Search

Background: This study explored the ethical issues associated with community-based HIV testing among African, Caribbean, and Black (ACB) populations in Canada, focusing on their perceptions of consent, privacy, and the management of HIV-related data and bio-samples. Methods: A qualitative community-based participatory research (CBPR) approach was employed to actively engage ACB community members in shaping the research process. The design included in-depth qualitative interviews with 33 ACB community members in Manitoba, Canada. The study was guided by a Community Guiding Circle, which contributed to study design, data analysis, and interpretation. A diverse sample was recruited through community agencies, social media, and flyers, with considerations for variations in age, gender, sexual orientation, and geographical location. The study employed iterative inductive thematic data analysis. Findings: Participants expressed significant concerns about the collection, sharing, and use of HIV data from healthcare encounters, revealing mistrust towards institutions like police, child welfare, and immigration accessing their health information. Their worries centered on the handling of biological samples, data misuse, potential human rights violations, HIV criminalization, deportations, challenging consent, privacy, and bodily autonomy principles. While open to contributing to medical research, they unanimously demanded greater transparency, informed consent, and control over the secondary use of their health data. Conclusions: The study underscores the need for culturally safe approaches in HIV testing and ethical governance in healthcare for ACB communities. It highlights the importance of prioritizing participant empowerment, ensuring transparency, practicing informed consent, and implementing robust data security measures to balance effective HIV information management with the protection of individual rights. [ABSTRACT FROM AUTHOR]

During the coronavirus disease-19 pandemic, Fukushima marked the 10th anniversary of its nuclear disaster of 2011. And although pandemic scientists around the world used technological surveillance to predict risks, the experiences from the Fukushima health crisis call into question such technological solutionism. The Japanese government and electronic companies had placed nuclear workers under intensive health surveillance for decades, but the health data rarely helped workers to protect themselves. Rather, the government has often used the data to decline workers' claims for medical compensation. I call this contradictory consequence of data Protective Abandonment, the systematic disposal of people through the promise of protection. Data are collected through surveillance, for the purpose of risk management, but the information ends up protecting only the existing political economic systems. Crucially, data collection disguises protection and hides the unequal distribution of care. I argue that protective abandonment may become a common experience in today's data-driven societies. [ABSTRACT FROM AUTHOR]

Purpose: General Data Protection Regulation (GDPR) of the European Union (EU) was passed to protect data privacy. Though the GDPR intended to address issues related to data privacy in the EU, it created an extra-territorial effect through Articles 3, 45 and 46. Extra-territorial effect refers to the application or the effect of local laws and regulations in another country. Lawmakers around the globe passed or intensified their efforts to pass laws to have personal data privacy covered so that they meet the adequacy requirement under Articles 45–46 of GDPR while providing comprehensive legislation locally. This study aims to analyze the Malaysian and Saudi Arabian legislation on health data privacy and their adequacy in meeting GDPR data privacy protection requirements. Design/methodology/approach: The research used a systematic literature review, legal content analysis and comparative analysis to critically analyze the health data protection in Malaysia and Saudi Arabia in comparison with GDPR and to see the adequacy of health data protection that could meet the requirement of EU data transfer requirement. Findings: The finding suggested that the private sector is better regulated in Malaysia than the public sector. Saudi Arabia has some general laws to cover health data privacy in both public and private sector organizations until the newly passed data protection law is implemented in 2024. The finding also suggested that the Personal Data Protection Act 2010 of Malaysia and the Personal Data Protection Law 2022 of Saudi Arabia could be considered "adequate" under GDPR. Originality/value: The research would be able to identify the key principles that could identify the adequacy of the laws about health data in Malaysia and Saudi Arabia as there is a dearth of literature in this area. This will help to propose suggestions to improve the laws concerning health data protection so that various stakeholders can benefit from it. [ABSTRACT FROM AUTHOR]

Background: As routinely collected patient data have become increasingly accessible over the years, more attention has been directed at the ethics of using such data for research. Patient data is often available to researchers through patient registries that typically collect data of patients with a specific condition. While ethical guidelines for using patient data are presented frequently in the literature, it is currently unknown how patient registries implement the recommendations from these guidelines in practice and how they communicate their practices. In this project, we assessed to what extent a sample of 51 patient registries provides information about a range of ethics practices. Methods: We searched for patient registries in the resource database of the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). Our ethics reporting checklist was based on three sources: the Registry Evaluation and Quality Standards Tool (REQueST), the Agency for Healthcare Research and Quality (AHRQ) guide for good registry practices, and a systematic review of the principles and norms related to health data sharing by Kalkman and colleagues. The checklist includes 26 questions about five ethics components: governance, conflicts of interest, informed consent, privacy and data protection, and use-and-access. Results: We found substantial heterogeneity in the way patient registries provide information about ethics practices. Patient registries often mentioned their governance structure and any potential conflicts of interests but typically did not describe the responsibilities and rights allocated to their funders. Information about informed consent was often provided to patients, but the available documents often lacked relevant information like the benefits and risks of participation. Privacy and data protection and use-and-access policies were typically discussed but not very concretely. Conclusions: We conclude that registries typically provide information about key ethics practices such as governance, conflicts of interest, informed consent, privacy and data protection, and use-and-access procedures, but this information is often not as detailed as recommended in existing guidelines. The ethics reporting checklist we designed could be helpful for the ethical assessments of patient registries and other types of registries in the future as well as for self-assessment of registries aiming to improve their ethics practices. [ABSTRACT FROM AUTHOR]

Effectively addressing ethical issues in precision medicine research in Africa requires a holistic social contract that integrates biomedical knowledge with local cultural values and Indigenous knowledge systems. Drawing on African epistemologies such as ubuntu and ujamaa and on our collective experiences in genomics and big data research for sickle cell disease, hearing impairment, and fragile X syndrome and the project Public Understanding of Big Data in Genomics Medicine in Africa, we envision a transformative shift in health research data governance in Africa that could help create a sense of shared responsibility between all stakeholders in genomics and data‐driven health research in Africa. This shift includes proposing a social contract for genomics and data science in health research that is grounded in African communitarianism such as solidarity, shared decision‐making, and reciprocity. We make several recommendations for a social contract for genomics and data science in health, including the coproduction of genomics knowledge with study communities, power sharing between stakeholders, public education on the ethical and social implications of genetics and data science, benefit sharing, giving voice to data subjects through dynamic consent, and democratizing data access to allow wide access by all research stakeholders. Achieving this would require adopting participatory approaches to genomics and data governance. [ABSTRACT FROM AUTHOR]

