Technology wrote about how data sharing could change the entire healthcare industry [ 31]. It discusses the partnership announced by Apple in 2018 with 13 major healthcare systems, including Johns Hopkins and the University of Pennsylvania, that will allow Apple to download patients' electronic health data onto its devices (with consent of the patients). This type of data sharing could transform the U.S. healthcare industry by empowering patients in new ways and improving care. It could even reduce organizational costs by streamlining care processes, because hospital staff would need to spend less time on making data available to patients. And artificial intelligence (AI) could use the patient data to answer patients' questions and direct them to the healthcare services they need.

The 'Ten Commandments of Translational Research Informatics' [ 32] are some guidelines related to data management and data integration in translational research projects. Some of the commandments relate to the sharing of data: clear arrangements about data access need to be made (commandment 4), agree about de-identification and anonymization (commandment 5), the FAIR guiding principles [ 33] should be adhered to (commandment 8), and researchers should think about what will happen to the data after the project (commandment 10): e.g., research can be shared in a public repository.

5. Data Sharing Initiatives

There are many initiatives around the world supporting the sharing of medical data, leading the way to open science while still respecting the privacy rights of the patients. This paragraph gives some recent examples of these initiatives, resulting from the literature analysis from the introduction.

GIFT-Cloud [ 34] is a platform for data sharing and collaboration in medical imaging research. The goal of GIFT-Cloud is to provide a flexible, clinician- and researcher-friendly system for anonymising and sharing data across multiple institutions. It was built to support the Guided Instrumentation for Fetal Therapy and Surgery (GIFT-Surg) project, an international research collaboration that is developing novel imaging methods for fetal surgery, but it also has general applicability to other areas of imaging research. It simplifies the transfer of imaging data from clinical to research institutions, facilitating the development and validation of medical research software and the sharing of results back to the clinical partners. GIFT-Cloud supports collaboration between multiple healthcare and research institutions while satisfying the demands of patient confidentiality, data security and data ownership. It achieves this by building upon existing, well-established cross-platform technologies. GIFT-Cloud stores data from each institution in a separate data group and access to these groups can be individually configured for each account, corresponding with what is arranged in the data sharing agreements.

Another development in data sharing is the Personalized Consent Flow [ 35]: a new consent model that allows people to control their personally collected health data and determine to what extent they want to share these for research purposes. Three main features characterize the consent flow: (1) Users are asked general questions about sharing data. When they wish to share data for scientific research, they may opt for "narrow" consent (treating each study separately) or "broad" consent (for multiple studies). Furthermore, users can decide which data will be shared for specific studies and with whom. (2) Users can choose to share existing data that they have collected passively, to share prospectively, collect data, or both. For prospective studies, researchers can invite specific users to collect selected data during a specific time period. Users can also be notified about future studies by signing up for the research program. (3) Expiration dates are connected to each consent choice, which ensures that a user reconsiders his decision. A default expiration date of one year will be assigned, but users may also select personal expiration dates, such as an expiration date connected to the duration of the study. Users can choose to quit sharing data at any time, as required by GDPR regulations. During all steps, users are informed about implications of consent options.

In the United States, the Sync for Science (S4S) [ 36] collaboration between Electronic Health Record (EHR) vendors, the National Institutes of Health (NIH), the Office of the National Coordinator for Health IT (ONC), and Harvard Medical School's Department of Biomedical Informatics was started in 2016. S4S allows individuals to access their health data and share these data with researchers to support studies that generate insights into human health and disease [ 37]. Different EHRs collect and store health data differently, so S4S has focused on promoting both authorization and healthcare data standards to make it possible for EHR systems to release, upon patient approval, high quality data that researchers can readily consume. The All of Us Research Program [ 38] was the first study to adopt S4S technology in a pilot program. The program began national enrollment in 2018 and is expected to last at least 10 years. An initiative like Sync for Science gives the power to the patient: the patient can decide what information to share with researchers, and under what conditions. Much like many countries now have organ donation registration systems in place, this 'data donation registration' might be something that will be implemented around the world in the near future.

In the EU, the 1+ Million Genomes Initiative [ 39] is a good example of how many datasets could be combined into one large database, enabling the study of, e.g., rare diseases. The declaration aims to bring together fragmented infrastructure and expertise supporting a shared and tangible goal: one million genomes accessible in the EU by 2022. The 22 participating European countries envision that the digital transformation of genomic medicine (and healthcare in general) will help health systems to meet the challenges they face and become more sustainable, thereby improving the provision of high-quality health services and the effectiveness of treatments. They believe that this requires a concerted effort to overcome data silos, lack of interoperability and fragmentation of initiatives across the EU. Another recent example of such a large-scale collaborative data sharing effort is the Pan-Cancer Analysis of Whole Genomes (PCAWG) [ 40], which was facilitated by international data sharing using compute clouds. PCAWG contains 2658 whole-cancer genomes and their matching normal tissues across 38 tumour types. More than 1300 scientists and clinicians from 37 countries were involved in the project.

