Sharing Health Data Research Articles

Knowledge and attitudes of the Italian population on sharing health data: a cross-sectional study

Abstract Background Healthcare digitization enhances data accessibility but faces data management and privacy challenges. The study sought to explore the Italian public’s understanding and views on storing and sharing health data for treatment and research purposes, as well as factors influencing their attitudes toward health data management and sharing. Methods A cross-sectional questionnaire assessed socio-demographic information, knowledge about health data sharing, and attitudes toward sharing data for treatment and scientific purposes. Descriptive analyses and multivariable logistic regressions were performed to examine the associations between sociodemographic factors and knowledge/attitudes about data storage and sharing. Results A total of 1,389 citizens participated. Most respondents (65.4%) thought that healthcare providers could access personal health data nationwide, while 94% expressed willingness to share personal health data nationwide. A high percentage of respondents (73%) fully agreed that storing and sharing personal health-related data could improve research and quality of care. Males and younger individuals (<41 years) were likelier to have higher data-sharing knowledge (adjusted Odds Ratio [adjOR] 1.50, 95% Confidence Interval (CI) 1.03 - 2.18, and adjOR 1.99, 95% CI 1.29 - 3.08 respectively). Lower educational-level respondents exhibited lower positive attitudes towards sharing health data for treatment and research purposes (adjOR 0.18, 95% CI 0.05-0.58 for high school, and adjOR 0.11, 95% CI 0.03-0.41 for middle school or lower, compared to those with a university degree). Conclusions The findings offer valuable perspectives for European policymakers, healthcare practitioners, and researchers who aim to enhance data handling, foster cooperation, and maximize the benefits of health data for tailored care and scientific progress. Key messages • Italian citizens show strong willingness to share health data nationwide for treatment and research, suggesting a favorable environment for collaborative healthcare initiatives. • Sociodemographic factors such as age and education level influence attitudes towards health data sharing, stressing interventions to ensure equitable access and participation in data-driven healthcare.

Exploring Electronic Personal Health Record usage in Northern Italy: a cross-sectional study

Abstract Background The Electronic Personal Health Record (EPHR) is a patient-centered tool that enables citizens to manage their health information. The EPHR has been implemented worldwide, including in Italy, and holds immense potential, but it has shown a generalized underutilisation. Therefore, this paper aims to identify its fulfillment and the reasons behind its poor use, potentially uncovering the key to unlocking its benefits. Methods A survey was distributed on social media and in the waiting rooms of vaccination facilities in a Region of Italy. Multivariable regression models were conducted to detect a connection between the general population’s usage of the EPHR and specific independent variables. Results Of the 1634 respondents, 710 activated the EPHR, especially women, Italians, highly educated, unemployed, with good health but affected with chronic diseases. Amongst those who activated the EPHR, the multivariate analysis showed that the number of accesses to the platform was significantly associated with lower age (adjusted Odds Ratio (adjOR) 0.96 p = 0.015), higher level of education (adjOR 2.17 p = 0.029), unemployment (adjOR 5.61 p = 0.008) and considering the information on the EPHR complete (adjOR 3.83 p = 0.001). As age increases, the perceived ease of access to the EPHR decreases (adjOR=0.98 p = 0.049). Having a higher level of education (adjOR=0.41 p = 0.042), autonomy in reading health materials (adjOR=0.32 p = 0.043), and less self-confidence in filling out medical forms (adjOR=0.61 p = 0.033) were linked to a decrease in perceived ease of access. Conclusions The study showed that the EPHR is more utilised by specific categories of citizens, especially the young and those with higher education. More efforts should be made to increase access to the EPHR for the whole community by making it more intuitive and user-friendly. The present study provides valuable foundations to foster the implementation of a platform for sharing health data at a European level. Key messages • The Electronic Personal Health Record (EPHR) is underutilised despite its potential; it’s mainly accessed by young and highly educated individuals. • Efforts to increase EPHR usage must focus on improving accessibility and ease of access, as well as enhancing user confidence and autonomy in managing health information.

