Taking patient involvement seriously: a critical ethical analysis of participatory approaches in data-intensive medical research

New horizons in prediction modelling using machine learning in older people's healthcare research

Machine learning (ML) and prediction modelling have become increasingly influential in healthcare, providing critical insights and supporting clinical decisions, particularly in the age of big data. This paper serves as an introductory guide for health researchers and readers interested in prediction modelling and explores how these technologies support clinical decisions, particularly with big data, and covers all aspects of the development, assessment and reporting of a model using ML. The paper starts with the importance of prediction modelling for precision medicine. It outlines different types of prediction and machine learning approaches, including supervised, unsupervised and semi-supervised learning, and provides an overview of popular algorithms for various outcomes and settings. It also introduces key theoretical ML concepts. The importance of data quality, preprocessing and unbiased model performance evaluation is highlighted. Concepts of apparent, internal and external validation will be introduced along with metrics for discrimination and calibration for different types of outcomes. Additionally, the paper addresses model interpretation, fairness and implementation in clinical practice. Finally, the paper provides recommendations for reporting and identifies common pitfalls in prediction modelling and machine learning. The aim of the paper is to help readers understand and critically evaluate research papers that present ML models and to serve as a first guide for developing, assessing and implementing their own.

How Do Molecular Systems Engineering Scientists Frame the Ethics of Their Research?

BACKGROUND

There are intense discussions about the ethical and societal implications of biomedical engineering, but little data to suggest how scientists think about the ethics of their work. The aim of this study is to describe how scientists frame the ethics of their research, with a focus on the field of molecular systems engineering.

METHODS

Semi-structured qualitative interviews were conducted during 2021-2022, as part of a larger study. This analysis includes a broad question about how participants view ethics as related to their work, with follow up probes about the topics they consider most important. Interviews were transcribed, inductively coded by two researchers to consensus, and analyzed thematically.

RESULTS

Twenty-four scientists participated in the study. Interviewees hold positions as professors, principal investigators, and senior staff researchers in universities or research institutes in the United States and Europe. Among those scientists who reported reflecting on ethical considerations in their work, many equated ethics with research ethics topics (e.g., safety, replicability), or with regulation and guidelines. Participants expressed the view that ethical issues are primarily relevant for clinical trials of bioengineered products, or for those working with animal or human subjects. Scientists described their research as "too early" or "not examining anything living" with regard to ethical reflection. Finally, many felt that ethics is seen as territory for experts and therefore beyond scientists' competencies.

CONCLUSIONS

Molecular systems engineering scientists currently focus on regulatory aspects as the framework for their ethical analyses. They describe using a framework to define when life arises, as a means to determine when further ethical engagement is warranted. Further research is needed to investigate how scientists relate to the ethics of their scientific work, and build consensus around concepts of life, autonomous behavior, and physiological relevance of bioengineered systems.

Patient and public involvement in research: a review of practical resources for young investigators

Patient and public involvement (PPI) in every aspect of research will add valuable insights from patients' experiences, help to explore barriers and facilitators to their compliance/adherence to assessment and treatment methods, bring meaningful outcomes that could meet their expectations, needs and preferences, reduce health care costs, and improve dissemination of research findings. It is essential to ensure competence of the research team by capacity building with available resources on PPI. This review summarizes practical resources for PPI in various stages of research projects-conception, co-creation, design (including qualitative or mixed methods), execution, implementation, feedback, authorship, acknowledgement and remuneration of patient research partners, and dissemination and communication of research findings with PPI. We have briefly summarized the recommendations and checklists, amongst others, for PPI in rheumatic and musculoskeletal research (e.g. the European Alliance of Associations for Rheumatology (EULAR) recommendations, the Core Outcome Measures in Effectiveness Trials (COMET) checklist and the Guidance for Reporting Involvement of Patients and the Public (GRIPP) checklist). Various tools that could be used to facilitate participation, communication and co-creation of research projects with PPI are highlighted in the review. We shed light on the opportunities and challenges for young investigators involving PPI in their research projects, and have summarized various resources that could be used to enhance PPI in various phases/aspects of research. A summary of web links to various tools and resources for PPI in various stages of research is provided in Additional file 1.

