The Effect of the General Data Protection Regulation on Medical Research

Medical Information Protection in Internet Hospital Apps in China: Scale Development and Content Analysis

BACKGROUND

Hospital apps are increasingly being adopted in many countries, especially since the start of the COVID-19 pandemic. Web-based hospitals can provide valuable medical services and enhanced accessibility. However, increasing concerns about personal information (PI) and strict legal compliance requirements necessitate privacy assessments for these platforms. Guided by the theory of contextual integrity, this study investigates the regulatory compliance of privacy policies for internet hospital apps in the mainland of China.

OBJECTIVE

In this paper, we aim to evaluate the regulatory compliance of privacy policies of internet hospital apps in the mainland of China and offer recommendations for improvement.

METHODS

We obtained 59 internet hospital apps on November 7, 2023, and reviewed 52 privacy policies available between November 8 and 23, 2023. We developed a 3-level indicator scale based on the information processing activities, as stipulated in relevant regulations. The scale comprised 7 level-1 indicators, 26 level-2 indicators, and 70 level-3 indicators.

RESULTS

The mean compliance score of the 52 assessed apps was 73/100 (SD 22.4%), revealing a varied spectrum of compliance. Sensitive PI protection compliance (mean 73.9%, SD 24.2%) lagged behind general PI protection (mean 90.4%, SD 14.7%), with only 12 apps requiring separate consent for processing sensitive PI (mean 73.9%, SD 24.2%). Although most apps (n=41, 79%) committed to supervising subcontractors, only a quarter (n=13, 25%) required users' explicit consent for subcontracting activities. Concerning PI storage security (mean 71.2%, SD 29.3%) and incident management (mean 71.8%, SD 36.6%), half of the assessed apps (n=27, 52%) committed to bear corresponding legal responsibility, whereas fewer than half (n=24, 46%) specified the security level obtained. Most privacy policies stated the PI retention period (n=40, 77%) and instances of PI deletion or anonymization (n=41, 79%), but fewer (n=20, 38.5%) committed to prompt third-party PI deletion. Most apps delineated various individual rights, but only a fraction addressed the rights to obtain copies (n=22, 42%) or to refuse advertisement based on automated decision-making (n=13, 25%). Significant deficiencies remained in regular compliance audits (mean 11.5%, SD 37.8%), impact assessments (mean 13.5%, SD 15.2%), and PI officer disclosure (mean 48.1%, SD 49.3%).

CONCLUSIONS

Our analysis revealed both strengths and significant shortcomings in the compliance of internet hospital apps' privacy policies with relevant regulations. As China continues to implement internet hospital apps, it should ensure the informed consent of users for PI processing activities, enhance compliance levels of relevant privacy policies, and fortify PI protection enforcement across the information processing stages.

Organ Donation and Transplantation Registries Across the Globe: A Review of the Current State

BACKGROUND

The current landscape of organ donation and transplantation (ODT) registries is not well established. This narrative review sought to identify and characterize the coverage, structure, and data capture of ODT registries globally.

METHODS

We conducted a literature search using Ovid Medline and web searches to identify ODT registries from 2000 to 2023. A list of ODT registries was compiled based on publications of registry design, studies, and reports. Extracted data elements included operational features of registries and the types of donor and recipient data captured.

RESULTS

We identified 129 registries encompassing patients from all continents except Antarctica. Most registries were active, received funding from government or professional societies, were national in scope, included both adult and pediatric patients, and reported patient-level data. Registries included kidney (n = 99), pancreas (n = 32), liver (n = 44), heart (n = 35), lung (n = 30), intestine (n = 15), and islet cell (n = 5) transplants. Most registries captured donor data (including living versus deceased) and recipient features (including demographics, cause of organ failure, and posttransplant outcomes) but there was underreporting of other domains (eg, donor comorbidities, deceased donor referral rates, waitlist statistics).

CONCLUSIONS

This review highlights existing ODT registries globally and serves as a call for increased visibility and transparency in data management and reporting practices. We propose that standards for ODT registries, a common data model, and technical platforms for collaboration, will enable a high-functioning global ODT system responsive to the needs of transplant candidates, recipients, and donors.

Mapping and evaluating national data flows: transparency, privacy, and guiding infrastructural transformation

The importance of big health data is recognised worldwide. Most UK National Health Service (NHS) care interactions are recorded in electronic health records, resulting in an unmatched potential for population-level datasets. However, policy reviews have highlighted challenges from a complex data-sharing landscape relating to transparency, privacy, and analysis capabilities. In response, we used public information sources to map all electronic patient data flows across England, from providers to more than 460 subsequent academic, commercial, and public data consumers. Although NHS data support a global research ecosystem, we found that multistage data flow chains limit transparency and risk public trust, most data interactions do not fulfil recommended best practices for safe data access, and existing infrastructure produces aggregation of duplicate data assets, thus limiting diversity of data and added value to end users. We provide recommendations to support data infrastructure transformation and have produced a website (https://DataInsights.uk) to promote transparency and showcase NHS data assets.

Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework

BACKGROUND

Conversational agents (CAs), also known as chatbots, are computer programs that simulate human conversations by using predetermined rule-based responses or artificial intelligence algorithms. They are increasingly used in health care, particularly via smartphones. There is, at present, no conceptual framework guiding the development of smartphone-based, rule-based CAs in health care. To fill this gap, we propose structured and tailored guidance for their design, development, evaluation, and implementation.

OBJECTIVE

The aim of this study was to develop a conceptual framework for the design, evaluation, and implementation of smartphone-delivered, rule-based, goal-oriented, and text-based CAs for health care.

METHODS

We followed the approach by Jabareen, which was based on the grounded theory method, to develop this conceptual framework. We performed 2 literature reviews focusing on health care CAs and conceptual frameworks for the development of mobile health interventions. We identified, named, categorized, integrated, and synthesized the information retrieved from the literature reviews to develop the conceptual framework. We then applied this framework by developing a CA and testing it in a feasibility study.

RESULTS

The Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER) conceptual framework includes 8 iterative steps grouped into 3 stages, as follows: design, comprising defining the goal, creating an identity, assembling the team, and selecting the delivery interface; development, including developing the content and building the conversation flow; and the evaluation and implementation of the CA. They were complemented by 2 cross-cutting considerations-user-centered design and privacy and security-that were relevant at all stages. This conceptual framework was successfully applied in the development of a CA to support lifestyle changes and prevent type 2 diabetes.