Proficiency in Personalized Medicine and Health Data Sharing in the EU

Abstract Personalized medicine, leveraging genetic, environmental, and lifestyle data, has already transformed healthcare by tailoring prevention, diagnosis, and treatment to individual patients. The successful development and implementation of personalized approaches rely on the public’s proficiency and awareness of personalized medicine, enabling access to innovative techniques and fostering a willingness to share health-related data. We distributed a survey to 6,581 respondents from 8 EU countries: France, Germany, the Netherlands, Italy, Spain, Poland, Hungary, and Romania. 52.5% of respondents were female (n = 3,458), with a mean age of 48.5 years (range 18-89 years, median = 49 years, SD = 15.96), and 37.91% of the participants reported achieving tertiary education. Our survey investigates the general public’s knowledge of personalized medicine, support for genetic testing, and willingness to share health data among EU citizens while assessing their perception and acceptance of these medical paradigms. Knowledge levels vary among different EU countries. Only 12.11% of respondents had a high knowledge of the topics presented in this survey. Support for implementing genetic testing in healthcare was high, with 81.52% supporting the implementation of genetic testing in their healthcare systems, and nuanced differences in acceptance were observed based on testing purposes. 52.35% reported willingness to share health data. Both support for implementing genetic testing and the willingness to share health data correlated positively with knowledge and education levels. Geographical differences within the EU highlighted variations in attitudes toward personalized medicine and data sharing, with respondents from Southern Europe displaying higher odds than their peers in Central and Eastern Europe. The results emphasize the need for targeted communication and education strategies to enhance public understanding and trust in personalized medicine and health data sharing. Key messages • High support for genetic testing across the EU highlights the need for enhanced public education on personalized medicine to boost understanding and data sharing willingness. • Geographical differences in attitudes toward personalized medicine in the EU suggest targeted strategies to build trust and knowledge, particularly in Central Europe.

Public Perceptions and Engagement in mHealth Across Eight European Countries

Abstract Background Mobile health technologies have the potential to revolutionize healthcare delivery and patient engagement. This study explores public attitudes toward health app usage and data sharing across eight European countries, addressing a significant gap in understanding the public’s perspective on mHealth adoption. Methods A cross-sectional survey was conducted involving 6,581 participants from Italy, the Netherlands, France, Germany, Spain, Poland, Romania, and Hungary. The survey assessed current health app usage, future interest, willingness to share health data, and concerns about data privacy and security. Results 21.87% of respondents currently use health apps, with 42.71% expressing interest in future use. Regarding data sharing, 52.82% are willing to share health data with healthcare providers, and 25.48% would share data with research institutions. However, 53.18% fear unauthorized use or hacking of their health data. Significant generational and geographical differences in mHealth engagement were observed, with older generations showing higher willingness to adopt health apps OR 1.22 (CI 1.03 - 1.46) for Baby Boomers, OR 1.45 (CI 0.97 - 2.16) for Silent Generation. Education level emerged as a crucial factor, with tertiary-educated individuals more likely to use health apps (OR 1.41 CI 1.24 - 1.58) and demand transparency (OR 1.61 CI 1.45 -1.78). Conclusions The findings underscore the need for targeted strategies to enhance digital literacy, ensure data privacy, and promote equitable access to mHealth technologies across Europe. Policymakers and healthcare stakeholders should prioritize these areas to foster greater public trust and engagement in mHealth, ultimately improving health outcomes and patient care. Key messages • Public attitudes toward mHealth vary significantly across generations, countries, and education levels in Europe. • Addressing data privacy concerns and enhancing digital literacy are crucial for widespread mHealth adoption.