Population level data on health of people with intellectual and developmental disabilities (IDD) are sorely needed to identify their health status, health disparities, and health needs. Key considerations to inform programs and policies need to address prevalence, problem identification, and progress assessment. Recent advances have been made in health data about people with disabilities generally that identify strategies for improving health data for people with IDD, including critical need for a standardized operational definition and survey identifiers of IDD. Past and current actions by federal agencies' to improve health data for health equity are summarized. Emerging developments in IDD health data are identified, including increasing use of self-report, data linking and harmonizing, intersectionality, and recognition of ableism. [ABSTRACT FROM AUTHOR]

Healthcare institutions and health registries often store patients' health data. In order to ensure privacy, sensitive medical information is stored separately from the identifying information of the patient. Generally, institutions anonymize medical information while sharing it for external use. However, internal users may also use it for identifying inaccuracies or missing information. Even though internal users may be legally permitted to access sensitive medical information, such access may lead to the identification of the patient, which can be vulnerable to patient privacy. Ensuring the accountability and responsibility of the internal users may lead to tractability in case of adversarial access with malicious intentions. Therefore, a secure system must be developed for the storage and retrieval of health data. To this end, in this paper, we propose a ledger-based system that cryptographically ensures that all access to health data must be logged into a ledger. Nevertheless, the ledger entries must be protected against adversarial access, too. At the same time, the ledger must be traversable by the patients as well as internal users. To address these needs, we propose techniques for the construction of a ledger to permit both internal users and patients to securely traverse and view only the entries to which they are linked. [ABSTRACT FROM AUTHOR]

The proposal for a European Health Data Space aims at creating a common space where individuals may control their health data in a trusted and secure way. The objective is not only improving healthcare delivery, but also enhancing the opportunities to use health data for research and innovation. To achieve these results, the proposal implements a mandatory self-certification scheme for European health records systems as well as for wellness devices and applications, setting up essential requirements related to interoperability and security. Although this is the first intervention that sets a horizontal framework that is mandatory for all Member States, the security requirements that are included in the legislative proposal are not sufficiently detailed and comprehensive. Given that cyberthreats are increasing and security incidents affecting health data may potentially have an impact on the lives of patients, it is important that cybersecurity measures are adopted and implemented in the most effective way. The paper will analyse the European Health Data Space proposal pointing to the open issues and doubts that may be emerging and it will compare them with the proposed Cyber Resilience Act, identifying the issues that may be solved thanks to this horizontal regulation and the ones that instead remain open. [ABSTRACT FROM AUTHOR]

In today's modern world, we have more than one global actor leading the economy and rapid technological development. The article focuses specifically on the right to sensitive data protection, or more broadly the right to privacy, in American and in EU legal system. This paper shows distinctions between the two and systematically demonstrates the protection of personal data in EU through years. Exploring these distinctions and different interpretations of the right to data protection is significant, because of the potential impacts on the consumer in particular, possibly resulting in being granted different rights when acquiring services in the EU or America. We will also analyse the fundamental legal acts, which are the cornerstones of data privacy. As its main focus, the article will also examine the provisions concerning sensitive personal data, in particular health data. Furthermore, the article will study some specific concerns in connection to the American smart phone, smartwatch and computer health apps that are not fully compliant with basic EU legal principles, human rights or the General Data Protection Regulation. While the technology is so advanced and users may access these apps from anywhere across the world, such apps, and their privacy policies or other typical contracts, should comply with the relevant legislation, valid in the state of user's nationality or remaining. The paper examines and substantiates the latter through two recent cases. In one, data breaches were punished by imposing a relatively high fine, and in the other case example, no punitive action was yet taken. That being said, the article argues the insufficient data protection framework that does not necessarily provide a consumer with appropriate safeguards, which is especially relevant in cases of transmission of personal health data. [ABSTRACT FROM AUTHOR]

Abstract Background This study explored the ethical issues associated with community-based HIV testing among African, Caribbean, and Black (ACB) populations in Canada, focusing on their perceptions of consent, privacy, and the management of HIV-related data and bio-samples. Methods A qualitative community-based participatory research (CBPR) approach was employed to actively engage ACB community members in shaping the research process. The design included in-depth qualitative interviews with 33 ACB community members in Manitoba, Canada. The study was guided by a Community Guiding Circle, which contributed to study design, data analysis, and interpretation. A diverse sample was recruited through community agencies, social media, and flyers, with considerations for variations in age, gender, sexual orientation, and geographical location. The study employed iterative inductive thematic data analysis. Findings Participants expressed significant concerns about the collection, sharing, and use of HIV data from healthcare encounters, revealing mistrust towards institutions like police, child welfare, and immigration accessing their health information. Their worries centered on the handling of biological samples, data misuse, potential human rights violations, HIV criminalization, deportations, challenging consent, privacy, and bodily autonomy principles. While open to contributing to medical research, they unanimously demanded greater transparency, informed consent, and control over the secondary use of their health data. Conclusions The study underscores the need for culturally safe approaches in HIV testing and ethical governance in healthcare for ACB communities. It highlights the importance of prioritizing participant empowerment, ensuring transparency, practicing informed consent, and implementing robust data security measures to balance effective HIV information management with the protection of individual rights.

(Series Information) European Papers - A Journal on Law and Integration, 2024 9(3), 852-864 | European Forum Insight of 24 December 2024 | (Table of Contents) I.Introduction. - II. Setting the facts of the case. - III. The ECJ's reasoning. - III.1. The nature of the GDPR remedies. - III.2. The breadth of the notion of health data. - IV. Commentary. - IV.1. The safeguards “outside” the GDPR: what role for unfair competition law? - IV.2. The safeguards “inside” the GDPR: what (if any) limits to the notion of sensitive data. - V. Conclusive remarks and broader implications. | (Abstract) On 4 October 2024, the Grand Chamber of the European Court of Justice (ECJ) issued a landmark judgment that adds significant depth to the interpretation of the GDPR, especially concerning its interplay with unfair competition law and the concept of “special categories of personal data”. After analysing the facts of the case and the ECJ’s reasoning, this Insight aims to highlight how the Lindenapotheke ruling, while aligned with the ECJ’s longstanding commitment to securing data subjects’ rights, represents a noteworthy advance: it reinforces the high level of protection afforded to data subjects by integrating GDPR safeguards – particularly those under art. 9 GDPR – with those established in other areas of EU secondary law. Finally, this Insight examines the critical aspects and practical implications of the ruling, especially for data controllers, as the ECJ’s approach may have an (excessive) deterrent effect on prac-tices that risk infringing these enhanced data protection standards.

