Personal Health Data Research Articles

Understanding the attitudes and experiences of people living with potentially stigmatised long-term health conditions with respect to collecting and sharing health and lifestyle data.

BackgroundThe emerging landscape of patient-generated data (PGData) provides an opportunity to collect large quantities of information that can be used to develop our understanding of different health conditions and potentially improve the quality of life for those living with long-term health condition (LTHCs). If the potential benefits of PGData are to be realised, we need a better understanding of the psychological barriers and facilitators to the collection and beneficial sharing of health and lifestyle data. Due to the understudied role that stigma plays in sharing PGData, we explore the attitudes and experiences of those living with potentially stigmatised LTHCs with respect to collecting and sharing health and lifestyle data.MethodsThis study used semi-structured interviews and a card sorting task to explore the attitudes and experiences of people living with potentially stigmatised LTHCs. Fourteen adult participants who reported having a range of conditions were recruited in England. Template analysis was used to analyse interview transcripts and descriptive statistics were used for the card sorting task.ResultsThe findings present four overarching themes: Preferences for collecting health and lifestyle data, Importance of anonymity, Expected use of data, and Sources of emotional support. Participants illustrated a general willingness to share health and lifestyle data; however, there were some notable differences in sharing experiences, varying both by information type and recipient group. Overall, participants did not identify health-related stigma as a barrier to collecting or sharing their personal health and lifestyle data.ConclusionsWe outline a number of preferences that participants feel would encourage them to collect and share data more readily, which may be considered when developing data sharing tools for the future.

Semantic Constraints Specification and Schematron-Based Validation for Internet of Medical Things’ Data

Fitness and activity trackers are hugely popular wearable devices that monitor various health-related metrics, such as step count, heartbeat rate, or even oxygen saturation. Utilizing personal health information obtained by users’ personal trackers provides promising results in the fields of telemedicine and personal well-being. However, we face challenges such as data quality, privacy and compliance with standards and regulations. This paper addresses such challenges, with the focus on the last one. Semantic constraints for healthcare datatypes are defined to ensure compliance with standards, making the information medically valid and relevant. A process of semantic verification and Schematron-based validation is proposed. The validation process suggested in this paper will enable the data to be transferred and incorporated into a formal Electronic Health Record. The process is then verified using datasets containing various health-related data types. The aim is to integrate personal health data into Electronic Health Record, which forms a part of Central Health Information System. This would provide personalized medical services to patients and help physicians to make more informed decisions.

Secondary use of Personal Health Data: when is it 'Further Processing' under the GDPR, and What Are the Implications for Data Controllers?

Secondary use of Personal Health Data: when is it 'Further Processing' under the GDPR, and What Are the Implications for Data Controllers?

Secondary Use of Personal Health Data: When is it “Further Processing” Under the Gdpr, and What are the Implications for Data Controllers?

Secondary Use of Personal Health Data: When is it “Further Processing” Under the Gdpr, and What are the Implications for Data Controllers?

Views of Nursing Students About Patients' Personal Health Data

Amaç: Öğrenci hemşirelerin kişisel sağlık verilerinin korunmasına yönelik tutumlarını belirlemek ve bu konudaki farkındalıklarını artırmaktır. Yöntem: Tanımlayıcı olarak yapılan bu araştırmaya 131 hemşirelik öğrencisi katılmıştır. Veriler online olarak, Tanıtıcı Özellikler Formu ve Hemşirelik Öğrencileri İçin Kişisel Sağlık Verilerinin Kayıt ve Korunması Tutum Ölçeği kullanılarak toplanmıştır. Veriler Sosyal Bilimler İçin İstatistik Programında analiz edilmiştir. Bulgular: Öğrencilerin %82.4’ünün kadın, %32.8’inin birinci sınıf olduğu, ölçekte aldıkları toplam puan ortalamasının 4.14 (±0.35) olduğu belirlenmiştir. Öğrencilerin, sınıflara göre aldıkları puanlarda anlamlı farklılık saptanmamıştır. Sonuç: Öğrencilerin kişisel sağlık verilerinin kayıt ve korunması konusunda farkındalıklarının yüksek olduğu saptanmıştır.

Nouvelles influences pour la nosographie psychiatrique

It seems legitimate to wonder about the influence that digital technologies could have on the unstable field of psychiatric classifications. For example, passive data collection is a form of monitoring that seems to promote dimensional approaches, more able to represent global trends or breaks than categorial classification based on symptomatology. The enthusiasm around these technologies needs to be recontextualized, and a realistic short-term development could lead to a form of digital assistance, requiring the development of a new type of therapeutic education: personal health data management.