CONCLUSIONS

Drawing on published evidence, the DISCOVER conceptual framework provides a step-by-step guide for developing rule-based, smartphone-delivered CAs. Further evaluation of this framework in diverse health care areas and settings and for a variety of users is needed to demonstrate its validity. Future research should aim to explore the use of CAs to deliver health care interventions, including behavior change and potential privacy and safety concerns.

Implementation of a pan-European ecosystem and an interoperable platform for Smart and Healthy Ageing in Europe: An Innovation Action research protocol

As life expectancy continues to increase in most EU Member States, smart technologies can help enable older people to continue living at home, despite the challenges accompanying the ageing process. The Innovation Action (IA) SHAPES 'Smart and Healthy Ageing through People Engaging in Supportive Systems' funded by the EU under the Horizon 2020 Research and Innovation Programme (grant agreement number 857159) attends to these topics to support active and healthy ageing and the wellbeing of older adults. This protocol article outlines the SHAPES project's objectives and aims, methods, structure, and expected outcomes. SHAPES seeks to build, pilot, and deploy a large-scale, EU-standardised interoperable, and scalable open platform. The platform will facilitate the integration of a broad range of technological, organisational, clinical, educational, and social solutions. SHAPES emphasises that the home is much more than a house-space; it entails a sense of belonging, a place and a purpose in the community. SHAPES creates an ecosystem - a network of relevant users and stakeholders - who will work together to scale-up smart solutions. Furthermore, SHAPES will create a marketplace seeking to connect demand and supply across the home, health and care services. Finally, SHAPES will produce a set of recommendations to support key stakeholders seeking to integrate smart technologies in their care systems to mediate care delivery. Throughout, SHAPES adopts a multidisciplinary research approach to establish an empirical basis to guide the development of the platform. This includes long-term ethnographic research and a large-scale pan-European campaign to pilot the platform and its digital solutions within the context of seven distinct pilot themes. The project will thereby address the challenges of ageing societies in Europe and facilitate the integration of community-based health and social care. SHAPES will thus be a key driver for the transformation of healthcare and social care services across Europe.

Practices and attitudes of Bavarian stakeholders regarding the secondary-use of health data for research purposes during the COVID-19 pandemic: a qualitative interview study (Preprint)

Background

The COVID-19 pandemic is a threat to global health and requires collaborative health research efforts across organizations and countries to address it. Although routinely collected digital health data are a valuable source of information for researchers, benefiting from these data requires accessing and sharing the data. Health care organizations focusing on individual risk minimization threaten to undermine COVID-19 research efforts, and it has been argued that there is an ethical obligation to use the European Union’s General Data Protection Regulation (GDPR) scientific research exemption during the COVID-19 pandemic to support collaborative health research.

Objective

This study aims to explore the practices and attitudes of stakeholders in the German federal state of Bavaria regarding the secondary use of health data for research purposes during the COVID-19 pandemic, with a specific focus on the GDPR scientific research exemption.

Methods

Individual semistructured qualitative interviews were conducted between December 2020 and January 2021 with a purposive sample of 17 stakeholders from 3 different groups in Bavaria: researchers involved in COVID-19 research (n=5, 29%), data protection officers (n=6, 35%), and research ethics committee representatives (n=6, 35%). The transcripts were analyzed using conventional content analysis.

Results

Participants identified systemic challenges in conducting collaborative secondary-use health data research in Bavaria; secondary health data research generally only happens when patient consent has been obtained, or the data have been fully anonymized. The GDPR research exemption has not played a significant role during the pandemic and is currently seldom and restrictively used. Participants identified 3 key groups of barriers that led to difficulties: the wider ecosystem at many Bavarian health care organizations, legal uncertainty that leads to risk-adverse approaches, and ethical positions that patient consent ought to be obtained whenever possible to respect patient autonomy. To improve health data research in Bavaria and across Germany, participants wanted greater legal certainty regarding the use of pseudonymized data for research purposes without the patient’s consent.

Conclusions

The current balance between enabling the positive goals of health data research and avoiding associated data protection risks is heavily skewed toward avoiding risks; so much so that it makes reaching the goals of health data research extremely difficult. This is important, as it is widely recognized that there is an ethical imperative to use health data to improve care. The current approach also creates a problematic conflict with the ambitions of Germany, and the federal state of Bavaria, to be a leader in artificial intelligence. A recent development in the field of German public administration known as norm screening (Normenscreening) could potentially provide a systematic approach to minimize legal barriers. This approach would likely be beneficial to other countries.

Federated Learning Approach to Protect Healthcare Data over Big Data Scenario

The benefits and drawbacks of various technologies, as well as the scope of their application, are thoroughly discussed. The use of anonymity technology and differential privacy in data collection can aid in the prevention of attacks based on background knowledge gleaned from data integration and fusion. The majority of medical big data are stored on a cloud computing platform during the storage stage. To ensure the confidentiality and integrity of the information stored, encryption and auditing procedures are frequently used. Access control mechanisms are mostly used during the data sharing stage to regulate the objects that have access to the data. The privacy protection of medical and health big data is carried out under the supervision of machine learning during the data analysis stage. Finally, acceptable ideas are put forward from the management level as a result of the general privacy protection concerns that exist throughout the life cycle of medical big data throughout the industry.

Ethical issues in biomedical research using electronic health records: a systematic review

Digitization of a health record changes its accessibility. An electronic health record (EHR) can be accessed by multiple authorized users. Health information from EHRs contributes to learning healthcare systems' development. The objective of this systematic review is to answer a question: What are ethical issues concerning research using EHRs in the literature? We searched Medline Ovid, Embase and Scopus for publications concerning ethical issues of research use of EHRs. We employed the constant comparative method to retrieve common ethical themes. We descriptively summarized empirical studies. The study reveals the breadth, depth, and complexity of ethical problems associated with research use of EHRs. The central ethical question that emerges from the review is how to manage access to EHRs. Managing accessibility consists of interconnected and overlapping issues: streamlining research access to EHRs, minimizing risk, engaging and educating patients, as well as ensuring trustworthy governance of EHR data. Most of the ethical problems concerning EHR-based research arise from rapid cultural change. The framing of concepts of privacy, as well as individual and public dimensions of beneficence, are changing. We are currently living in the middle of this transition period. Human emotions and mental habits, as well as laws, are lagging behind technological developments. In the medical tradition, individual patient's health has always been in the center. Transformation of healthcare care, its digitalization, seems to have some impacts on our perspective of health care ethics, research ethics and public health ethics.

