Extension of the primary care research object model (PCROM) as clinical research information model (CRIM) for the "learning healthcare system".

BACKGROUND: Patient data from general practices is already used for many types of epidemiological research and increasingly, primary care systems to facilitate randomized clinical trials. The EU funded project TRANSFoRm aims to create a "Learning Healthcare System" at a European level that is able to support all types of research using primary care data, to recruit patients and follow patients in clinical studies and to improve diagnosis and therapy. The implementation of such a Learning Healthcare System needs an information model for clinical research (CRIM), as an informational backbone to integrate aspects of primary care with clinical trials and database searches. METHODS: Workflow descriptions and corresponding data objects of two clinical use cases (Gastro-Oesophageal Reflux Disease and Type 2 Diabetes) were described in UML activity diagrams. The components of activity diagrams were mapped to information objects of PCROM (Primary Care Research Object Model) and BRIDG (Biomedical Research Integrated Domain Group) and evaluated. The class diagram of PCROM was adapted to comply with workflow descriptions. RESULTS: The suitability of PCROM, a primary care information model already used for clinical trials, to act as an information model for TRANSFoRm was evaluated and resulted in its extension with 14 new information object types, two extensions of existing objects and the introduction of two new high-ranking concepts (CARE area and ENTRY area). No PCROM component was redundant. Our result illustrates that in primary care based research an important but underestimated portion of research activity takes place in the area of care (e.g. patient consultation, screening, recruitment and response to adverse events). The newly introduced CARE area for care-related research activities accounts for this shift and includes Episode of Care and Encounter as two new basic elements. In the ENTRY area different aspects of data collection were combined, including data semantics for observations, assessment activities, intervention activities and patient reporting to enable case report form (CRF) based data collection combined with decision support. CONCLUSIONS: Research with primary care data needs an extended information model that covers research activities at the care site which are characteristic for primary care based research and the requirements of the complicated data collection processes.

Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

PURPOSE: The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. METHODS: Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. RESULTS: 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. CONCLUSIONS: Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks.

An ontological treatment of clinical prediction rules implementing the Alvarado score.

A lack of acceptance has hindered the widespread adoption and implementation of clinical prediction rules (CPRs). The use of clinical decision support systems (CDSSs) has been advocated as one way of facilitating a broader dissemination and validation of CPRs. This requires computable models of clinical evidence based on open standards rather than closed proprietary content. The on-going TRANSFoRm project has developed ontological models of CPRs suitable for providing CPR based decision support. This paper presents a description of the design and implementation of the ontology model for CPRs that has been proposed. The conceptual validity of the ontology is discussed using the example of a specific CPR in the form of the Alvarado Score for acute appendicitis. We demonstrate how the model is used to query the structure of this particular rule, providing a computable representation suitable for CPRs in general.

A model for the electronic support of practice-based research networks.

PURPOSE: The principal goal of the electronic Primary Care Research Network (ePCRN) is to enable the development of an electronic infrastructure to support clinical research activities in primary care practice-based research networks (PBRNs). We describe the model that the ePCRN developed to enhance the growth and to expand the reach of PBRN research. METHODS: Use cases and activity diagrams were developed from interviews with key informants from 11 PBRNs from the United States and United Kingdom. Discrete functions were identified and aggregated into logical components. Interaction diagrams were created, and an overall composite diagram was constructed describing the proposed software behavior. Software for each component was written and aggregated, and the resulting prototype application was pilot tested for feasibility. A practical model was then created by separating application activities into distinct software packages based on existing PBRN business rules, hardware requirements, network requirements, and security concerns. RESULTS: We present an information architecture that provides for essential interactions, activities, data flows, and structural elements necessary for providing support for PBRN translational research activities. The model describes research information exchange between investigators and clusters of independent data sites supported by a contracted research director. The model was designed to support recruitment for clinical trials, collection of aggregated anonymous data, and retrieval of identifiable data from previously consented patients across hundreds of practices. CONCLUSIONS: The proposed model advances our understanding of the fundamental roles and activities of PBRNs and defines the information exchange commonly used by PBRNs to successfully engage community health care clinicians in translational research activities. By describing the network architecture in a language familiar to that used by software developers, the model provides an important foundation for the development of electronic support for essential PBRN research activities.

