Publications
171 results found
Chapman M, Balatsoukas P, Ashworth M, et al., 2019, Computational Argumentation-based Clinical Decision Support, 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), Publisher: ASSOC COMPUTING MACHINERY, Pages: 2345-2347
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- Citations: 6
Essers K, Chapman M, Kokciyan N, et al., 2018, The CONSULT System: Demonstration, 6th International Conference on Human-Agent Interaction (HAI), Publisher: ASSOC COMPUTING MACHINERY, Pages: 385-386
Cyras K, Delaney B, Prociuk D, et al., 2018, Argumentation for explainable reasoning with conflicting medical recommendations, Reasoning with Ambiguous and Conflicting Evidence and Recommendations in Medicine (MedRACER 2018), Pages: 14-22
Designing a treatment path for a patient suffering from mul-tiple conditions involves merging and applying multiple clin-ical guidelines and is recognised as a difficult task. This isespecially relevant in the treatment of patients with multiplechronic diseases, such as chronic obstructive pulmonary dis-ease, because of the high risk of any treatment change havingpotentially lethal exacerbations. Clinical guidelines are typi-cally designed to assist a clinician in treating a single condi-tion with no general method for integrating them. Addition-ally, guidelines for different conditions may contain mutuallyconflicting recommendations with certain actions potentiallyleading to adverse effects. Finally, individual patient prefer-ences need to be respected when making decisions.In this work we present a description of an integrated frame-work and a system to execute conflicting clinical guidelinerecommendations by taking into account patient specific in-formation and preferences of various parties. Overall, ourframework combines a patient’s electronic health record datawith clinical guideline representation to obtain personalisedrecommendations, uses computational argumentation tech-niques to resolve conflicts among recommendations while re-specting preferences of various parties involved, if any, andyields conflict-free recommendations that are inspectable andexplainable. The system implementing our framework willallow for continuous learning by taking feedback from thedecision makers and integrating it within its pipeline.
Porat T, Kokciyan N, Sassoon I, et al., 2018, Stakeholders’ views on a collaborative decision support system to promote multimorbidity self-management: barriers, facilitators and design implications, AMIA 2018 Annual Symposium
Kokciyan N, Sassoon I, Young AP, et al., 2018, Towards an Argumentation System for Supporting Patients in Self-Managing their Chronic Conditions, Thirty-Second AAAI Conference on Artificial Intelligence
Verheij RA, Curcin V, Delaney BC, et al., 2018, Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse (Preprint), Journal of Medical Internet Research, ISSN: 1438-8871
Robert V, Vasa C, Delaney BC, et al., 2018, Possible sources of bias in primary care electronic health record data (re-)use, Journal of Medical Internet Research, Vol: 20, ISSN: 1438-8871
Background - Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use this data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. Objective - In this paper, we discuss whether data that is recorded routinely as part of the health care process in primary care is actually fit to use for these other purposes, how the original purpose may affect the extent to which the data is fit for another purpose and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the (re-)use of this type of data. Methods –This discussion paper is based on the authors’ experience as users of electronic health records data, as a general practitioner, health informatics experts, and health services researchers. It is a product of the discussions they had during the TRANSFoRm project, which was funded by the EU and sought to develop, pilot and evaluate a core information architecture for the Learning Health System (LHS) in Europe, based on primary care electronic health records. Results – We first describe the different stages in the processing of EHR data, as well as the different purposes for which this data is used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step, that can generate biased outcomes. We identified thirteen possible sources of bias. Four of them are related to the organization of a health care system, some are of a more technical nature. Conclusions - There are a substantial number of possible sources of bias, and very little is known about the size and direction of their impact. Howev
Tapuria A, Bruland P, Delaney B, et al., 2018, Comparison and transformation between CDISC ODM and EN13606 EHR standards in connecting EHR data with clinical trial research data, DIGITAL HEALTH, Vol: 4, ISSN: 2055-2076
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- Citations: 4
Ethier J-F, McGilchrist M, Barton A, et al., 2018, The TRANSFoRm project: Experience and lessons learned regarding functional and interoperability requirements to support primary care, Learning Health Systems, Vol: 2, ISSN: 2379-6146
IntroductionThe 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.MethodVarious 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.ResultImplemented 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.ConclusionThe 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.