Research Electronic Data Capture (REDCap): tackling data collection, management, storage, and privacy challenges

Data are the basis of research; without data, there is no research. However, growing internationalization of research, increased complexity of study designs, and stricter legislation make high-quality data collection, management, and storage more important, but also more challenging than ever. This article provides an overview of common challenges clinical researchers face when collecting, managing, and storing data and how REDCap, Research Electronic Data Capture, can be a possible solution to address these challenges.

National and international kidney failure registries: characteristics, commonalities, and contrasts

Registries are essential for health infrastructure planning, benchmarking, continuous quality improvement, hypothesis generation, and real-world trials. To date, data from these registries have predominantly been analyzed in isolated "silos," hampering efforts to analyze "big data" at the international level, an approach that provides wide-ranging benefits, including enhanced statistical power, an ability to conduct international comparisons, and greater capacity to study rare diseases. This review serves as a valuable resource to clinicians, researchers, and policymakers, by comprehensively describing kidney failure registries active in 2021, before proposing approaches for inter-registry research under current conditions, as well as solutions to enhance global capacity for data collaboration. We identified 79 kidney-failure registries spanning 77 countries worldwide. International Society of Nephrology exemplar initiatives, including the Global Kidney Health Atlas and Sharing Expertise to support the set-up of Renal Registries (SharE-RR), continue to raise awareness regarding international healthcare disparities and support the development of universal kidney-disease registries. Current barriers to inter-registry collaboration include underrepresentation of lower-income countries, poor syntactic and semantic interoperability, absence of clear consensus guidelines for healthcare data sharing, and limited researcher incentives. This review represents a call to action for international stakeholders to enact systemic change that will harmonize the current fragmented approaches to kidney-failure registry data collection and research.

Challenges and proposed solutions in making clinical research on COVID-19 ethical: a status quo analysis across German research ethics committees

BACKGROUND

In the course of the COVID-19 pandemic, the biomedical research community's attempt to focus the attention on fighting COVID-19, led to several challenges within the field of research ethics. However, we know little about the practical relevance of these challenges for Research Ethics Committees (RECs).

METHODS

We conducted a qualitative survey across all 52 German RECs on the challenges and potential solutions with reviewing proposals for COVID-19 studies. We de-identified the answers and applied thematic text analysis for the extraction and synthesis of challenges and potential solutions that we grouped under established principles for clinical research ethics.

RESULTS

We received an overall response rate of 42%. The 22 responding RECs reported that they had assessed a total of 441 study proposals on COVID-19 until 21 April 2020. For the review of these proposals the RECs indicated a broad spectrum of challenges regarding (1) social value (e.g. lack of coordination), (2) scientific validity (e.g. provisional study planning), (3) favourable risk-benefit ratio (e.g. difficult benefit assessment), (4) informed consent (e.g. strict isolation measures), (5) independent review (e.g. lack of time), (6) fair selection of trial participants (e.g. inclusion of vulnerable groups), and (7) respect for study participants (e.g. data security). Mentioned solutions ranged from improved local/national coordination, over guidance on modified consent procedures, to priority setting across clinical studies.

CONCLUSIONS

RECs are facing a broad spectrum of pressing challenges in reviewing COVID-19 studies. Some challenges for consent procedures are well known from research in intensive care settings but are further aggravated by infection measures. Other challenges such as reviewing several clinical studies at the same time that potentially compete for the recruitment of in-house COVID-19 patients are unique to the current situation. For some of the challenges the proposed solutions in our survey could relatively easy be translated into practice. Others need further conceptual and empirical research. Our findings together with the increasing body of literature on COVID-19 research ethics, and further stakeholder engagement should inform the development of hands-on guidance for researchers, funders, RECs, and further oversight bodies.

Ethics of Artificial Intelligence in Medicine and Ophthalmology

BACKGROUND

This review explores the bioethical implementation of artificial intelligence (AI) in medicine and in ophthalmology. AI, which was first introduced in the 1950s, is defined as "the machine simulation of human mental reasoning, decision making, and behavior". The increased power of computing, expansion of storage capacity, and compilation of medical big data helped the AI implementation surge in medical practice and research. Ophthalmology is a leading medical specialty in applying AI in screening, diagnosis, and treatment. The first Food and Drug Administration approved autonomous diagnostic system served to diagnose and classify diabetic retinopathy. Other ophthalmic conditions such as age-related macular degeneration, glaucoma, retinopathy of prematurity, and congenital cataract, among others, implemented AI too.

PURPOSE

To review the contemporary literature of the bioethical issues of AI in medicine and ophthalmology, classify ethical issues in medical AI, and suggest possible standardizations of ethical frameworks for AI implementation.

METHODS

Keywords were searched on Google Scholar and PubMed between October 2019 and April 2020. The results were reviewed, cross-referenced, and summarized. A total of 284 references including articles, books, book chapters, and regulatory reports and statements were reviewed, and those that were relevant were cited in the paper.

RESULTS

Most sources that studied the use of AI in medicine explored the ethical aspects. Bioethical challenges of AI implementation in medicine were categorized into 6 main categories. These include machine training ethics, machine accuracy ethics, patient-related ethics, physician-related ethics, shared ethics, and roles of regulators.

CONCLUSIONS

There are multiple stakeholders in the ethical issues surrounding AI in medicine and ophthalmology. Attention to the various aspects of ethics related to AI is important especially with the expanding use of AI. Solutions of ethical problems are envisioned to be multifactorial.

Data Sharing Under the General Data Protection Regulation: Time to Harmonize Law and Research Ethics?

Supplemental Digital Content is available in the text.