Abstract Background Clinical data warehouses provide harmonized access to healthcare data for medical researchers. Informatics for Integrating Biology and the Bedside (i2b2) is a well-established open-source solution with the major benefit that data representations can be tailored to support specific use cases. These data representations can be defined and improved via an iterative approach together with domain experts and the medical researchers using the platform. To facilitate these discussions, it is important to understand how users interact with the system. Objective The objective of this work was to develop metrics for describing user interactions with clinical data warehouses in general and i2b2 in particular. Moreover, we aimed to develop a dashboard featuring interactive visualizations that inform data engineers and data stewards about potential improvements. Methods We first identified metrics for different data usage dimensions and extracted the relevant metadata about previous user queries from the i2b2 database schema for further analysis. We then implemented associated visualizations in Python and integrated the results into an interactive dashboard using Dash. Results The identified categories of metrics include frequency of use, session duration, and use of functionality and features. We created a dashboard that extends our local i2b2 data warehouse platform, focusing on the latter category, further broken down into the number of queries, frequently queried concepts, and query complexity. The implementation is available as open-source software. Conclusion A range of metrics can be derived from metadata logged in the i2b2 database schema to provide data engineers and data stewards with a comprehensive understanding of how users interact with the platform. This can help to identify the strengths and limitations of specific instances of the platform for specific use cases and aid their iterative improvement.

Artificial intelligence is actively taking over sphere after sphere and particular successes and achievements can be seen in the medical sector and medical technology. However, the introduction of AI raises a number of practical and ethical issues. One of the main ones is the issue of handling personal data, as access to a large number of patients’ health data plays a key role in the development and use of AI in medicine. The most convenient solution for their use is to anonymise them beforehand. However, with anonymisation, there is a risk of re-identification and the potential for loss of data informativeness may be lost. In the framework of this article the experience in the sphere of legal regulation of personal health data handling when using artificial intelligence systems in medicine is considered in the example of the USA, EU and Singapore. Each country is endeavoring to strike a balance between the protection of personal data privacy and the advancement of technological innovations. The analysis suggests that the emphasis on artificial intelligence development necessitates specific premises in the domain of personal data protection. Conversely, stringent standards for the protection of personal data could potentially exert a restrictive influence.

Abstract Background Chronic kidney disease (CKD) poses significant health risks due to its asymptomatic nature in early stages and its association with increased cardiovascular and kidney events. Early detection and management are critical for improving outcomes. Objective This study aimed to develop and validate a prediction model for hospitalization for ischemic heart disease (IHD) or cerebrovascular disease (CVD) and major kidney events in Japanese individuals with mild CKD using readily available health check and prescription data. Methods A retrospective cohort study was conducted using data from approximately 850,000 individuals in the PREVENT Inc. database, collected between April 2013 and April 2023. Cox proportional hazard regression models were utilized to derive and validate risk scores for hospitalization for IHD/CVD and major kidney events, incorporating traditional risk factors and CKD-specific variables. Model performance was assessed using the concordance index (c-index) and 5-fold cross-validation. Results A total of 40,351 individuals were included. Key predictors included age, sex, diabetes, hypertension, and lipid levels for hospitalization for IHD/CVD and major kidney events. Age significantly increased the risk score for both hospitalization for IHD/CVD and major kidney events. The baseline 5-year survival rates are 0.99 for hospitalization for IHD/CVD and major kidney events are 0.99. The developed risk models demonstrated predictive ability, with mean c-indexes of 0.75 for hospitalization for IHD/CVD and 0.69 for major kidney events. Conclusions This prediction model offers a practical tool for early identification of Japanese individuals with mild CKD at risk for hospitalization for IHD/CVD and major kidney events, facilitating timely interventions to improve patient outcomes and reduce healthcare costs. The models stratified patients into risk categories, enabling identification of those at higher risk for adverse events. Further clinical validation is required.

Abstract Transitioning to an individualized risk-based approach can significantly enhance cervical cancer screening programs. We aimed to derive and internally validate a prediction model for assessing the risk of cervical intraepithelial neoplasia grade 3 or higher (CIN3+) and cancer in women eligible for screening. This retrospective study utilized data from the Estonian electronic health records, including 517,884 women from the health insurance database and linked health registries. We employed Cox proportional hazard regression, incorporating reproductive and medical history variables (14 covariates), and utilized the least absolute shrinkage and selection operator (LASSO) for variable selection. A 10-fold cross-validation for internal validation of the model was used. The main outcomes were the performance of discrimination and calibration. Over the 8-year follow-up, we identified 1326 women with cervical cancer and 5929 with CIN3+, with absolute risks of 0.3% and 1.1%, respectively. The prediction model for CIN3 + and cervical cancer had good discriminative power and was well calibrated Harrell’s C of 0.74 (0.73–0.74) (calibration slope 1.00 (0.97–1.02) and 0.67 (0.66–0.69) (calibration slope 0.92 (0.84–1.00) respectively. A developed model based on nationwide electronic health data showed potential utility for risk stratification to supplement screening efforts. This work was supported through grants number PRG2218 from the Estonian Research Council, and EMP416 from the EEA (European Economic Area) and Norway Grants.

With the development of cross-border telemedicine, international cooperation in clinical trials of drugs and vaccines, cross-border academic discussions and clinical research, cross-border flow of health and medical data has become an irreversible trend. Rules for cross-border flow of health data require doing a balance among the objectives and policies of maintaining national health security, protecting citizens' privacy, and promoting the development of health industry. This paper uses literature research, comparative analysis and other methods to study the legislative regulation and regulatory mechanism of cross-border health data in Australia. The study's findings reveal that Australia prioritizes health data security, enforcing stringent oversight on the cross-border movement of personally identifiable health data through dedicated agencies and comprehensive legislation. China should build a scientific, complete and strict legal and policy system for health data protection and cross-border flow, and organically combine and implement laws and regulations with policy guidelines; improve the terms of standard contract for health data exit and play the function of the standard contract for regulation; actively use the existing hierarchical classification of health data to build a flexible and diverse health data exit regulation model; and strengthen exchanges and cooperation with countries along the "Belt and Road", especially ASEAN member countries, in the field of cross-border health data flow.

