Data Collection Challenges Research Articles

Teacher praise to reprimand ratios during small and large group instruction: a video pilot study

Few studies have examined teachers’ natural use of praise and reprimand in the absence of training, possibly due to the challenges of live data collection. This study sought to determine the feasibility of using technology to examine teachers’ use of praise and reprimand during class-wide and small group instruction in four elementary classrooms. In both settings, teachers used more general praise than behavior-specific praise and these differences were statistically significant. No differences were found between reprimand type, regardless of instructional context. During class-wide instruction, praise was delivered more frequently to individual students. No significant difference in praise or reprimand rates were observed between instructional contexts. Implications for training teachers to increase their praise-to-reprimand use and future research via video data collection are offered.

Rare and Complex Urology: Clinical Overview of ERN eUROGEN

BackgroundIn 2017, the European Commission launched 24 European Reference Networks (ERNs). ERN eUROGEN is the network for urorectogenital diseases and complex conditions, and started with 29 full member healthcare providers (HCPs) in 11 countries. It then covered 19 different disease areas distributed over three workstreams (WSs). ObjectiveTo provide an overview and identify challenges in data collection at European level of the ERN eUROGEN patient population treated by HCPs in the network. Design, setting, and participantsA retrospective cohort study was conducted of the 29 HCPs who were full members between 2013 and 2019. Outcome measurements and statistical analysisData were extracted from the original HCP applications and the ERN continuous monitoring system. Patient volumes, new patient numbers, and procedures were compared between different WSs, countries, and HCPs. Discrepancies between monitoring and application data were identified. Results and limitationsBetween 2013 and 2019, 122 040 patients required long-term care within the 29 HCPs. The volume of patients treated and procedures undertaken per year increased over time. Large discrepancies were found between patient numbers contained in the application forms and those reported in the continuous monitoring system (0–1357% deviation). ConclusionsPatient numbers and procedures increased across ERN eUROGEN HCPs. Reliable data extraction appeared challenging, illustrated by the patient volume discrepancies between application forms and the continuous monitoring data. Improved disease definitions, re-evaluation of affiliated HCPs, and valid data extraction are needed for future improvements. Patient summaryWe analysed the patient population with rare urorectogenital diseases or complex conditions within the ERN eUROGEN network between 2013 and 2019. Clinical activity was found to increase, but differences in patient numbers were evident between healthcare providers. In order to acquire valid patient numbers, both improved definitions of diagnostic codes and greater insight into the data-gathering process are required.

Partnering with healthcare facilities to understand psychosocial distress screening practices among cancer survivors: pilot study implications for study design, recruitment, and data collection

BackgroundWe sought to understand barriers and facilitators to implementing distress screening (DS) of cancer patients to inform and promote uptake in cancer treatment facilities. We describe the recruitment and data collection challenges and recommendations for assessing DS in oncology treatment facilities.MethodsWe recruited CoC-accredited facilities and collected data from each facility’s electronic health record (EHR). Collected data included cancer diagnosis and demographics, details on DS, and other relevant patient health data. Data were collected by external study staff who were given access to the facility’s EHR system, or by facility staff working locally within their own EHR system. Analyses are based on a pilot study of 9 facilities.ResultsChallenges stemmed from being a multi-facility-based study and local institutional review board (IRB) approval, facility review and approval processes, and issues associated with EHR systems and the lack of DS data standards. Facilities that provided study staff remote-access took longer for recruitment; facilities that performed their own extraction/abstraction took longer to complete data collection.ConclusionExamining DS practices and follow-up among cancer survivors necessitated recruiting and working directly with multiple healthcare systems and facilities. There were a number of lessons learned related to recruitment, enrollment, and data collection. Using the facilitators described in this manuscript offers increased potential for working successfully with various cancer centers and insight into partnering with facilities collecting non-standardized DS clinical data.

Collecting experimental network data from interventions on critical links in workplace networks

This article describes and discusses challenges associated with interventionist network data gathering in organizational settings, with a special focus on dyadic interventions. While pointing out major risks of these approaches, we argue that collecting data in combination with dyadic network alteration methods can enable social network researchers to explore network mechanisms from a new angle and potentially benefit the members of the targeted social networks and the entire collectives, if certain research design and implementation principles are followed. We introduce a facilitated self-disclosure method for strengthening critical dyads in social networks in combination with longitudinal and retrospective network measurement. We assess the participants’ perceptions of the different stages of this process by qualitative interviews. The study illustrates that experimental network data collection includes some extra challenges in addition to the many challenges of observational network data collection but participants also reported practical benefits that would not be gained through observational network surveys alone. The results highlight the importance of early and continuous communication during the data collection process with all research participants, not just the management, and the benefits of sharing more of the preliminary results. The lessons learnt through this study can inform the design of experimental network data collection to prioritize the preferences of the participants and their benefits.