Verkn\xfcpfung von Abrechnungsdaten gesetzlicher Krankenkassen mit Daten epidemiologischer Krebsregister: l\xe4nderspezifische M\xf6glichkeiten und Limitationen

ZusammenfassungHintergrundIn den letzten Jahren wird verstärkt gefordert, Forschungsdaten gemäß den sog. FAIR-Prinzipien für eine Nachnutzung aufzubereiten. Dadurch könnten zukünftige Projekte auf einer breiteren Datengrundlage durchgeführt sowie durch Verknüpfung verschiedener Datenquellen neue Fragestellungen untersucht werden.FragestellungEruiert werden soll, inwieweit Abrechnungsdaten gesetzlicher Krankenversicherungen mit den Daten der Landeskrebsregister (LKR) überregional verknüpft werden können, um die in den Abrechnungsdaten fehlenden Informationen zu Krebserkrankungen ergänzen und die Validität der dortigen Angaben zur Tumordiagnose beurteilen zu können. Der Fokus liegt dabei auf der Beschreibung der länderspezifischen Anforderungen für einen solchen Datenabgleich.Material und MethodenAls Datenquellen wurden die Pharmakoepidemiologische Forschungsdatenbank GePaRD des Leibniz-Instituts für Präventionsforschung und Epidemiologie – BIPS und sechs Krebsregister herangezogen. Zur Verknüpfung wurden vergleichend das logistisch aufwendige direkte Linkage- und ein weniger aufwendiges indirektes Linkage-Verfahren angewandt. Dazu mussten für GePaRD und für jedes LKR die Genehmigungen der jeweils zuständigen Behörde eingeholt werden.ErgebnisseHinsichtlich der Verknüpfung von LKR-Daten mit GePaRD zeigten sich gravierende Unterschiede in der Datenbereitstellung (vollständige Ablehnung bis hin zu einer unkomplizierten Umsetzung).DiskussionIn Deutschland müssen einheitliche Rahmenbedingungen geschaffen werden, um eine angemessene Nachnutzung und eine Verknüpfung von personenbezogenen Gesundheitsdaten zu Forschungszwecken im Sinne der FAIR-Prinzipien zu ermöglichen. Bezüglich der Verknüpfung von LKR-Daten mit anderen Datenquellen könnte das neue Gesetz zur Zusammenführung von Krebsregisterdaten Abhilfe schaffen.

산후 개인생성건강기록 활용 리포트 연구

This study aims to design and develop the self-monitoring contents and Person Generated Health Data report for the postpartum management group. So far, many mobile apps and wearable health devices for personal health data have been released. However, few services manage postpartum management groups’ health based on Korean medicine knowledge. Our postpartum PGHD report proposed in this paper reflects the clinical indicators of Korean medicine and the actual postpartum record pattern. It provides information to users based on categories of related PGHD items. When the PGHD platform provides this report information, the user can easily interpret PGHD items and monitor their health status integrated compared to the existing simple list-type record information check. In addition, category-specific information based on oriental significance indicator analysis data can be used to connect with oriental medicine treatment in the postpartum management group. In the future, it will be able to contribute to the oriental medical treatment of the postpartum management group if this postpartum PGHD report can be provided by linking the oriental medicine PHR platform in connection with the oriental clinic medical record chart.

Toward the Value Sensitive Design of eHealth Technologies to Support Self-management of Cardiovascular Diseases: Content Analysis.