Towards conceptualizing patients as partners in health systems: a systematic review and descriptive synthesis

BACKGROUND

With the sharp increase in the involvement of patients (including family and informal caregivers) as active participants, collaborators, advisors and decision-makers in health systems, a new role has emerged: the patient partner. The role of patient partner differs from other forms of patient engagement in its longitudinal and bidirectional nature. This systematic review describes extant work on how patient partners are conceptualized and engaged in health systems. In doing so, it furthers the understanding of the role and activities of patient partners, and best practices for future patient partnership activities.

METHODS

A systematic review was conducted of peer-reviewed literature published in English or French that describes patient partner roles between 2000 and 2021 in any country or sector of the health system. We used a broad search strategy to capture descriptions of longitudinal patient engagement that may not have used words such as "partner" or "advisor".

RESULTS

A total of 506 eligible papers were identified, representing patient partnership activities in mostly high-income countries. These studies overwhelmingly described patient partnership in health research. We identified clusters of literature about patient partnership in cancer and mental health. The literature is saturated with single-site descriptive studies of patient partnership on individual projects or initiatives. There is a lack of work synthesizing impacts, facilitating factors and outcomes of patient partnership in healthcare.

CONCLUSIONS

There is not yet a consolidated understanding of the role, activities or impacts of patient partners. Advancement of the literature has been stymied by a lack of consistently used terminology. The literature is ready to move beyond single-site descriptions, and synthesis of existing pockets of high-quality theoretical work will be essential to this evolution.

A digital innovation typology: Navigating the complexity of emerging technologies to negotiate health systems research with young people

Objective

This study aims to explore young people's perspectives of emerging technologies and health systems research in an adolescent health community of practice.

Methods

The context of this integrated knowledge translation study is the Wellbeing Health & Youth Centre of Research Excellence in Adolescent Health. A theory-building, non-systematic review was conducted to examine the concepts and interrelationships of emerging technologies associated with digital innovation and health systems. This typology informed the design of an online workshop with young people to explore their views, concerns, and ideas about health systems research.

Results

A digital innovation typology was identified to differentiate and explain emerging technology concepts and interrelationships that can be applied to the health systems context. Aligned with this typology, youth perspectives about digital health challenges and opportunities were identified to support future research, policy, and practice.

Conclusion

The integrated findings from this study can assist the navigation of complex emerging technologies, and the negotiation of equitable health systems research, between youth and adult stakeholders. Further, with these typology-related resources, mutual learning and the public involvement of young people in health systems research and priority setting agendas can be supported.

Public Views About Involvement in Decision-Making on Health Data Sharing, Access, Use and Reuse: The Importance of Trust in Science and Other Institutions

Background

Data-intensive and needs-driven research can deliver substantial health benefits. However, concerns with privacy loss, undisclosed surveillance, and discrimination are on the rise due to mounting data breaches. This can undermine the trustworthiness of data processing institutions and reduce people's willingness to share their data. Involving the public in health data governance can help to address this problem by imbuing data processing frameworks with societal values. This study assesses public views about involvement in individual-level decisions concerned with health data and their association with trust in science and other institutions.

Methods

Cross-sectional study with 162 patients and 489 informal carers followed at two reference centers for rare diseases in an academic hospital in Portugal (June 2019–March 2020). Participants rated the importance of involvement in decision-making concerning health data sharing, access, use, and reuse from “not important” to “very important”. Its association with sociodemographic characteristics, interpersonal trust, trust in national and international institutions, and the importance of trust in research teams and host institutions was tested.

Results

Most participants perceived involvement in decision-making about data sharing (85.1%), access (87.1%), use (85%) and reuse (79.9%) to be important or very important. Participants who ascribed a high degree of importance to trust in research host institutions were significantly more likely to value involvement in such decisions. A similar position was expressed by participants who valued trust in research teams for data sharing, access, and use. Participants with low levels of trust in national and international institutions and with lower levels of education attributed less importance to being involved in decisions about data use.

Conclusion

The high value attributed by participants to involvement in individual-level data governance stresses the need to broaden opportunities for public participation in health data decision-making, namely by introducing a meta consent approach. The important role played by trust in science and in other institutions in shaping participants' views about involvement highlights the relevance of pairing such a meta consent approach with the provision of transparent information about the implications of data sharing, the resources needed to make informed choices and the development of harm mitigation tools and redress.

