Access To Health Data Research Articles

A robust algorithm for authenticated health data access via blockchain and cloud computing.

In modern healthcare, providers increasingly use cloud services to store and share electronic medical records. However, traditional cloud hosting, which depends on intermediaries, poses risks to privacy and security, including inadequate control over access, data auditing, and tracking data origins. Additionally, current schemes face significant limitations such as scalability concerns, high computational overhead, practical implementation challenges, and issues with interoperability and data standardization. Unauthorized data access by cloud providers further exacerbates these concerns. Blockchain technology, known for its secure and decentralized nature, offers a solution by enabling secure data auditing in sharing systems. This research integrates blockchain into healthcare for efficient record management. We proposed a blockchain-based method for secure EHR management and integrated Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The proposed algorithm combines blockchain and smart contracts with a cloud-based healthcare Service Management System (SMS) to ensure secure and accessible EHRs. Smart contracts automate key management, encryption, and decryption processes, enhancing data security and integrity. The blockchain ledger authenticates data transactions, while the cloud provides scalability. The SMS manages access requests, enhancing resource allocation and response times. A dual authentication system confirms patient keys before granting data access, with failed attempts leading to access revocation and incident logging. Our analyses show that this algorithm significantly improves the security and efficiency of health data exchanges. By combining blockchain's decentralized structure with the cloud's scalability, this approach significantly improves EHR security protocols in modern healthcare setting.

Knowledge-Driven Data Sharing Governance: Developing a data provenance model for constructing context-aware data sharing frameworks for linkage data within the All of Us Research Program.

Health data access and sharing in the U.S. have been hindered by complex privacy and security conditions, limiting research and improvement of health outcomes. This challenge is even greater in minority and sensitive populations - who would benefit most from integrative research. The All of Us Research Program is a National Institutes of Health funded initiative aiming to enhance health research through the analysis of lifestyle, environment, and genetics data from over one million participants, with a focus on communities historically underrepresented in biomedical research. The program is enhancing the utility of data by incorporating passive data streams, such as claims, electronic health records (EHRs), environmental, geocodes, mortality, and residential history. Further, through the use of national health information exchange standards, All of Us also aims to address technical challenges in linking EHRs from health information networks to deliver a more complete longitudinal participant record. However, such an initiative necessitates a thoughtful approach through data awareness, advancing data access in ways that preserve individual privacy while increasing scientific utility. Here, we describe our privacy-preserving record linkage (PPRL) and data privacy risk disclosure control strategy, emphasizing fit-for-purpose data provisioning. Our knowledge-driven data ecosystem architecture leverages a combination of PPRL, data provenance, and geocoding at multiple levels to support context-aware data sharing granular access control.

Pushing health data access boundaries - adapting NHS governance for 3 challenges: clinical free-text, safe commercial access and AI development

This presentation will demonstrate expansions to TRE governance frameworks developed to facilitate safe data access in three advanced areas: commercial access, clinical free-text, and innovation with AI/Machine Learning. NHS research governance frameworks - originally set up for clinical trials and expanded into the TRE and secondary data use space – are not an immediate fit for advanced techniques in data science research. Over 2 years, public, expert and user engagement approaches were utilised to support developing frameworks within DataLoch, a Health and Social Care TRE and data access service in South-East Scotland. Surveys, workshops and continuous user and data controller feedback allowed the testing of tools and assessments to adapt traditional NHS governance structures. These frameworks utilise the Five Safes framework to make NHS data controllers supportive of innovative research in the public and health service’s benefit. The presentation will summarise the challenges faced for one TRE and researchers working to support these areas, and the frameworks that are now are being tested within an NHS Scotland Health Board. The development and implementation demonstrates that governance structures can be adapted collaboratively and paves the way for more flexible TRE governance to allow the use of NHS patient data to remain legally and ethically robust while supporting advanced techniques of analysis.