A Welfare Test for Sharing Health Data

A Welfare Test for Sharing Health Data

Anomalous Network Behaviour Detection in Interoperable Health Systems using Machine Learning in Resource Limited Areas

The connection of devices in distributed environments produces and shares a vast amount of data useful for different organisational decision-making. In healthcare service organisations, for example, multiple e-health systems from different departments or facilities connect and share health data and information. During sharing, proper management is important to ensure the information is secure against intruders. Machine learning as a non-conventional security technique can be used along conventional techniques like firewalls, antivirus and intrusion detection systems to predict future network threats and other anomalies using historical backgrounds and other features. However, some machine learning algorithms have complex computation thus requiring resourceful systems in terms of network bandwidth, CPU power, memory, and storage capacity. In resource-constrained environments, therefore, special consideration is needed to ensure that the analysis of the big data is successful and that the benefits associated with them are effectively obtained. In this paper, a Machine Learning algorithm was selected among four algorithms whose performance was compared through various performance metrics. Classification accuracy, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Absolute Error (RAE) among other performance metrics were used to compare the ANN, Random forest, Decision trees, and Naïve byes classification algorithms using an extract from CICDDOS2019 dataset. Using the Weka version 3.8.6, the algorithms were compared to choose the best one to classify the data. By using three computers with different resources, the experiments were carried out to determine the performance of those machine learning algorithms. The result revealed that the random forest produced a good average classification performance in resource-limited systems since it surpassed other algorithms in classifying the data at an average of 99 per cent with a low average mean absolute error of 0.0001. Furthermore, as an ensemble that classifies with multiple decision trees algorithm, it likewise uses reasonable time to build and test the model therefore recommended for resource-limited systems.

Nordic Citizens' Willingness to Share Digital Health Data.

The quality of the digital healthcare systems relies on citizens' willingness to share their digital health data. This makes citizens' use, perceptions, and attitudes towards digital healthcare systems pivotal. The study presented here examines Nordic citizens' willingness to share digital health data with healthcare providers and for research purposes. A cross-sectional study design was applied to obtain answers from citizens in Denmark, Finland, Iceland, Norway, and Sweden. The results are based on answers from 5078 citizens across the five countries. Results based on descriptive statistics indicate that the majority of Nordic citizens are willing to share health data that has clinical relevance with healthcare providers and for research purposes. The odds ratio analysis reveals that citizens' odds of sharing health data decreases with age and increases with the level of education. Conclusively, this study shows that most Nordic citizens are willing to share their health data, influenced by age and level of education. Awareness of and efforts to support citizens who are unable or unwilling to actively use and engage with the digital healthcare system is recommended.

Evaluating Patient Perceptions of Smartphone Use for Active and Passive Collection of Health Data

Background: The objectives of this study are to assess patient willingness to share smartphone generated health data with providers and to characterize patient populations who express hesitation around providing these data to providers. Methods: Bivariate analysis involved unpaired t-test or analysis of variance for comparing continuous variables. An alpha value of 0.05 was used. A multivariable regression to investigate demographic factors associated with comfort with sharing health data was performed. Odds ratios compared participants by age group, educational attainment, and race. Results: A total of 485 participants completed the survey, with an average age of 38 years old. Respondents were 65% male and 73% Caucasian. The majority (70%) of participants were comfortable answering questions about their health on their phone, as well as sending pictures of their health problems or wounds. Over half of participants were comfortable with passive social and physical activity information being sent to their doctor and would allow their doctors to download a data tracking application on their smartphone (55% and 59%, respectively). Younger participants (OR 2.0, P<0.05) and participants with increased education (OR 2.5, P < 0.01) were more likely to be comfortable in sharing their data. Privacy concerns were cited as the most common reason patients desired to refrain from sharing data. Conclusion: Most study participants were comfortable sharing smartphone generated health data.