Election Data Transparency: Obtaining Precinct-Level Election Returns

Election data transparency, accessibility, and usability constitute key dimensions of electoral integrity, as they allow for an additional accountability check on how states and localities run elections. Yet, it is an understudied topic, because of challenges in systematic data collection. Lack of data transparency has important implications for a democratic election process: It can reflect non-compliance with federal and state election laws, discriminatory practices, or lack of capacity in modernizing the conduct of elections. Thus far, there is no standard way for states to report election data, and there is no standard way to request, collect, and disseminate them. This paper presents the various ways state and local governments make official precinct-level election results publicly available, based on an OpenElections collection effort that covered primary and general elections in 2016 and 2018 in every U.S. state. We describe methods for obtaining official precinct results, ranging from formal records requests under state law, photographing pages in-person, to results sent by fax, and the benefits and costs of those methods. Using this information, local and state officials could adopt processes and policies to promote public access to official precinct-level election results.

Remote Interviewer Training for COVID-19 Data Collection: Challenges and Lessons Learned From 3 Countries in Sub-Saharan Africa.

There is an urgent need for data to inform coronavirus disease (COVID-19) pandemic response efforts. At the same time, the pandemic has created challenges for data collection, one of which is interviewer training in the context of social distancing. In sub-Saharan Africa, in-person interviewer training and face-to-face data collection remain the norm, requiring researchers to think creatively about transitioning to remote settings to allow for safer data collection that respects government guidelines. Performance Monitoring for Action (PMA, formerly PMA2020) has collected both cross-sectional and longitudinal data on key reproductive health measures in Africa and Asia since 2013. Relying on partnerships with in-country research institutes and cadres of female interviewers recruited from sampled communities, the project was well-positioned to transition to collecting data on COVID-19 from the onset of the pandemic. This article presents PMA's development of a remote training system for COVID-19 surveys in the Democratic Republic of the Congo, Kenya, and Nigeria, including challenges faced and lessons learned. We demonstrate that remote interviewer training can be a viable approach when data are critically needed and in-person learning is not possible. We also argue against systematic replacement of in-person trainings with remote learning, instead recommending consideration of local context and a project's individual circumstances when contemplating a transition to remote interviewer training.

Research Design in Socially Deviant Tourist Behavior Studies: A Mixed-Method Approach

Most extant research on socially deviant tourist behavior is qualitative in nature and lacks empirical investigation due to the topic's sensitivity and data collection difficulties. This research note addresses the methodological and data collection challenges in deviant tourist behavior studies and provides a comprehensive mixed-method research design by demonstrating an empirical study of drug tourism among Chinese visitors in Amsterdam. A sequential explanatory design is presented, which includes ethnography and two-staged qualitative interview data gathering and analysis, followed by a cross-sectional quantitative survey study. This research note provides methodological contributions to solve the data collection problems in special interest tourism and other sensitive research topics in social science.

Online multiplayer games as virtual laboratories for collecting data on social-ecological decision making.

Article impact statement: Online games can improve the collection of data on human decision-making in situations relevant to conservation and stakeholder trade-offs. This article is protected by copyright. All rights reserved.

The Use of Patient-Reported Outcome Measures in Rare Diseases and Implications for Health Technology Assessment.

BackgroundPatient-reported outcome measures (PROMs) are used in health technology assessment (HTA) to measure patient experiences with disease and treatment, allowing a deeper understanding of treatment impact beyond clinical endpoints. Developing and administering PROMs for rare diseases poses unique challenges because of small patient populations, disease heterogeneity, lack of natural history knowledge, and short-term studies.ObjectiveThis research aims to identify key factors to consider when using different types of PROMs in HTA for rare disease treatments (RDTs).MethodsA scoping review of scientific and grey literature was conducted, with no date or publication type restrictions. Information on the advantages of and the challenges and potential solutions when using different types of PROMs for RDTs, including psychometric properties, was extracted and synthesized.ResultsOf 79 records from PubMed, 32 were included, plus 12 records from the grey literature. PROMs for rare diseases face potential data collection and psychometric challenges resulting from small patient populations and disease heterogeneity. Generic PROMs are comparable across diseases but not sensitive to disease specificities. Disease-specific instruments are sensitive but do not exist for many rare diseases and rarely provide the utility values required by some HTA bodies. Creating new PROMs is time and resource intensive. Potential solutions include pooling data (multi-site/international data collection), using computer-assisted technology, or using generic and disease-specific PROMs in a complementary way.ConclusionsPROMs are relevant in HTA for RDTs but pose a number of difficulties. A deeper understanding of the potential advantages of and the challenges and potential solutions for each can help manage these difficulties.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40271-020-00493-w.