The General Data Protection Regulation (GDPR) became binding law in the European Union Member States in 2018, as a step toward harmonizing personal data protection legislation in the European Union. The Regulation governs almost all types of personal data processing, hence, also, those pertaining to biomedical research. The purpose of this article is to highlight the main practical issues related to data and biological sample sharing that biomedical researchers face regularly, and to specify how these are addressed in the context of GDPR, after consulting with ethics/legal experts. We identify areas in which clarifications of the GDPR are needed, particularly those related to consent requirements by study participants. Amendments should target the following: (1) restricting exceptions based on national laws and increasing harmonization, (2) confirming the concept of broad consent, and (3) defining a roadmap for secondary use of data. These changes will be achieved by acknowledged learned societies in the field taking the lead in preparing a document giving guidance for the optimal interpretation of the GDPR, which will be finalized following a period of commenting by a broad multistakeholder audience. In parallel, promoting engagement and education of the public in the relevant issues (such as different consent types or residual risk for re-identification), on both local/national and international levels, is considered critical for advancement. We hope that this article will open this broad discussion involving all major stakeholders, toward optimizing the GDPR and allowing a harmonized transnational research approach.

Effectiveness of Smartphone-Based Cognitive Behavioral Therapy Among Patients With Major Depression: Systematic Review of Health Implications

BACKGROUND

Depression is often associated with rapid changes in mood and quality of life that persist for a period of 2 weeks. Despite medical innovations, there are problems in the provision of care. Long waiting times for treatment and high recurrence rates of depression cause enormous costs for health care systems. At the same time, comprehensive limitations in physical, psychological, and social dimensions are observed for patients with depression, which significantly reduce their quality of life. In addition to patient-specific limitations, undersupply and inappropriate health care have been determined. For this reason, new forms of care are discussed. Smartphone-based therapy is considered to have great potential due to its reach and easy accessibility. Low socioeconomic groups, which are always difficult to reach for public health interventions, can now be accessed due to the high dispersion of smartphones. There is still little information about the impact and mechanisms of smartphone-based therapy on depression. In a systematic literature review, the health implications of smartphone-based therapy were presented in comparison with standard care.

OBJECTIVE

The objective of this review was to identify and summarize the existing evidence regarding smartphone-based cognitive behavioral therapy for patients with depression and to present the health implications of smartphone-based cognitive behavioral therapy of considered endpoints.

METHODS

A systematic literature review was conducted to identify relevant studies by means of inclusion and exclusion criteria. For this purpose, the PubMed and Psyndex databases were systematically searched using a search syntax. The endpoints of depressive symptoms, depression-related anxiety, self-efficacy or self-esteem, and quality of life were analyzed. Identified studies were evaluated for study quality and risk of bias. After applying the inclusion and exclusion criteria, 8 studies were identified.

RESULTS

The studies examined in this review reported contradictory results regarding the investigated endpoints. In addition, due to clinical and methodological heterogeneity, it was difficult to derive evident results. All included studies reported effects on depressive symptoms. The other investigated endpoints were only reported by isolated studies. Only 50% (4/8) of the studies reported effects on depression-related anxiety, self-efficacy or self-esteem, and quality of life.

CONCLUSIONS

No clear implications of smartphone-based cognitive behavioral therapy could be established. Evidence for the treatment of depression using smartphone-based cognitive behavioral therapy is limited. Additional research projects are needed to demonstrate the effects of smartphone-based cognitive behavioral therapy in the context of evidence-based medicine and to enable its translation into standard care. Participatory technology development might help to address current problems in mobile health intervention studies.

Preferences for Accessing Electronic Health Records for Research Purposes: Views of Parents Who Have a Child With a Known or Suspected Genetic Condition

OBJECTIVES

The purpose of this study was to examine parental preferences for researchers accessing their child's electronic health record across 3 groups: those with a child with (1) a known genetic condition (fragile X syndrome FXS), (2) a suspected genetic condition (autism spectrum disorder [ASD]), and (3) no known genetic condition (typically developing).

METHODS

After extensive formative work, a discrete choice experiment was designed consisting of 5 attributes, each with 2 or 3 levels, including (1) type of researcher, (2) the use of personally identifiable information, (3) the use of sensitive information, (4) personal importance of research, and (5) return of results. Stratified mixed logit and latent class conditional logit models were examined.

RESULTS

Parents of children with FXS or ASD had relatively higher preferences for research conducted by nonprofits than parents of typically developing children. Parents of children with ASD also preferred research using non-identifiable and nonsensitive information. Parents of children with FXS or ASD also had preferences for research that was personally important and returned either summary or individual results. Although a few child and family characteristics were related to preferences, they did not overall define the subgroups of parents.

CONCLUSIONS

Although electronic health record preference research has been conducted with the general public, this is the first study to examine the opinions of parents who have a child with a known or suspected genetic condition. These parents were open to studies using their child's electronic health record because they may have more to gain from this type of research.

Reflections on dynamic consent in biomedical research: the story so far

Dynamic consent (DC) was originally developed in response to challenges to the informed consent process presented by participants agreeing to ‘future research’ in biobanking. In the past 12 years, it has been trialled in a number of different projects, and examined as a new approach for consent and to support patient engagement over time. There have been significant societal shifts during this time, namely in our reliance on digital tools and the use of social media, as well as a greater appreciation of the integral role of patients in biomedical research. This paper reflects on the development of DC to understand its importance in an age where digital health is becoming the norm and patients require greater oversight and control of how their data may be used in a range of settings. As well as looking back, it looks forwards to consider how DC could be further utilised to enhance the patient experience and address some of the inequalities caused by the digital divide in society.

Immersive technologies as a solution for general data protection regulation in Europe and impact on the COVID-19 pandemic

BACKGROUND

General data protection regulation (GDPR) provides rules according to which data should be managed and processed in a secure and appropriate way for patient requirements and security. Currently, everyone in Europe is covered by GDPR. Thus, the medical practice also requires access to patient data in a safe and secure way.

METHODS

Holographic technology allows users to see everything visible on a computer screen in a new and less restricted way, i.e. without the limitations of traditional computers and screens.

RESULTS

In this study, a three-dimensional holographic doctors' assistant is designed and implemented in a way that meets the GDPR requirements. The HoloView application, which is tailored to run on Microsoft HoloLens, is proposed toallow display and access to personal data and so-called sensitive information of all individual patients without the risk that it will be presented to unauthorized persons.