BackgroundeHealth can revolutionize the way self-management support is offered to chronically ill individuals such as those with a cardiovascular disease (CVD). However, patients’ fluctuating motivation to actually perform self-management is an important factor for which to account. Tailoring and personalizing eHealth to fit with the values of individuals promises to be an effective motivational strategy. Nevertheless, how specific eHealth technologies and design features could potentially contribute to values of individuals with a CVD has not been explicitly studied before.ObjectiveThis study sought to connect a set of empirically validated, health-related values of individuals with a CVD with existing eHealth technologies and their design features. The study searched for potential connections between design features and values with the goal to advance knowledge about how eHealth technologies can actually be more meaningful and motivating for end users.MethodsUndertaking a technical investigation that fits with the value sensitive design framework, a content analysis of existing eHealth technologies was conducted. We matched 11 empirically validated values of CVD patients with 70 design features from 10 eHealth technologies that were previously identified in a systematic review. The analysis consisted mainly of a deductive coding stage performed independently by 3 members of the study team. In addition, researchers and developers of 6 of the 10 reviewed technologies provided input about potential feature-value connections.ResultsIn total, 98 connections were made between eHealth design features and patient values. This meant that some design features could contribute to multiple values. Importantly, some values were more often addressed than others. CVD patients’ values most often addressed were related to (1) having or maintaining a healthy lifestyle, (2) having an overview of personal health data, (3) having reliable information and advice, (4) having extrinsic motivators to accomplish goals or health-related activities, and (5) receiving personalized care. In contrast, values less often addressed concerned (6) perceiving low thresholds to access health care, (7) receiving social support, (8) preserving a sense of autonomy over life, and (9) not feeling fear, anxiety, or insecurity about health. Last, 2 largely unaddressed values were related to (10) having confidence and self-efficacy in the treatment or ability to achieve goals and (11) desiring to be seen as a person rather than a patient.ConclusionsPositively, existing eHealth technologies could be connected with CVD patients’ values, largely through design features that relate to educational support, self-monitoring support, behavior change support, feedback, and motivational incentives. Other design features such as reminders, prompts or cues, peer-based or expert-based human support, and general system personalization were also connected with values but in narrower ways. In future studies, the inferred feature-value connections must be validated with empirical data from individuals with a CVD or similar chronic conditions.

Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality. The privacy of health data can only be preserved by keeping it in an encrypted form, but it affects usability and flexibility in terms of effective search. Attribute-based searchable encryption (ABSE) has proven its worth by providing fine-grained searching capabilities in the shared cloud storage. However, it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations. In a healthcare cloud-based cyber-physical system (CCPS), the data is often collected by resource-constraint devices; therefore, here also, we cannot directly apply ABSE schemes. In the proposed work, the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network. Thus, it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS. With the assistance of blockchain technology, the proposed scheme offers two main benefits. First, it is free from a trusted authority, which makes it genuinely decentralized and free from a single point of failure. Second, it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network. Specifically, the task of initializing the system, which is considered the most computationally intensive, and the task of partial search token generation, which is considered as the most frequent operation, is now the responsibility of the consensus nodes. This eliminates the need of the trusted authority and reduces the burden of data users, respectively. Further, in comparison to existing decentralized fine-grained searchable encryption schemes, the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users. It has been verified both theoretically and practically in the performance analysis section.

Harmonizing regulatory regimes for the governance of patient-generated health data

Patient-generated health data (PGHD), created and captured from patients via wearable devices and mobile apps, are proliferating outside of clinical settings. Examples include sleep trackers, fitness trackers, continuous glucose monitors, and RFID-enabled implants, with many additional biometric or health surveillance applications in development or envisioned. These data are included in growing stockpiles of personal health data (PHI) being mined for insight by health economists, policy analysts, researchers, and health system organizations. Dominant narratives position these highly personal data as valuable resources to transform healthcare, stimulate innovation in medical research, and engage individuals in their health and healthcare. Large tech companies are also increasingly implicated in these areas, through mobile health application sales and data acquisitions. Given the many possible uses and users for PGHD, ensuring privacy, security, and equity of benefits from PGHD will be challenging. This is due in part to disparate regulatory policies and practices across technology firms, health system organizations, and health researchers. Rapid developments with PGHD technologies and the lack of harmonization between regulatory regimes may render existing safeguards to preserve patient privacy and control over their PGHD ineffective, while also failing to guide PGHD-related innovation in socially desirable directions. Using a policy regime lens to explore these challenges, we examine three existing data protection regimes relevant to PGHD in the United States that are currently in tension with one another: federal and state health-sector laws, regulations on data use and reuse for research and innovation, and industry self-regulation of consumer privacy by large tech companies. We argue that harmonization of these regimes is necessary to meet the challenges of PGHD data governance. We next examine emerging governing instruments, identifying three types of structures (organizational, regulatory, technological/algorithmic), which synergistically could help enact needed regulatory oversight while limiting the friction and economic costs of regulation that may hinder innovation. This policy analysis provides a starting point for further discussions and negotiations among stakeholders and regulators to do so.

