Building ontology-based temporal databases for data reuse: An applied example on hospital organizational structures.

Keeping track of data semantics and data changes in the databases is essential to support retrospective studies and the reproducibility of longitudinal clinical analysis by preventing false conclusions from being drawn from outdated data. A knowledge model combined with a temporal model plays an essential role in organizing the data and improving query expressiveness across time and multiple institutions. This paper presents a modelling framework for temporal relational databases using an ontology to derive a shareable and interoperable data model. The framework is based on: OntoRela an ontology-driven database modelling approach and Unified Historicization Framework a temporal database modelling approach. The method was applied to hospital organizational structures to show the impact of tracking organizational changes on data quality assessment, healthcare activities and data access rights. The paper demonstrated the usefulness of an ontology to provide a formal, interoperable, and reusable definition of entities and their relationships, as well as the adequacy of the temporal database to store, trace, and query data over time.

Effect of on-demand vs continuous prescription of proton pump inhibitors on symptom burden and quality of life: results of a real-world randomized controlled trial in primary care patients with gastroesophageal reflux disease.

OBJECTIVES: This study aimed to assess the impact of on-demand versus continuous prescribing of proton pump inhibitors (PPIs) on symptom burden and health-related quality of life in patients with gastroesophageal reflux disease (GERD) presenting to primary care. METHODS: Thirty-six primary care centres across Europe enrolled adult GERD patients from electronic health records. Participants were randomised to on-demand or continuous PPI prescriptions and were followed for 8 weeks. PPI intake, symptom burden, and quality of life were compared between the two groups using mixed-effect regression analyses. Spearman's correlation was used to assess the association between changes in PPI dose and patient-reported outcomes. RESULTS: A total of 488 patients (median age 51 years, 58% women) completed the initial visit, with 360 attending the follow-up visit. There was no significant difference in PPI use between the continuous and on-demand prescription groups (b=.57, 95%CI:0.40-1.53), although PPI use increased in both groups (b = 1.33, 95%CI:0.65 - 2.01). Advice on prescribing strategy did not significantly affect patient-reported outcomes. Both symptom burden (Reflux Disease Questionnaire, b=-0.61, 95%CI:-0.73 - -0.49) and quality of life (12-item Short Form Survey physical score b = 3.31, 95%CI:2.17 - 4.45) improved from baseline to follow-up in both groups. Increased PPI intake correlated with reduced reflux symptoms (n = 347, ρ=-0.12, p = 0.02) and improved quality of life (n = 217, ρ = 0.16, p = 0.02). CONCLUSION: In real-world settings, both continuous and on-demand PPI prescriptions resulted in similar increases in PPI consumption with no difference in treatment effects. Achieving an adequate PPI dose to alleviate reflux symptom burden improves quality of life in GERD patients. EudraCT number 2014-001314-25.

Continuous and on-demand prescription increase in proton pump inhibitor consumption equally in real-world settings and did not result in different outcomes.Reaching a sufficient dose of proton pump inhibitor to reduce reflux symptom burden improves quality of life in patients with gastroesophageal reflux disease.

Patients' and Members of the Public's Wishes Regarding Transparency in the Context of Secondary Use of Health Data: Scoping Review.

