SemEHR:A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research

OBJECTIVE: Unlocking the data contained within both structured and unstructured components of electronic health records (EHRs) has the potential to provide a step change in data available for secondary research use, generation of actionable medical insights, hospital management, and trial recruitment. To achieve this, we implemented SemEHR, an open source semantic search and analytics tool for EHRs. METHODS: SemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualized mentions of a wide range of biomedical concepts within EHRs. Natural language processing annotations are further assembled at the patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data are serviced via ontology-based search and analytics interfaces. RESULTS: SemEHR has been deployed at a number of UK hospitals, including the Clinical Record Interactive Search, an anonymized replica of the EHR of the UK South London and Maudsley National Health Service Foundation Trust, one of Europe's largest providers of mental health services. In 2 Clinical Record Interactive Search-based studies, SemEHR achieved 93% (hepatitis C) and 99% (HIV) F-measure results in identifying true positive patients. At King's College Hospital in London, as part of the CogStack program (github.com/cogstack), SemEHR is being used to recruit patients into the UK Department of Health 100 000 Genomes Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast at searching phenotypes; time for recruitment criteria checking was reduced from days to minutes. Validated on open intensive care EHR data, Medical Information Mart for Intensive Care III, the vital signs extracted by SemEHR can achieve around 97% accuracy. CONCLUSION: Results from the multiple case studies demonstrate SemEHR's efficiency: weeks or months of work can be done within hours or minutes in some cases. SemEHR provides a more comprehensive view of patients, bringing in more and unexpected insight compared to study-oriented bespoke IE systems. SemEHR is open source, available at https://github.com/CogStack/SemEHR

CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital.

BACKGROUND: Traditional health information systems are generally devised to support clinical data collection at the point of care. However, as the significance of the modern information economy expands in scope and permeates the healthcare domain, there is an increasing urgency for healthcare organisations to offer information systems that address the expectations of clinicians, researchers and the business intelligence community alike. Amongst other emergent requirements, the principal unmet need might be defined as the 3R principle (right data, right place, right time) to address deficiencies in organisational data flow while retaining the strict information governance policies that apply within the UK National Health Service (NHS). Here, we describe our work on creating and deploying a low cost structured and unstructured information retrieval and extraction architecture within King's College Hospital, the management of governance concerns and the associated use cases and cost saving opportunities that such components present. RESULTS: To date, our CogStack architecture has processed over 300 million lines of clinical data, making it available for internal service improvement projects at King's College London. On generated data designed to simulate real world clinical text, our de-identification algorithm achieved up to 94% precision and up to 96% recall. CONCLUSION: We describe a toolkit which we feel is of huge value to the UK (and beyond) healthcare community. It is the only open source, easily deployable solution designed for the UK healthcare environment, in a landscape populated by expensive proprietary systems. Solutions such as these provide a crucial foundation for the genomic revolution in medicine

Predictors of multiple treatment failure of antipsychotics in early-onset psychosis

This is the author accepted manuscript. the final version is available from Elsevier via the DOI in this recordBackground: In adult-onset psychosis, a number of factors are associated with poor clinical outcomes, which include the severity of negative symptoms at illness onset, the presence of premorbid difficulties, and a family history of psychotic disorder [1]. If and how these factors effect psychosis prognosis in children and adolescents is unclear [2]. Objective: Using a retrospective cohort design and data from a large electronic case register, we sought to investigate, in a sample of children and adolescents with first-episode psychosis (FEP), the prospective association of demographic and clinical variables at first presentation to services with reduced antipsychotic effectiveness. We used multiple treatment failure (MTF) as a proxy for reduced treatment effectiveness, defined as the initiation of a third trial of novel antipsychotic as a result of insufficient response, nontolerable adverse effects or non-adherence. We hypothesized that presence of premorbid difficulties (e.g. comorbid neurodevelopmental disorders), baseline negative symptoms and family history of psychosis would be positively associated with MTF. Methods: Data were obtained from a clinical cohort of 638 children (51% male) with FEP, aged 10−17 years, referred to inpatient and community-based services in South London, UK, using the Clinical Record Interactive Search (CRIS) system. Age, sex, ethnicity, adaptive function, inpatient status, presence of negative symptoms at first presentation, first degree family history of psychosis, co-morbid neurodevelopmental disorders (autism spectrum disorders [ASD], intellectual disability [ID], and hyperkinetic disorders), unique antipsychotic medications prescribed in a 5-year observation period, and reasons for MTF were extracted from electronic patient records. The effect of neurodevelopmental comorbidity, presenting with two or more Marder Negative Symptoms, and family history of psychosis, on the development of MTF over a 5-year period was modelled using Cox regression. Results: One hundred and twenty-four children with FEP (19.3% of the sample) developed MTF prior to the age of eighteen. Of those, 10.5% (n = 13) showed a persistently insufficient response to two consecutive trials of different antipsychotics, 14.5% (n = 18) experienced persistently non-tolerable adverse effects, 4% (n = 5) showed persistent non-adherence, and 78% (n = 77) showed a combination of these reasons. After controlling for a range of potential confounders, a fully adjusted cox proportional hazards model found that co-morbid ASD (adjusted hazard ratio [aHR] 1.89; 95% CI 1.03–3.46; p<0.05) was significantly associated with MTF. The presence of two or more NS around the first episode (aHR 2.00; 1.16– 3.42; p<0.05), family history of psychosis (aHR 2.08; 1.15–3.75; p<0.05), and Black ethnicity, relative to white British ethnicity (aHR 2.09; 1.31–3.33; p<0.05) were all significantly associated with an increased risk of MTF over a 5 year follow-up period. Adaptive function score was inversely associated with MTF (aHR 0.98; 0.97–0.99, p<0.05). Conclusion: Prominent negative symptoms, co-morbid ASD and first degree family history of psychosis at first presentation can delineate a subset of children and adolescents with psychosis who have a higher risk of reduced antipsychotic effectiveness. Children with these risk factors may require early optimisation of current therapeutic approaches or early alternatives to conventional antipsychotic treatment to improve their prognosis