A cluster randomised controlled trial evaluating the effectiveness of eHealth-supported patient recruitment in primary care research: the TRANSFoRm study protocol

mHealth in sub-Saharan Africa and Europe: A systematic review comparing the use and availability of mHealth approaches in sub-Saharan Africa and Europe

Background

mHealth can help with healthcare service delivery for various health issues, but there's a significant gap in the availability and use of mHealth systems between sub-Saharan Africa and Europe, despite the ongoing digitalization of the global healthcare system.

Objective

This work aims to compare and investigate the use and availability of mHealth systems in sub-Saharan Africa and Europe, and identify gaps in current mHealth development and implementation in both regions.

Methods

The study adhered to the PRISMA 2020 guidelines for article search and selection to ensure an unbiased comparison between sub-Saharan Africa and Europe. Four databases (Scopus, Web of Science, IEEE Xplore, and PubMed) were used, and articles were evaluated based on predetermined criteria. Details on the mHealth system type, goal, patient type, health concern, and development stage were collected and recorded in a Microsoft Excel worksheet.

Results

The search query produced 1020 articles for sub-Saharan Africa and 2477 articles for Europe. After screening for eligibility, 86 articles for sub-Saharan Africa and 297 articles for Europe were included. To minimize bias, two reviewers conducted the article screening and data retrieval. Sub-Saharan Africa used SMS and call-based mHealth methods for consultation and diagnosis, mainly for young patients such as children and mothers, and for issues such as HIV, pregnancy, childbirth, and child care. Europe relied more on apps, sensors, and wearables for monitoring, with the elderly as the most common patient group, and the most common health issues being cardiovascular disease and heart failure.

Conclusion

Wearable technology and external sensors are heavily used in Europe, whereas they are seldom used in sub-Saharan Africa. More efforts should be made to use the mHealth system to improve health outcomes in both regions, incorporating more cutting-edge technologies like wearables internal and external sensors. Undertaking context-based studies, identifying determinants of mHealth systems use, and considering these determinants during mHealth system design could enhance mHealth availability and utilization.

Telehealth in emergency medicine: A consensus conference to map the intersection of telehealth and emergency medicine

INTRODUCTION

Telehealth has the potential to significantly change the specialty of emergency medicine (EM) and has rapidly expanded in EM during the COVID pandemic; however, it is unclear how EM should intersect with telehealth. The field lacks a unified research agenda with priorities for scientific questions on telehealth in EM.

METHODS

Through the 2020 Society for Academic Emergency Medicine's annual consensus conference, experts in EM and telehealth created a research agenda for the topic. The multiyear process used a modified Delphi technique to develop research questions related to telehealth in EM. Research questions were excluded from the final research agenda if they did not meet a threshold of at least 80% of votes indicating "important" or "very important."

RESULTS

Round 1 of voting included 94 research questions, expanded to 103 questions in round 2 and refined to 36 questions for the final vote. Consensus occurred with a final set of 24 important research questions spanning five breakout group topics. Each breakout group domain was represented in the final set of questions. Examples of the questions include: "Among underserved populations, what are mechanisms by which disparities in emergency care delivery may be exacerbated or ameliorated by telehealth" (health care access) and "In what situations should the quality and safety of telehealth be compared to in-person care and in what situations should it be compared to no care" (quality and safety).

CONCLUSION

The primary finding from the process was the breadth of gaps in the evidence for telehealth in EM and telehealth in general. Our consensus process identified priority research questions for the use of and evaluation of telehealth in EM to fill the current knowledge gaps. Support should be provided to answer the research questions to guide the evidenced-based development of telehealth in EM.

A method for the graphical modeling of relative temporal constraints

Searching for patient cohorts in electronic patient data often requires the definition of temporal constraints between the selection criteria. However, beyond a certain degree of temporal complexity, the non-graphical, form-based approaches implemented in current translational research platforms may be limited when modeling such constraints. In our opinion, there is a need for an easily accessible and implementable, fully graphical method for creating temporal queries. We aim to respond to this challenge with a new graphical notation. Based on Allen's time interval algebra, it allows for modeling temporal queries by arranging simple horizontal bars depicting symbolic time intervals. To make our approach applicable to complex temporal patterns, we apply two extensions: with duration intervals, we enable the inference about relative temporal distances between patient events, and with time interval modifiers, we support counting and excluding patient events, as well as constraining numeric values. We describe how to generate database queries from this notation. We provide a prototypical implementation, consisting of a temporal query modeling frontend and an experimental backend that connects to an i2b2 system. We evaluate our modeling approach on the MIMIC-III database to demonstrate that it can be used for modeling typical temporal phenotyping queries.