Public governance of medical artificial intelligence research in the UK: an integrated multi-scale model

There is a growing consensus among scholars, national governments, and intergovernmental organisations of the need to involve the public in decision-making around the use of artificial intelligence (AI) in society. Focusing on the UK, this paper asks how that can be achieved for medical AI research, that is, for research involving the training of AI on data from medical research databases. Public governance of medical AI research in the UK is generally achieved in three ways, namely, via lay representation on data access committees, through patient and public involvement groups, and by means of various deliberative democratic projects such as citizens' juries, citizen panels, citizen assemblies, etc.-what we collectively call "citizen forums". As we will show, each of these public involvement initiatives have complementary strengths and weaknesses for providing oversight of medical AI research. As they are currently utilized, however, they are unable to realize the full potential of their complementarity due to insufficient information transfer across them. In order to synergistically build on their contributions, we offer here a multi-scale model integrating all three. In doing so we provide a unified public governance model for medical AI research, one that, we argue, could improve the trustworthiness of big data and AI related medical research in the future.

The involvement matrix as a framework for involving youth with severe communication disabilities in developing health education materials

Introduction

Involving youth with severe communication disabilities in health research is foregrounded in a perspective of rights and participation. Researchers aligned with a participatory and inclusive research agenda recommend that involving youth in health research should be a deliberate and well‐planned process. However, limited examples exist of how researchers can facilitate the involvement of youth with severe communication disabilities in research projects.

Method

The aim of this paper was to describe the application of the Involvement Matrix as a conceptual framework to guide the three phases of a research project with youth with severe communication disabilities.

Results

Six youth aged 19–34 years consented to be involved in the project. All youth had a severe communication disability and used augmentative and alternative communication (AAC) to support their involvement in the research project. The Involvement Matrix provided a structure to delineate four involvement roles in three research phases: In Phase 1, youth were listeners to research information and advisors in the needs analysis. In Phase 2, as advisors and decision‐makers, youth provided their opinions on selecting picture communication symbols for health materials. In Phase 3, as partners, they were copresenters at an online youth forum.

Conclusion

The Involvement Matrix was used to plan and implement the involvement of youth with severe communication disabilities in codeveloping health materials for use during the COVID‐19 pandemic. The Involvement Matrix can be applied together with AAC to enable meaningful involvement of youth in a health research project as listeners, advisors, decision‐makers and partners.

Patient or Public Contribution

This study project was codeveloped with youth with severe communication disabilities who use AAC in South Africa. A person with lived experience was involved as an advisor to the health material development process and in the drafting of the manuscript.

Quality indicators for dementia and older people nearing the end of life: A systematic review

BACKGROUND

Robust quality indicators (QIs) are essential for monitoring and improving the quality of care and learning from good practice. We aimed to identify and assess QIs for the care of older people and people with dementia who are nearing the end of life and recommend QIs for use with routinely collected electronic data across care settings.

METHODS

A systematic review was conducted, including five databases and reference chaining. Studies describing the development of QIs for care of older people and those with dementia nearing the end of life were included. QIs were categorized as relating to processes or outcomes, and mapped against six care domains. The psychometric properties (acceptability, evidence base, definition, feasibility, reliability, and validity) of each QI were assessed; QIs were categorized as robust, moderate, or poor.

RESULTS

From 12,980 titles and abstracts screened, 37 papers and 976 QIs were included. Process and outcome QIs accounted for 780 (79.7%) and 196 (20.3%) of all QIs, respectively. Many of the QIs concerned physical aspects of care (n = 492, 50.4%), and very few concerned spiritual and cultural aspects of care (n = 19, 1.9%). Three hundred and fifteen (32.3%) QIs were robust and of those 220 were measurable using routinely collected electronic data. The final shortlist of 71 QIs came from seven studies.

CONCLUSIONS

Of the numerous QIs developed for care of older adults and those with dementia nearing the end of life, most had poor or moderate psychometric properties or were not designed for use with routinely collected electronic datasets. Infrastructure for data availability, combined with use of robust QIs, is important for enhancing understanding of care provided to this population, identifying unmet needs, and improving service provision.

Chronic Heart Failure Patients’ Attitudes towards Digital Device Data for Self-Documentation and Research in Germany: A Cross-Sectional Survey Study (Preprint)

Background

In recent years, the use of digital mobile measurement devices (DMMDs) for self-documentation in cardiovascular care in Western industrialized health care systems has increased. For patients with chronic heart failure (cHF), digital self-documentation plays an increasingly important role in self-management. Data from DMMDs can also be integrated into telemonitoring programs or data-intensive medical research to collect and evaluate patient-reported outcome measures through data sharing. However, the implementation of data-intensive devices and data sharing poses several challenges for doctors and patients as well as for the ethical governance of data-driven medical research.