BACKGROUND: Secondary use of health data has reached unequaled potential to improve health systems governance, knowledge, and clinical care. Transparency regarding this secondary use is frequently cited as necessary to address deficits in trust and conditional support and to increase patient awareness. OBJECTIVE: We aimed to review the current published literature to identify different stakeholders' perspectives and recommendations on what information patients and members of the public want to learn about the secondary use of health data for research purposes and how and in which situations. METHODS: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we conducted a scoping review using Medline, CINAHL, PsycINFO, Scopus, Cochrane Library, and PubMed databases to locate a broad range of studies published in English or French until November 2022. We included articles reporting a stakeholder's perspective or recommendations of what information patients and members of the public want to learn about the secondary use of health data for research purposes and how or in which situations. Data were collected and analyzed with an iterative thematic approach using NVivo. RESULTS: Overall, 178 articles were included in this scoping review. The type of information can be divided into generic and specific content. Generic content includes information on governance and regulatory frameworks, technical aspects, and scientific aims. Specific content includes updates on the use of one's data, return of results from individual tests, information on global results, information on data sharing, and how to access one's data. Recommendations on how to communicate the information focused on frequency, use of various supports, formats, and wording. Methods for communication generally favored broad approaches such as nationwide publicity campaigns, mainstream and social media for generic content, and mixed approaches for specific content including websites, patient portals, and face-to-face encounters. Content should be tailored to the individual as much as possible with regard to length, avoidance of technical terms, cultural competence, and level of detail. Finally, the review outlined 4 major situations where communication was deemed necessary: before a new use of data, when new test results became available, when global research results were released, and in the advent of a breach in confidentiality. CONCLUSIONS: This review highlights how different types of information and approaches to communication efforts may serve as the basis for achieving greater transparency. Governing bodies could use the results: to elaborate or evaluate strategies to educate on the potential benefits; to provide some knowledge and control over data use as a form of reciprocity; and as a condition to engage citizens and build and maintain trust. Future work is needed to assess which strategies achieve the greatest outreach while striking a balance between meeting information needs and use of resources.

Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications.

BACKGROUND: A large volume of heavily fragmented data is generated daily in different healthcare contexts and is stored using various structures with different semantics. This fragmentation and heterogeneity make secondary use of data a challenge. Data integration approaches that derive a common data model from sources or requirements have some advantages. However, these approaches are often built for a specific application where the research questions are known. Thus, the semantic and structural reconciliation is often not reusable nor reproducible. A recent integration approach using knowledge models has been developed with ontologies that provide a strong semantic foundation. Nonetheless, deriving a data model that captures the richness of the ontology to store data with their full semantic remains a challenging task. OBJECTIVES: This article addresses the following question: How to design a sharable and interoperable data model for storing heterogeneous healthcare data and their semantic to support various applications? METHOD: This article describes a method using an ontological knowledge model to automatically generate a data model for a domain of interest. The model can then be implemented in a relational database which efficiently enables the collection, storage, and retrieval of data while keeping semantic ontological annotations so that the same data can be extracted for various applications for further processing. RESULTS: This article (1) presents a comparison of existing methods for generating a relational data model from an ontology using 23 criteria, (2) describes standard conversion rules, and (3) presents O n t o R e l a , a prototype developed to demonstrate the conversion rules. CONCLUSION: This work is a first step toward automating and refining the generation of sharable and interoperable relational data models using ontologies with a freely available tool. The remaining challenges to cover all the ontology richness in the relational model are pointed out.

The Prescription of Drug Ontology 2.0 (PDRO): More Than the Sum of Its Parts.

While drugs and related products have profoundly changed the lives of people around the world, ongoing challenges remain, including inappropriate use of a drug product. Inappropriate uses can be explained in part by ambiguous or incomplete information, for example, missing reasons for treatments, ambiguous information on how to take a medication, or lack of information on medication-related events outside the health care system. In order to fully assess the situation, data from multiple systems (electronic medical records, pharmacy and radiology information systems, laboratory management systems, etc.) from multiple organizations (outpatient clinics, hospitals, long-term care facilities, laboratories, pharmacies, registries, governments) on a large geographical scale is needed. Formal knowledge models like ontologies can help address such an information integration challenge. Existing approaches like the Observational Medical Outcomes Partnership are discussed and contrasted with the use of ontologies and systems using them for data integration. The PRescription Drug Ontology 2.0 (PDRO 2.0) is then presented and entities that are paramount in addressing this problematic are described. Finally, the benefits of using PDRO are discussed through a series of exemplar situation.

Expected clinical utility of automatable prediction models for improving palliative and end-of-life care outcomes: Toward routine decision analysis before implementation.