Data sharing goes beyond the academic world. Many public-private partnerships have been set up, in order to make sure that discoveries are not only published, but also applied in a product such as a medical device, a medicine or a computer program. Funding programmes, such as the Horizon 2020 Research and Innovation Programme of the European Union, very much stimulate data sharing with companies. In May 2016, it was announced that Deepmind, a company owned by Google and most famous for its innovative use of AI, was given access to the healthcare data of up to 1.6 million patients from three hospitals run by a major London NHS trust [ 41]. And in January 2018, Apple announced that they created a pact with 13 prominent health systems, including prestigious centers like Johns Hopkins and the University of Pennsylvania, allowing Apple to download the electronic health data of patients onto its devices, with consent [ 42]. Of course, when sharing data with commercial parties, privacy needs to be taken into account. For example, if GDPR applies, the patient needs to 'opt-in' for sharing their data for commercial use. Besides industry using data generated by academia, the opposite is also possible; these collaborations are called "data collaboratives" [ 43]. Data collaboratives are a new form of partnership in which privately held data are made accessible for analysis and use by external parties working in the public interest. By having researchers from both industry and academia work on the data, new insights and innovations can be created, and the potential of privately-owned data can be unlocked.

6. Federated Data

Data federation is a recent development in medical science, which is a possible solution for the data sharing vs. patient privacy dilemma. In this section, three examples of federated data systems for sharing medical data are discussed, resulting from the literature analysis from the introduction.

When applying machine learning methods on healthcare data, large samples sizes are required. Often these sizes can only be achieved by combining data from several studies. But this kind of pooling of information is difficult because of patient privacy and data protection needs. Privacy preserving distributed learning technology has the potential to overcome these limitations. The general idea behind distributed learning is that sites share a statistical model and its parameters, instead of sharing sensitive data. Each site runs computations on a local data store that generate these aggregated statistics. In this setting, organizations can collaborate by exchanging aggregated data/statistics while keeping the underlying data safely on site and undisclosed. VANTAGE6 [ 44] provides a way to use distributed learning technology, using open source software. It is one of the federated data systems that has recently become available in order to share data while preserving the patients' privacy rights.

The Personal Health Train (PHT) [ 45, 46] is another example of a federated data system; it aims to connect distributed health data and create value by increasing the use of existing health data for citizens, healthcare, and scientific research. The key concept in the Personal Health Train is to bring algorithms ('trains') to the data where they happen to be ('stations'), rather than bringing all data together in a central database. The Personal Health Train is designed to give controlled access to heterogeneous data sources, while ensuring privacy protection and maximum engagement of individual subjects. As a prerequisite, health data are made FAIR (Findable, Accessible, Interoperable and Reusable) [ 33]. Stations containing FAIR data may be controlled by individuals, (general) physicians, biobanks, hospitals and public or private data repositories. The Personal Health Train was applied recently to a project with 20,000+ lung cancer patients [ 47] and will also be used in the Coronary ARtery disease: Risk estimations and Interventions for prevention and EaRly detection (CARRIER) project [ 48]. Likely more projects will follow.

Another example of a federated data system is DataSHIELD [ 49]. It provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. It facilitates research in settings where sharing the data itself is not possible (due to government restrictions, intellectual property issues, or data size). Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. DataSHIELD has been used by the Healthy Obese Project and the Environmental Core Project of the Biobank Standardisation and Harmonisation for Research Excellence in the European Union (BioSHaRE-EU [ 50]) for the federated analysis of 10 datasets across eight European countries.

