Dataset Information

Ontology to identify pregnant women in electronic health records: primary care sentinel network database study.

ABSTRACT: OBJECTIVE:To develop an ontology to identify pregnant women from computerised medical record systems with dissimilar coding systems in a primary care sentinel network. MATERIALS AND METHODS:We used a three-step approach to develop our pregnancy ontology in two different coding schemata, one hierarchical and the other polyhierarchical. We developed a coding system-independent pregnancy case identification algorithm using the Royal College of General Practitioners Research and Surveillance Centre sentinel network database which held 1.8 million patients' data drawn from 150 primary care providers. We tested the algorithm by examining individual patient records in a 10% random sample of all women aged 29 in each year from 2004 to 2016. We did an external comparison with national pregnancy data. We used ?2 test to compare results obtained for the two different coding schemata. RESULTS:243 005 women (median age 29 years at start of pregnancy) had 405 591 pregnancies from 2004 to 2016 of which 333 689 went to term. We found no significant difference between results obtained for two populations using different coding schemata. Pregnancy mean ages did not differ significantly from national data. DISCUSSION:This ontologically driven algorithm enables consistent analysis across data drawn from populations using different coding schemata. It could be applied to other hierarchical coding systems (eg, International Classification of Disease) or polyhierarchical systems (eg, SNOMED CT to which our health system is currently migrating). CONCLUSION:This ontological approach will improve our surveillance in particular of influenza vaccine exposure in pregnancy.

SUBMITTER: Liyanage H

PROVIDER: S-EPMC7062332 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Ontology to identify pregnant women in electronic health records: primary care sentinel network database study.

Liyanage Harshana H Williams John J Byford Rachel R de Lusignan Simon S

BMJ health & care informatics 20190704 1

<h4>Objective</h4>To develop an ontology to identify pregnant women from computerised medical record systems with dissimilar coding systems in a primary care sentinel network.<h4>Materials and methods</h4>We used a three-step approach to develop our pregnancy ontology in two different coding schemata, one hierarchical and the other polyhierarchical. We developed a coding system-independent pregnancy case identification algorithm using the Royal College of General Practitioners Research and Surve ...[more]

PMID: 31272998

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Project description:BackgroundCreating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at either the domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was an extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform.ObjectiveThis study aimed to develop an application ontology for COVID-19 that can be deployed across the various use-case domains of the RCGP RSC research and surveillance activities.MethodsWe described our domain-specific use case. The actor was the RCGP RSC sentinel network, the system was the course of the COVID-19 pandemic, and the outcomes were the spread and effect of mitigation measures. We used our established 3-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system-independent COVID-19 case identification algorithm. As there were no gold-standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association Primary Health Care Informatics working group and extended networks.ResultsOur use-case domains included primary care, public health, virology, clinical research, and clinical informatics. Our ontology supported (1) case identification, microbiological sampling, and health outcomes at an individual practice and at the national level; (2) feedback through a dashboard; (3) a national observatory; (4) regular updates for Public Health England; and (5) transformation of a sentinel network into a trial platform. We have identified a total of 19,115 people with a definite COVID-19 status, 5226 probable cases, and 74,293 people with possible COVID-19, within the RCGP RSC network (N=5,370,225).ConclusionsThe underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential.

Project description:BackgroundEpidemiological research on small cell lung cancer (SCLC) is limited and based on cancer registry data. We evaluated the feasibility and validity of using primary care electronic health records (The Health Improvement Network [THIN]) in the UK to identify and characterise SCLC.MethodsWe searched THIN records of individuals aged 18-89 years between 2000 and 2014 for a first diagnostic code suggestive of lung cancer (group 1) or small cell cancer (SCC; group 2) and for text strings among free text comments to identify and characterise incident SCLC cases. We validated our case identification strategy by manual review of patient EHRs, including free text comments, for a random sample of 400 individuals initially detected with a diagnostic code (300 from group 1 and 100 from group 2).ResultsTwenty five thousand two hundred fourty one individuals had a code for lung cancer (n = 24,508 [97.1%]) or SCC (733 [2.9%]). Following free-text searches, there were 3530 incident SCLC cases (2956 from group 1; 574 from group 2) corresponding to an incidence rate of 1.01 per 10,000 person-years. In the validation exercise, SCLC confirmation rates were 99% (group 1) and 85% (group 2). Mean age at diagnosis among confirmed cases was 68.5 years; staging information was present in 63.5% of cases of whom 17.8% had limited disease and 82.2% had extensive disease. The majority (84.5%) had a recorded symptom suggestive of lung cancer; chest infection was the most common (18%) followed by cough (15.8%) and chest/abdominal/back pain (15.2%). The first year crude mortality rates was 9.9 per 100 person-months (95% confidence interval [CI] 9.5-10.4), was higher among men and those aged 80 years and above. A total of 144 (37.8%) confirmed cases had metastases recorded. Median survival among the whole study cohort was 7.37 months.ConclusionsOur SCLC case identification method appears to be valid and could potentially be adapted to identify other cancer types. However, complete characterisation of staging requires information from additional data sources including cancer registries.

Dataset Information

Ontology to identify pregnant women in electronic health records: primary care sentinel network database study.

Publications

Ontology to identify pregnant women in electronic health records: primary care sentinel network database study.

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