Abstract Methodological developments in different sectors like health, biomedical and biological areas are the recent burning issue in the statistical literature. The approach of implementing declining hazard function which is obtained by compounding truncated Poisson distribution and a lifetime distribution is a special concern in a few studies. In this paper we proposed a newly introduced distribution called inverse Lomax-Uniform Poisson distribution mostly applied in the health, biomedical, biological, and related sectors. Some basic statistical properties of the distribution are discussed. The capability of the model is checked by comparing it with six potential models by using a practical real data set. Based on the comparison techniques, the newly proposed model out performs all its counterparts. The simulation study is also conducted. Furthermore, the joint modelling of repeatedly measured and time-to-vent processes is discussed in detail with the real data set in the health sector.

AbstractBackgroundMethodsResultsConclusionValue-Based Health Care (VBHC) represents a pivotal shift from volume-based to outcome-driven quality metrics centered on patient-valued outcomes. This approach requires collaboration across all participants in the health care value chain; providers, payers, pharma, policymakers and patients (collectively known as the 5Ps). Despite substantial theoretical endorsement of VBHC’s potential for improving health outcomes and system efficiency, empirical evidence detailing its practical implementation remains limited. This field study evaluates the real-word implementation of VBHC within a health care organization.In 2022, a health care collaboration Think Tank initiated this investigation during a breakout session, gathering insights from 12 leading international organizations to construct an empirical VBHC transformation reference guide. Real-world data was collected through structured interviews over a 1-year period, covering the 5 P value chain in various healthcare settings. The VBHC initiatives were analyzed through four stages: initiation, data acquisition, collaborative frameworks, and results evaluation.The 12 interviews identified five key enablers for successful VBHC implementation: 1. Organizational Purpose: defining core motivators for change; 2. People: identifying pivotal roles and leadership to endorse change; 3. Resources: securing personnel and financial support; 4. Data Infrastructure: developing interoperable IT systems for effective data sharing and collection; 5. Execution: prioritizing sustained implementation processes.The findings highlight that VBHC implementation and adoption is complex and requires incremental advancements, dedicated leadership, and resilient strategic framework spanning over multiple years. A comprehensive understanding of patient populations, risk stratification, and appropriate outcome metrics are essential to measure and deliver the VBHC transformation. Executive endorsement and transition funding during the transformation process are paramount to support this systemic shift. Collaboration among all 5 P stakeholders is essential for success. This field study underscores the importance of continuous learning and adaptation, providing a practical guide to enhance health care quality and efficiency that serves all stakeholders. [ABSTRACT FROM AUTHOR]

Background: Clinical data warehouses provide harmonized access to healthcare data for medical researchers. Informatics for Integrating Biology and the Bedside (i2b2) is a well-established open-source solution with the major benefit that data representations can be tailored to support specific use cases. These data representations can be defined and improved via an iterative approach together with domain experts and the medical researchers using the platform. To facilitate these discussions, it is important to understand how users interact with the system. Objective: The objective of this work was to develop metrics for describing user interactions with clinical data warehouses in general and i2b2 in particular. Moreover, we aimed to develop a dashboard featuring interactive visualizations that inform data engineers and data stewards about potential improvements. Methods: We first identified metrics for different data usage dimensions and extracted the relevant metadata about previous user queries from the i2b2 database schema for further analysis. We then implemented associated visualizations in Python and integrated the results into an interactive dashboard using Dash. Results: The identified categories of metrics include frequency of use, session duration, and use of functionality and features. We created a dashboard that extends our local i2b2 data warehouse platform, focusing on the latter category, further broken down into the number of queries, frequently queried concepts, and query complexity. The implementation is available as open-source software. Conclusion: A range of metrics can be derived from metadata logged in the i2b2 database schema to provide data engineers and data stewards with a comprehensive understanding of how users interact with the platform. This can help to identify the strengths and limitations of specific instances of the platform for specific use cases and aid their iterative improvement. [ABSTRACT FROM AUTHOR]

Considering the difficult procedure of obtaining consent, consent-free health data reuse is believed to be one major contributor towards the creation of thriving health innovation ecosystem. While classic researchers are supported by the GDPR to reuse health data without consent, the innovator's situation remains unclear, and this uncertainty continues to exist upon the introduction of the EHDS. To shed light on grey areas, positions of GDPR and EHDS on the ability of innovators for consent-free health data reuse were assessed. The study concludes that, while academicians are able to reuse health data without consent, a private company's ability has been impeded by individual member states' deviations from Article 9(2) GDPR. Furthermore, in contracts to EHDS (2022) which appeared in favour of innovators through the elimination of consent mechanism, EHDS (2024) with the inclusion of opt-out mechanism, and exclusion of private sector bodies from the Article 35(f), has negatively affected private companies. [ABSTRACT FROM AUTHOR]

Data standards play a key role in the governance of commercial actors on the market for health data. This commentary explores expectations for standard-setting in the European Health Data Space (EHDS), a pivotal legal initiative for facilitation of cross-border health data exchange. While EHDS aims to establish shared standards for interoperability and innovation in the European Union (EU) and affiliated countries, the analysis reveals some of the inherent complexities. We draw on insights from policy analysis and Science and Technology Studies (STS) that underscore how standards are performative and have unpredictable and uneven implications. To exemplify this, the commentary delves into the intricate issues surrounding the harmonization of diagnostic codes and pharmaceutical prescriptions. In conclusion, it asserts that while standard setting has unpredictable effects, it still involves prioritizing some purposes and benefits over others. We raise questions about the distribution of costs and benefits among stakeholders in the evolving landscape of health data governance and call for further empirical investigation. [ABSTRACT FROM AUTHOR]

Transitioning to an individualized risk-based approach can significantly enhance cervical cancer screening programs. We aimed to derive and internally validate a prediction model for assessing the risk of cervical intraepithelial neoplasia grade 3 or higher (CIN3+) and cancer in women eligible for screening. This retrospective study utilized data from the Estonian electronic health records, including 517,884 women from the health insurance database and linked health registries. We employed Cox proportional hazard regression, incorporating reproductive and medical history variables (14 covariates), and utilized the least absolute shrinkage and selection operator (LASSO) for variable selection. A 10-fold cross-validation for internal validation of the model was used. The main outcomes were the performance of discrimination and calibration. Over the 8-year follow-up, we identified 1326 women with cervical cancer and 5929 with CIN3+, with absolute risks of 0.3% and 1.1%, respectively. The prediction model for CIN3 + and cervical cancer had good discriminative power and was well calibrated Harrell's C of 0.74 (0.73–0.74) (calibration slope 1.00 (0.97–1.02) and 0.67 (0.66–0.69) (calibration slope 0.92 (0.84–1.00) respectively. A developed model based on nationwide electronic health data showed potential utility for risk stratification to supplement screening efforts. This work was supported through grants number PRG2218 from the Estonian Research Council, and EMP416 from the EEA (European Economic Area) and Norway Grants. [ABSTRACT FROM AUTHOR]