CONCLUSIONS

To enhance the user experience and remain consistent with GSPR, a holographic desk is proposed that allows displaying patient data and sensitive information only in front of the doctor's eyes using mixed reality glasses. Last but not least, it boasts of a reduction in infection risk for the staff during the COVID-19 pandemic, affording medical care to be carried out by as few doctors as possible.

Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System

BACKGROUND

Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system.

OBJECTIVE

The study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information.

METHODS

We retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly).

RESULTS

Our analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively.

CONCLUSIONS

We presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

Bridging the Gap Between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems

Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificial Intelligence (AI) fitting into a clinical environment requires identifying/bridging the gap between AI and Healthcare sides. Since the biggest problem in Medical Imaging lies in data paucity, confirming the clinical relevance for diagnosis of research-proven image augmentation techniques is essential. Therefore, we hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). This analysis confirms our pathology-aware GANs’ clinical relevance as a clinical decision support system and non-expert physician training tool. Our findings would play a key role in connecting inter-disciplinary research and clinical applications, not limited to the Japanese medical context and pathology-aware GANs.

Optimizing the Analytical Value of Oncology-Related Data Based on an In-Memory Analysis Layer: Development and Assessment of the Munich Online Comprehensive Cancer Analysis Platform

Background

Many comprehensive cancer centers incorporate tumor documentation software supplying structured information from the associated centers’ oncology patients for internal and external audit purposes. However, much of the documentation data included in these systems often remain unused and unknown by most of the clinicians at the sites.

Objective

To improve access to such data for analytical purposes, a prerollout of an analysis layer based on the business intelligence software QlikView was implemented. This software allows for the real-time analysis and inspection of oncology-related data. The system is meant to increase access to the data while simultaneously providing tools for user-friendly real-time analytics.

Methods

The system combines in-memory capabilities (based on QlikView software) with innovative techniques that compress the complexity of the data, consequently improving its readability as well as its accessibility for designated end users. Aside from the technical and conceptual components, the software’s implementation necessitated a complex system of permission and governance.

Results

A continuously running system including daily updates with a user-friendly Web interface and real-time usage was established. This paper introduces its main components and major design ideas. A commented video summarizing and presenting the work can be found within the Multimedia Appendix.

Conclusions

The system has been well-received by a focus group of physicians within an initial prerollout. Aside from improving data transparency, the system’s main benefits are its quality and process control capabilities, knowledge discovery, and hypothesis generation. Limitations such as run time, governance, or misinterpretation of data are considered.

Availability, access, analysis and dissemination of small-area data

In this era of 'big data', there is growing recognition of the value of environmental, health, social and demographic data for research. Open government data initiatives are growing in number and in terms of content. Remote sensing data are finding widespread use in environmental research, including in low- and middle-income settings. While our ability to study environment and health associations across countries and continents grows, data protection rules and greater patient control over the use of their data present new challenges to using health data in research. Innovative tools that circumvent the need for the physical sharing of data by supporting non-disclosive sharing of information, or that permit spatial analysis without researchers needing access to underlying patient data can be used to support analyses while protecting data confidentiality. User-friendly visualizations, allowing small-area data to be seen and understood by non-expert audiences, are revolutionizing public and researcher interactions with data. The UK Small Area Health Statistics Unit's Environment and Health Atlas for England and Wales, and the US National Environmental Public Health Tracking Network offer good examples. Open data facilitates user-generated outputs, and 'mash-ups', and user-generated inputs from social media, mobile devices and wearable tech are new data streams that will find utility in future studies, and bring novel dimensions with respect to ethical use of small-area data.

A Blind Man Leads a Blind Man? Personalised Nutrition-Related Attitudes, Knowledge and Behaviours of Fitness Trainers in Hungary

It is well-documented that fitness trainers could play an important role in the nutrition-related behaviour of their clients based on their personalised nutrition-related counselling activities, but there are considerable concerns all over the world about the level of their knowledge to become nutritional coaches. In the framework of the current study based on qualitative (focus-group interviews) and quantitative (questionnaire and analysis of responses by multivariable methods, as well as structural equation modelling) methods, it has been proven that (1) theoretically, both the trainers and the dietitians acknowledge the importance of cooperation in the optimisation of coaching efficiency and advisory work due to some "professional jealousness" and differences in professional background, as well as in culture, so it is hard to find a common platform for cooperation, especially in market segments characterised by relative low levels of purchasing power; (2) due to lack of regulation, there is a high heterogeneity of professional competences of trainers in general and their nutritional competences, in particular; (3) the majority of trainers do not have an objective picture on his/her effective nutritional knowledge, and they often offer a much wider scope of services (e.g., nutritional counselling for clients with chronic diseases) which are well beyond their professional knowledge and (4) the dietary guidelines have not become an integral part of professional knowledge, even at the level of specialists. To improve the current-in some cases, dangerous-situation, the following steps should be taken: (1) enhancement of the level of professional qualification of future trainers, integrating the practice-oriented approaches and emphasising the role of teamwork by simulation-based practices; (2) highlighting in a clear way the professional and ethical boundaries of the activities of trainers and (3) working out an efficient incentive system for the continuous professional development of trainers.

Data Access Committees

BACKGROUND

Sharing de-identified individual-level health research data is widely promoted and has many potential benefits. However there are also some potential harms, such as misuse of data and breach of participant confidentiality. One way to promote the benefits of sharing while ameliorating its potential harms is through the adoption of a managed access approach where data requests are channeled through a Data Access Committee (DAC), rather than making data openly available without restrictions. A DAC, whether a formal or informal group of individuals, has the responsibility of reviewing and assessing data access requests. Many individual groups, consortiums, institutional and independent DACs have been established but there is currently no widely accepted framework for their organization and function.

MAIN TEXT

We propose that DACs, should have the role of both promotion of data sharing and protection of data subjects, their communities, data producers, their institutions and the scientific enterprise. We suggest that data access should be granted by DACs as long as the data reuse has potential social value and provided there is low risk of foreseeable harms. To promote data sharing and to motivate data producers, DACs should encourage secondary uses that are consistent with the interests of data producers and their own institutions. Given the suggested roles of DACs, there should be transparent, simple and clear application procedures for data access. The approach to review of applications should be proportionate to the potential risks involved. DACs should be established within institutional and legal frameworks with clear lines of accountability, terms of reference and membership. We suggest that DACs should not be modelled after research ethics committees (RECs) because their functions and goals of review are different from those of RECs. DAC reviews should be guided by the principles of public health ethics instead of research ethics.