OBJECTIVE: The study sought to evaluate the expected clinical utility of automatable prediction models for increasing goals-of-care discussions (GOCDs) among hospitalized patients at the end of life (EOL). MATERIALS AND METHODS: We built a decision model from the perspective of clinicians who aim to increase GOCDs at the EOL using an automated alert system. The alternative strategies were 4 prediction models-3 random forest models and the Modified Hospital One-year Mortality Risk model-to generate alerts for patients at a high risk of 1-year mortality. They were trained on admissions from 2011 to 2016 (70 788 patients) and tested with admissions from 2017-2018 (16 490 patients). GOCDs occurring in usual care were measured with code status orders. We calculated the expected risk difference (beneficial outcomes with alerts minus beneficial outcomes without alerts among those at the EOL), the number needed to benefit (number of alerts needed to increase benefit over usual care by 1 outcome), and the net benefit (benefit minus cost) of each strategy. RESULTS: Models had a C-statistic between 0.79 and 0.86. A code status order occurred during 2599 of 3773 (69%) hospitalizations at the EOL. At a risk threshold corresponding to an alert prevalence of 10%, the expected risk difference ranged from 5.4% to 10.7% and the number needed to benefit ranged from 5.4 to 10.9 alerts. Using revealed preferences, only 2 models improved net benefit over usual care. A random forest model with diagnostic predictors had the highest expected value, including in sensitivity analyses. DISCUSSION: Prediction models with acceptable predictive validity differed meaningfully in their ability to improve over usual decision making. CONCLUSIONS: An evaluation of clinical utility, such as by using decision curve analysis, is recommended after validating a prediction model because metrics of model predictiveness, such as the C-statistic, are not informative of clinical value.

Meta-consent for the secondary use of health data within a learning health system: a qualitative study of the public's perspective.

BACKGROUND: The advent of learning healthcare systems (LHSs) raises an important implementation challenge concerning how to request and manage consent to support secondary use of data in learning cycles, particularly research activities. Current consent models in Quebec were not established with the context of LHSs in mind and do not support the agility and transparency required to obtain consent from all involved, especially the citizens. Therefore, a new approach to consent is needed. Previous work identified the meta-consent model as a promising alternative to fulfill the requirements of LHSs, particularly large-scale deployments. We elicited the public's attitude toward the meta-consent model to evaluate if the model could be understood by the citizens and would be deemed acceptable to prepare for its possible implementation in Quebec. METHODS: Eight focus groups, with a total of 63 members of the general public from various backgrounds were conducted in Quebec, Canada, in 2019. Explicit attention was given to literacy levels, language spoken at home and rural vs urban settings. We assessed attitudes, concerns and facilitators regarding key components of the meta-consent model: predefined categories to personalized consent requests, a dynamic web-based infrastructure to record meta-consent, and default settings. To analyse the discussions, a thematic content analysis was performed using a qualitative software. RESULTS: Our findings showed that participants were supportive of this new approach of consent as it promotes transparency and offers autonomy for the management of their health data. Key facilitators were identified to be considered in the implementation of a meta-consent model in the Quebec LHSs: information and transparency, awareness campaigns, development of educational tools, collaboration of front-line healthcare professionals, default settings deemed acceptable by the society as well as close partnerships with recognized and trusted institutions. CONCLUSIONS: This qualitative study reveals the openness of a sample of the Quebec population regarding the meta-consent model for secondary use of health data for research. This first exploratory study conducted with the public is an important step in guiding decision-makers in the next phases of implementing the various strategies to support access and use of health data in Quebec.

Citizens, Research Ethics Committee Members and Researchers' Attitude Toward Information and Consent for the Secondary Use of Health Data: Implications for Research Within Learning Health Systems.

A survey was conducted to assess citizens, research ethics committee members, and researchers' attitude toward information and consent for the secondary use of health data for research within learning health systems (LHSs). Results show that the reuse of health data for research to advance knowledge and improve care is valued by all parties; consent regarding health data reuse for research has fundamental importance particularly to citizens; and all respondents deemed important the existence of a secure website to support the information and consent processes. This survey was part of a larger project that aims at exploring public perspectives on alternate approaches to the current consent models for health data reuse to take into consideration the unique features of LHSs. The revised model will need to ensure that citizens are given the opportunity to be better informed about upcoming research and have their say, when possible, in the use of their data.

Informed consent within a learning health system: A scoping review.