The ICES Indigenous Health Data Training Program: Building knowledge and strengthening capacity within Indigenous communities and organizations

Objective and ApproachThe responsible use of population-level health data requires equitable access, and considerations of Indigenous data sovereignty. There is often a baseline level of knowledge and understanding required to access data. To facilitate data access and strengthen capacity within Indigenous communities and organizations, we created the ICES Indigenous Health Data Training Program (IHDTP). ResultsThe IHDTP provides learning opportunities for Indigenous communities and organizational members to gain a better understanding of ICES data with a focus on Indigenous health. It provides an opportunity to gain practical knowledge on the research process, including how to develop a research question, understanding and interpreting research outputs, and knowledge translation. The program offers ongoing one-on-one technical support to participants, as well as the opportunity to submit a research project request for participating communities and organizations. ConclusionsThe IHDPT has been run several times, including both virtually and in-person, with a range of Indigenous partners. The materials presented in each administration of the program were tailored to the partner. Recorded sessions have also been created to allow a greater number of additional Indigenous partners to take the course flexibly. Program participants have indicated that the information learned would be used to advance issues that are important to their communities via policy, advocacy, and planning. ImplicationsThe IHDTP has enabled a variety of Indigenous partners to strengthen their understanding of health data and research to ultimately facilitate equitable and inclusive public health data access.

Will the GDPR Restrain Health Data Access Bodies Under the European Health Data Space (EHDS)?

The plans for a European Health Data Space (EHDS) envisage an ambitious and radical platform that will inter alia make the sharing of secondary health data easier. It will encourage the systematic sharing of health data and provide a legal framework for it to be shared by Health Data Access Bodies (HDABs) based in each of the Member States. Whilst this promises to bring about major benefits for research and innovation, it also raises serious questions given the intrinsic sensitivity of health data. Fears concerning privacy harms on the individual level and detrimental effects on the societal level have been raised. This article discusses two of the main protective pillars designed to allay such concerns. The first is that the proposal clearly outlines several contexts for which a Health Data Access Permit (HDAP) should and should not be granted. The second is that a request for an HDAP must also be compliant with the GDPR (inter alia requiring a valid legal basis and respecting data processing principles such as ‘minimization’ and ‘storage limitation’). As this article discusses, in some instances the need to have a valid legal basis under the GDPR may make it difficult to obtain a data access permit, in particular for some of the commercially orientated grounds outlined within the EHDS proposal. A further important issue concerns the ability of HDABs to analyse the compatibility permit requests under the GDPR and relevant national law at both speed and scale.

Toward the European Health Data Space: The IMPaCT-Data secure infrastructure for EHR-based precision medicine research

Background:Art. 50 of the proposal for a Regulation on the European Health Data Space (EHDS) states that “health data access bodies shall provide access to electronic health data only through a secure processing environment, with technical and organizational measures and security and interoperability requirements”. Objective:To identify specific security measures that nodes participating in health data spaces shall implement based on the results of the IMPaCT-Data project, whose goal is to facilitate the exchange of electronic health records (EHR) between public entities based in Spain and the secondary use of this information for precision medicine research in compliance with the General Data Protection Regulation (GDPR). Data and methods:This article presents an analysis of 24 out of a list of 72 security measures identified in the Spanish National Security Scheme (ENS) and adopted by members of the federated data infrastructure developed during the IMPaCT-Data project. Results:The IMPaCT-Data case helps clarify roles and responsibilities of entities willing to participate in the EHDS by reconciling technical system notions with the legal terminology. Most relevant security measures for Data Space Gatekeepers, Enablers and Prosumers are identified and explained. Conclusion:The EHDS can only be viable as long as the fiduciary duty of care of public health authorities is preserved; this implies that the secondary use of personal data shall contribute to the public interest and/or to protect the vital interests of the data subjects. This condition can only be met if all nodes participating in a health data space adopt the appropriate organizational and technical security measures necessary to fulfill their role.

The Growing Impact of Natural Language Processing in Healthcare and Public Health.

Natural Language Processing (NLP) is a subset of Artificial Intelligence, specifically focused on understanding and generating human language. NLP technologies are becoming more prevalent in healthcare and hold potential solutions to current problems. Some examples of existing and future uses include: public sentiment analysis in relation to health policies, electronic health record (EHR) screening, use of speech to text technology for extracting EHR data from point of care, patient communications, accelerated identification of eligible clinical trial candidates through automated searches and access of health data to assist in informed treatment decisions. This narrative review aims to summarize the current uses of NLP in healthcare, highlight successful implementation of computational linguistics-based approaches, and identify gaps, limitations, and emerging trends within the subfield of NLP in public health. The online databases Google Scholar and PubMed were scanned for papers published between 2018 and 2023. Keywords "Natural Language Processing, Health Policy, Large Language Models" were utilized in the initial search. Then, papers were limited to those written in English. Each of the 27 selected papers was subject to careful analysis, and their relevance in relation to NLP and healthcare respectively is utilized in this review. NLP and deep learning technologies scan large datasets, extracting valuable insights in various realms. This is especially significant in healthcare where huge amounts of data exist in the form of unstructured text. Automating labor intensive and tedious tasks with language processing algorithms, using text analytics systems and machine learning to analyze social media data and extracting insights from unstructured data allows for better public sentiment analysis, enhancement of risk prediction models, improved patient communication, and informed treatment decisions. In the recent past, some studies have applied NLP tools to social media posts to evaluate public sentiment regarding COVID-19 vaccine use. Social media data also has the capacity to be harnessed to develop pandemic prediction models based on reported symptoms. Furthermore, NLP has the potential to enhance healthcare delivery across the globe. Advanced language processing techniques such as Speech Recognition (SR) and Natural Language Understanding (NLU) tools can help overcome linguistic barriers and facilitate efficient communication between patients and healthcare providers.