CONCLUSIONS

In this paper we have suggested a framework under which DACs should operate, how they should be organised, and how to constitute them.

The Data Tags Suite (DATS) model for discovering data access and use requirements

BACKGROUND

Data reuse is often controlled to protect the privacy of subjects and patients. Data discovery tools need ways to inform researchers about restrictions on data access and re-use.

RESULTS

We present elements in the Data Tags Suite (DATS) metadata schema describing data access, data use conditions, and consent information. DATS metadata are explained in terms of the administrative, legal, and technical systems used to protect confidential data.

CONCLUSIONS

The access and use metadata items in DATS are designed from the perspective of a researcher who wants to find and re-use existing data. We call for standard ways of describing informed consent and data use agreements that will enable automated systems for managing research data.

Big Data and diabetes: the applications of Big Data for diabetes care now and in the future

We review current applications of Big Data in diabetes care and consider the future potential by carrying out a scoping study of the academic literature on Big Data and diabetes care. Healthcare data are being produced at ever-increasing rates, and this information has the potential to transform the provision of diabetes care. Big Data is beginning to have an impact on diabetes care through data research. The use of Big Data for routine clinical care is still a future application. Vast amounts of healthcare data are already being produced, and the key is harnessing these to produce actionable insights. Considerable development work is required to achieve these goals.

Broad consent under the GDPR: an optimistic perspective on a bright future

Broad consent - the act of gaining one consent for multiple potential future research projects - sits at the core of much current genomic research practice. Since the 25th May 2018, the General Data Protection Regulation (GDPR) has applied as valid law concerning genomic research in the EU and now occupies a dominant position in the legal landscape. Yet, the position of the GDPR concerning broad consent has recently been cause for concern in the genomic research community. Whilst the text of the GDPR apparently supports the practice, recent jurisprudence contains language which is decidedly less positive. This article takes an in-depth look at the situation concerning broad consent under the GDPR and - despite the understandable concern flowing from recent jurisprudence - offers a positive outlook. This positive outlook is argued from three perspectives, each of which is significant in defining the current, and ongoing, legitimacy and utility of broad consent under the GDPR: the principled, the legal technical, and the practical.

Automating Dynamic Consent Decisions for the Processing of Social Media Data in Health Research

Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the participant. Machine learning algorithms show much promise for facilitating a "middle-ground" approach: using trained models to predict and automate granular consent decisions. Such techniques, however, raise a myriad of follow-on ethical and technical considerations. In this article, we present an exploratory user study (n = 67) in which we find that we can predict the appropriate flow of health-related social media data with reasonable accuracy, while minimizing undesired data leaks. We then attempt to deconstruct the findings of this study, identifying and discussing a number of real-world implications if such a technique were put into practice.

Prioritizing research challenges and funding for allergy and asthma and the need for translational research-The European Strategic Forum on Allergic Diseases

The European Academy of Allergy and Clinical Immunology (EAACI) organized the first European Strategic Forum on Allergic Diseases and Asthma. The main aim was to bring together all relevant stakeholders and decision-makers in the field of allergy, asthma and clinical Immunology around an open debate on contemporary challenges and potential solutions for the next decade. The Strategic Forum was an upscaling of the EAACI White Paper aiming to integrate the Academy's output with the perspective offered by EAACI's partners. This collaboration is fundamental for adapting and integrating allergy and asthma care into the context of real-world problems. The Strategic Forum on Allergic Diseases brought together all partners who have the drive and the influence to make positive change: national and international societies, patients' organizations, regulatory bodies and industry representatives. An open debate with a special focus on drug development and biomedical engineering, big data and information technology and allergic diseases and asthma in the context of environmental health concluded that connecting science with the transformation of care and a joint agreement between all partners on priorities and needs are essential to ensure a better management of allergic diseases and asthma in the advent of precision medicine together with global access to innovative and affordable diagnostics and therapeutics.

Data Work: Meaning-Making in the Era of Data-Rich Medicine

In the era of data-rich medicine, an increasing number of domains of people’s lives are datafied and rendered usable for health care purposes. Yet, deriving insights for clinical practice and individual life choices and deciding what data or information should be used for this purpose pose difficult challenges that require tremendous time, resources, and skill. Thus, big data not only promises new clinical insights but also generates new—and heretofore largely unarticulated—forms of work for patients, families, and health care providers alike. Building on science studies, medical informatics, Anselm Strauss and colleagues’ concept of patient work, and subsequent elaborations of articulation work, in this article, we analyze the forms of work engendered by the need to make data and information actionable for the treatment decisions and lives of individual patients. We outline three areas of data work, which we characterize as the work of supporting digital data practices, the work of interpretation and contextualization, and the work of inclusion and interaction. This is a first step toward naming and making visible these forms of work in order that they can be adequately seen, rewarded, and assessed in the future. We argue that making data work visible is also necessary to ensure that the insights of big and diverse datasets can be applied in meaningful and equitable ways for better health care.

Mobile health tools for the management of chronic respiratory diseases

BACKGROUND

The market of mobile health (mHealth) technology is rapidly evolving, making new mobile technologies potentially available for healthcare systems. Patient empowerment through self-monitoring of symptoms, shared decision making with the physician, and easily accessible education are important features extending the reach of mHealth technology beyond traditional care.

METHODS

Two digital distribution platforms (Apple App Store and Google Play Store) were searched for currently available mobile applications (apps) for patients with chronic respiratory diseases (CRDs). A new index (score ranging from 0 to 10) was developed to assess the potential of apps as a tool to empower patients through mobile technology (based on self-monitoring, personalized feedback, and patient education app features).