Identity, language and culture: Using Africanist Sista-hood and Deaf cultural discourse in research with minority social workers

Central to any anti-oppressive research endeavour is the importance of reflexivity and the genuine attempt from researchers to turn the research tools on themselves (Hermans, 2019). Beyond research, the social work profession has much to learn from the reflexive accounts of researchers. Issues of identity, language and culture are widely recognised as important when working with service users and carers in social work; however, this is much less the case when considering identities of social work practitioners. Starting with personal and professional positions of Black female and Deaf female social workers, this article reveals the reflexive journey of the Black female hearing researcher undertaking the research. The article takes an original approach to theory construction by introducing ‘ Africanist Sista-hood in Britain’ and marrying this with Deaf cultural discourse in the form of Deafhood, Deaf ethnicity and Deaf Gain, all of which make valuable contribution to existing debates in identity politics and the importance of self-naming and self-actualisation. Within the article the author discusses epistemological challenges in theory construction, data collection, language, transcription and dissemination, as they linked to power, privilege and the different forms this took within the research. The article makes a number of significant contributions. It introduces Africanist Sista-hood in Britain as a useful theoretical framework in research and at the same time encourages theoretical alliances across other marginalised groups. In discussing issues of knowledge production beyond existing hegemonic frames, it offers a broadening of the lens beyond the ways in which identity, culture and ethnicity are currently understood in the mainstream. In discussing Deaf cultural discourse and its influence on methodological choices it invites researchers to better engage with these issues from perspectives articulated by Deaf people.

Development and validation of a photographic atlas of food portions for accurate quantification of dietary intakes in China.

BackgroundAccurate estimation of food portion sizes remains an important challenge in dietary data collection. The present study aimed to develop a food atlas with adequate visual reference to improve the accuracy of dietary surveys in China.MethodsA food atlas for dietary surveys in China was developed using three visual reference systems, namely, regularly placed food portions, the two‐dimensional background coordinates and common objects known in daily life. The atlas was validated by estimating a meal before and after using the food atlas, and differences in weight estimation were compared using a paired t‐test. In total, 50 college students participated in the study.ResultsAfter determination of food varieties; design of the food display; purchase, processing, cooking and weighing of food; photographing food; post‐image processing and data processing, a total of 799 pictures of 303 types of food and two types of tableware were produced. The mean value of food weight estimated with the atlas was closer to the actual weight, and the variation range of these values was smaller and more stable than that estimated without the atlas. The differences estimated before and after using the atlas for all foods were significant (P < 0.05). Comparing the differences in weight before using the atlas, the error ranges of food samples were reduced.ConclusionsA food atlas has been developed for a retrospective dietary survey in China, which can be used to enable a better understanding of nutritional adequacy in the Chinese population.

Environmental DNA captures the genetic diversity of bowhead whales (Balaena mysticetus) in West Greenland

AbstractEnvironmental changes are prominent in Arctic ecosystems, where the distribution, abundance, life history, and health of marine organisms such as the bowhead whale (Balaena mysticetus) are tightly connected to sea ice and sea temperature. However, due to logistical and other challenges of data collection in the Arctic, appropriate assessments of past, present and future effects of climate change and human activities are lacking for many Arctic species. Environmental DNA (eDNA) is emerging as a noninvasive and cost‐effective way of obtaining genetic material from the environment and has the potential to complement traditional methods for biodiversity and genetic monitoring. In this study, we investigate whether eDNA isolated from seawater samples has the capacity to capture the genetic diversity of bowhead whales in Disko Bay, West Greenland, for the implementation of long‐term genetic monitoring programs of key Arctic marine species. A total of 41 eDNA “footprint” samples were obtained from the water surface after a whale had dived and an additional 54 eDNA samples were collected along transect lines. Samples were screened for bowhead DNA using a species‐specific qPCR primer and probe assay, and a subset of 30 samples were successfully Sanger‐sequenced to generate individual mitochondrial control region haplotypes. Moreover, by shotgun sequencing ten footprint samples on an Illumina NovaSeq platform we show that footprints generally contain less than 1% endogenous DNA, resulting in partial mitochondrial genomes in four samples out of ten samples. Our findings suggest that sampling in the footprint or wake of traveling animals is a promising method for capturing the genetic diversity of bowhead whales and other marine megafauna. With optimization of sampling and target DNA sequencing for higher endogenous DNA yield, seawater eDNA samples have a large potential for implementation in the long‐term population genetic monitoring of marine megafauna in the Arctic and elsewhere.