RmvPFBAM: Removing Primers from BAM Files Based on Amplicon-Based Next-Generation Sequencing and Cloud Computing When Analyzing Personal Genome Data

Personal genomic data constitute one important part of personal health data. However, due to the large amount of personal genomic data obtained by the next-generation sequencing technology, special tools are needed to analyze these data. In this article, we will explore a tool analyzing cloud-based large-scale genome sequencing data. Analyzing and identifying genomic variations from amplicon-based next-generation sequencing data are necessary for the clinical diagnosis and treatment of cancer patients. When processing the amplicon-based next-generation sequencing data, one essential step is removing primer sequences from the reads to avoid detecting false-positive mutations introduced by nonspecific primer binding and primer extension reactions. At present, the removing primer tools usually discard primer sequences from the FASTQ file instead of BAM file, but this method could cause some downstream analysis problems. Only one tool (BAMClipper) removes primer sequences from BAM files, but it only modified the CIGAR value of the BAM file, and false-positive mutations falling in the primer region could still be detected based on its processed BAM file. So, we developed one cutting primer tool (rmvPFBAM) removing primer sequences from the BAM file, and the mutations detected based on the processed BAM file by rmvPFBAM are highly credible. Besides that, rmvPFBAM runs faster than other tools, such as cutPrimers and BAMClipper.

Een mensgericht, gezondheidsgegevens-gedreven ecosysteem

A human-centered, health data-driven ecosystem Value-based, connected and integrated healthcare are gaining momentum in the healthcare landscape. Industry 4.0 is transforming healthcare into a data-driven sector. Data and innovation are the foundations of future value-driven healthcare ecosystems. But how will the human aspect remain to play a lasting role? Healthcare continuums are being rolled out as healthcare goes beyond traditional diagnosis and treatment towards prevention and early detection. Health institutions are facing a new generation of ‘health-conscious’ consumers and ‘technology-minded and -adapted’ healthcare professionals. In order to accelerate innovation within healthcare institutions, it must be powered by the personal health-data of an individual, independent of location, life phase and health status. This data will be continuously generated by the daily use of different technologies. Based upon these concepts and shifts, this paper describes a human-centered, health data-driven ecosystem that is built upon the interaction and balance of human actors in every life phase, different environments and societal changes and with different technologies. International and national guidelines and regulations, and ethical norms and values need to be taken into account. Implementation of this health data model will create future value at any time, place and location within this ecosystem. This ecosystem will create value through the realized integrated care by connecting diverse human actors, different environments and various technologies, while still maintaining the empathic relations between the caretaker and the patient/client.

A proactive GDPR-compliant solution for fostering medical scientific research as a secondary use of per-sonal health data

A proactive GDPR-compliant solution for fostering medical scientific research as a secondary use of per-sonal health data

Security of Cloud-Based Medical Internet of Things (MIoTs)

In this digital era expectations for medical quality have increased. As the number of patients continues to increase, conventional health care methods are having to deal with new complications. In light of these observations, researchers suggested a hybrid combination of conventional health care methods with IoT technology and develop MIoT. The goal of IoMT is to ensure that patients can respond more effectively and efficiently to their treatment. But preserving user privacy is a critical issue when it comes to collecting and handling highly sensitive personal health data. However, IoMTs have limited processing power; hence, they can only implement minimal security techniques. Consequently, throughout the health data transfer through MIoT, patient’s data is at risk of data leakage. This manuscript per the authors emphasizes the need of implementing suitable security measures to increase the IoMT's resilience to cyberattacks. Additionally, this manuscript per the authors discusses the main security and privacy issues associated with IoMT and provide an overview of existing techniques.

A cognitive approach for blockchain-based cryptographic curve hash signature (BC-CCHS) technique to secure healthcare data in Data Lake.

In today's digital world, information is exchanged among various sources, and it is expected that each interaction or transaction among the sources must be reliable and secure. In these circumstances, blockchain technology can be applied to ensure healthcare data security in an efficient way. Blockchain is an ordered list of records linked together through a chain of blocks in a distributed ledger. It is a decentralized and tamper-proof database system. It can be used to store the medical records of patients and play a vital role in healthcare to maintain and share medical data securely. At present, many scholars are focusing on the privacy and security in electronic health record (EHR) sharing based on blockchain technology. But still, the security of health data plays a significant challenge. A Cognitive Approach blockchain-based cryptographic curve hash signature (BC-CCHS) technique is proposed to secure patients' medical records and share their personal health data safely and conveniently. The proposed approach is carried out in the Hyperledger framework. Here, several phases like registration, authentication, uploading, and requesting are involved in enhancing the security mechanism. The proposed methodology is experimentally tested and validated with the existing techniques regarding encryption time, decryption time, throughput, delay, and overall processing time.