Envisioning a learning health care system: the electronic primary care research network, a case study.

PURPOSE: The learning health care system refers to the cycle of turning health care data into knowledge, translating that knowledge into practice, and creating new data by means of advanced information technology. The electronic Primary Care Research Network (ePCRN) was a project, funded by the U.S. National Institutes of Health, with the aim to facilitate clinical research using primary care electronic health records (EHRs). METHODS: We identified the requirements necessary to deliver clinical studies via a distributed electronic network linked to EHRs. After we explored a variety of informatics solutions, we constructed a functional prototype of the software. We then explored the barriers to adoption of the prototype software within U.S. practice-based research networks. RESULTS: We developed a system to assist in the identification of eligible cohorts from EHR data. To preserve privacy, counts and flagging were performed remotely, and no data were transferred out of the EHR. A lack of batch export facilities from EHR systems and ambiguities in the coding of clinical data, such as blood pressure, have so far prevented a full-scale deployment. We created an international consortium and a model for sharing further ePCRN development across a variety of ongoing projects in the United States and Europe. CONCLUSIONS: A means of accessing health care data for research is not sufficient in itself to deliver a learning health care system. EHR systems need to use sophisticated tools to capture and preserve rich clinical context in coded data, and business models need to be developed that incentivize all stakeholders from clinicians to vendors to participate in the system.

Developing a survey instrument to assess the readiness of primary care data, genetic and disease registries to conduct linked research: TRANSFoRm International Research Readiness (TIRRE) survey instrument.

BACKGROUND: Clinical data are collected for routine care in family practice; there are also a growing number of genetic and cancer registry data repositories. The Translational Research and Patient Safety in Europe (TRANSFoRm) project seeks to facilitate research using linked data from more than one source. We performed a requirements analysis which identified a wide range of data and business process requirements that need to be met before linking primary care and either genetic or disease registry data. OBJECTIVES: To develop a survey to assess the readiness of data repositories to participate in linked research - the Transform International Research Readiness (TIRRE) survey. METHOD: We develop the questionnaire based on our requirement analysis; with questions at micro-, meso- and macro levels of granularity, study-specific questions about diabetes and gastro-oesophageal reflux disease (GORD), and research track record. The scope of the data required was extensive. We piloted this instrument, conducting ten preliminary telephone interviews to evaluate the response to the questionnaire. RESULTS: Using feedback gained from these interviews we revised the questionnaire; clarifying questions that were difficult to answer and utilising skip logic to create different series of questions for the various types of data repository. We simplified the questionnaire replacing free-text responses with yes/no or picking list options, wherever possible. We placed the final questionnaire online and encouraged its use (www.clininf.eu/jointirre/info.html). CONCLUSION: Limited field testing suggests that TIRRE is capable of collecting comprehensive and relevant data about the suitability and readiness of data repositories to participate in linked data research.

A loosely coupled framework for terminology controlled distributed EHR search for patient cohort identification in clinical research.

Heterogeneous data models and coding schemes for electronic health records present challenges for automated search across distributed data sources. This paper describes a loosely coupled software framework based on the terminology controlled approach to enable the interoperation between the search interface and heterogeneous data sources. Software components interoperate via common terminology service and abstract criteria model so as to promote component reuse and incremental system evolution.

Health information systems in Jordan and Palestine: the need for health informatics training.