Alper P, Belhajjame K, Curcin V, et al., 2018, <i>Label</i>Flow Framework for Annotating Workflow Provenance, INFORMATICS-BASEL, Vol: 5
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- Citations: 4
Porat T, Liao Z, Curcin V, 2018, Engaging stakeholders in the design and usability evaluation of a decision aid to improve secondary stroke prevention., Studies in Health Technology and Informatics, Vol: 247, Pages: 765-769, ISSN: 0926-9630
Stroke survivors have a nearly 40% risk of recurrent stroke during the first 10 years. Effective secondary stroke prevention strategies are sub-optimally used, and hence, developing interventions to enable healthcare professionals and stroke survivors to manage risk factors more effectively are required. In this paper we describe the usability evaluation of a decision aid designed in collaboration with stakeholders to reduce the risk of a recurrent stroke. The decision aid was found usable and acceptable by both general practitioners and stroke survivors. Concerns and suggestions for improving the decision aid are discussed.
Fairweather E, Alper P, Porat T, et al., 2018, Architecture for Template-Driven Provenance Recording, 7th International Provenance and Annotation Workshop (IPAW), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 217-221, ISSN: 0302-9743
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- Citations: 1
Alper P, Fairweather E, Curcin V, 2018, Simulated Domain-Specific Provenance, 7th International Provenance and Annotation Workshop (IPAW), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 71-83, ISSN: 0302-9743
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- Citations: 1
Xu S, Fairweather E, Rogers T, et al., 2018, Implementing Data Provenance in Health Data Analytics Software, 7th International Provenance and Annotation Workshop (IPAW), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 173-176, ISSN: 0302-9743
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- Citations: 1
Wongkoblap A, Vadillo MA, Curcin V, 2018, Classifying Depressed Users With Multiple Instance Learning from Social Network Data, 6th IEEE International Conference on Healthcare Informatics (ICHI), Publisher: IEEE COMPUTER SOC, Pages: 436-436, ISSN: 2575-2634
Satrjeenpong P, Tapuria A, Curcin V, et al., 2018, Identifying audit trail viewer requirements for user-focused design: a qualitative focus group study, 6th IEEE International Conference on Healthcare Informatics (ICHI), Publisher: IEEE COMPUTER SOC, Pages: 405-406, ISSN: 2575-2634
Wongkoblap A, Vadillo MA, Curcin V, 2018, A Multilevel Predictive Model for Detecting Social Network Users with Depression, 6th IEEE International Conference on Healthcare Informatics (ICHI), Publisher: IEEE COMPUTER SOC, Pages: 130-135, ISSN: 2575-2634
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- Citations: 8
Liang S-F, Sun X, Gulliford M, et al., 2018, Inclusion and Exclusion of Medical Codes for Primary Care Data Extraction, 6th IEEE International Conference on Healthcare Informatics (ICHI), Publisher: IEEE COMPUTER SOC, Pages: 394-395, ISSN: 2575-2634
Ethier J-F, Curcin V, McGilchrist M, et al., 2017, eSource for clinical trials: implementation and evaluation of a standards-based approach in a real world trial, International Journal of Medical Informatics, Vol: 106, Pages: 17-24, ISSN: 1872-8243
ObjectiveThe 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 andMethodsThe 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.ResultsUsing 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.DiscussionThe 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.ConclusionFurther 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.
Wongkoblap A, Vadillo MA, Curcin V, 2017, Researching Mental Health Disorders in the Era of Social Media: Systematic Review, JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 19, ISSN: 1438-8871
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- Citations: 105
Corrigan D, Munelley G, Kazienko P, et al., 2017, Requirements and validation of a prototype learning health system for clinical diagnosis, Learning Health Systems, Vol: 1, ISSN: 2379-6146
IntroductionDiagnostic error is a major threat to patient safety in the context of family practice. The patient safety implications are severe for both patient and clinician. Traditional approaches to diagnostic decision support have lacked broad acceptance for a number of well-documented reasons: poor integration with electronic health records and clinician workflow, static evidence that lacks transparency and trust, and use of proprietary technical standards hindering wider interoperability. The learning health system (LHS) provides a suitable infrastructure for development of a new breed of learning decision support tools. These tools exploit the potential for appropriate use of the growing volumes of aggregated sources of electronic health records.MethodsWe describe the experiences of the TRANSFoRm project developing a diagnostic decision support infrastructure consistent with the wider goals of the LHS. We describe an architecture that is model driven, service oriented, constructed using open standards, and supports evidence derived from electronic sources of patient data. We describe the architecture and implementation of 2 critical aspects for a successful LHS: the model representation and translation of clinical evidence into effective practice and the generation of curated clinical evidence that can be used to populate those models, thus closing the LHS loop.Results/ConclusionsSix core design requirements for implementing a diagnostic LHS are identified and successfully implemented as part of this research work. A number of significant technical and policy challenges are identified for the LHS community to consider, and these are discussed in the context of evaluating this work: medico-legal responsibility for generated diagnostic evidence, developing trust in the LHS (particularly important from the perspective of decision support), and constraints imposed by clinical terminologies on evidence generation.