Contextual Challenges in the Implementation of the Alliance for Maternal and Newborn Health Improvement, Prospective Cohort Study, an Experience from Rural Pakistan

Introduction: Improving maternal and newborn survival needs robust data on patterns of morbidity and mortality from well-characterized cohorts. It is equally important for researchers to document and understand the contextual challenges of data collection and how they are addressed. Methods: This was a prospective cohort study implemented from December 2012 to August 2014 in Matiari, Pakistan. A total of 11,315 pregnancies were enrolled. Participants were approached at home for sequential data collection through the standard pretested structured questionnaires. Some indicators were sourced through health facility records. Information on field challenges gathered through field diaries and minutes of meetings with field staff. Results: Inaccurate reporting of last menstrual period (LMP) dates caused difficulties in the planning and completion of antenatal data collection visits at scheduled gestational weeks. We documented ultrasound reports wherever available, relied on quickening technique, and implemented a seasonal event calendar to help mothers’ recall their LMP. Health system coordinators of public sector and private healthcare providers were individually approached for maximum data collection. But an unregulated private health system with poor record maintenance and health care providers’ reluctance for cooperation posed a greater challenge in data collection. Conclusions: Within a broader understanding of the health systems and socio-cultural environment, temporal and spatial feasibility of data collection should be considered thoroughly at the early stages of study designing, planning, resource allocation, and implementation. Pre-defined regular and need-based meetings with each tier of data collection teams and study managers help to reinvigorate field execution plans and optimize both quantity and quality of study data.

Learning Transferable Driven and Drone Assisted Sustainable and Robust Regional Disease Surveillance for Smart Healthcare.

Smart healthcare has been applied in many fields such as disease surveillance and telemedicine, etc. However, there are some challenges for device deployment, data collection and guarantee of stainability in regional disease surveillance. First, it is difficult to deploy sensors and adjust the sensor network in unknown region for dynamic disease surveillance. Second, the limited life-cycle of sensor network may cause the loss of surveillance data. Thus, it is important to provide a sustainable and robust regional disease surveillance system. Given a set of Disease surveillance Area (DsA)s and Point of disease Surveillance (PoS)s, some sensors are deployed to monitor these PoSs, and a drone collect data from the sensors as well as charge the sensors to extend their life-cycles. The drone replenish its energy by relying on the bus network. We first formulate the drone assisted regional disease surveillance problem under the constraints of life-cycle of sensors and energy of drone, and propose an approximation algorithm to find a feasible cycle of drone to minimize the traveling time cost of drone. To satisfy the diversity requirements and dynamic scalability of regional disease surveillance, we deploy one robot in each DsA instead of sensors. We further formulate the learning transferable driven regional disease surveillance problem, and propose a joint schedule algorithm of drone and robots. The results of both theoretical analysis and extensive simulations show that the proposed algorithms can reduce the total time cost by 39.71 and 48.74 percent, average waiting time by 42.00 and 50.14 percent, and increase the average accessing ratio of PoSs by 15.53 and 22.30 percent, through the assistance of bus network and learning transferable features.

On-Device Deep Personalization for Robust Activity Data Collection.