BACKGROUND: Some Arab countries have health information systems (HIS) in place but they lack well trained IT staff. Poor management and lack of appreciation of the importance of HIS are major barriers to development and adoption of HIS in Arab hospitals. AIMS: This research is part of a survey carried out to determine health informatics (HI) use and to assess the training needs of health professionals in Jordan and Palestine. METHODS: A survey was conducted in 2017 among employees in all health professions at 14 hospitals in Jordan and Palestine to assess their use of the HI system and to assess the HI skills needed in both countries. RESULTS: The majority of respondents reported that their hospital departments were employing computer systems to run services. More than half had received training in computer skills but also half said they needed specialized training in HI. Between 58.0% and 73.6% agreed that their hospitals provided the necessary support to operate HI systems. The vast majority (86.0%) of health professionals reported that they needed skills to monitor diagnosis and treatment, including access to clinical findings. Other skills needed included using shared hospital services (85.6%), using medical records (84.7%), managing electronic patient data (84.5%), using patient medical records to conduct clinical research (83.4%), and using tele-care services and technologies effectively (75.9%). CONCLUSIONS: Health professionals in Palestine and Jordan are in need of training in HI and therefore educational programmes in the area of HI are strongly recommended.

The 'PEARL' Data Warehouse: Initial Challenges Faced with Semantic and Syntactic Interoperability.

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.

Models and inference methods for clinical systems: a principled approach.

Previous papers have argued for the existence of three different models in many clinical information systems--for the medical record, for inference in guidelines, and for concepts and re-usable facts. This paper presents a principled approach to deciding which information belongs in each model based on the nature of the queries or inference to be performed: necessary or contingent, open or closed world, algorithmic vs heuristic. It then discusses an important class of systems--"ontologically indexed knowledge bases"--and issues of metadata within this framework.

An approach for utilizing clinical statements in HL7 RIM to evaluate eligibility criteria.

The HL7 RIM (Reference Information Model) is a commonly used standard for the exchange of clinical data and can be employed for integrating the patient care and clinical research domains. Yet it is not sufficiently well specified to ensure a canonical representation of structured clinical data when used for the automated evaluation of eligibility criteria from a clinical trial protocol. We present an approach to further constrain the RIM to create a common information model to hold clinical data. In order to demonstrate our approach, we identified 132 distinct data elements from 10 rich clinical trails. We then defined a taxonomy to (i) identify the types of data elements that would need to be stored and (ii) define the types of predicate that would be used to evaluate them. This informed the definition of a pattern used to represent the data, which was shown to be sufficient for storing and evaluating the clinical statements required by the trials.

A standardised graphic method for describing data privacy frameworks in primary care research using a flexible zone model.

PURPOSE: To develop a model describing core concepts and principles of data flow, data privacy and confidentiality, in a simple and flexible way, using concise process descriptions and a diagrammatic notation applied to research workflow processes. The model should help to generate robust data privacy frameworks for research done with patient data. METHODS: Based on an exploration of EU legal requirements for data protection and privacy, data access policies, and existing privacy frameworks of research projects, basic concepts and common processes were extracted, described and incorporated into a model with a formal graphical representation and a standardised notation. The Unified Modelling Language (UML) notation was enriched by workflow and own symbols to enable the representation of extended data flow requirements, data privacy and data security requirements, privacy enhancing techniques (PET) and to allow privacy threat analysis for research scenarios. RESULTS: Our model is built upon the concept of three privacy zones (Care Zone, Non-care Zone and Research Zone) containing databases, data transformation operators, such as data linkers and privacy filters. Using these model components, a risk gradient for moving data from a zone of high risk for patient identification to a zone of low risk can be described. The model was applied to the analysis of data flows in several general clinical research use cases and two research scenarios from the TRANSFoRm project (e.g., finding patients for clinical research and linkage of databases). The model was validated by representing research done with the NIVEL Primary Care Database in the Netherlands. CONCLUSIONS: The model allows analysis of data privacy and confidentiality issues for research with patient data in a structured way and provides a framework to specify a privacy compliant data flow, to communicate privacy requirements and to identify weak points for an adequate implementation of data privacy.