Objective

This study aims to explore the potential and challenges of digital device data in cardiology research from patients’ perspectives. Leading research questions of the study concerned the attitudes of patients with cHF toward health-related data collected in the use of digital devices for self-documentation as well as sharing these data and consenting to data sharing for research purposes.

Methods

A cross-sectional survey of patients of a research in cardiology was conducted at a German university medical center (N=159) in 2020 (March to July). Eligible participants were German-speaking adult patients with cHF at that center. A pen-and-pencil questionnaire was sent by mail.

Results

Most participants (77/105, 73.3%) approved digital documentation, as they expected the device data to help them observe their body and its functions more objectively. Digital device data were believed to provide cognitive support, both for patients’ self-assessment and doctors’ evaluation of their patients’ current health condition. Interestingly, positive attitudes toward DMMD data providing cognitive support were, in particular, voiced by older patients aged >65 years. However, approximately half of the participants (56/105, 53.3%) also reported difficulty in dealing with self-documented data that lay outside the optimal medical target range. Furthermore, our findings revealed preferences for the self-management of DMMD data disclosed for data-intensive medical research among German patients with cHF, which are best implemented with a dynamic consent model.

Conclusions

Our findings provide potentially valuable insights for introducing DMMD in cardiovascular research in the German context. They have several practical implications, such as a high divergence in attitudes among patients with cHF toward different data-receiving organizations as well as a large variance in preferences for the modes of receiving information included in the consenting procedure for data sharing for research. We suggest addressing patients’ multiple views on consenting and data sharing in institutional normative governance frameworks for data-intensive medical research.

Participatory codesign of patient involvement in a Learning Health System: How can data-driven care be patient-driven care?

BACKGROUND

A Learning Health System (LHS) is a model of how routinely collected health data can be used to improve care, creating 'virtuous cycles' between data and improvement. This requires the active involvement of health service stakeholders, including patients themselves. However, to date, research has explored the acceptability of being 'data donors' rather than considering patients as active contributors. The study aimed to understand how patients should be actively involved in an LHS.

DESIGN

Ten participatory codesign workshops were conducted with eight experienced public contributors using visual, collective and iterative methods. This led contributors to challenge and revise not only the idea of an LHS but also revise the study aims and outputs.

RESULTS

The contributors proposed three exemplar roles for patients in patient-driven LHS, which aligned with the idea of three forms of transparency: informational, participatory and accountability. 'Epistemic injustice' was considered a useful concept to express the risks of an LHS that did not provide active roles to patients (testimonial injustice) and that neglected their experience through collecting data that did not reflect the complexity of their lives (hermeneutic injustice).

DISCUSSION

Patient involvement in an LHS should be 'with and by' patients, not 'about or for'. This requires systems to actively work with and respond to patient feedback, as demonstrated within the study itself by the adaptive approach to responding to contributor questions, to work in partnership with patients to create a 'virtuous alliance' to achieve change.

PATIENT OR PUBLIC CONTRIBUTION

Public contributors were active partners throughout, and co-authored the paper.

Public involvement and engagement in big data research: protocol for a scoping review and a systematic review of delivery and effectiveness of strategies for involvement and engagement

INTRODUCTION

Big data research has grown considerably over the last two decades. This presents new ethical challenges around consent, data storage and anonymisation. Big data research projects require public support to succeed and it has been argued that one way to achieve this is through public involvement and engagement. To better understand the role public involvement and engagement can play in big data research, we will review the current literature. This protocol describes the planned review methods.

METHODS AND ANALYSIS

Our review will be conducted in two stages. In the first stage, we will conduct a scoping review using Arksey and O'Malley methodology to comprehensively map current evidence on public involvement and engagement in big data research. Databases (CINAHL, Health Research Premium Collection, PubMed, Scopus, Web of Science) and grey literature will be searched for eligible papers. We provide a narrative description of the results based on a thematic analysis. In the second stage, out of papers found in the scoping review which discuss involvement and engagement strategies, we will conduct a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, exploring the delivery and effectiveness of these strategies. We will conduct a qualitative synthesis. Relevant results from the quantitative studies will be extracted and placed under qualitative themes. Individual studies will be appraised through Mixed Methods Appraisal Tool (MMAT), we will then assess the overall confidence in each finding through Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual). Results will be reported in a thematic and narrative way.

