Time for research networks to be trained in informatics and based
within informatics centres

Lusignan, Simon de

doi:10.14236/jhi.v20i1.42

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Whilst our ontology was created to be as comprehensive as possible, it is limited by the fact that some concepts are not present in the Read code terminology, whilst other are non-specific or do not sufficiently differentiate between acute and chronic. Also of note, the NHS in England is to change from using Read codes to using the SNOMED CT by April 2018, and Read codes had stopped being updated at the time of this study [ 26 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

An ontological approach to identifying cases of chronic kidney disease from routine primary care data: a cross-sectional study

et al. 2018

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BackgroundAccurately identifying cases of chronic kidney disease (CKD) from primary care data facilitates the management of patients, and is vital for surveillance and research purposes. Ontologies provide a systematic and transparent basis for clinical case definition and can be used to identify clinical codes relevant to all aspects of CKD care and its diagnosis.MethodsWe used routinely collected primary care data from the Royal College of General Practitioners Research and Surveillance Centre. A domain ontology was created and presented in Ontology Web Language (OWL). The identification and staging of CKD was then carried out using two parallel approaches: (1) clinical coding consistent with a diagnosis of CKD; (2) laboratory-confirmed CKD, based on estimated glomerular filtration rate (eGFR) or the presence of proteinuria.ResultsThe study cohort comprised of 1.2 million individuals aged 18 years and over. 78,153 (6.4%) of the population had CKD on the basis of an eGFR of < 60 mL/min/1.73m2, and a further 7366 (0.6%) individuals were identified as having CKD due to proteinuria. 19,504 (1.6%) individuals without laboratory-confirmed CKD had a clinical code consistent with the diagnosis. In addition, a subset of codes allowed for 1348 (0.1%) individuals receiving renal replacement therapy to be identified.ConclusionsFinding cases of CKD from primary care data using an ontological approach may have greater sensitivity than less comprehensive methods, particularly for identifying those receiving renal replacement therapy or with CKD stages 1 or 2. However, the possibility of inaccurate coding may limit the specificity of this method.

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Section: Discussionmentioning

confidence: 99%

An ontological approach to identifying cases of chronic kidney disease from routine primary care data: a cross-sectional study

et al. 2018

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“…[ 65 ] PCRNs have yet to document the extent to which they have created social and intellectual capital, which might soon be lost as they are amalgamated into comprehensive research networks. The case for investment in structural capital, particularly better use of informatics, has been made on the basis that this may be one of the biggest bars to recruitment [ 66 ]. A Canadian series on research networks emphasises the central role of the EPR in research [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

Defining dimensions of research readiness: a conceptual model for primary care research networks

et al. 2014

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BackgroundRecruitment to research studies in primary care is challenging despite widespread implementation of electronic patient record (EPR) systems which potentially make it easier to identify eligible cases.MethodsLiterature review and applying the learning from a European research readiness assessment tool, the TRANSFoRm International Research Readiness instrument (TIRRE), to the context of the English NHS in order to develop a model to assess a practice’s research readiness.ResultsSeven dimensions of research readiness were identified: (1) Data readiness: Is there good data quality in EPR systems; (2) Record readiness: Are EPR data able to identify eligible cases and other study data; (3) Organisational readiness: Are the health system and socio-cultural environment supportive; (4) Governance readiness: Does the study meet legal and local health system regulatory compliance; (5) Study-specific readiness; (6) Business process readiness: Are business processes tilted in favour of participation: including capacity and capability to take on extra work, financial incentives as well as intangibles such as social and intellectual capital; (7) Patient readiness: Are systems in place to recruit patients and obtain informed consent?ConclusionsThe model might enable the development of interventions to increase participation in primary care-based research and become a tool to measure the progress of practice networks towards the most advanced state of readiness.

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“…SNOMED CT is a clinical terminology, and has been adopted by the NHS for use as a coding standard. However, concerns have been raised regarding its complexity having a detrimental impact on finding data coded to it [52]. MeSH is more intuitive to biomedical researchers and has been shown to be capable of annotating all GWAS phenotypes at an informative level of granularity, albeit at a coarser granularity than originally described in some cases.…”

Section: Discussionmentioning

confidence: 99%

Semantically enabling a genome-wide association study database

et al. 2012

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BackgroundThe amount of data generated from genome-wide association studies (GWAS) has grown rapidly, but considerations for GWAS phenotype data reuse and interchange have not kept pace. This impacts on the work of GWAS Central – a free and open access resource for the advanced querying and comparison of summary-level genetic association data. The benefits of employing ontologies for standardising and structuring data are widely accepted. The complex spectrum of observed human phenotypes (and traits), and the requirement for cross-species phenotype comparisons, calls for reflection on the most appropriate solution for the organisation of human phenotype data. The Semantic Web provides standards for the possibility of further integration of GWAS data and the ability to contribute to the web of Linked Data.ResultsA pragmatic consideration when applying phenotype ontologies to GWAS data is the ability to retrieve all data, at the most granular level possible, from querying a single ontology graph. We found the Medical Subject Headings (MeSH) terminology suitable for describing all traits (diseases and medical signs and symptoms) at various levels of granularity and the Human Phenotype Ontology (HPO) most suitable for describing phenotypic abnormalities (medical signs and symptoms) at the most granular level. Diseases within MeSH are mapped to HPO to infer the phenotypic abnormalities associated with diseases. Building on the rich semantic phenotype annotation layer, we are able to make cross-species phenotype comparisons and publish a core subset of GWAS data as RDF nanopublications.ConclusionsWe present a methodology for applying phenotype annotations to a comprehensive genome-wide association dataset and for ensuring compatibility with the Semantic Web. The annotations are used to assist with cross-species genotype and phenotype comparisons. However, further processing and deconstructions of terms may be required to facilitate automatic phenotype comparisons. The provision of GWAS nanopublications enables a new dimension for exploring GWAS data, by way of intrinsic links to related data resources within the Linked Data web. The value of such annotation and integration will grow as more biomedical resources adopt the standards of the Semantic Web.

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Time for research networks to be trained in informatics and based within informatics centres

Cited by 4 publications

References 6 publications

An ontological approach to identifying cases of chronic kidney disease from routine primary care data: a cross-sectional study

An ontological approach to identifying cases of chronic kidney disease from routine primary care data: a cross-sectional study

Defining dimensions of research readiness: a conceptual model for primary care research networks

Semantically enabling a genome-wide association study database

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