The opportunities and challenges of pragmatic point-of-care randomised trials using routinely collected electronic records: evaluations of two exemplar trials.

BACKGROUND: Pragmatic trials compare the effects of different decisions in usual clinical practice. OBJECTIVES: To develop and evaluate methods to implement simple pragmatic trials using routinely collected electronic health records (EHRs) and recruiting patients at the point of care; to identify the barriers and facilitators for general practitioners (GPs) and patients and the experiences of trial participants. DESIGN: Two exemplar randomised trials (Retropro and eLung) with qualitative evaluations. SETTING: Four hundred and fifty-nine English and Scottish general practices contributing EHRs to a research database, of which 17 participated in the trials. PARTICIPANTS: Retropro aimed to recruit 300 patients with hypercholesterolaemia and high cardiovascular risk and eLung aimed to recruit 150 patients with a chronic obstructive pulmonary disease exacerbation. INTERVENTIONS: Retropro randomised between simvastatin and atorvastatin and eLung between immediate antibiotics and deferred or non-use. eLung recruited during an unscheduled consultation using EHR flagging. MAIN OUTCOME MEASURE: Successful trial completion with implementation of information technology (IT) system for flagging and data processing and documentation of operational and scientific experiences. DATA SOURCES: EHR research database. RESULTS: The governance approval process took over 3 years. A total of 58.8% of the practices (n = 270) expressed interest in participating. The number of interested practices dropped substantially with each stage of the governance process. In Retropro, 6.5% of the practices (n = 30) were eventually approved and 3.7% (n = 17) recruited patients; in eLung, these numbers were 6.8% (n = 31) and 1.3% (n = 6) respectively. Retropro successfully completed recruitment (301 patients) whereas eLung recruited 31 patients. Retropro recruited 20.6% of all statin starters in recruiting practices and 1.1% in the EHR database; the comparable numbers for eLung were 32.3% and 0.9% respectively. The IT system allowed for complex eligibility criteria with central on and off control of recruitment and flagging at a practice. Good Clinical Practice guidelines, governance and consent procedures were found to have substantially affected the intended simple nature of the trials. One qualitative study of 13 clinicians found that clinicians were generally positive about the principle of computerised trial recruitment (flagging during consultation). However, trials which did not include patients with acute illness were favoured. The second qualitative process evaluation interviewed 27 GPs about their actual experiences, including declining, recruiting and non-recruiting GPs. Opportunistic patient recruitment during a routine GP consultation was found to be the most controversial element. The actual experiences of recruiting patients during unscheduled consultation were generally more positive than the hypothetical views of GPs. Several of the recruiting GPs reported the process took 5 minutes and was straightforward and feasible on most occasions. Almost all GPs expressed their strong support for the use of EHRs for trials. Ten eLung participants were interviewed, all of whom considered it acceptable to be recruited during a consultation and to use EHRs for trials. CONCLUSIONS: EHR point-of-care trials are feasible, although the recruitment of clinicians is a major challenge owing to the complexity of trial approvals. These trials will provide substantial evidence on clinical effectiveness only if trial interventions and participating clinicians and patients are typical of usual clinical care and trials are simple to initiate and conduct. Recommendations for research include the development of evidence and implementation of risk proportionality in trial governance and conduct. TRIAL REGISTRATION: Current Controlled Trials ISRCTN33113202 and ISRCTN72035428. FUNDING: This project was funded by the NIHR Health Technology Assessment programme and the Wellcome Trust and will be published in full in Health Technology Assessment; Vol. 18, No. 43. See the NIHR Journals Library website for further project information.

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.

An adaptable architecture for patient cohort identification from diverse data sources.