One of the biggest challenges of activity data collection is the need to rely on users and keep them engaged to continually provide labels. Recent breakthroughs in mobile platforms have proven effective in bringing deep neural networks powered intelligence into mobile devices. This study proposes a novel on-device personalization for data labeling for an activity recognition system using mobile sensing. The key idea behind this system is that estimated activities personalized for a specific individual user can be used as feedback to motivate user contribution and improve data labeling quality. First, we exploited fine-tuning using a Deep Recurrent Neural Network to address the lack of sufficient training data and minimize the need for training deep learning on mobile devices from scratch. Second, we utilized a model pruning technique to reduce the computation cost of on-device personalization without affecting the accuracy. Finally, we built a robust activity data labeling system by integrating the two techniques outlined above, allowing the mobile application to create a personalized experience for the user. To demonstrate the proposed model’s capability and feasibility, we developed and deployed the proposed system to realistic settings. For our experimental setup, we gathered more than 16,800 activity windows from 12 activity classes using smartphone sensors. We empirically evaluated the proposed quality by comparing it with a baseline using machine learning. Our results indicate that the proposed system effectively improved activity accuracy recognition for individual users and reduced cost and latency for inference for mobile devices. Based on our findings, we highlight critical and promising future research directions regarding the design of efficient activity data collection with on-device personalization.

Security risk management: a case of Qalhat liquefied natural gas terminal

Purpose At the substantive level, there exists a gap in knowledge about the position of security risk management (i.e. SRM) during the terminals’ operations and management; particularly when there is potential for deliberate anti-security acts. Correspondingly, the purpose of this paper is a need for more practical research to find out the justification for the existence of the SRM and different techniques for its appropriate execution on these logistics infrastructures principally with due regard to the potential requirements in the near future. Design/methodology/approach Both qualitative and quantitative techniques are used in this study incorporating fuzzy set theory and risk assessment matrix to achieve the research objective. Findings A designed SRM framework tailored for Qalhat liquefied petroleum gas (LNG) terminal in Sultanate of Oman was established to manage the security threats which can be resulted from any probable terrorist attacks. Research limitations/implications The limited numbers of experts for the purpose of the addressed SRM are causing challenges in data collection. Practical implications The pressures for enhanced attention to critical infrastructure security have fostered new challenges for petrochemical seaports and terminals (PSTs). These tendencies dictate to maintain comprehensive security regimens that can be integrated with national and international strategies to support the country’s security against terrorism. Originality/value The development of the security risk factor table model in the case of Qalhat LNG Terminal.

Sustainable and inclusive – Evaluating urban sustainability indicators’ suitability for measuring progress towards SDG-11

The adoption of the sustainable development goals marks a transition in the global sustainability discourse to a growing focus on equity, with urban areas’ role in achieving sustainable and inclusive growth more explicit in sustainable development goal-11. Within this discourse, urban sustainability indicators could be used to monitor environmental quality and equity within individual cities, while promising to deepen our understanding of how urban areas contribute to global environmental sustainability. We examine 484 indicators of urban and regional environmental sustainability sourced from 40 indexes and online data repositories to determine their suitability for measuring both urban environmental performance and equity. Despite the large number of existing indicators related to urban environmental monitoring, we find that they are inadequate as tools for evaluating progress towards sustainable development goal-11’s integrated goal of sustainable and inclusive (i.e. equitable) urban areas, due to a lack of benchmarks, targets, and explicit measurement of equity considerations. Future research should emphasize data collection that can be disaggregated geographically to make it possible to measure distributional equity and establish locally appropriate benchmarks and realistic targets for urban sustainability indicators. Lastly, we argue that utilizing large-scale, high-resolution datasets has the potential to help overcome these data collection challenges.

Advancing clinical leadership to improve the implementation of evidence-based practice in surgery: a longitudinal mixed-method study protocol

BackgroundClinical leadership is fundamental in facilitating service improvements in healthcare. Few studies have attempted to understand or model the different approaches to leadership which are used when promoting the uptake and implementation of evidence-based interventions. This research aims to uncover and explain how distributed clinical leadership can be developed and improved to enhance the use of evidence in practice. In doing so, this study examines implementation leadership in orthopaedic surgery to explain leadership as a collective endeavour which cannot be separated from the organisational context.MethodsA mixed-method study consisting of longitudinal and cross-sectional interviews and an embedded social network analysis will be performed in six NHS hospitals. A social network analysis will be undertaken in each hospital to uncover the organisational networks, the focal leadership actors and information flows in each organisation. This will be followed by a series of repeated semi-structured interviews, conducted over 4 years, with orthopaedic surgeons and their professional networks. These longitudinal interviews will be supplemented by cross-sectional interviews with the national established surgical leaders. All qualitative data will be analysed using a constructivist grounded theory approach and integrated with the quantitative data. The participant narratives will enrich the social network to uncover the leadership configurations which exist, and how different configurations of leadership are functioning in practice to influence implementation processes and outcomes.DiscussionThe study findings will facilitate understanding about how and why different configurations of leadership develop and under what organisational conditions and circumstances they are able to flourish. The study will guide the development of leadership interventions that are grounded in the data and aimed at advancing leadership for service improvement in orthopaedics. The strength of the study lies in the combination of multi-component, multi-site, multi-agent methods to examine leadership processes in surgery. The findings may be limited by the practical challenges of longitudinal qualitative data collection, such as ensuring participant retention, which need to be balanced against the theoretical and empirical insights generated through this comprehensive exploration of leadership across and within a range of healthcare organisations.