This paper concerns the modeling of smart health sensors for data monitoring, where the data are eventually noisy, with correlated noise. In this work, we applied Clifford-based wavelets/multiwavelets for correlated noise in multi-sensors health data monitoring by estimating the set of sensor nodes minimizing an eventual error computed by the signal values at the selected nodes mostly caused by the correlated noise. Instead of directly minimizing the estimation error, we focused on evaluating a multi-level scheme based on multiwavelets for the estimation of the error between the parameter vector and its sub-vector of those nodes. Numerical simulations are provided with a comparison to some recent existing works. This model showed a performance and a fast time execution compared to those existing works. This model exceeds these models by the non-necessity to assume a priory structure of the data. Wavelets are capable to detect, localize, and eliminate the noise, even correlated, efficiently via the independent uncorrelated multiwavelets’ components. [ABSTRACT FROM AUTHOR]

Introduction: The incorporation of health-related sensors in wearable devices has increased their use as essential monitoring tools for a wide range of clinical applications. However, the signals obtained from these devices often present challenges such as artifacts, spikes, high-frequency noise, and data gaps, which impede their direct exploitation. Additionally, clinically relevant features are not always readily available. This problem is particularly critical within the H2020 BRAINTEASER project, funded by the European Community, which aims at developing models for the progression of Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS) using data from wearable devices. Methods: The objective of this study is to present the automated pipeline developed to process signals and extract features from the Garmin Vivoactive 4 smartwatch, which has been chosen as the primary wearable device in the BRAINTEASER project. The proposed pipeline includes a signal processing step, which applies retiming, gap-filling, and denoising algorithms to enhance the quality of the data. The feature extraction step, on the other hand, utilizes clinical partners' knowledge and feedback to select the most relevant variables for analysis. Results: The performance and effectiveness of the proposed automated pipeline have been evaluated through pivotal beta testing sessions, which demonstrated the ability of the pipeline to improve the data quality and extract features from the data. Further clinical validation of the extracted features will be performed in the upcoming steps of the BRAINTEASER project. Discussion: Developed in Python, this pipeline can be used by researchers for automated signal processing and feature extraction from wearable devices. It can also be easily adapted or modified to suit the specific requirements of different scenarios. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) poses significant health risks due to its asymptomatic nature in early stages and its association with increased cardiovascular and kidney events. Early detection and management are critical for improving outcomes. Objective: This study aimed to develop and validate a prediction model for hospitalization for ischemic heart disease (IHD) or cerebrovascular disease (CVD) and major kidney events in Japanese individuals with mild CKD using readily available health check and prescription data. Methods: A retrospective cohort study was conducted using data from approximately 850,000 individuals in the PREVENT Inc. database, collected between April 2013 and April 2023. Cox proportional hazard regression models were utilized to derive and validate risk scores for hospitalization for IHD/CVD and major kidney events, incorporating traditional risk factors and CKD-specific variables. Model performance was assessed using the concordance index (c-index) and 5-fold cross-validation. Results: A total of 40,351 individuals were included. Key predictors included age, sex, diabetes, hypertension, and lipid levels for hospitalization for IHD/CVD and major kidney events. Age significantly increased the risk score for both hospitalization for IHD/CVD and major kidney events. The baseline 5-year survival rates are 0.99 for hospitalization for IHD/CVD and major kidney events are 0.99. The developed risk models demonstrated predictive ability, with mean c-indexes of 0.75 for hospitalization for IHD/CVD and 0.69 for major kidney events. Conclusions: This prediction model offers a practical tool for early identification of Japanese individuals with mild CKD at risk for hospitalization for IHD/CVD and major kidney events, facilitating timely interventions to improve patient outcomes and reduce healthcare costs. The models stratified patients into risk categories, enabling identification of those at higher risk for adverse events. Further clinical validation is required. [ABSTRACT FROM AUTHOR]

Global health emergencies often lead to a proliferation of health-related research and resultant data, which is shared across borders to help control the outbreak of disease and support decision-making regarding public health interventions. However, efforts to share data can be hindered by diverse international ethical and legal frameworks. The frameworks aim to govern coordinated processing, sharing and transfer of health data across borders thus placing burdens on researchers who are willing or obligated to share data. In this paper, we investigate ethical values and legal principles that can be applied to ensure a more streamlined approach in the governance of cross-border health data sharing during global health emergencies. A scoping review approach was used to better understand emerging evidence about issues related to the ethical and legal governance of cross-border health data sharing. We first highlight the challenges of sharing health data across borders. We then discuss how the challenges might be overcome through the application of ethical values and legal principles that have been identified in literature. This is followed by a proposal for possible ways of streamlining the governance of health data sharing from ethical and legal perspectives in a bid to better prepare for future global health emergencies and promote the responsible sharing of health data. [ABSTRACT FROM AUTHOR]

The creation of the European Health Data Space is a milestone. It establishes something new and makes it possible to have faster and more efficient health treatment, but also to untap the potential of unused data for policy-making and development of new technologies. However, for the EHDS to be functional, it is imperative that a balance it struck between the aim of having useful data to achieve important public objectives on one side and the goal of enabling patients to have control over their health data. The original Draft Report of the European Parliament came closest to achieving these objectives and should have represented the blueprint for the final compromise. Having a harmonised opt-out for secondary use is crucial for striking the right balance between the mentioned objectives. The end result is far from perfect regarding primary use, but represents a good balance in the area of secondary use concerning the opt-out. [ABSTRACT FROM AUTHOR]