Person-centred data sharing: Empirical studies in private individuals’ attitudes

Background Recognising the power of data analytics, researchers are anxious to gain access to personal data either directly from data subjects or via research data sets. This requires a secure environment, such as a trusted research environment (TRE). However, it is unclear how the data subjects themselves regard sharing their data with TREs, especially if research goals are difficult to specify upfront or data are used for secondary purposes, making informed consent difficult to manage. We review three empirical studies to throw some light on individual attitudes to sharing health data. Methods Three anonymous, online surveys were run. The first involving 800 UK residents aimed at understanding how participants view the health data security. The second involving 500 UK residents aimed at identifying private individual views on privacy. These two surveys used a crowdsourcing platform. The third involved 1086 students at a UK university reporting their engagement with a trial diagnostic method for SARS-CoV-2. Results The first survey demonstrated that private individuals could make security decisions though they usually assume the recipient of their personal data to be responsible for all aspects of keeping the data safe. The second highlighted that individuals were aware of privacy risks but are motivated to share their data based on different contextual assumptions. The third, involving the incidental sharing of sensitive data during the SARS-CoV-2 pilot highlighted that prosocial motivations override potential personal benefit of such testing. Conclusions The three, unconnected surveys make clear that there are tensions between private individual understanding of data security and privacy risk, on the one hand, and how they behave, on the other. Respondents rely on data stewards to keep their data safe, though are likely to share even sensitive data for prosocial benefit. These findings have implications for those offering TRE services for research.

Intelligent Health Monitoring in Smart Homes

Intelligent Health Monitoring in Smart Homes is a burgeoning field that integrates advanced technologies to revolutionize healthcare delivery within residential environments. This paper explores the concept of smart homes as proactive health monitoring ecosystems, leveraging interconnected devices, sensors, and data analytics to monitor residents' health parameters and behaviors in real-time. Through a comprehensive review of existing literature and case studies, this study elucidates the potential benefits, challenges, and emerging trends in intelligent health monitoring within smart home environments.Key components of intelligent health monitoring systems, including wearable devices, ambient sensors, and machine learning algorithms, are examined in detail, highlighting their roles in detecting early signs of health deterioration, predicting health-related events, and facilitating timely interventions. Furthermore, the integration of telemedicine platforms and communication technologies enhances remote patient monitoring and enables seamless interaction between residents and healthcare providers.The paper also addresses critical considerations such as privacy, data security, and interoperability standards to ensure the ethical and responsible implementation of smart health monitoring solutions. By fostering collaboration among healthcare professionals, technology developers, and policymakers, intelligent health monitoring in smart homes has the potential to improve health outcomes, enhance quality of life, and reduce healthcare costs. The integration of artificial intelligence, Internet of Things (IoT) devices, and cloud computing enables seamless communication and coordination among various components of the smart home ecosystem. Through real-time monitoring and analysis, potential health risks can be identified early, enabling timely interventions and proactive healthcare management. Additionally, the ability to remotely access and share health data facilitates collaborative care and empowers individuals to take control of their own health and well-being. However, the widespread adoption of intelligent health monitoring in smart homes also raises important ethical, privacy, and regulatory considerations. Safeguarding the security and privacy of sensitive health data, ensuring transparency and accountability in algorithmic decision-making, and promoting equitable access to technology are essential aspects that require careful attention and proactive measures. In conclusion, Intelligent Health Monitoring in Smart Homes holds tremendous potential to revolutionize healthcare delivery, promoting preventive care, empowering individuals, and ultimately improving health outcomes. By addressing technical, ethical, and regulatory challenges, this transformative approach can pave the way for a more connected, proactive, and personalized healthcare experience.This paper studies recent state-of-the-art research on the field of IoT for health monitoring and smart homes, examines several potential use-cases of blending the technology, and proposes integration with an existing smart home test bed for further study. Challenges of adoption and future research on the topic are also discussed, Keywords: Intelligent Health Monitoring, Smart Homes, Healthcare, IoT, Machine Learning, Data Analytics, Remote Monitoring, Proactive Healthcare, Privacy, Ethics, Regulatory Compliance.

Balancing stakeholder interests and paradoxes in health data sharing within health ecosystems

Personal health data sharing can facilitate value co-creation between multiple stakeholders, including individuals, public organisations, private companies, research institutes, and policymakers. Yet, when companies are involved in sharing health data to develop health-related solutions, it can lead to conflicts and contradictions between stakeholders. We apply a qualitative research approach over two cases in the Finnish healthcare sector to explore the tensions and contradictions in sharing personal health data that companies can utilise in the development of new products/or services in the healthcare sector. We identify the tensions and paradoxes from multiple stakeholders’ perspectives and provide management approaches on three levels: (I) The micro-level focusing on individuals as users, (II) The meso-level concerns businesses operating within the digital health ecosystem. (III) The macro-level addresses broader societal impact and policies governing the secondary use of health data.