INTRODUCTION: A major consideration for the implementation of a learning health system (LHS) is consent from participants to the use of their data for research purposes. The main objective of this paper was to identify in the literature which types of consent have been proposed for participation in research observational activities in a LHS. We were particularly interested in understanding which approaches were seen as most feasible and acceptable and in which context, in order to inform the development of a Quebec-based LHS. METHODS: Using a scoping review methodology, we searched scientific and legal databases as well as the gray literature using specific terms. Full-text articles were reviewed independently by two authors on the basis of the following concepts: (a) LHS and (b) approach to consent. The selected papers were imported in NVivo software for analysis in the light of a conceptual framework that distinguishes various, largely independent dimensions of consent. RESULTS: A total of 93 publications were analysed for this review. Several studies reach opposing conclusions concerning the best approach to consent within a LHS. However, in the light of the conceptual framework we developed, we found that many of these results are distorted by the conflation between various characteristics of consent. Thus, when these characteristics are distinguished, the results mainly suggest the prime importance of the communication process, by contrast to the scope of consent or the kind of action required by participants (opt-in/opt-out). We identified two models of consent that were especially relevant for our purpose: metaconsent and dynamic consent. CONCLUSIONS: Our review shows the importance of distinguishing carefully the various features of the consent process. It also suggests that the metaconsent model is a valuable model within a LHS, as it addresses many of the issues raised with regards to feasibility and acceptability. We propose to complement this model by adding the modalities of the information process to the dimensions relevant in the metaconsent process.

The SPOR-Canadian Data Platform: a national initiative to facilitate data rich multi-jurisdictional research.

Administrative health data is recognized for its value for conducting population-based research that has contributed to numerous improvements in health. In Canada, each province and territory is responsible for administering its own publicly funded health care program, which has resulted in multiple sets of administrative health data. Challenges to using these data within each of these jurisdictions have been identified, which are further amplified when the research involves more than one jurisdiction. The benefits to conducting multi-jurisdictional studies has been recognized by the Canadian Institutes of Health Research (CIHR), which issued a call in 2017 for proposals that address the challenges. The grant led to the creation of Health Data Research Network Canada (HDRN), with a vision is to establish a distributed network that facilitates and accelerates multi-jurisdictional research in Canada. HDRN received funding for seven years that will be used to support the objectives and activities of an initiative called the Strategy for Patient-Oriented Research Canadian Data Platform (SPOR-CDP). In this paper, we describe the challenges that researchers face while using, or considering using, administrative health data to conduct multi-jurisdictional research and the various ways that the SPOR-CDP will attempt to address them. Our objective is to assist other groups facing similar challenges associated with undertaking multi-jurisdictional research.

A pharmacist-physician intervention model using a computerized alert system to reduce high-risk medication use in primary care.

PURPOSE: Potentially inappropriate medications (PIMs) have been associated with a greater risk of adverse drug events and hospitalizations. To reduce PIMs use, a family health team (FHT) implemented a knowledge translation (KT) strategy that included a pharmacist-physician intervention model based on alerts from a computerized alert system (CAS). METHODS: Our pragmatic, single-site, pilot study was conducted in an FHT clinic in Quebec, Canada. We included community-dwelling older adults (≥ 65 years), with at least 1 alert for selected PIMs and a medical appointment during the study period. PIMs were selected from the Beers and STOPP criteria. The primary outcome was PIMs cessation, decreased dose, or replacement. The secondary outcome was the clinical relevance of the alerts as assessed by the pharmacists. RESULTS: During the 134 days of the study, the CAS screened 369 individuals leading to the identification of 65 (18%) patients with at least 1 new alert. For those 65 patients, the mean age was 77 years, men accounted for 29% of the group and 55% were prescribed 10 or more drugs. One or more clinically relevant alerts were generated for 27 of 65 included patients for an overall clinical relevance of the alerts of 42%. Of the 27 patients with at least 1 relevant alert, 17 (63%) had at least 1 medication change as suggested by the pharmacist. CONCLUSION: An interdisciplinary pharmacist-physician intervention model, based on alerts generated by a CAS, reduced the use of PIMs in community-dwelling older adults followed by an FHT.

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.

Reduction in targeted potentially inappropriate medication use in elderly inpatients: a pragmatic randomized controlled trial.

