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.

ABSTRACTObjectiveUnlocking 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 forsecondary research use, generation of actionable medical insights, hospital management and trial recruitment. To achieve this, we implemented SemEHR - a semantic search and analytics, open source tool for EHRs.MethodsSemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualised mentions of a wide range of biomedical concepts within EHRs. Natural Language Processing (NLP) annotations are further assembled at patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data is serviced via ontology-based search and analytics interfaces.ResultsSemEHR has been deployed to a number of UK hospitals including the Clinical Record Interactive Search (CRIS), an anonymised replica of the EHR of the UK South London and Maudsley (SLaM) NHS Foundation Trust, one of Europes largest providers of mental health services. In two CRIS-based studies, SemEHR achieved 93% (Hepatitis C case) and 99% (HIV case) F-Measure results in identifying true positive patients. At King’s College Hospital in London, as part of the CogStack programme (github.com/cogstack), SemEHR is being used to recruit patients into the UK Dept of Health 100k Genome Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast in searching phenotypes - time for recruitment criteria checking reduced from days to minutes. Validated on an open intensive care EHR data - MIMICIII, the vital signs extracted by SemEHR can achieve around 97% accuracy.ConclusionResults 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 a patient, bringing in more and unexpected insight compared to study-oriented bespoke information extraction systems.SemEHR is open source available at https://github.com/CogStack/SemEHR.

The topology of the patient flow network in a hospital is complex, comprising hundreds of overlapping patient journeys, and is a determinant of operational efficiency. To understand the network architecture of patient flow, we performed a data-driven network analysis of patient flow through two acute hospital sites of King’s College Hospital NHS Foundation Trust. Administration databases were queried for all intra-hospital patient transfers in an 18- month period and modelled as a dynamic weighted directed graph. A ‘core’ subnetwork containing only 13-17% of all edges channelled 83-90% of the patient flow, while an ‘ephemeral’ network constituted the remainder. Unsupervised cluster analysis and differential network analysis identified sub-networks where traffic is most associated with A&E performance the following day. Increased flow to clinical decision units was associated with the best A&E performance in both sites. The component analysis also detected a weekend effect on patient transfers which was not associated with performance. We have performed the first data-driven hypothesis-free analysis of patient flow which can enhance understanding of whole healthcare systems. Such analysis can drive transformation in healthcare as it has in industries such as manufacturing.

BackgroundDeriving structured data from unstructured clinical notes in electronic health records (EHRs) requires natural language processing and clinical expertise, which is often costly, and frequently a one-off investment. We implemented SemEHR, a semantic search system that reduces the expertise and effort required in this context. We aimed to use it to characterise and select patients for projects such as the UK Department of Health 100,000 Genome Project.MethodsBuilt upon the off-the-shelf toolkits, Bio-YODIE and CogStack, SemEHR integrates heterogeneous EHR documents and identifies contextualised (negation, temporality, and experiencer) mentions of a wide range of biomedical concepts including SNOMED CT, ICD-10, LOINC, and Drug Ontology. Text mining and semantics techniques are incorporated to derive a longitudinal patient panorama, combining structured profiles and unstructured records, available through semantic search interfaces.FindingsWe deployed SemEHR in various UK hospital EHRs, including the South London and Maudsley NHS Foundation Trust, where 46 million concept mentions were identified from 18 million documents. In a liver disease study, SemEHR identified 94 of 100 hepatitis C positive manually annotated patients. In a HIV study, SemEHR identified 21 of 23 true positives in a 1000-patient cohort. At King's College Hospital, SemEHR is being used to recruit patients into the 100,000 Genomes Project, where ontological associations are integrated to match recruitment criteria and populate complex phenotype models. A preliminary evaluation suggests that the tool is able to validate previously submitted cases and is very fast in searching phenotypes.InterpretationUsing SemEHR, a query such as “find patients with a family history of hepatitis C”, which previously might have required the user to have natural language processing expertise, becomes a simple search, for which SemEHR retrieves a relevant patient cohort, populates patient-level summaries, and provides a link to each mention in the original source. Results and feedback from the multiple studies have proven its efficiency: previously weeks or months of work can be done within minutes in some cases.FundingMedical Research Council (MC_PC_14089); NIHR Biomedical Research Centre for Mental Health, Biomedical Research Unit for Dementia; the European Union's Horizon 2020 (No 644753 KConnect); Wellcome Trust Seed Award in Science (109823/Z/15/Z); National Institute for Health Research University College London Hospital's Biomedical Research Centre; Arthritis Research UK; British Heart Foundation; Cancer Research UK; Chief Scientist Office; Economic and Social Research Council; Engineering and Physical Sciences Research Council; National Institute for Social Care and Health Research; Wellcome Trust (grant MR/K006584/1).