Visualizing Knowledge Evolution Trends and Research Hotspots of Personal Health Data Research: Bibliometric Analysis.

BackgroundThe recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots.ObjectiveThe aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis.MethodsWe collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence.ResultsThe findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention.ConclusionsOur analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology. These findings indicate opportunities for interdisciplinary cooperation in several PHD research areas: (1) AI applications for PHD; (2) regulatory issues and governance of PHD; (3) education of all stakeholders about AI technology; and (4) value-based health care including “allocative value,” “technology value,” and “personalized value.”

MAPPING HEALTH: HOW DANES EXPERIENCE THEIR DIGITAL HEALTH DATA

This paper explores how Danish citizens experience digital health data and how these in turn affect their understanding of digital health data and their self-understanding as a patient. Previous research on digital health data examines primarily opportunities and challenges as well as structural effects concluding that having access to one's medical data is generally beneficial for patients but also comes with literacy challenges. The aim of this research is to look deeper into personal experiences with digital health data in order to understand what is at stake when people become digitally mapped patients and how experiences of empowerment, independence, perplexity, and doubt intermingle when reading one’s own health data. Taking a user’s view, the paper draws theoretically on the concept of ‘assemblage’ understanding digital health data as a complex nexus of user-data relationships. The empirical analysis draws on 16 in-depth purposefully sampled interviews that have been coded thematically. The primary analysis shows that digital health data creates unique, deeply emotional experiences that lead towards a variety of existential questions. Combining the theoretical lens with the empirical analysis this paper contributes with what we call ‘health assemblages’ that highlight the emerging relationships and personal emotional attachments users make with their digital health data. In conclusion, it can be stated that seeing oneself mapped in data creates unique experiences, often challenging the self-understanding of the patient.

GlucoMine

The growing popularity of wearable devices for continuous sensing has made personal health data increasingly available, yet methods for data interpretation are still a work in progress. This paper investigates potential under-utilization of wearable device data in diabetes management and develops an analytic approach - GlucoMine - to uncover individualized patterns in extended periods of such data to support and improve care. In addition, we conduct a user study with clinicians to assess and compare conventional tools used for reviewing wearable device data in diabetes management with the proposed solution. Using 3-6 months of continuous glucose monitor (CGM) data from 54 patients with type 1 diabetes, we found that: 1) the recommended practice of reviewing only short periods (e.g., the most recent 2-weeks) of CGM data based on correlation analysis is not sufficient for finding hidden patterns of poor management; 2) majority of subjects (96% in this study) had clinically-recognized episodes of recurrent adverse glycemic events observable from analysis of extended periods of their CGM data; 3) majority of clinicians (89% in this study) believe there is benefit to be gained in having an algorithm for extracting patterns of adverse glycemic events from longer periods of wearable device data. Findings from our user study also provides insights, including strengths and weakness of various data presentation tools, to guide development of better solutions that improve the use of wearable device data for patient care.

Development of a MyData Platform Based on the Personal Health Record Data Sharing System in Korea

Objectives: recently, there has been a government-level movement to guarantee the rights of individual entities regarding the use of their personal data worldwide. This movement has been specifically named as ‘MyData’ in South Korea and has variants such as ‘Self data’, ‘Midata’, ‘MesInfos’, ‘Personal Information Management Services’, ‘Personal Data Economy’ and ‘Internet of Me’ in other countries. This research project aimed to establish and demonstrate a system called ‘HiMD’, which allows individuals to select data sharing institutions and control a range of data utilization parameters under the MyData ecosystem. Method: we developed the MyData Platform, a personal health record data sharing system. The HiMD included several user-empowerment functions such as self-determination for data sharing. Actual platform users were recruited from three university-level hospitals for system assessment. Result: females comprised the majority of users with 991 participants (78.1%). Additionally, data consensus results revealed a decrease in given user permissions (from 94.9% to 79.4%) as the range and depth of permissions increased. Most users agreed to open their medical data for commercial uses (n = 1007, 79.4%) and most of those users were interested in DNA analysis (n = 888, 81.2%). Finally, all results for the five questions presented positive answers. All average values on the five questions scored above three on the Likert scale. In other words, over 50% participants gave point 4 and point 5. Conclusion: the contribution of this study is that it developed and demonstrated a MyData system that reflects the right to data portability. It means that the users can proactively make decisions regarding sharing and transferring their own data. These results are expected to contribute to developing future personal health record (PHR) systems of user-oriented and utilization of personal health data.