ETHICS AND DISSEMINATION

This protocol sets out how the review will be conducted to ensure rigour and transparency. Public advisors were involved in its development. Ethics approval is not required. Review findings will be presented at conferences and published in peer-reviewed journals.

Children and young people's contributions to public involvement and engagement activities in health-related research: A scoping review

BACKGROUND

There has been an increasing interest in how children and young people can be involved in patient and public involvement and engagement (PPIE) in health research. However, relatively little robust evidence exists about which children and young people are reported as being involved or excluded from PPIE; the methods reported as being used to involve them in PPIE; and the reasons presented for their involvement in PPIE and what happens as a result. We performed a scoping review to identify, synthesise and present what is known from the literature about patient and public involvement and engagement activities with children and young people in health related research.

METHODS

Relevant studies were identified by searches in Scopus, Medline, CINAHL, Cochrane and PsychInfo databases, and hand checking of reference lists and grey literature. An adapted version of the Guidance for Reporting Involvement of Patients and the Public (GRIPP2) was used as a framework to collate the data. Two reviewers independently screened articles and decisions were consensually made.

MAIN FINDINGS

A total of 9805 references were identified (after duplicates were removed) through the literature search, of which 233 full-text articles were assessed for eligibility. Forty studies published between 2000 and 2019 were included in the review. The review reveals ambiguities in the quality of reporting of PPIE with children with clear reporting on demographics and health conditions. The review found that children and young people were commonly involved in multiple stages of research but there was also significant variation in the level at which children and young people were involved in PPIE. Evaluation of the impact of children and young people's involvement in PPIE was limited.

CONCLUSIONS

Consultation, engagement and participation can all offer children and young people worthwhile ways of contributing to research with the level, purpose and impact of involvement determined by the children and young people themselves. However, careful decisions need to be made to ensure that it is suited to the context, setting and focus so that the desired PPIE impacts are achieved. Improvements should be made to the evaluation and reporting of PPIE in research. This will help researchers and funders to better understand the benefits, challenges and impact of PPIE with children and young people on health research.

Public and patient involvement in health data governance (DATAGov): protocol of a people-centred, mixed-methods study on data use and sharing for rare diseases care and research

INTRODUCTION

International policy imperatives for the public and patient involvement in the governance of health data coexist with conflicting cross-border policies on data sharing. This can challenge the planning and implementation of participatory data governance in healthcare services locally. Engaging with local stakeholders and understanding how their needs, values and preferences for governing health data can be articulated with policies made at the supranational level is crucial. This paper describes a protocol for a project that aims to coproduce a people-centred model for involving patients and the public in decision-making processes about the use and sharing of health data for rare diseases care and research.

METHODS AND ANALYSIS

This multidisciplinary project draws on an explanatory sequential mixed-methods study. A hospital-based survey with patients, informal carers, health professionals and technical staff recruited at two reference centres for rare diseases in Portugal will be conducted first. The qualitative study will follow consisting of semi-structured interviews and scenario-based workshops with a subsample of the participant groups recruited at baseline. Quantitative data will be analysed using descriptive and inferential statistics. Inductive and deductive approaches will be combined to analyse the qualitative interviews. Data from scenario-based workshops will be iteratively compared using the constant comparison method to identify cross-cutting themes and categories.

ETHICS AND DISSEMINATION

The Ethics Committee for Health from the University Hospital Centre São João/Faculty of Medicine of University of Porto approved the study protocol (Ref. 99/19). Research findings will be disseminated at academic conferences and science promotion events, and through public meetings involving patient representatives, practitioners, policy-makers and students, a project website and peer-reviewed journal publications.

My Data, My Choice? - German Patient Organizations' Attitudes towards Big Data-Driven Approaches in Personalized Medicine. An Empirical-Ethical Study