Sadler E, Porat T, Marshall I, et al., 2017, Shaping innovations in long-term care for stroke survivors with multimorbidity through stakeholder engagement, PLoS ONE, Vol: 12, ISSN: 1932-6203
BackgroundStroke, like many long-term conditions, tends to be managed in isolation of its associated risk factors and multimorbidity. With increasing access to clinical and research data there is the potential to combine data from a variety of sources to inform interventions to improve healthcare. A ‘Learning Health System’ (LHS) is an innovative model of care which transforms integrated data into knowledge to improve healthcare. The objective of this study is to develop a process of engaging stakeholders in the use of clinical and research data to co-produce potential solutions, informed by a LHS, to improve long-term care for stroke survivors with multimorbidity.MethodsWe used a stakeholder engagement study design informed by co-production principles to engage stakeholders, including service users, carers, general practitioners and other health and social care professionals, service managers, commissioners of services, policy makers, third sector representatives and researchers. Over a 10 month period we used a range of methods including stakeholder group meetings, focus groups, nominal group techniques (priority setting and consensus building) and interviews. Qualitative data were recorded, transcribed and analysed thematically.Results37 participants took part in the study. The concept of how data might drive intervention development was difficult to convey and understand. The engagement process led to four priority areas for needs for data and information being identified by stakeholders: 1) improving continuity of care; 2) improving management of mental health consequences; 3) better access to health and social care; and 4) targeting multiple risk factors. These priorities informed preliminary design interventions. The final choice of intervention was agreed by consensus, informed by consideration of the gap in evidence and local service provision, and availability of robust data. This shaped a co-produced decision support tool to improve secondary
Alexakis C, Saxena S, Chhaya V, et al., 2017, Do Thiopurines Reduce the Risk of Surgery in Elderly Onset Inflammatory Bowel Disease? A 20-Year National Population-Based Cohort Study., Inflammatory Bowel Diseases, Vol: 23, Pages: 672-680, ISSN: 1536-4844
BACKGROUND: Evidence that thiopurines impact on the risk of surgery in elderly onset inflammatory bowel disease (EO-IBD) is lacking. We aimed to compare the rates of surgery in EO-IBD (>60 years at diagnosis) with adult-onset IBD (18-59 yrs), and examine the impact of thiopurines on surgical risk in EO-IBD. METHODS: Using a U.K. database between 1990 and 2010, we compared rates of surgery between adult-onset IBD and EO-IBD using survival analysis. Ulcerative colitis (UC) and Crohn's disease (CD) were analyzed separately. Cox proportional hazard modeling was used to determine the adjusted relative risk of surgery. We further assessed the impact of duration of thiopurine treatment on risk of surgery. RESULTS: We identified 2758 of 9515 patients with UC and 1349 of 6490 patients with CD, with EO-IBD. Cumulative 1, 5, and 10 years risk of colectomy was similar in EO-UC (2.2, 4.5, and 5.8%, respectively) and AO-UC (2.2, 5.0, and 7.3%, respectively; P = 0.15). Cumulative 1, 5, and 10 years risk of first intestinal surgery was lower in EO-CD (9.5, 14.6, and 17.9%, respectively) than AO-CD (12.2, 19.0, and 24.4%, respectively; P < 0.001). Early steroid use, steroid dependency, and thiopurine use was associated with higher risk of colectomy in EO-UC. Among EO-UC receiving thiopurines for >12 months, there was a 70% reduction in risk of colectomy (hazard ratio. 0.30; 95% confidence interval, 0.15-0.58). Thiopurines were not associated with a reduced risk of surgery in EO-CD. CONCLUSIONS: Risk of colectomy in EO-UC does not differ from AO-UC, but the risk of surgery in EO-CD is significantly lower than in AO-CD. Sustained thiopurine use of 12 months or more duration in EO-UC reduces the risk colectomy, but does not impact on the risk of surgery in EO-CD. These findings are important given the greater risk of thiopurine-associated lymphoma in the elderly.
Curcin V, 2017, Embedding data provenance into the Learning Health System to facilitate reproducible research, LEARNING HEALTH SYSTEMS, Vol: 1, ISSN: 2379-6146
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- Citations: 18
Mahmoud S, Boyd A, Curcin V, et al., 2017, The 'PEARL' Data Warehouse: Initial Challenges Faced with Semantic and Syntactic Interoperability., Studies in Health Technology and Informatics, Vol: 235, Pages: 156-160, ISSN: 0926-9630
Data about patients are available from diverse sources, including those routinely collected as individuals interact with service providers, and those provided directly by individuals through surveys. Linking these data can lead to a more complete picture about the individual, to inform either care decision making or research investigations. However, post-linkage, differences in data recording systems and formats present barriers to achieving these aims. This paper describes an approach to combine linked GP records with study observations, and reports initial challenges related to semantic and syntactic interoperability issues.