OBJECTIVE: We define and validate an architecture for systems that identify patient cohorts for clinical trials from multiple heterogeneous data sources. This architecture has an explicit query model capable of supporting temporal reasoning and expressing eligibility criteria independently of the representation of the data used to evaluate them. METHOD: The architecture has the key feature that queries defined according to the query model are both pre and post-processed and this is used to address both structural and semantic heterogeneity. The process of extracting the relevant clinical facts is separated from the process of reasoning about them. A specific instance of the query model is then defined and implemented. RESULTS: We show that the specific instance of the query model has wide applicability. We then describe how it is used to access three diverse data warehouses to determine patient counts. DISCUSSION: Although the proposed architecture requires greater effort to implement the query model than would be the case for using just SQL and accessing a data-based management system directly, this effort is justified because it supports both temporal reasoning and heterogeneous data sources. The query model only needs to be implemented once no matter how many data sources are accessed. Each additional source requires only the implementation of a lightweight adaptor. CONCLUSIONS: The architecture has been used to implement a specific query model that can express complex eligibility criteria and access three diverse data warehouses thus demonstrating the feasibility of this approach in dealing with temporal reasoning and data heterogeneity.

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.

Reuse of routinely collected health data for clinical research: considerations in a central England case study.

An increasing amount of electronic health data is being collected for patient care, and with it the opportunity to explore reusing this data, for example to support clinical research. We explore how researchers can be supported in identifying potential study participants using a semi-automated approach to query anonymised datasets remotely. This paper describes the socio-technical considerations when answering this question in a central England case study.

Cohort identification for clinical research: querying federated electronic healthcare records using controlled vocabularies and semantic types.

In the United Kingdom (UK), local initiatives have started to federate electronic healthcare records from different primary care clinical systems, mainly for the purposes of ensuring that health care services effectively meet the needs of the population. The use of such information is being investigated for clinical research, notably in patient cohort identification and recruitment. To achieve these aims, it is essential that the information from different systems can be searched from a single interface. While interoperability is a widely researched topic, interoperable methods and data sources in primary care are largely missing. This paper describes our approach to enabling primary care data in England to be searchable on a platform developed for performing large national collaborative primary care research studies throughout the United States.

Characterizing primary care research: PCROM versus BRIDG.

BRIDG is a widely known domain model of protocol-driven biomedical/clinical research. During a project to design a clinical trials management system for primary care research we developed a standard, computable representation of an RCT--the Primary Care Research Object Model (PCROM). We compared the PCROM Model to BRIDG and found significant overlap of concepts but also several areas important to our work that were either not yet represented or represented quite differently in BRIDG.

The Primary Care Research Object Model (PCROM): a computable information model for practice-based primary care research.

OBJECTIVES: Chronic disease prevalence and burden is growing, as is the need for applicable large community-based clinical trials of potential interventions. To support the development of clinical trial management systems for such trials, a community-based primary care research information model is needed. We analyzed the requirements of trials in this environment, and constructed an information model to drive development of systems supporting trial design, execution, and analysis. We anticipate that this model will contribute to a deeper understanding of all the dimensions of clinical research and that it will be integrated with other clinical research modeling efforts, such as the Biomedical Research Integrated Domain Group (BRIDG) model, to complement and expand on current domain models. DESIGN: We used unified modeling language modeling to develop use cases, activity diagrams, and a class (object) model to capture components of research in this setting. The initial primary care research object model (PCROM) scope was the performance of a randomized clinical trial (RCT). It was validated by domain experts worldwide, and underwent a detailed comparison with the BRIDG clinical research reference model. RESULTS: We present a class diagram and associated definitions that capture the components of a primary care RCT. Forty-five percent of PCROM objects were mapped to BRIDG, 37% differed in class and/or subclass assignment, and 18% did not map. CONCLUSION: The PCROM represents an important link between existing research reference models and the real-world design and implementation of systems for managing practice-based primary care clinical trials. Although the high degree of correspondence between PCROM and existing research reference models provides evidence for validity and comprehensiveness, existing models require object extensions and modifications to serve primary care research.