Personalized medicine (PM) operates with biological data to optimize therapy or prevention and to achieve cost reduction. Associated data may consist of large variations of informational subtypes e.g. genetic characteristics and their epigenetic modifications, biomarkers or even individual lifestyle factors. Present innovations in the field of information technology have already enabled the procession of increasingly large amounts of such data (‘volume’) from various sources (‘variety’) and varying quality in terms of data accuracy (‘veracity’) to facilitate the generation and analyzation of messy data sets within a short and highly efficient time period (‘velocity’) to provide insights into previously unknown connections and correlations between different items (‘value’). As such developments are characteristics of Big Data approaches, Big Data itself has become an important catchphrase that is closely linked to the emerging foundations and approaches of PM. However, as ethical concerns have been pointed out by experts in the debate already, moral concerns by stakeholders such as patient organizations (POs) need to be reflected in this context as well. We used an empirical-ethical approach including a website-analysis and 27 telephone-interviews for gaining in-depth insight into German POs’ perspectives on PM and Big Data. Our results show that not all POs are stakeholders in the same way. Comparing the perspectives and political engagement of the minority of POs that is currently actively involved in research around PM and Big Data-driven research led to four stakeholder sub-classifications: ‘mediators’ support research projects through facilitating researcher’s access to the patient community while simultaneously selecting projects they preferably support while ‘cooperators’ tend to contribute more directly to research projects by providing and implemeting patient perspectives. ‘Financers’ provide financial resources. ‘Independents’ keep control over their collected samples and associated patient-related information with a strong interest in making autonomous decisions about its scientific use. A more detailed terminology for the involvement of POs as stakeholders facilitates the adressing of their aims and goals. Based on our results, the ‘independents’ subgroup is a promising candidate for future collaborations in scientific research. Additionally, we identified gaps in PO’s knowledge about PM and Big Data. Based on these findings, approaches can be developed to increase data and statistical literacy. This way, the full potential of stakeholder involvement of POs can be made accessible in discourses around PM and Big Data.

Health data research on sudden cardiac arrest: perspectives of survivors and their next-of-kin

BACKGROUND

Consent for data research in acute and critical care is complex as patients become at least temporarily incapacitated or die. Existing guidelines and regulations in the European Union are of limited help and there is a lack of literature about the use of data from this vulnerable group. To aid the creation of a patient-centred framework for responsible data research in the acute setting, we explored views of patients and next-of-kin about the collection, storage, sharing and use of genetic and health-related data for observational research.

METHODS

We conducted qualitative interviews (n = 19) with Dutch sudden cardiac arrest survivors who donated clinical and socio-economic data and genetic samples to research. We also interviewed their next-of-kin. Topics were informed by ethics literature and we used scenario-sketches to aid discussion of complex issues.

RESULTS

Sudden cardiac arrest survivors displayed limited awareness of their involvement in health data research and of the content of their given consent. We found that preferences regarding disclosure of clinically actionable genetic findings could change over time. When data collection and use were limited to the medical realm, patients trusted researchers to handle data responsibly without concern for privacy or other risks. There was no consensus as to whether deferred consent should be explicitly asked from survivors. If consent is asked, this would ideally be done a few months after the event when cognitive capacities have been regained. Views were divided about the need to obtain proxy consent for research with deceased patients' data. However, there was general support for the disclosure of potentially relevant post-mortem genetic findings to relatives.

CONCLUSIONS

Sudden cardiac arrest patients' donation of data for research was grounded in trust in medicine overall, blurring the boundary between research and care. Our findings also highlight questions about the acceptability of a one-time consent and about responsibilities of patients, researchers and ethics committees. Finally, further normative investigation is needed regarding the (continued) use of participants' data after death, which is of particular importance in this setting. Our findings are thought to be of relevance for other acute and life-threatening illnesses as well.

Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities

In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables. In this viewpoint, we review the rationale behind the push to integrate SBDH data into health care predictive models and explore the technical, strategic, and ethical challenges faced as this process unfolds across the United States. We also offer several recommendations to overcome these challenges to reach the promise of SBDH predictive analytics to improve health and reduce health care disparities.

Clinical Research Informatics

Objectives : To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2019.

Method : A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers.

Results : Among the 517 papers, published in 2019, returned by the search, that were in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes the use of a homomorphic encryption technique to enable federated analysis of real-world data while complying more easily with data protection requirements. The authors of the second best paper demonstrate the evidence value of federated data networks reporting a large real world data study related to the first line treatment for hypertension. The third best paper reports the migration of the US Food and Drug Administration (FDA) adverse event reporting system database to the OMOP common data model. This work opens the combined analysis of both spontaneous reporting system and electronic health record (EHR) data for pharmacovigilance.

Conclusions : The most significant research efforts in the CRI field are currently focusing on real world evidence generation and especially the reuse of EHR data. With the progress achieved this year in the areas of phenotyping, data integration, semantic interoperability, and data quality assessment, real world data is becoming more accessible and reusable. High quality data sets are key assets not only for large scale observational studies or for changing the way clinical trials are conducted but also for developing or evaluating artificial intelligence algorithms guiding clinical decision for more personalized care. And lastly, security and confidentiality, ethical and regulatory issues, and more generally speaking data governance are still active research areas this year.