PURPOSE: The use of potentially inappropriate medications (PIMs) in hospitalized older adults is a complex problem, but the use of computerized alert systems (CAS) has shown some potential. The study's objective is to assess the change in PIM use with a CAS-based pharmacist-physician intervention model compared to usual clinical care. METHODS: Pragmatic single-site randomized controlled trial was conducted at a university teaching hospital. Hospitalizations identified with selected Beers or STOPP criteria were randomized to usual clinical care or to the CAS-based pharmacist-physician intervention. The primary outcome was PIM drug cessation or dosage decrease. Clinical relevance of the CAS alerts was assessed. RESULTS: Analyses included 231 patients who had 128 and 126 hospitalizations in the control and intervention groups, respectively. Patients had a mean age of 81, and 60% were female. In the intervention compared to the control group, drug cessation or dosage decrease were more frequent at 48 h post-alert (45.8 vs 15.9%; absolute difference 30.0%; 95%CI 13.8 to 46.1%) and at discharge from the hospital (48.1 vs 27.3%; absolute difference 20.8%; 95%CI 4.6 to 37.0%). In a post hoc analysis of all alerts, regardless of their clinical relevance, the absolute difference in drug cessation or dosage decrease between the intervention and control groups was 16.2% (95%CI 2.9 to 29.6%) at 48 h and 8.0% (95%CI -4.0 to 20.0%) at discharge from the hospital. CONCLUSIONS: In hospitalized older adults, a CAS-based pharmacist-physician intervention, compared to usual clinical care, resulted in significant higher number of drug cessation and dosage reductions for targeted PIMs.

An ontological analysis of medical Bayesian indicators of performance.

BACKGROUND: Biomedical ontologies aim at providing the most exhaustive and rigorous representation of reality as described by biomedical sciences. A large part of medical reasoning deals with diagnosis and is essentially probabilistic. It would be an asset for biomedical ontologies to be able to support such a probabilistic reasoning and formalize Bayesian indicators of performance: sensitivity, specificity, positive predictive value and negative predictive value. In doing so, one has to consider that not only the positive and negative predictive values, but also sensitivity and specificity depend upon the group under consideration: this is the "spectrum effect". METHODS: The sensitivity value of an index test IT for a disease M in a group g is identified with the proportion of people in g who have M who would get a positive result to IT if the test IT was realized on them. This value can be estimated by selecting a reference test RT for M and a sample s of g, and measuring the proportion, among members of s having a positive result to RT, of those who got a positive result to IT. Similar approximation strategies hold for prevalence, specificity, PPV and NPV. Indicators of diagnostic performances and their estimations are formalized in the context of the OBO Foundry, built on the realist upper ontology Basic Formal Ontology (BFO). RESULTS: Entities and relations from the Ontology for Biomedical investigations (OBI) and the Information Artifact Ontology (IAO) are used and complemented to represent reference tests and index tests, tests executions, tests results and the relations involving those entities, as well as the values of indicators of performance and their estimates. The computations taking as input several estimates of an indicator of performance to produce a finer estimate are also represented. The value of e.g. sensitivity estimates should be dissociated from the real sensitivity value - which involves possible, non-actual conditions, namely the result a person would get if a medical test would be performed on her. Such conditions could not be directly represented in a realist ontology, but a representation is proposed that introduces only actual entities by considering a disposition whose probability value is the real sensitivity value. A sensitivity estimate is a data item which is about such a disposition. CONCLUSIONS: This model provides theoretical basis for the representation of entities supporting Bayesian reasoning in ontologies.

Knowledge Translation Strategy to Reduce the Use of Potentially Inappropriate Medications in Hospitalized Elderly Adults.