Population Neuroscience: Strategies to Promote Data Sharing While Protecting Privacy.

Population neuroscience aims to advance our understanding of how genetic and environmental factors influence brain development and brain health over the life span, by integrating genomics, epidemiology, and neuroscience at population scale. This big data approach depends on data sharing strategies at both the micro- and macro-level, as well as attention to effective data management and protection of participant privacy. At the micro-level, researchers participate in international consortia that support collaboration, standards, and data sharing. They also seek to link together cohort studies, administrative health databases, and measures of the physical, built, and social environment in creative ways. Large-scale, longitudinal, and multi-modal cohorts are being designed to support explorations of genetic and environmental impacts on the brain. At a macro-level, funding agency policies now require data across health research domains to be managed according to the FAIR (findable, accessible, interoperable, and re-useable) Data principles and made available to the research community in a timely manner to support reproducibility and re-use. Data repositories provide technical infrastructure for storing, accessing, and increasingly also analyzing rich population-level data. Federated and cloud-based approaches are being leveraged to improve the security, remote accessibility, and performance of repositories. Finally, legal frameworks are being developed to facilitate secure health data access, integration, and analysis, providing new opportunities for the field.

Health data security sharing method based on hybrid blockchain

With the rapid development of Internet of Things(IoT) technology, it is more and more common to use IoT devices to obtain health data. Health data sharing is a crucial component of health data utilization, yet it encounters serious privacy and security challenges. How to solve the problem of data security is the key factor to encourage users to participate, and existing data sharing methods generally have the problem of single point of failure. Based on this, this paper proposes a hybrid blockchain based health data sharing method HSHB, which divides sharing transactions into private chain data sharing and alliance chain data sharing according to different sharing entities. According to the identity and access purpose of different entities, HSHB has constructed the health data access control policy HDAC to avoid sharing interference among different entities. By building a b+ tree index of health data, HSHB provides users with an efficient and stable query scheme for historical health data. In addition, agent re-encryption and smart contract technology are used to share health data among institutions while ensuring the security of health data. Finally, the HSHB scheme is tested on XuperChain. The experimental results show that the health data sharing scheme HSHB proposed in this paper has advantages in computing overhead, throughput, and latency.

‘There’s a lot less time on small talk’: Rural patient perspectives on shifting to technology-enabled healthcare in Canada during COVID-19

The objective of this qualitative research study was to explore system, provider, and patient level factors from the perspective of rural-living citizens in Canada and how these factors influenced their telehealth experiences. Participants were recruited in follow-up to an online survey which asked for interest in participation in focus groups to talk about telehealth experiences. Twenty-two rural citizens participated in one of five focus groups. The qualitative data from the focus groups were thematically analyzed. The overarching theme that described rural participants’ experiences of telehealth during the pandemic was navigating the shifting care model. Two main themes were constructed from the data: shifts in the patient-provider relationship and mismatch between the telehealth requirements and provider and system support. Relational shifts involved a transactional or business-like relationship with their providers, that was reflected in changes in etiquette practices, personalization of care, and communication dynamics. Mismatch in telehealth system requirements and support was reflected in shifting personal and infrastructure technology requirements, blurred boundaries of health data access and privacy, and shifting appointment logistics. Continued use and expansion of technology-enabled healthcare must consider patient perspectives.