Large data-intensive health research programmes benefit from collaboration amongst researchers who may be located in different institutions and international contexts. However, complexities in navigating privacy frameworks and data protection laws across various jurisdictions pose significant challenges to researchers seeking to share or transfer data outside of institutional boundaries. Research on the awareness of data protection and privacy laws amongst stakeholders is limited. Our qualitative study, drawn from a larger project in Singapore, revealed insights into stakeholders' perceptions of the role of law in cross-national health data research. Stakeholders in our study demonstrated a range of perceptions regarding the role of data protection law in governing the collection and transfer of health data for research. The main criticisms included inadequate legal protection to data and lack of uniformed data protection standards. Despite these criticisms, participants recognised the importance of data protection law in supporting cross-border data transfers and proposed measures to improve perceived limitations of existing laws. These measures include strengthening existing legal framework, establishing contractual agreements and imposing severe punishments for data misuse. [ABSTRACT FROM AUTHOR]

Case C-21/23 Lindenapotheke, Judgment of the Court of Justice of the European Union on 4 October 2024 ND, a pharmacy operating under the name 'Lindenapotheke', sells pharmacy-only medicines via Amazon. Based on a German law against unfair competition, competitor DR filed an injunction against ND before a German national court to stop the online sale of these medicines, alleging that ND processes health data without consent, in violation of the GDPR. The Court of Justice of the European Union (ECJ) ruled on two questions referred for a preliminary ruling. First, the ECJ held that the GDPR does not preclude national rules that empower competitors to bring proceedings before a national court for infringements of that regulation against the infringer based on the prohibition of unfair competition. Second, the ECJ clarified that the personal data that customers enter when ordering pharmacy-only medicines online constitutes health data within the meaning of the GDPR. Building on the Meta PlatformsvBundeskartellamt judgment, in which the ECJ has held that the incorrect application of the GDPR affects the assessment of an abuse of a dominant position under EU competition law, Lindenapotheke now extends this principle to a national law against unfair competition. Consequently, this 'Meta Model' could contribute to enhancing data subjects' fundamental rights and addressing data-related competition issues more effectively. However, the ECJ's broad interpretation of health data, departing from Advocate General Szpunar's opinion, introduces a new probability test with significant implications for business models that extend beyond online pharmacies involving health-related data, such as fitness apps, and for the application of other data-related regulatory frameworks, including the Digital Markets Act and the European Health Data Space. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic response in the UK, as in other countries, drew heavily on health and social care data, making its utility extremely visible as necessary for timely government decision-making and planning. The urgency created by the crisis, however, meant that additional data collection and sharing under emergency legislation was implemented with minimal public consultation. To understand the public perception of these new data measures and initiatives, three citizens' juries took place in the spring of 2021. This article reports on qualitative observations of the small group deliberations from these juries. The analysis shows that jurors frequently drew on normative discourses of transparency and trust in discussions, and the different roles they were assumed to fulfil. Transparency was expected to offer greater visibility into the organisations involved in health and social care data sharing, but this was made difficult by the increased complexity of the health data economy. Transparency into the political justifications for additional health data collection was important for jurors. The utilitarian narratives used by the government were considered problematic, restricting opportunities for individuals to express concerns and leading to cynicism. The findings will be situated with the critical literature on visibility practices to highlight the need to unpick what the promise of transparency and trust offers to the public and how it links to power and control. Lastly, it will examine what the deliberations around transparency mean for wider policy on health and social care data-sharing. [ABSTRACT FROM AUTHOR]

In this paper, we bring together the concepts of data valences and data journeys to examine how ideational and material factors work together to shape the movement of health data from the UK healthcare sector to universities for reuse in research. Specifically, we focus on the interaction of university-based researchers' constructs about data with the material conditions of health data circulation in the UK and how these dynamics drive greater circulation of health data through the data sharing infrastructure. Building on our empirical research, we identify four data valences or expectations about data present in the discourses of university-based researchers – vanguard, discovery, truthiness and actionability – and three material factors – investment in data, infrastructure and labour. We argue that the interaction of these factors has created a favourable environment for making data flow from the healthcare sector into the hands of university-based researchers. This work contributes to a better understanding of why health data reuse practices are expanding and being sustained, and it challenges previous health data reuse research that treats the drivers shaping data flows as self-evident or already determined. [ABSTRACT FROM AUTHOR]

Researchers and practitioners are increasingly using machine‐generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while—potentially—mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of synthetic data is often understood to be its detachment from the data subjects whose measurement data is used to generate it. However, we argue that addressing the ethical issues synthetic data raises might require bringing data subjects back into the picture, finding ways that researchers and data subjects can be more meaningfully engaged in the construction and evaluation of datasets and in the creation of institutional safeguards that promote responsible use. [ABSTRACT FROM AUTHOR]

Tensor decomposition has recently been gaining attention in the machine learning community for the analysis of individual traces, such as Electronic Health Records. However, this task becomes significantly more difficult when the data follows complex temporal patterns. This paper introduces the notion of a temporal phenotype as an arrangement of features over time and it proposes SWoTTeD (Sliding Window for Temporal Tensor Decomposition), a novel method to discover hidden temporal patterns. SWoTTeD integrates several constraints and regularizations to enhance the interpretability of the extracted phenotypes. We validate our proposal using both synthetic and real-world datasets, and we present an original usecase using data from the Greater Paris University Hospital. The results show that SWoTTeD achieves at least as accurate reconstruction as recent state-of-the-art tensor decomposition models, and extracts temporal phenotypes that are meaningful for clinicians. [ABSTRACT FROM AUTHOR]

The FINRISK risk calculator is a tool for the evaluation of the 10-year risk of cardiovascular diseases (CVDs). It is based on a Finnish population survey and is frequently used in Finnish healthcare. Currently, risks are calculated manually by inputting the required values. An alternative source of input values could be the Finnish Kanta Patient Data Repository (PDR). Risk calculation based on the Kanta PDR could enable monitoring of predicted CVD risks at the level of the Finnish population and targeting of preventive healthcare to high-risk individuals. The goal of this study is to assess if the FINRISK risk calculator can be made to work effectively using only structured data available in the Kanta PDR. We approach the issue from two perspectives. The first is the availability of individual inputs, and the second is the ability to categorize people to risk categories used in healthcare. The study was based on patient histories of roughly 60 000 persons who met the inclusion and exclusion criteria of the FINRISK risk calculator. The data had been recorded into the Kanta PDR between January 2014 and June 2022. To enable risk calculation even when individual input values are missing, we calculated a minimum and maximum risk for each person based on data that was available at a prediction time point. Risks were predicted at seven different time points between the years 2016 and 2022 to analyse the development of categorization performance over the years. The highest categorization performance was achieved for the year 2022, where we were able to identify the FINRISK risk category of 1.69% of persons. With additional categories for low or moderate and moderate or high risk, 4.15% of persons could be categorized. The risk level of the remaining 95.85% of individuals could not be specified. The reason behind the poor performance was the inadequacy of input values. Namely, family history of CVDs was not available in structured format and smoking information was rarely found. Information about blood pressure, blood cholesterol, and diabetes were more frequent. We conclude that population level risk assessment is not viable based on only the structured data in the Kanta PDR. Extracting inputs like smoking status from text data could improve the situation. As the FINRISK risk calculator is often used by inputting structured data into an electronic form, the lack of same inputs in the Kanta PDR highlights incomplete flow of data in the Finnish health data infrastructure.