7. Conclusions

This review discusses the current state of data sharing in healthcare. It shows that data sharing is widely supported by governments, funding programs and publishers, but that there are also issues. Clinicians or researchers might be reluctant to share data, because of publication pressure and fear for competition. This might be solved by the "open science" initiatives mentioned in this paper, which need to be supported by governments as well as the scientific communities itself in order to make it a success. Next to this, there are also patient-related issues such as stricter privacy laws. A possible (technical) solution here is the use of federated data systems such as the Personal Health Train, which enable algorithms to reach out the data without having the need to bring all data together. The challenges around privacy might also be solved by non-technical means such as using standardized consent forms to enable future use of data for research and/or commercial purposes. For the (near) future, there are some more developments that might be influential on the acceptance of open science and the sharing of data. One of these developments is medical crowdsourcing [ 51], which offers hope to patients who suffer from complex health conditions or rare diseases that are difficult to diagnose. Medical crowdsourcing platforms empower patients to use the "wisdom of the crowd" by providing access to a large pool of diverse medical information. One example medical crowdsourcing platform is CrowdMed [ 52]. This platform was appreciated by some patients with undiagnosed illnesses, because they received helpful guidance from crowdsourcing their diagnoses during their difficult diagnostic journeys [ 53]. Greater participation in crowdsourcing increases the likelihood of encountering a correct solution, and this might help to encourage patients to share their data. However, more participation can also lead to more noise, making the identification of the most likely solution from a broader pool of recommendations difficult. The challenge for medical crowdsourcing platforms is to increase participation of both patients and solution providers, while simultaneously increasing the efficacy and accuracy of solutions. Moreover, caution should be taken when giving people without a medical background the power to diagnose others. Another future development is the increase in "data generalists"; experts that focus entirely on data sharing and communication [ 54]. A data generalist takes on all responsibility for the sharing of data and needs critical thinking skills to integrate, evaluate and communicate the benefits and drawbacks of providing open data. They also have a role in data analysis. The emergence of this role should encourage better sharing of data. In a time where much attention is going to the data scientist, it could be the data generalist that really has the job of the future.

Funding

This research received no external funding.

Conflicts of Interest

Tim Hulsen is employed by Philips Research.

References

9. The European Parliament and the Council of the European Union Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) Off. J. Eur. Union. 2016 Apr 27;59:1-88. [ Google Scholar]

23. Knoppers B.M., Thorogood A.M. Ethics and big data in health. Curr. Opin. Syst. Biol. 2017;4:53-57. doi: 10.1016/j.coisb.2017.07.001. [[ CrossRef] Google Scholar]

32. Hulsen T. The ten commandments of translational research informatics. Data Sci. 2019;2:341-352. doi: 10.3233/DS-190020. [[ CrossRef] Google Scholar]

46. van Soest J., Sun C., Mussmann O., Puts M., van den Berg B., Malic A., van Oppen C., Towend D., Dekker A., Dumontier M. Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data. Stud. Health Technol. Inf. 2018;247:581-585. [[ PubMed] Google Scholar]

51. Dissanayake I., Nerur S., Singh R., Lee Y. Medical Crowdsourcing: Harnessing the "Wisdom of the Crowd" to Solve Medical Mysteries. J. Assoc. Inf. Syst. 2019;20:4. [ Google Scholar]

Articles from International Journal of Environmental Research and Public Health are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

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Sharing Is Caring-Data Sharing Initiatives in Healthcare

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In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these 'big data' put together can be utilized to optimize treatments for each ...

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Czech Association for Rare Diseases

https://vzacna-onemocneni.cz/about-us/

Patient Hub - a project run by the Ministry of Healthcare

https://www.pacientskyhub.cz/en/

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Česká asociace pro vzácná onemocnění z.s.

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About us - Česká asociace pro vzácná onemocnění z.s.

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We unite people affected by rare diseases. We share experiences and help each other. We bring together patients and their families, professionals, politicians and civil servants to change the health care and social systems in their favour Mission, vision and goals The mission of the Rare Diseases Czech Republic (RDCR) is to bring together rare [...]

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Rare disease patients' experiences with data sharing - playlist

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Rare disease patients' experiences with data sharing - playlist

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https://www.youtube.com/watch?v=sVBZiRCgNCw

https://www.youtube.com/watch?v=3JKLjzVh1Xg

https://www.youtube.com/watch?v=HAn3pTNDodE

https://www.youtube.com/watch?v=OpbUPkZNub0

https://www.youtube.com/watch?v=a4CwmVueZ8c

https://www.youtube.com/watch?v=9miMGLYq-88

https://www.youtube.com/watch?v=C6nbs2G44ls

https://www.youtube.com/watch?v=fscaRh2OtXk --> very interesting

https://www.youtube.com/watch?v=aHOGCKAX7-8

https://www.youtube.com/watch?v=evC3HGI-Lnc

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Justin's Odyssey: A journey in rare disease data sharing

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pferreira@fpce.up.pt

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hernanizao@gmail.com

holiveira@uevora.pt

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ERN Animation clip for patients and health professionals

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This video explains to patients affected by rare, low prevalence and complex diseases what the European Reference Networks (ERN) are and how they might support them to identify diagnosis or treatment, in the case their health professional considers the support of the ERNs is necessary.