Diabetes Care Editors' Expert Forum 2018: Managing Big Data for Diabetes Research and Care

Technological progress in the past half century has greatly increased our ability to collect, store, and transmit vast quantities of information, giving rise to the term "big data." This term refers to very large data sets that can be analyzed to identify patterns, trends, and associations. In medicine-including diabetes care and research-big data come from three main sources: electronic medical records (EMRs), surveys and registries, and randomized controlled trials (RCTs). These systems have evolved in different ways, each with strengths and limitations. EMRs continuously accumulate information about patients and make it readily accessible but are limited by missing data or data that are not quality assured. Because EMRs vary in structure and management, comparisons of data between health systems may be difficult. Registries and surveys provide data that are consistently collected and representative of broad populations but are limited in scope and may be updated only intermittently. RCT databases excel in the specificity, completeness, and accuracy of their data, but rarely include a fully representative sample of the general population. Also, they are costly to build and seldom maintained after a trial's end. To consider these issues, and the challenges and opportunities they present, the editors of Diabetes Care convened a group of experts in management of diabetes-related data on 21 June 2018, in conjunction with the American Diabetes Association's 78th Scientific Sessions in Orlando, FL. This article summarizes the discussion and conclusions of that forum, offering a vision of benefits that might be realized from prospectively designed and unified data-management systems to support the collective needs of clinical, surveillance, and research activities related to diabetes.

The TRANSFoRm project: Experience and lessons learned regarding functional and interoperability requirements to support primary care

INTRODUCTION

The current model of medical knowledge production, transfer, and application suffers from serious shortcomings. Learning health systems (LHS) have recently emerged as a potential solution-systems in which health information generated from patients is continuously analyzed to improve knowledge that will be transferred to patient care.

METHOD

Various approaches of data integration already exist and could be considered for the implementation of a LHS. We discuss what are the possible informatics approaches to address the functional requirements of LHS, in the specific context of primary care, and present the experience and lessons learned from the TRANSFoRm project.

RESULT

Implemented in 4 countries around 5 systems, TRANSFoRm is based on a local-as-view data mediation approach integrating the structural and terminological models in the same framework. It clearly demonstrated that it has the potential to address the requirements for a LHS in primary care, by dealing with data fragmented across multiple points of service. Also, it has the potential to support the generation of hypotheses from the context of clinical care, retrospective and prospective research, and decision support systems that improve the relevance of medical decisions.

CONCLUSION

The LHS approach embodies a shift from an institution-centered to a patient-centered perspective in knowledge production and transfer and can address important challenges in the primary care setting.

eSource for clinical trials: Implementation and evaluation of a standards-based approach in a real world trial

OBJECTIVE

The Learning Health System (LHS) requires integration of research into routine practice. 'eSource' or embedding clinical trial functionalities into routine electronic health record (EHR) systems has long been put forward as a solution to the rising costs of research. We aimed to create and validate an eSource solution that would be readily extensible as part of a LHS.

MATERIALS AND METHODS

The EU FP7 TRANSFoRm project's approach is based on dual modelling, using the Clinical Research Information Model (CRIM) and the Clinical Data Integration Model of meaning (CDIM) to bridge the gap between clinical and research data structures, using the CDISC Operational Data Model (ODM) standard. Validation against GCP requirements was conducted in a clinical site, and a cluster randomised evaluation by site nested into a live clinical trial.

RESULTS

Using the form definition element of ODM, we linked precisely modelled data queries to data elements, constrained against CDIM concepts, to enable automated patient identification for specific protocols and pre-population of electronic case report forms (e-CRF). Both control and eSource sites recruited better than expected with no significant difference. Completeness of clinical forms was significantly improved by eSource, but Patient Related Outcome Measures (PROMs) were less well completed on smartphones than paper in this population.

DISCUSSION

The TRANSFoRm approach provides an ontologically-based approach to eSource in a low-resource, heterogeneous, highly distributed environment, that allows precise prospective mapping of data elements in the EHR.

CONCLUSION

Further studies using this approach to CDISC should optimise the delivery of PROMS, whilst building a sustainable infrastructure for eSource with research networks, trials units and EHR vendors.

Embedding data provenance into the Learning Health System to facilitate reproducible research

INTRODUCTION

The learning health system (LHS) community has taken up the challenge of bringing the complex relationship between clinical research and practice into this brave new world. At the heart of the LHS vision is the notion of routine capture, transformation, and dissemination of data and knowledge, with various use cases, such as clinical studies, quality improvement initiatives, and decision support, constructed on top of specific routes that the data is taking through the system. In order to stop this increased data volume and analytical complexity from obfuscating the research process, it is essential to establish trust in the system through implementing reproducibility and auditability throughout the workflow.

