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

Abstract

BackgroundTraditional 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.ResultsTo 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.ConclusionWe 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.

ACE-inhibitors and Angiotensin-2 Receptor Blockers are not associated with severe SARS-COVID19 infection in a multi-site UK acute Hospital Trust

“…The data (demographic, emergency department letters, discharge summaries, clinical notes, radiology reports, medication orders, lab results) was retrieved and analyzed in near real-time from the structured and unstructured components of the electronic health record (EHR) using a variety of natural language processing (NLP) informatics tools belonging to the CogStack ecosystem, 8 namely DrugPipeline, 9 MedCAT 10 and MedCATTrainer. 11 The CogStack NLP pipeline captures negation, synonyms, and acronyms for medical SNOMED-CT concepts as well as surrounding linguistic context using deep learning and long short-term memory networks.…”

From Learning About Machines to Machine Learning: Applications for Mental Health Rehabilitation

“…The SLaM Biomedical Research Centre Case Register and its Clinical Record Interactive Search (CRIS) application were developed in 2008, generating a research repository of real-time, anonymised, structured and open-text data derived from the electronic health record system used by SLaM, a large mental healthcare provider in southeast London [3]. The psychosis risk detection and alerting system leverages the CogStack platform [7], which is an open-source information retrieval system. CogStack implements data mining techniques such as natural language processing to automate information extraction of medical concepts.…”

Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-automated Simulation Based on the LeoPARDS Trial

“…Stuctured and free text data from the EHR were combined into a searchable indexed repository using the CogStack [13] platform, which contains pipelines for document processing and indexing, fast text searching, and distributed analysis. We used the SemEHR [14] biomedical document processing system on CogStack, with Elasticsearch 1 for full free text search to explore text and annotations and Bio-Yodie [15] (an NLP application) to annotate text using the Unified Medical Language System (UMLS) [16].…”