RESULTS

One hundred and twelve apps were retained for analysis and could be classified in 5 categories: Asthma (n = 71), COPD (n = 15), Asthma and COPD (n = 15), Rhinitis and Asthma (n = 5), and Rhinosinusitis (n = 6). Eighty percent were developed by medical technology companies compared to 18% by medical doctors and 2% by pharmaceutical companies. Two-thirds of apps allow disease self-monitoring, whereas over half of apps provide patient feedback through graphs. Sixty percent of apps contain easily accessible patient education material. Only three percent of apps reach a score of ≥7 on the newly designed patient empowerment index.

CONCLUSIONS

A variety of apps are available for patients with CRDs of which only few were developed by or jointly with medical doctors. The majority of these apps include self-monitoring tools, but only few also provide personalized feedback, which is needed to adopt these apps into daily care.

Open Humans: A platform for participant-centered research and personal data exploration

BACKGROUND

Many aspects of our lives are now digitized and connected to the internet. As a result, individuals are now creating and collecting more personal data than ever before. This offers an unprecedented chance for human-participant research ranging from the social sciences to precision medicine. With this potential wealth of data comes practical problems (e.g., how to merge data streams from various sources), as well as ethical problems (e.g., how best to balance risks and benefits when enabling personal data sharing by individuals).

RESULTS

To begin to address these problems in real time, we present Open Humans, a community-based platform that enables personal data collections across data streams, giving individuals more personal data access and control of sharing authorizations, and enabling academic research as well as patient-led projects. We showcase data streams that Open Humans combines (e.g., personal genetic data, wearable activity monitors, GPS location records, and continuous glucose monitor data), along with use cases of how the data facilitate various projects.

CONCLUSIONS

Open Humans highlights how a community-centric ecosystem can be used to aggregate personal data from various sources, as well as how these data can be used by academic and citizen scientists through practical, iterative approaches to sharing that strive to balance considerations with participant autonomy, inclusion, and privacy.

Shaping tomorrow’s facilities management

Purpose

Against the background of earlier publications on the future of facilities management (FM) and acknowledging digitalization and sustainability as two major shaping forces, the purpose of this paper is to place contributions to the special issue in the perspective of current opportunities for FM research.

Design/methodology/approach

After a review of publications since the 1980s, dealing with the future of FM, there is an analysis of how the forces of digitalization and sustainability have emerged over five decades. The articles of this special issue are introduced against this background. Opportunities for future FM research are identified, and the relation between research, education and practice is discussed.

Findings

Megatrends outlined in the 1980s still shape how FM develops. Digitalization supports sustainability not only through workplace change and building design but also through performance measurement, certification schemes and an awareness of the wider urban context.

Research limitations/implications

Opportunities for FM research are created by digitalization and concerns with sustainability, combining environmental and social aspects. Relations between organizations studied in an FM context are important. Within organizations, employee issues and risk management are emphasized.

Practical implications

Policies and schemes for sustainable buildings should be linked to sustainable FM more clearly. The relation between research, education and practice needs to be consolidated as a basis for research and development, as illustrated by a number of studies belonging to this special issue. To reach the goals of sustainable development, we need to develop the knowledge and theoretical frameworks that can be applied to and used by practice. The recent ISO FM definition appears as narrow and should be extended to recognize facilities’ life-cycle issues as well as broader urban and social concerns.

Originality/value

This paper highlights the importance of basing FM research on an understanding of the fundamental forces that shape change.

OpenEHR and General Data Protection Regulation: Evaluation of Principles and Requirements

Background

Concerns about privacy and personal data protection resulted in reforms of the existing legislation in the European Union (EU). The General Data Protection Regulation (GDPR) aims to reform the existing directive on the topic of personal data protection of EU citizens with a strong emphasis on more control of the citizens over their data and in the establishment of rules for the processing of personal data. OpenEHR is a standard that embodies many principles of interoperable and secure software for electronic health records (EHRs) and has been advocated as the best approach for the development of hospital information systems.

Objective

This study aimed to understand to what extent the openEHR standard can help in the compliance of EHR systems to the GDPR requirements.

Methods

A list of requirements for an EHR to support GDPR compliance and also a list of the openEHR design principles were made. The requirements were categorized and compared with the principles by experts on openEHR and GDPR.

Results

A total of 50 GDPR requirements and 8 openEHR design principles were identified. The openEHR principles conformed to 30% (15/50) of GDPR requirements. All the openEHR principles were aligned with GDPR requirements.

Conclusions

This study showed that the openEHR principles conform well to GDPR, underlining the common wisdom that truly realizing security and privacy requires it to be built in from the start. By using an openEHR-based EHR, the institutions are closer to becoming compliant with GDPR while safeguarding the medical data.

Implications for nursing and healthcare research of the general data protection regulation and retrospective reviews of patients' data

BACKGROUND

The European Union's general data protection regulation (GDPR) came into effect in May 2018. It is intended to prevent the unwanted sharing of private data and it has significant implications for healthcare research. A well-established research methodology that GDPR is likely to affect is the retrospective reviewing of patients' data. This has been used widely in healthcare research and commonly involves examining patients' medical records.

AIM

To examine GDPR and its potential effects on the use of patients' data in healthcare research.

DISCUSSION

Previous misuse of patients' data has affected public confidence in healthcare research. GDPR is intended to improve the public's confidence in the handling of their data, but it may negatively impact healthcare research. Researchers who want to review patients' data will need to consider consent issues carefully. GDPR does include exceptions to the rules of consent, but there is uncertainty about this process.

CONCLUSION

If GDPR results in stricter requirements to achieve patients' consent in research, the validity of some studies may be affected. Nurse researchers and organisations may need to consider innovative ways of engaging patients in research.

IMPLICATIONS FOR PRACTICE

Research using patients' data has played an important role in shaping nursing and healthcare policy and practice. Imminent Europe-wide changes prompted by GDPR could affect how patients' data are used in research.

Impact of the General Data Protection Regulation on Clinical Proteomics Research

The recently implemented General Data Protection Regulation (GDPR) has promising attributes for ensuring the protection of personal data collected and processed for clinical proteomic investigations. However, there exist ever increasing alarming concerns regarding its implications upon the future of clinical proteomics research both within and beyond the European Union. The main issues of concern regard GDPR legislative requirements for informed consent for study subjects' data collection and processing, data anonymization, and data storage and/or sharing, particularly in research areas which readily utilize databanks and biobanks, such as clinical proteomics investigations. The potential impacts of the aforementioned issues upon on-going and future clinical proteomics investigations are detailed, whilst recommendations for potentially resolving these emerging issues are proposed. Consensus between government, legislative, and research stakeholders, as well as impact assessments of final measures to be applied for medical research, is necessary so as to ensure the favorable perpetuation of clinical proteomics investigations and subsequent impact upon optimal patient health.