Health Data Sharing towards Knowledge Creation

Data sharing and service reuse in the health sector pose significant privacy and security challenges. The European Commission recognizes health data as a unique and cost-effective resource for research, while the OECD emphasizes the need for privacy-protecting data governance systems. In this paper, we propose a novel approach to health data access in a hospital environment, leveraging homomorphic encryption to ensure privacy and secure sharing of medical data among healthcare entities. Our framework establishes a secure environment that enforces GDPR adoption. We present an Information Sharing Infrastructure (ISI) framework that seamlessly integrates artificial intelligence (AI) capabilities for data analysis. Through our implementation, we demonstrate the ease of applying AI algorithms to treated health data within the ISI environment. Evaluating machine learning models, we achieve high accuracies of 96.88% with logistic regression and 97.62% with random forest. To address privacy concerns, our framework incorporates Data Sharing Agreements (DSAs). Data producers and consumers (prosumers) have the flexibility to express their prefearences for sharing and analytics operations. Data-centric policy enforcement mechanisms ensure compliance and privacy preservation. In summary, our comprehensive framework combines homomorphic encryption, secure data sharing, and AI-driven analytics. By fostering collaboration and knowledge creation in a secure environment, our approach contributes to the advancement of medical research and improves healthcare outcomes. A real case application was implemented between Portuguese hospitals and universities for this data sharing.

Secondary Use Provisions in the European Health Data Space Proposal and Policy Recommendations for Korea.

This article explores the secondary use provisions of the European Health Data Space (EHDS), proposed by the European Commission in May 2022, and offers policy recommendations for South Korea. The authors analyzed the texts of the EHDS proposal and other documents published by the European Union, as well as surveyed the relevant literature. The EHDS proposal seeks to create new patient rights over electronic health data collected and used for primary care; and establish a data sharing system for the re-use of electronic health data for secondary purposes, including research, the provision of personalized healthcare, and developing healthcare artificial intelligence (AI) applications. These provisions envisage requiring both private and public data holders to share certain types of electronic health data on a mandatory basis with third parties. New government bodies, called health data access bodies, would review data access applications and issue data permits. The overarching aim of the EHDS proposal is to make electronic health data, which are currently held in the hands of a small number of organizations, available for re-use by third parties to stimulate innovation and research. While it will be very challenging for South Korea to adopt a similar scheme and require private entities to share their proprietary data with third parties, the South Korean government should consider making at least health data collected through publicly funded research more readily available for secondary use.

Interoperability and governance in the European Health Data Space regulation

The proposal for a regulation on the European Health Data Space (EHDS) is a much-awaited project. It aspires to create a harmonised framework – a common European data space – for the administration of health data (primary use) across Member States and the promotion of healthcare research and innovation (by establishing rules for the secondary use of health data). As such, although the EHDS proposal is a legal document, in its essence, it includes provisions that introduce not only legal, but also institutional, and technical-infrastructural changes. Overall, together with the Regulation 2017/745 on medical devices, the Data Governance Act (DGA), the Data Act, the AI Act, and the General Data Protection Regulation (GDPR), the EHDS proposal will complete the regulatory canvas for the use of health data in the European Union. Although we are supportive of the EHDS initiative, there are aspects of the proposal that require further debate, reconsideration, and amendments. Following previous work on potential power asymmetries encapsulated in the Proposal, in this commentary, we focus on the provisions of/for interoperability of the Electronic Health Record (EHR) systems (Ar. 14–32) as well as the provisions on the structure of Health Data Access bodies and their cross-border organisation (section 3). We recommend a series of amendments to orientate the EHDS project better to its constitutive goals: the promotion of public health research and respect for the rights of the individuals.

Influential Article Review - Approaches for Integrated Medical Infrastructure Utilizing Blockchain Technology

This paper examines system innovation. We present insights from a highly influential paper. Here are the highlights from this paper: With a growing trend in medicine towards individualized, patient-centric care, traditional health information technology limits progress. With high administrative costs and the lack of universal data access, contemporary electronic medical records serve more the institution rather than the patient. Blockchain technology, as presently described, was initially developed for use in financial markets, serving as a decentralized, distributed ledger of transactions. However, certain inherent characteristics of this technology suit it for use in the healthcare sector. Potential applications of the blockchain in medicine include interoperable health data access, data storage and security, value-based payment mechanisms, medical supply chain efficiency, amongst others. While the technology remains in nascent stages, it is essential that members of the healthcare community understand the fundamental concepts behind blockchain and recognize its potential impact on the future of medical care. For our overseas readers, we then present the insights from this paper in Spanish, French, Portuguese, and German.

Evolution or Revolution? Recommendations to Improve the Swiss Health Data Framework.