Curcin V, Fairweather E, Danger R, et al., 2017, Templates as a method for implementing data provenance in decision support systems, JOURNAL OF BIOMEDICAL INFORMATICS, Vol: 65, Pages: 1-21, ISSN: 1532-0464
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- Citations: 23
Tapuria A, Evans M, Curcin V, et al., 2017, Establishment of Requirements and Methodology for the Development and Implementation of GreyMatters, a Memory Clinic Information System, INFORMATICS FOR HEALTH: CONNECTED CITIZEN-LED WELLNESS AND POPULATION HEALTH, Vol: 235, Pages: 18-22, ISSN: 0926-9630
Wongkoblap A, Vadillo MA, Curcin V, 2017, Detecting and Treating Mental Illness on Social Networks, 5th IEEE International Conference on Healthcare Informatics (ICHI), Publisher: IEEE, Pages: 330-330
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- Citations: 5
Chhaya V, Saxena S, Cecil E, et al., 2016, Emerging trends and risk factors for perianal surgery in Crohn's disease: a 20-year national population-based cohort study, European Journal of Gastroenterology & Hepatology, Vol: 28, Pages: 890-895, ISSN: 0954-691X
Background: Little is known about the rates of perianal surgery (PAS) in Crohn’s disease (CD). Our aim was to determine trends in PAS, the timing of surgery relative to the diagnosis of CD and to identify subgroups at risk of PAS.Materials and methods: We identified 9391 incident cases of CD between 1989 and 2009. We defined three eras: era 1 (1989–1995), era 2 (1996–2002) and era 3 (2003–2009), and determined trends in procedure type and the time to first PAS relative to the date of diagnosis. We used Kaplan–Meier analysis to calculate the rate of first PAS and performed Cox regression to determine subgroups at risk of PAS.Results: Among the 9391 incident cases of CD, 405 (4.3%) underwent PAS. The overall rate of PAS was 5.5% [95% confidence interval (CI): 4.9–6.2%] 10 years after diagnosis. 34% (n=137) of all patients undergoing PAS had surgery in the 5 years before CD diagnosis. Abscess drainage increased from 34 to 58%, whereas proctectomy decreased from 16 to 6% between eras 1 and 3, respectively. Men [hazard rate (HR) 1.51, 95% CI: 1.24–1.84], those aged 17–40 years (HR 1.69, 95% CI: 1.09–2.02 vs. those aged >40 years) and those with a history of previous intestinal resection (HR 28.5, 95% CI: 22.2–36.5) were more likely to have PAS.Conclusion: Around one-third of patients have a PAS in the 5 years preceding their diagnosis of CD. Surgical practice has changed over 20 years, with a decrease in proctectomy and a concurrent increase in abscess drainage that is likely to reflect improvements in therapeutic practice.
Vamos EP, Pape UJ, Curcin V, et al., 2016, Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes., Canadian Medical Association Journal, ISSN: 0008-4409
BACKGROUND: The health burden caused by seasonal influenza is substantial. We sought to examine the effectiveness of influenza vaccination against admission to hospital for acute cardiovascular and respiratory conditions and all-cause death in people with type 2 diabetes. METHODS: We conducted a retrospective cohort study using primary and secondary care data from the Clinical Practice Research Datalink in England, over a 7-year period between 2003/04 and 2009/10. We enrolled 124 503 adults with type 2 diabetes. Outcome measures included admission to hospital for acute myocardial infarction (MI), stroke, heart failure or pneumonia/influenza, and death. We fitted Poisson regression models for influenza and off-season periods to estimate incidence rate ratios (IRR) for cohorts who had and had not received the vaccine. We used estimates for the summer, when influenza activity is low, to adjust for residual confounding. RESULTS: Study participants contributed to 623 591 person-years of observation during the 7-year study period. Vaccine recipients were older and had more comorbid conditions compared with nonrecipients. After we adjusted for covariates and residual confounding, vaccination was associated with significantly lower admission rates for stroke (IRR 0.70, 95% confidence interval [CI] 0.53-0.91), heart failure (IRR 0.78, 95% CI 0.65-0.92) and pneumonia or influenza (IRR 0.85, 95% CI 0.74-0.99), as well as all-cause death (IRR 0.76, 95% CI 0.65-0.83), and a nonsignificant change for acute MI (IRR 0.81, 95% CI 0.62-1.04) during the influenza seasons. INTERPRETATION: In this cohort of patients with type 2 diabetes, influenza vaccination was associated with reductions in rates of admission to hospital for specific cardiovascular events. Efforts should be focused on improvements in vaccine uptake in this important target group as part of comprehensive secondary prevention.
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