An evaluation of the replicability of analyses using synthetic health data

Synthetic data generation is being increasingly used as a privacy preserving approach for sharing health data. In addition to protecting privacy, it is important to ensure that generated data has high utility. A common way to assess utility is the ability of synthetic data to replicate results from the real data. Replicability has been defined using two criteria: (a) replicate the results of the analyses on real data, and (b) ensure valid population inferences from the synthetic data. A simulation study using three heterogeneous real-world datasets evaluated the replicability of logistic regression workloads. Eight replicability metrics were evaluated: decision agreement, estimate agreement, standardized difference, confidence interval overlap, bias, confidence interval coverage, statistical power, and precision (empirical SE). The analysis of synthetic data used a multiple imputation approach whereby up to 20 datasets were generated and the fitted logistic regression models were combined using combining rules for fully synthetic datasets. The effects of synthetic data amplification were evaluated, and two types of generative models were used: sequential synthesis using boosted decision trees and a generative adversarial network (GAN). Privacy risk was evaluated using a membership disclosure metric. For sequential synthesis, adjusted model parameters after combining at least ten synthetic datasets gave high decision and estimate agreement, low standardized difference, as well as high confidence interval overlap, low bias, the confidence interval had nominal coverage, and power close to the nominal level. Amplification had only a marginal benefit. Confidence interval coverage from a single synthetic dataset without applying combining rules were erroneous, and statistical power, as expected, was artificially inflated when amplification was used. Sequential synthesis performed considerably better than the GAN across multiple datasets. Membership disclosure risk was low for all datasets and models. For replicable results, the statistical analysis of fully synthetic data should be based on at least ten generated datasets of the same size as the original whose analyses results are combined. Analysis results from synthetic data without applying combining rules can be misleading. Replicability results are dependent on the type of generative model used, with our study suggesting that sequential synthesis has good replicability characteristics for common health research workloads.

Health data sharing attitudes towards primary and secondary use of data: a systematic review

To receive the best care, people share their health data (HD) with their health practitioners (known as sharing HD for primary purposes). However, during the past two decades, sharing for other (i.e., secondary) purposes has become of great importance in numerous fields, including public health, personalized medicine, research, and development. We aimed to conduct the first comprehensive overview of all studies that investigated people's HD sharing attitudes-along with associated barriers/motivators and significant influencing factors-for all data types and across both primary and secondary uses. We searched PubMed, MEDLINE, PsycINFO, Web of Science, EMBASE, and CINAHL for relevant studies published in English between database inception and February 28, 2023, using a predefined set of keywords. Studies were included, regardless of their design, if they reported outcomes related to attitudes towards sharing HD. We extracted key data from the included studies, including the type of HD involved and findings related to: HD sharing attitudes (either in general or depending on type of data/user); barriers/motivators/benefits/concerns of the study participants; and sociodemographic and other variables that could impact HD sharing behaviour. The qualitative synthesis was conducted by dividing the studies according to the data type (resulting in five subgroups) as well as the purpose the data sharing was focused on (primary, secondary or both). The Newcastle-Ottawa Scale (NOS) was used to assess the quality of non-randomised studies. This work was registered with PROSPERO, CRD42023413822. Of 2109 studies identified through our search, 116 were included in the qualitative synthesis, yielding a total of 228,501 participants and various types of HD represented: person-generated HD (n=17 studies and 10,771 participants), personal HD in general (n=69 studies and 117,054 participants), Biobank data (n=7 studies and 27,073 participants), genomic data (n=13 studies and 54,716 participants), and miscellaneous data (n=10 studies and 18,887 participants). The majority of studies had a moderate level of quality (83 [71.6%] of 116 studies), but varying levels of quality were observed across the included studies. Overall, studies suggest that sharing intentions for primary purposes were observed to be high regardless of data type, and it was higher than sharing intentions for secondary purposes. Sharing for secondary purposes yielded variable findings, where both the highest and the lowest intention rates were observed in the case of studies that explored sharing biobank data (98% and 10%, respectively). Several influencing factors on sharing intentions were identified, such as the type of data recipient, data, consent. Further, concerns related to data sharing that were found to be mutual for all data types included privacy, security, and data access/control, while the perceived benefits included those related to improvements in healthcare. Findings regarding attitudes towards sharing varied significantly across sociodemographic factors and depended on data type and type of use. In most cases, these findings were derived from single studies and therefore warrant confirmations from additional studies. Sharing health data is a complex issue that is influenced by various factors (the type of health data, the intended use, the data recipient, among others) and these insights could be used to overcome barriers, address people's concerns, and focus on spreading awareness about the data sharing process and benefits. None.