Background: Facilitating access to health data for public health and research purposes is an important element in the health policy agenda of many countries. Improvements in this sense can only be achieved with the development of an appropriate data infrastructure and the implementations of policies that also respect societal preferences. Switzerland is a revealing example of a country that has been struggling to achieve this aim. The objective of the study is to reflect on stakeholders' recommendations on how to improve the health data framework of this country.Methods: We analysed the recommendations collected as part of a qualitative study including 48 expert stakeholders from Switzerland that have been working principally with health databases. Recommendations were divided in themes and subthemes according to applied thematic analysis.Results: Stakeholders recommended several potential improvements of the health data framework in Switzerland. At the general level of mind-set and attitude, they suggested to foster the development of an explicit health data strategy, better communication and the respect of societal preferences. In terms of infrastructure, there were calls for the creation of a national data center, the improvement of IT solutions and the use of a Unique Identifier for patient data. Lastly, they recommended harmonising procedures for data access and to clarify data protection and consent rules.Conclusion: Recommendations show several potential improvements of the health data framework, but they have to be reconciled with existing policies, infrastructures and ethico-legal limitations. Achieving a gradual implementation of the recommended solutions is the preferable way forward for Switzerland and a lesson for other countries that are also seeking to improve health data access for public health and research purposes.

The challenges and opportunities of mental health data sharing in the UK

The challenges and opportunities of mental health data sharing in the UK

A Sensitive Data Access Model in Support of Learning Health Systems

Given the ever-growing body of knowledge, healthcare improvement hinges more than ever on efficient knowledge transfer to clinicians and patients. Promoted initially by the Institute of Medicine, the Learning Health System (LHS) framework emerged in the early 2000s. It places focus on learning cycles where care delivery is tightly coupled with research activities, which in turn is closely tied to knowledge transfer, ultimately injecting solid improvements into medical practice. Sensitive health data access across multiple organisations is therefore paramount to support LHSs. While the LHS vision is well established, security requirements to support them are not. Health data exchange approaches have been implemented (e.g., HL7 FHIR) or proposed (e.g., blockchain-based methods), but none cover the entire LHS requirement spectrum. To address this, the Sensitive Data Access Model (SDAM) is proposed. Using a representation of agents and processes of data access systems, specific security requirements are presented and the SDAM layer architecture is described, with an emphasis on its mix-network dynamic topology approach. A clinical application benefiting from the model is subsequently presented and an analysis evaluates the security properties and vulnerability mitigation strategies offered by a protocol suite following SDAM and in parallel, by FHIR.

Value from health data: European opportunity to catalyse progress in digital health

Value from health data: European opportunity to catalyse progress in digital health

Solidarity and Data Access: Challenges and Potentialities

This paper provides an account of the challenges and potentialities of a solidarity-based approach to data access and governance. To do that, it offers an infraethical understanding of solidarity that describes it as a structural moral enabler that can sustain collective action and risk taking. The paper ends with a brief discussion of health data access as a possible case study to test this approach.

Using Self-Study and Peer-to-Peer Support to Change "Sick" Care to "Health" Care: The Patient Perspective.

Background: Access to digital health technologies is contributing to a paradigm shift where sickcare may become authentic healthcare. Individuals can now access personal health data through wearable sensors, affordable lab screenings, genetic and genomic sequencing, and real-time health tracking apps. Personal health data access creates opportunities to study health indicators 24/7 and in real time. This is especially useful for patients with hard-to-diagnose or treat diseases, which led to a self-formed patient group called Project Apollo. Project Apollo is composed of highly motivated patients with common experiences of undiagnosed conditions, a lack of clear treatment options, and shared frustrations with navigating the U.S. healthcare system. These experiences have led the Apollo cohort to supplement their health knowledge through self-study research.Objective: To qualify the experience and expectations of patients affiliated with Project Apollo.Methods: A qualitative approach involved record review and semi-structured interviews. One-hour semi-structured interviews were conducted to solicit motivations, expectations, and potential barriers and facilitators to self-study followed by a brief survey on digital tool use. Interviews were digitally recorded, transcribed, and analyzed to identify themes and patterns.Results: Participants included six females and three males ranging in age from 30 to 70+ years. Responses were organized under five key themes including: frustration with healthcare system; community support; self-study/N-of-1 research; access to experts; moving from sick to healthcare. Facilitators include motivation, albeit stemming from frustration, a safe community where patients derive support, and access to experts for guidance. Increasing awareness of clinicians about the potential value of partnering with patients who are advancing health knowledge through self-study is critical.Conclusions: N-of-1 self-study research, coupled with community support and digital health tools, appears to be one plausible pathway to shifting the paradigm from sickcare toward patient-partnered healthcare.