Is Blockchain Technology Suitable for Managing Personal Health Records? Mixed-Methods Study to Test Feasibility

Background

There are many perspectives on the advantages of introducing blockchain in the medical field, but there are no published feasibility studies regarding the storage, propagation, and management of personal health records (PHRs) using blockchain technology.

Objective

The purpose of this study was to investigate the usefulness of blockchains in the medical field in relation to transactions with and propagation of PHRs in a private blockchain.

Methods

We constructed a private blockchain network using Ethereum version 1.8.4 and conducted verification using the de-identified PHRs of 300 patients. The private blockchain network consisted of one hospital node and 300 patient nodes. In order to verify the effectiveness of blockchain-based PHR management, PHRs at a time were loaded in a transaction between the hospital and patient nodes and propagated to the whole network. We obtained and analyzed the time and gas required for data transaction and propagation on the blockchain network. For reproducibility, these processes were repeated 100 times.

Results

Of 300 patient records, 74 (24.7%) were not loaded in the private blockchain due to the data block size of the transaction block. The remaining 226 individual health records were classified into groups A (80 patients with outpatient visit data less than 1 year old), B (84 patients with outpatient data from between 1 and 3 years before data collection), and C (62 patients with outpatient data 3 to 5 years old). With respect to mean transaction time in the blockchain, C (128.7 seconds) had the shortest time, followed by A (132.2 seconds) and then B (159.0 seconds). The mean propagation times for groups A, B, and C were 1494.2 seconds, 2138.9 seconds, and 4111.4 seconds, respectively; mean file sizes were 5.6 KB, 18.6 KB, and 45.38 KB, respectively. The mean gas consumption values were 1,900,767; 4,224,341; and 4,112,784 for groups A, B, and C, respectively.

Conclusions

This study confirms that it is possible to exchange PHR data in a private blockchain network. However, to develop a blockchain-based PHR platform that can be used in practice, many improvements are required, including reductions in data size, improved personal information protection, and reduced operating costs.

A Groupware Tool to Facilitate Caregiving for Home-Dwelling Frail Older Persons in the Netherlands: Mixed-Methods Study

Background

Collaboration among informal and formal caregivers in a mixed care network of home-dwelling elderly may benefit from using a groupware app for digital networked communication (DNC).

Objective

This study aimed to describe and explain differences in the use and evaluation of a DNC app by members of the care network and to come up with a list of conditions that facilitate (or restrict) the implementation of a DNC app by a home care organization.

Methods

A pilot study collected information on digital communication in 7 care networks of clients of a home care organization in the Netherlands. Semistructured interviews with 4 care recipients, 7 informal carers (of which 3 spoke on behalf of the care receiver as well on account of receivers’ suffering from dementia), 3 district nurses, 5 auxiliary nurses, and 3 managers were conducted 3 times in a period of 6 months. In addition, we observed relevant workshops initiated by the home care organization and studied log-in data created by the users of the DNC app.

Results

The qualitative data and the monthly retrieved quantitative log-in data revealed 3 types of digital care networks: arranging the care network, discuss the care network, and staying connected network. Differences between network types were attributed to health impairment and digital illiteracy of the care recipients, motivation of informal caregivers, and commitment of formal caregivers. The easy availability of up-to-date information, the ability to promote a sense of safety for the carers, and short communication lines in case of complex care situations were positively evaluated.

Conclusions

It is concluded that digital communication is beneficial for organizing and discussing the care within a care network. More research is needed to study its impact on care burden of informal carers, on quality of care, and on quality of life of home-dwelling frail older adults.

On the personal utility of Alzheimer's disease-related biomarker testing in the research context

Many healthy volunteers choose to take part in Alzheimer's disease (AD) prevention studies because they want to know whether they will develop dementia-and what they can do to reduce their risk-and are therefore interested in learning the results of AD biomarker tests. Proponents of AD biomarker disclosure often refer to the personal utility of AD biomarkers, claiming that research participants will be able to use AD biomarker information for personal purposes, such as planning ahead or making important life decisions. In this paper, the claim that AD biomarkers have personal utility for asymptomatic individuals is critically assessed. It demonstrates that in the absence of clinical validity, AD biomarkers cannot have personal utility and do not serve research participants' autonomy. Over the next few years, many research groups will be confronted with participants' preferences to learn the results of AD biomarker tests. When researchers choose to make results available upon explicit request, they should ensure adequate information provision and education, notably on the uncertain clinical significance of AD biomarker information. Routine disclosure of AD biomarkers to cognitively unimpaired individuals in research settings cannot be justified with an appeal to the personal utility of AD biomarker information.

Contextual Anonymization for Secondary Use of Big Data in Biomedical Research: Proposal for an Anonymization Matrix

Background

The current law on anonymization sets the same standard across all situations, which poses a problem for biomedical research.

Objective

We propose a matrix for setting different standards, which is responsive to context and public expectations.

Methods

The law and ethics applicable to anonymization were reviewed in a scoping study. Social science on public attitudes and research on technical methods of anonymization were applied to formulate a matrix.

Results

The matrix adjusts anonymization standards according to the sensitivity of the data and the safety of the place, people, and projects involved.

Conclusions

The matrix offers a tool with context-specific standards for anonymization in data research.

Contextual Anonymization for Secondary Use of Big Data in Biomedical Research: Proposal for an Anonymization Matrix

BACKGROUND

The current law on anonymization sets the same standard across all situations, which poses a problem for biomedical research.

OBJECTIVE

We propose a matrix for setting different standards, which is responsive to context and public expectations.

METHODS

The law and ethics applicable to anonymization were reviewed in a scoping study. Social science on public attitudes and research on technical methods of anonymization were applied to formulate a matrix.

RESULTS

The matrix adjusts anonymization standards according to the sensitivity of the data and the safety of the place, people, and projects involved.

CONCLUSIONS

The matrix offers a tool with context-specific standards for anonymization in data research.

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.