Developing a Trustworthy Cloud Service Framework for Cloud Computing Security

Cloud computing is quickly becoming an essential platform for sharing infrastructure, software, apps, and corporate resources. Cloud computing has many advantages, but users still have a lot of questions about the dependability and safety of cloud services. Concerns about the hazards associated with the possible exploitation of this technology to undertake criminal operations might threaten the undeniable success of cloud computing. To ensure happy customers, the cloud model must prioritize safety, openness, and dependability.Its main purpose is data security, which concerns everyone contemplating cloud services. A cloud-based assault protection system will safeguard data, communications, and information.According to studies, the recommended technique is successful, however updating tags and blocks when data is amended requires computation and communication expenses. Scalability, data secrecy, and decentralized double encryption improve security.The proposed method employs cloud servers for computation-intensive tasks and protects data content by depriving data owners and users of privilege information. Also ensures responsibility. Sharing health data on the cloud is feasible, cost-effective, efficient, adaptive, and better for individuals. This"Advanced Encryption Standard with Lightweight Cipher-text-Identity and Attribute-based Encryption" (AES-lightweight CP-ABE) aims to protect sensitive data.

Bioinformatics: Using “Big” Data to Solve Health Mysteries

Health data—information from sources like medical records, surveys, and even electronic devices like smartwatches—are becoming increasingly important for keeping people healthy. Computers and the internet make it easy to store and share health data. Scientists and researchers can use these data to understand and prevent diseases or to develop better treatments. To do so, they combine biology, computer science, and math to understand data and find patterns. But using health data is not easy. Scientists must first find the right information among the many data sources available. They also need to make sure the data are “clean” and correct. Once health data are collected and checked, scientists analyze those data to make important discoveries. Health data are both personal and valuable, so they must be kept safe and private. By protecting people’s privacy, we encourage even more data sharing, which helps scientists learn even more and continue to improve human health.

How Health Data Integrity Can Earn Trust and Advance Health

Efforts to share health data across borders snag on legal and regulatory barriers. Before detangling the fine print, let’s agree on overarching principles.

BJWT-EHR: A novel JWT based Blockchain System for Electronic Health Records

Since most transactions in the world now take place digitally, it is crucial to maintain data integrity, share data securely, and provide quick access to it. Our reliance on computers in our daily lives is growing, and new data will inevitably be generated to meet this need. Providing for safe data exchange is becoming increasingly crucial every day. Maintaining and safeguarding the security of this data is crucial. Recently, the term "blockchain" has been used extensively for data security. A decentralized digital ledger called blockchain is in charge of preserving data integrity and facilitating safe data sharing. Blockchain transfers data by the principle of transparency, records data one way, and prevents reverse engineering. According to reports, blockchain is the general term for the technology that promotes common data sharing of these recorded data and reduces access times, essential for preserving data security. There are several risks to patients' privacy when information from medical records is stolen or altered. The damage caused by malicious people with this information causes serious harm to both the patient and the financing institution. Therefore, cloud-based EHR (electronic health records) is becoming a focal point in many areas. Storing patient data, ensuring data accuracy, protecting data confidentiality, and ensuring fast data transmission in the cloud system is a significant concerns in the healthcare industry. In this study, the layered architecture structure is also addressed in addition to the blockchain for sharing health data with the help of cloud service providers while providing data access control and auditing for EHR records. To enter this layered architectural system, new solutions are proposed by applying JWT (JSON web token) and hashing function of patient information. This proposed method manages and controls the patient's health records electronically. Therefore, these technologies used in the blockchain platform open new doors to more secure and sustainable solutions.