We report on a qualitative Group Survey study involving four healthcare professionals (HCPs) and eight people living with HIV who were recipients of care in the United Kingdom (UK). The survey aimed to bring participants' perspectives into dialogue and establish consensus about how communication between HCPs delivering HIV care and their patients could be improved in the context of the routine care consultation. Responses from both parties were anonymously collated, thematically analysed, and shared back with participants in two subsequent survey rounds to support consensus-building on matters of concern and identify thematic insights. In this paper, we report three themes for informing future designs of tools and services to support communication between patients and HCPs: Patient-clinician relationship for trusted sharing; Self-reporting psychosocial information to support Whole-person care; and Perceived barriers for online trusted sharing with HCPs. Our findings highlight key areas of concern and further investigation is needed to understand how self-reported information may be meaningfully captured, interpreted and processed by HCPs in ways that are trusted by patients who voice privacy and security concerns. [ABSTRACT FROM AUTHOR]

This paper conducts a comparative analysis of data governance mechanisms concerning the secondary use of health data in Taiwan and the European Union (EU). Both regions have adopted distinctive approaches and regulations for utilizing health data beyond primary care, encompassing areas such as medical research and healthcare system enhancement. Through an examination of these models, this study seeks to elucidate the strategies, frameworks, and legal structures employed by Taiwan and the EU to strike a delicate balance between the imperative of data-driven healthcare innovation and the safeguarding of individual privacy rights. This paper examines and compares several key aspects of the secondary use of health data in Taiwan and the EU. These aspects include data governance frameworks, legal and regulatory frameworks, data access and sharing mechanisms, and privacy and security considerations. This comparative exploration offers invaluable insights into the evolving global landscape of health data governance. It provides a deeper understanding of the strategies implemented by these regions to harness the potential of health data while upholding the ethical and legal considerations surrounding its secondary use. The findings aim to inform best practices for responsible and effective health data utilization, particularly in the context of medical AI applications. [ABSTRACT FROM AUTHOR]

Objective: Both Indigenous and non-Indigenous governments and organizations have increasingly called for improved Indigenous health data in order to improve health equity among Indigenous peoples. This scoping review identifies best practices, potential consequences and barriers for advancing Indigenous health data and Indigenous data sovereignty globally. Methods: A scoping review was conducted to capture the breadth and nature of the academic and grey literature. We searched academic databases for academic records published between 2000 and 2021. We used Google to conduct a review of the grey literature. We applied Harfield's Aboriginal and Torres Strait Islander Quality Appraisal Tool (QAT) to all original research articles included in the review to assess the quality of health information from an Indigenous perspective. Results: In total, 77 academic articles and 49 grey literature records were included. Much of the academic literature was published in the last 12 years, demonstrating a more recent interest in Indigenous health data. Overall, we identified two ways for Indigenous health data to be retrieved. The first approach is health care organizations asking clients to voluntarily self-identify as Indigenous. The other approach is through data linkage. Both approaches to improving Indigenous health data require awareness of the intergenerational consequences of settler colonialism along with a general mistrust in health care systems among Indigenous peoples. This context also presents special considerations for health care systems that wish to engage with Indigenous communities around the intention, purpose, and uses of the identification of Indigenous status in administrative databases and in health care settings. Partnerships with local Indigenous nations should be developed prior to the systematic collection of Indigenous identifiers in health administrative data. The QAT revealed that many research articles do not include adequate information to describe how Indigenous communities and stakeholders have been involved in this research. Conclusion: There is consensus within the academic literature that improving Indigenous health should be of high priority for health care systems globally. To address data disparities, governments and health organizations are encouraged to work in collaboration with local Indigenous nations and stakeholders at every step from conceptualization, data collection, analysis, to ownership. This finding highlights the need for future research to provide transparent explanation of how meaningful Indigenous collaboration is achieved in their research. [ABSTRACT FROM AUTHOR]

Purpose: The use of wearable devices to monitor aspects of personal health is increasing. The Fitbit is an example of a popular device used for this purpose. It is unknown whether users' privacy (i.e. sensitive data collected from wearable devices) would be leaked via unauthorized access. So, this investigation will answer the following questions; are the data transmissions protected against unauthorised access or modification? what data are transmitted between the device and the server? how much data can be collected by unauthorized access? Method: This paper describes an investigation into data access in the Fitbit Blaze and, specifically, whether this is possible without connecting to the Fitbit server. A Man-In-The-Middle (MITM) attack was used in this investigation. Result: In this experiment, the firmware image, transferred when the device connects to the Fitbit server, is first captured and analysed to obtain data. This was done to attempt to identify the encryption method and obtain the unique device MAC address. Secondly, some fitness data, namely, the authentication key, the cryptographic key and the Nonce, were extracted from the Fitbit application. We attempted to connect the Fitbit Blaze device and the Fitbit application directly without connecting via the Fitbit server. We also attempted direct access to the Fitbit Blaze using a charger cable. In addition, Fitbit Java files were extracted from the Fitbit application. Conclusion: Finally, the outcomes of this investigation are compared with investigations into other Fitbit devices in the previous research. [ABSTRACT FROM AUTHOR]

The Global Alliance for Genomics and Health is an international consortium that is developing the Data Use Ontology (DUO) as a standard providing machine-readable codes for automation in data discovery and responsible sharing of genomics data. DUO concepts, which are encoded using OWL, only contain the textual descriptions of the conditions for data use they represent, and do not specify the intended permissions, prohibitions, and obligations explicitly – which limits their usefulness. We present an exploration of how the Open Digital Rights Language (ODRL) can be used to explicitly represent the information inherent in DUO concepts to create policies that are then used to represent conditions under which datasets are available for use, conditions in requests to use them, and to generate agreements based on a compatibility matching between the two. We also address a current limitation of DUO regarding specifying information relevant to privacy and data protection law by using the Data Privacy Vocabulary (DPV) which supports expressing legal concepts in a jurisdiction-agnostic manner as well as for specific laws like the GDPR. Our work supports the existing socio-technical governance processes involving use of DUO by providing a complementary rather than replacement approach. To support this and improve DUO, we provide a description of how our system can be deployed with a proof of concept demonstration that uses ODRL rules for all DUO concepts, and uses them to generate agreements through matching of requests to data offers. All resources described in this article are available at: https://w3id.org/duodrl/repo. [ABSTRACT FROM AUTHOR]