OBJECTIVES: To evaluate the effect of a knowledge translation (KT) strategy to reduce potentially inappropriate medication (PIM) use in hospitalized elderly adults. DESIGN: Segmented regression analysis of an interrupted time series. SETTING: Teaching hospital. PARTICIPANTS: Individuals aged 75 and older discharged from the hospital in 2013/14 (mean age 83.3, 54.5% female). INTERVENTION: The KT strategy comprises the distribution of educational materials, presentations by geriatricians, pharmacist-physician interventions based on alerts from a computerized alert system, and comprehensive geriatric assessments. MEASUREMENTS: Rate of PIM use (number of patient-days with use of at least one PIM/number of patient-days of hospitalization for individuals aged ≥75). RESULTS: For 8,622 patients with 14,071 admissions, a total of 145,061 patient-days were analyzed. One or more PIMs were prescribed on 28,776 (19.8%) patient-days; a higher rate was found for individuals aged 75 to 84 (24.0%) than for those aged 85 and older (14.4%) (P < .001), and in women (20.8%) than in men (18.6%) (P < .001). The drug classes most frequently accounting for the PIM were gastrointestinal agents (21%), antihistamines (18%), and antidepressants (17%). An absolute decrease of 3.5% (P < .001) of patient-days with at least one PIM was observed immediately after the intervention. CONCLUSION: A KT strategy resulted in decreased use of PIM in elderly adults in the hospital. Additional interventions will be implemented to maintain or further reduce PIM use.

Translational Medicine and Patient Safety in Europe: TRANSFoRm--Architecture for the Learning Health System in Europe.

UNLABELLED: The Learning Health System (LHS) describes linking routine healthcare systems directly with both research translation and knowledge translation as an extension of the evidence-based medicine paradigm, taking advantage of the ubiquitous use of electronic health record (EHR) systems. TRANSFoRm is an EU FP7 project that seeks to develop an infrastructure for the LHS in European primary care. METHODS: The project is based on three clinical use cases, a genotype-phenotype study in diabetes, a randomised controlled trial with gastroesophageal reflux disease, and a diagnostic decision support system for chest pain, abdominal pain, and shortness of breath. RESULTS: Four models were developed (clinical research, clinical data, provenance, and diagnosis) that form the basis of the projects approach to interoperability. These models are maintained as ontologies with binding of terms to define precise data elements. CDISC ODM and SDM standards are extended using an archetype approach to enable a two-level model of individual data elements, representing both research content and clinical content. Separate configurations of the TRANSFoRm tools serve each use case. CONCLUSIONS: The project has been successful in using ontologies and archetypes to develop a highly flexible solution to the problem of heterogeneity of data sources presented by the LHS.

Detailed clinical modelling approach to data extraction from heterogeneous data sources for clinical research.

The reuse of routinely collected clinical data for clinical research is being explored as part of the drive to reduce duplicate data entry and to start making full use of the big data potential in the healthcare domain. Clinical researchers often need to extract data from patient registries and other patient record datasets for data analysis as part of clinical studies. In the TRANSFoRm project, researchers define their study requirements via a Query Formulation Workbench. We use a standardised approach to data extraction to retrieve relevant information from heterogeneous data sources, using semantic interoperability enabled via detailed clinical modelling. This approach is used for data extraction from data sources for analysis and for pre-population of electronic Case Report Forms from electronic health records in primary care clinical systems.

A unified structural/terminological interoperability framework based on LexEVS: application to TRANSFoRm.

OBJECTIVE: Biomedical research increasingly relies on the integration of information from multiple heterogeneous data sources. Despite the fact that structural and terminological aspects of interoperability are interdependent and rely on a common set of requirements, current efforts typically address them in isolation. We propose a unified ontology-based knowledge framework to facilitate interoperability between heterogeneous sources, and investigate if using the LexEVS terminology server is a viable implementation method. MATERIALS AND METHODS: We developed a framework based on an ontology, the general information model (GIM), to unify structural models and terminologies, together with relevant mapping sets. This allowed a uniform access to these resources within LexEVS to facilitate interoperability by various components and data sources from implementing architectures. RESULTS: Our unified framework has been tested in the context of the EU Framework Program 7 TRANSFoRm project, where it was used to achieve data integration in a retrospective diabetes cohort study. The GIM was successfully instantiated in TRANSFoRm as the clinical data integration model, and necessary mappings were created to support effective information retrieval for software tools in the project. CONCLUSIONS: We present a novel, unifying approach to address interoperability challenges in heterogeneous data sources, by representing structural and semantic models in one framework. Systems using this architecture can rely solely on the GIM that abstracts over both the structure and coding. Information models, terminologies and mappings are all stored in LexEVS and can be accessed in a uniform manner (implementing the HL7 CTS2 service functional model). The system is flexible and should reduce the effort needed from data sources personnel for implementing and managing the integration.