METHODS

Data provenance technologies can automatically capture the trace of the research task and resulting data, thereby facilitating reproducible research. While some computational domains, such as bioinformatics, have embraced the technology through provenance-enabled execution middlewares, disciplines based on distributed, heterogeneous software, such as medical research, are only starting on the road to adoption, motivated by the institutional pressures to improve transparency and reproducibility.

RESULTS

Guided by the experiences of the TRANSFoRm project, we present the opportunities that data provenance offers to the LHS community. We illustrate how provenance can facilitate documenting 21 CFR Part 11 compliance for Food and Drug Administration submissions and provide auditability for decisions made by the decision support tools and discuss the transformational effect of routine provenance capture on data privacy, study reporting, and publishing medical research.

CONCLUSIONS

If the scaling up of the LHS is to succeed, we have to embed mechanisms to verify trust in the system inside our research instruments. In the research world increasingly reliant on electronic tools, provenance gives us a lingua franca to achieve traceability, which we have shown to be essential to building these mechanisms. To realize the vision of making computable provenance a feasible approach to implementing reproducibility in the LHS, we have to provide viable mechanisms for adoption. These include defining meaningful provenance models for problem domains and also introducing provenance support to existing tools in a minimally invasive manner.

Integrating technology into complex intervention trial processes: a case study

BACKGROUND

Trials of complex interventions are associated with high costs and burdens in terms of paperwork, management, data collection, validation, and intervention fidelity assessment occurring across multiple sites. Traditional data collection methods rely on paper-based forms, where processing can be time-consuming and error rates high. Electronic source data collection can potentially address many of these inefficiencies, but has not routinely been used in complex intervention trials. Here we present the use of an on-line system for managing all aspects of data handling and for the monitoring of trial processes in a multicentre trial of a complex intervention. We custom built a web-accessible software application for the delivery of ENGAGE-HD, a multicentre trial of a complex physical therapy intervention. The software incorporated functionality for participant randomisation, data collection and assessment of intervention fidelity. It was accessible to multiple users with differing levels of access depending on required usage or to maintain blinding. Each site was supplied with a 4G-enabled iPad for accessing the system. The impact of this system was quantified through review of data quality and collation of feedback from site coordinators and assessors through structured process interviews.

RESULTS

The custom-built system was an efficient tool for collecting data and managing trial processes. Although the set-up time required was significant, using the system resulted in an overall data completion rate of 98.5% with a data query rate of 0.1%, the majority of which were resolved in under a week. Feedback from research staff indicated that the system was highly acceptable for use in a research environment. This was a reflection of the portability and accessibility of the system when using the iPad and its usefulness in aiding accurate data collection, intervention fidelity and general administration.

CONCLUSIONS

A combination of commercially available hardware and a bespoke online database designed to support data collection, intervention fidelity and trial progress provides a viable option for streamlining trial processes in a multicentre complex intervention trial. There is scope to further extend the system to cater for larger trials and add further functionality such as automatic reporting facilities and participant management support.

TRIAL REGISTRATION

ISRCTN65378754 , registered on 13 March 2014.

TRANSFoRm eHealth solution for quality of life monitoring

Patient Recorded Outcome Measures (PROMs) are an essential part of quality of life monitoring, clinical trials, improvement studies and other medical tasks. Recently, web and mobile technologies have been explored as means of improving the response rates and quality of data collected. Despite the potential benefit of this approach, there are currently no widely accepted standards for developing or implementing PROMs in CER (Comparative Effectiveness Research). Within the European Union project Transform (Translational Research and Patient Safety in Europe) an eHealth solution for quality of life monitoring has been developed and validated. This paper presents the overall architecture of the system as well as a detailed description of the mobile and web applications.

Feasibility and acceptability of TRANSFoRm to improve clinical trial recruitment in primary care

BACKGROUND

Recruitment of study participants is a challenging process for health professionals and patients. The Translational Medicine and Patient Safety in Europe (TRANSFoRm) clinical trial tools enable automated identification, recruitment and follow-up in clinical trials, potentially saving time, effort and costs for all parties involved.

OBJECTIVES

This study evaluates the acceptability and feasibility of TRANSFoRm to improve clinical trial recruitment in primary care.

METHODS

A feasibility study was conducted in three general practices in Poland. Participants were physicians and patients with gastro-oesophageal reflux disease. Semi-structured interviews were held to obtain feedback about the usefulness, ease of use and overall experience with the TRANSFoRm tools and to identify potential usability issues. Data were analysed thematically.

RESULTS

A total of 5 physicians and 10 patients participated in the study. Physicians were satisfied with the usefulness of the system, as it enabled easier and faster identification, recruitment and follow-up of patients compared with existing methods. Patients found the TRANSFoRm apps easy to use to report patient outcomes. However, they also felt that the apps may not be useful for patients with limited exposure to smartphone and web technologies. Two main usability issues were identified: physicians could not access the result of the randomization at the end of each visit, and participants could not locate the follow-up reminder email.