Special Section on Ethics in Health Informatics

OBJECTIVE

To summarize significant research contributions on ethics in medical informatics published in 2019.

METHODS

An extensive search using PubMed/Medline was conducted to identify the scientific contributions published in 2019 that address ethics issues in medical informatics. The selection process comprised three steps: 1) 15 candidate best papers were first selected by the two section editors; 2) external reviewers from internationally renowned research teams reviewed each candidate best paper; and 3) the final selection of three best papers was conducted by the editorial committee of the Yearbook.

RESULTS

The three selected best papers explore timely issues of concern to the community and demonstrate how ethics considerations influence applied informatics.

CONCLUSION

With regard to ethics in informatics, data sharing and privacy remain primary areas of concern. Ethics issues related to the development and implementation of artificial intelligence is an emerging topic of interest.

Big data analytics in medical engineering and healthcare: methods, advances and challenges

Big data analytics are gaining popularity in medical engineering and healthcare use cases. Stakeholders are finding big data analytics reduce medical costs and personalise medical services for each individual patient. Big data analytics can be used in large-scale genetics studies, public health, personalised and precision medicine, new drug development, etc. The introduction of the types, sources, and features of big data in healthcare as well as the applications and benefits of big data and big data analytics in healthcare is key to understanding healthcare big data and will be discussed in this article. Major methods, platforms and tools of big data analytics in medical engineering and healthcare are also presented. Advances and technology progress of big data analytics in healthcare are introduced, which includes artificial intelligence (AI) with big data, infrastructure and cloud computing, advanced computation and data processing, privacy and cybersecurity, health economic outcomes and technology management, and smart healthcare with sensing, wearable devices and Internet of things (IoT). Current challenges of dealing with big data and big data analytics in medical engineering and healthcare as well as future work are also presented.

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals.

Patient Participation in Nursing Care: Views From Ghanaian Nurses, Nursing Students, and Patients

Previous studies on patient participation reported inconclusive benefits of patient participation in health care. Consequently, the World Health Organization is actively highlighting the need for the participation of patients and their families in their care. The aim of this study was to explore the views of nurses, nursing students, and patients on patient participation in Ghanaian hospitals. Sixty-five participants made up of 15 patients, 25 registered general nurses, and 25 undergraduate nursing students were involved in the study. Data collection was done through interviews and focus group discussions. Content analysis was utilized in analyzing the data to generate four main categories. These categories were as follows: (a) meaning of patient participation in Ghana, (b) patient participation encouraged more during discharge education, (c) patient participation in nursing care higher in private and smaller hospitals, and (d) perceived facilitators and inhibitors of patient participation in nursing care. Participants in this study indicated that patient participation in nursing care meant involvement of patient in treatment decisions and nursing care procedures. Participants agreed that patient participation in nursing care was mostly encouraged during discharge education. Participation was perceived to be higher in private and smaller hospitals. Wealth and higher education were perceived as facilitators of patient participation while workload and high patient acuity were perceived as inhibitors.

A taxonomy of impacts on clinical and translational research from community stakeholder engagement

Background

Community engagement is increasingly recognized as a valuable tool in clinical and translational research; however, the impact of engagement is not fully understood. No standard nomenclature yet exists to clearly define how research changes when community stakeholders are engaged across the research spectrum. This severely limits our ability to assess the value of community engagement in research. To address this gap, we developed a taxonomy for characterizing and classifying changes in research due to community engagement.

Methods

Using an iterative process, we (a) identified areas of potential impact associated with community engagement from author experience, (b) categorized these in taxonomic bins based on research stages, (c) conducted semi‐structured interviews with researchers and community stakeholders, (d) validated the codebook in a sample dataset and (e) refined the taxonomy based on the validation. Community stakeholders were involved in every step of the process including as members of the primary study team.

Results

The final taxonomy catalogues changes into eleven domains corresponding to research phases. Each domain includes 2‐4 dimensions depicting concepts within the domain's scope and, within each dimension, 2‐10 elements labelling activities through which community engagement could change research.

Conclusions

Community engagement has great potential to enhance clinical and translational research. This taxonomy provides a common vocabulary and framework for understanding the impact of community engagement and suggests metrics for assessing the value of community engagement in research.