Potential for establishing an injury surveillance system in India: a review of data sources and reporting systems

BackgroundUnintentional injuries account for 10% of deaths worldwide; the majority due to road traffic injuries, falls, drowning, poisoning and burns. Effective surveillance systems provide evidence for informed injury prevention and treatment and improve recovery outcomes. Our objectives were to review existing sources of unintentional injury data, and quality of the data on the burden, distribution, risk factors and trends of unintentional injuries in India and to describe strengths and limitations of health facility-based data for potential use in injury surveillance systems.MethodsWe searched national and international organisations’ websites to identify unintentional injury-related mortality and morbidity data sources in India. We reviewed and evaluated data collection methods for surveillance attributes recommended by World Health Organization (WHO). We visited health facilities at all levels from public and private sectors, emergency transport centres, insurance offices and police stations in settings reporting significant number of injuries. In these sites, we interviewed key stakeholders using an explorative approach on current data collection processes and challenges to establishing an injury surveillance system based on WHO guidelines.ResultsMajor gaps were highlighted in injury mortality and morbidity data in India, including ill-defined causes of injury deaths and lack of standardisation in classification and coding. Site visits revealed that reporting standards of injuries varied, with issues around clarity of definitions, accountability, time points and lack of reporter/coder training. Major challenges were lack of dedicated staff and training.ConclusionsThere is an important need to build human resource capacity, integrate data sources, standardise and streamline data collected, ensure accountability and capitalise on digital health information systems including insurance databases.

Online monitoring of patient self-reported adverse events in early phase clinical trials: Views from patients, clinicians, and trial staff.

Background/aimsNew classes of cancer drugs bring a range of unknown and undesirable adverse events. Adverse event monitoring is essential in phase I trials to assess toxicity and safety. In phase II, the focus is also on efficacy but robust data on adverse events continue to inform the safety and the adverse event profile. Standard, clinician-led monitoring has been shown to underestimate patients’ symptoms. Hence, patient-reported adverse event monitoring has been argued to complement and improve the information on adverse events in early phase clinical trials. With advances in information technology, real-time patient self-reported adverse events in trials are feasible. This study explored the experiences and procedures for reporting adverse events in early phase trials among patients, clinical staff, and trial staff, and their views on using an electronic patient-reported outcome adverse event system in this setting.MethodsQualitative interviews were conducted with patients, purposively sampled across ages, gender, and different phases of trials, and with clinical and trial-related staff involved in early phase trials (e.g. consultants, research nurses, hospital-based trial assistants/data managers, trial unit management staff). Interviews explored patient experiences and views on current adverse event reporting processes and electronic patient-reported outcome adverse event reporting. Framework analysis techniques were used to analyse the data.ResultsInterviewees were from two hospital trusts with early phase portfolios in England and a trial unit, and included sixteen patients, five consultants, four research nurses, five hospital-based trial staff, and two trial unit staff. Interviews identified three key themes (patient experiences, data flow, and views on electronic patient-reported outcome adverse event reporting). Stakeholders emphasised the intensity of trials for patients and the importance of extensive information provision within the uncertainty of early phase trial drugs. Regular face-to-face appointments for patients supplemented by telephone contact aimed to capture any adverse events. Delayed or under-reporting of mild- or low-severity symptoms was evident among patients. Hospital-based staff highlighted the challenges of current data collection including intense timescales, monitoring by trial sponsors, and high workload. Positive views on electronic patient-reported outcome adverse events highlighted that this could provide a more comprehensive and accurate view on the side effects of new drugs. Clinical staff emphasised patient safety and the need for clear responsibilities for monitoring. The need for careful decision-making about data flow and symptom attribution was highlighted; with trial unit staff emphasising the need for clinician review.ConclusionTechnology advances mean it is timely to explore the benefits and challenges of electronic patient-reported outcome adverse event reporting. This is a complex area warranting further consideration within the trial community. We have developed an online patient self-reporting tool and a small pilot with early phase trial patients is underway.