Organizational Factors in Clinical Data Sharing for Artificial Intelligence in Health Care

Limited sharing of data sets that accurately represent disease and patient diversity limits the generalizability of artificial intelligence (AI) algorithms in health care. To explore the factors associated with organizational motivation to share health data for AI development. This qualitative study investigated organizational readiness for sharing health data across the academic, governmental, nonprofit, and private sectors. Using a multiple case studies approach, 27 semistructured interviews were conducted with leaders in data-sharing roles from August 29, 2022, to January 9, 2023. The interviews were conducted in the English language using a video conferencing platform. Using a purposive and nonprobabilistic sampling strategy, 78 individuals across 52 unique organizations were identified. Of these, 35 participants were enrolled. Participant recruitment concluded after 27 interviews, as theoretical saturation was reached and no additional themes emerged. Concepts defining organizational readiness for data sharing and the association between data-sharing factors and organizational behavior were mapped through iterative qualitative analysis to establish a framework defining organizational readiness for sharing clinical data for AI development. Interviews included 27 leaders from 18 organizations (academia: 10, government: 7, nonprofit: 8, and private: 2). Organizational readiness for data sharing centered around 2 main constructs: motivation and capabilities. Motivation related to the alignment of an organization's values with data-sharing priorities and was associated with its engagement in data-sharing efforts. However, organizational motivation could be modulated by extrinsic incentives for financial or reputational gains. Organizational capabilities comprised infrastructure, people, expertise, and access to data. Cross-sector collaboration was a key strategy to mitigate barriers to access health data. This qualitative study identified sector-specific factors that may affect the data-sharing behaviors of health organizations. External incentives may bolster cross-sector collaborations by helping overcome barriers to accessing health data for AI development. The findings suggest that tailored incentives may boost organizational motivation and facilitate sustainable flow of health data for AI development.

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.

Public preference on sharing health data to inform research, health policy and clinical practice in Australia: A stated preference experiment.

To investigate public willingness to share sensitive health information for research, health policy and clinical practice. A total of 1,003 Australian respondents answered an online, attribute-driven, survey in which participants were asked to accept or reject hypothetical choice sets based on a willingness to share their health data for research and frontline-medical support as part of an integrated health system. The survey consisted of 5 attributes: Stakeholder access for analysis (Analysing group); Type of information collected; Purpose of data collection; Information governance; and Anticipated benefit; the results of which were analysed using logistic regression. When asked about their preference for sharing their health data, respondents had no preference between data collection for the purposes of clinical practice, health policy or research, with a slight preference for having government organisations manage, govern and curate the integrated datasets from which the analysis was being conducted. The least preferred option was for personal health records to be integrated with insurance records or for their data collected by privately owned corporate organisations. Individuals preferred their data to be analysed by a public healthcare provider or government staff and expressed a dislike for any private company involvement. The findings from this study suggest that Australian consumers prefer to share their health data when there is government oversight, and have concerns about sharing their anonymised health data for clinical practice, health policy or research purposes unless clarity is provided pertaining to its intended purpose, limitations of use and restrictions to access. Similar findings have been observed in the limited set of existing international studies utilising a stated preference approach. Evident from this study, and supported by national and international research, is that the establishment and preservation of a social license for data linkage in health research will require routine public engagement as a result of continuously evolving technological advancements and fluctuating risk tolerance. Without more work to understand and address stakeholder concerns, consumers risk being reluctant to participate in data-sharing and linkage programmes.