Introduction: In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are unique digital assets on the blockchain, representing ownership of digital objects, including health data. By minting their data as NFTs, patients can track access, monetize its use, and build secure, private health information systems. However, research on NFTs in healthcare is in its infancy, warranting a comprehensive review. Methods: This study conducted a systematic literature review and thematic analysis of NFTs in healthcare to identify use cases, design models, and key challenges. Five multidisciplinary research databases (Scopus, Web of Science, Google Scholar, IEEE Explore, Elsevier Science Direct) were searched. The approach involved four stages: paper collection, inclusion/exclusion criteria application, screening, full-text reading, and quality assessment. A classification and coding framework was employed. Thematic analysis followed six steps: data familiarization, initial code generation, theme searching, theme review, theme definition/naming, and report production. Results: Analysis of 19 selected papers revealed three primary use cases: patientcentric data management, supply chain management for data provenance, and digital twin development. Notably, most solutions were prototypes or frameworks without real-world implementations. Four overarching themes emerged: data governance (ownership, tracking, privacy), data monetization (commercialization, incentivization, sharing), data protection, and data storage. The focus lies on user-controlled, private, and secure health data solutions. Additionally, data commodification is explored, with mechanisms proposed to incentivize data maintenance and sharing. NFTs are also suggested for tracking medical products in supply chains, ensuring data integrity and provenance. Ethereum and similar platforms dominate NFT minting, while compact NFT storage options are being explored for faster data access. Conclusion: NFTs offer significant potential for secure, traceable, decentralized healthcare data exchange systems. However, challenges exist, including dependence on blockchain, interoperability issues, and associated costs. The review identified research gaps, such as developing dual ownership models and data pricing strategies. Building an open standard for interoperability and adoption is crucial. The scalability, security, and privacy of NFT-backed healthcare applications require further investigation. Thus, this study proposes a research agenda for adopting NFTs in healthcare, focusing on governance, storage models, and perceptions. [ABSTRACT FROM AUTHOR]

L'intelligence artificielle (IA) se développe à une vitesse impressionnante dans le domaine de la santé. L'IA ouvre la voie à de nouvelles pratiques d'aide au diagnostic, de médecine préventive, de suivi individualisé du patient, d'appui au choix d'une thérapie, d'aide à l'interprétation de résultats d'examens complémentaires... Loin d'être une solution miracle, la délégation de tâches à l'IA soulève de nombreuses questions éthiques portant notamment sur la supervision des algorithmes, l'égalité d'accès à l'innovation et l'utilisation des données des patients. Il est encore difficile de prédire si l'IA restera un « filet de sécurité » pour le médecin ou si elle le supplantera. La France et l'Europe ont posé le principe de « garantie humaine » à l'interprétation des résultats diagnostiques et thérapeutiques, impliquant que la machine ne peut agir seule et consacrant la responsabilité du professionnel de santé. Cet article vise à comprendre à quel point l'IA est complexe à encadrer au plan juridique au risque d'en négliger le secret, que ce soit au plan individuel ou collectif. Artificial intelligence (AI) is developing at an impressive speed in the field of health. AI paves the way for new practices in diagnostic assistance, preventive medicine, individualized patient follow-up, support in choosing a therapy, assistance in interpreting the results of additional examinations, etc. Far from being a magic bullet, delegating tasks to AI raises many ethical questions relating in particular to the supervision of algorithms, equal access to innovation and the use of patient data. It is still difficult to predict whether AI will remain a "safety net" for the doctor or if it will supplant him. France and Europe have laid down the principle of "human guarantee" in the interpretation of diagnostic and therapeutic results, implying that the machine cannot act alone and enshrining the responsibility of the health professional. This article aims to understand how complex AI is to regulate legally at the risk of neglecting its secrecy, whether at the individual or collective level. [ABSTRACT FROM AUTHOR]

The growth of wearable technology has enabled the collection of even more personalized information on individuals. New health-related devices marketed to consumers collect health information that might not fall under the traditional category of Protected Health Information, and thus, HIPAA protections do not fully apply. Meaning, commercial wearable health devices do not fall under FDA oversight, and data not paired with a doctor–patient relationship do not fall under HIPAA privacy protection; thus, much of the gathered health-related metrics are left without regulation and open to be sold to data brokers. As such, these data can be leveraged by health insurance, law enforcement, and employers, to name a few. This manuscript explores the loopholes in current regulations and suggests a framework that categorizes wearable data and addresses challenges in data transfer. Furthermore, taking a user perspective, the suggested framework offers solutions that aim to guide users and policymakers in navigating privacy issues in wearable technology. [ABSTRACT FROM AUTHOR]

In 2019, the International Consortium for Personalised Medicine (ICPerMed) developed a vision on how the use of personalized medicine (PM) approaches will promote "next-generation" medicine in 2030 more firmly centered on the individual's personal characteristics, leading to improved health outcomes within sustainable healthcare systems through research, development, innovation, and implementation for the benefit of patients, citizens, and society. Nevertheless, there are significant hurdles that healthcare professionals, researchers, policy makers, and patients must overcome to implement PM. The ICPerMed aims to provide recommendations to increase stakeholders' awareness on actionable measures to be implemented for the realization of PM. Starting with best practice examples of PM together with consultation of experts and stakeholders, a careful analysis that underlined hurdles, opportunities, recommendations, and information, aiming at developing knowledge on the requirements for PM implementation in healthcare practices, has been provided. A pragmatic roadmap has been defined for PM integration into healthcare systems, suggesting actions to overcome existing barriers and harness the potential of PM for improved health outcomes. In fact, to facilitate the adoption of PM by diverse stakeholders, it is mandatory to have a comprehensive set of resources tailored to stakeholder needs in critical areas of PM. These include engagement strategies, collaboration frameworks, infrastructure development, education and training programs, ethical considerations, resource allocation guidelines, regulatory compliance, and data management and privacy. [ABSTRACT FROM AUTHOR]