CONCLUSIONS

This study provides new evidence on the acceptability and feasibility of TRANSFoRm to enable automated identification, recruitment and follow-up of study participants in primary care trials. It also helps to better understand and address users' requirements in eHealth-supported clinical research.

Implementation of a Care Pathway for Primary Palliative Care in 5 research clusters in Belgium: quasi-experimental study protocol and innovations in data collection (pro-SPINOZA)

BACKGROUND

Starting with early identification of palliative care patients by general practitioners (GPs), the Care Pathway for Primary Palliative Care (CPPPC) is believed to help primary health care workers to deliver patient- and family-centered care in the last year of life. The care pathway has been pilot-tested, and will now be implemented in 5 Belgian regions: 2 Dutch-speaking regions, 2 French-speaking regions and the bilingual capital region of Brussels. The overall aim of the CPPPC is to provide better quality of primary palliative care, and in the end to reduce the hospital death rate. The aim of this article is to describe the quantitative design and innovative data collection strategy used in the evaluation of this complex intervention.

METHODS/DESIGN

A quasi-experimental stepped wedge cluster design is set up with the 5 regions being 5 non-randomized clusters. The primary outcome is reduced hospital death rate per GPs' patient population. Secondary outcomes are increased death at home and health care consumption patterns suggesting high quality palliative care. Per research cluster, GPs will be recruited via convenience sampling. These GPs -volunteering to be involved will recruit people with reduced life expectancy and their informal care givers. Health care consumption data in the last year of life, available for all deceased people having lived in the research clusters in the study period, will be used for comparison between patient populations of participating GPs and patient populations of non-participating GPs. Description of baseline characteristics of participating GPs and patients and monitoring of the level of involvement by GPs, patients and informal care givers will happen through regular, privacy-secured web-surveys. Web-survey data and health consumption data are linked in a secure way, respecting Belgian privacy laws.

DISCUSSION

To evaluate this complex intervention, a quasi-experimental stepped wedge cluster design has been set up. Context characteristics and involvement level of participants are important parameters in evaluating complex interventions. It is possible to securely link survey data with health consumption data. By appealing to IT solutions we hope to be able to partly reduce respondent burden, a known problem in palliative care research.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02266069.

Transgenic DNA modules with pre-programmed self-destruction: Universal molecular devices to escape 'genetic litter' in gene and cell therapy

Gene delivery to human somatic cells is a well-established therapeutic strategy to treat a variety of diseases. In addition, gene transfer to human cells is required to generate human induced pluripotent cells and also to eliminate tumorigenic undifferentiated cells in many types of stem-cell derived transplantation material. The expression of transgenes in these medical technologies is often required only in some of the recipient cells and only in specific limited time-windows, with inappropriately located or untimely expressed transgenes presenting a risk of undesired collateral effects. Unfortunately, current gene transfer procedures commonly result in a number of cells in the patient's body containing fragments of transferred genetic material which are either not therapeutically necessary at all, are no longer necessary or are necessary but in some other cells. Such transgenic material in the patient, created as a by-product of the chosen therapeutic procedure, constitutes, in fact, 'genetic litter', that is, persisting potentially-hazardous foreign genetic material which is neither required therapeutically nor explicitly chosen by an informed and free-willing person as an artificial body element. Wider use and more frequent administration of gene and cell therapy in the future are likely to give greater prominence to the issue of misdelivered genetic medicines and of their unwanted remainders accumulating in human bodies. Thus, novel DNA templates, which, on the one hand, are capable of providing transgene expression over broad time-windows, and, on the other hand, do not leave unwanted permanent 'genetic traces', are required. I propose that the problem of 'genetic litter' in patients' bodies can be addressed through the employment of a new type of gene vectors delivering DNA-based transgenic modules with pre-programmed self-destruction. Such vectors could deliver therapeutic DNA cargo and then execute self-liquidation through pre-scheduled activation of co-delivered genome editing tools, such as CRISPR/Cas9 nucleases, specific for the DNA to be eliminated. In this model, all unnecessary transgenic DNA is edited away precisely at a desired time point. Activity of the gene correction apparatus for the specific and effective destruction of transgenic DNA could be turned on by well-timed external signals or could be triggered through intracellular sensors of particular epigenetic signatures. It is expected that the employment of the proposed DNA-based gene vectors equipped with a transgene self-destruct mechanism can extend the safe and ethical application of gene and cell therapy to a broader range of curative and lifestyle-choice medical treatments, e.g., full body prophylactic gene therapy of cancer.