Electronic Health Records (EHRs) are now widely deployed, and in many cases these electronic systems have accumulated a considerable history of clinical data. Each clinical site therefore represents a potentially significant data resource. There is considerable structured coding of clinical events and related results, and the structured data capture is highly targeted to specific purposes (primarily billing or reporting). Such structured diagnosis lists, problem lists or test lists often only partially capture the full clinical picture of a patient as the primary means of clinical communication and documentation is in the form of free text letters, notes and reports [1]. Most analytical quantitative research have focused on the structured elements only as the unstructured free text recorded in EHRs have traditionally been difficult to access and analyse [2–4] + +In conventional healthcare workflows, both structured and unstructured aspects of EHR’s are read by business intelligence staff and translated into standardised codes (termed ’clinical coders’) for submissions into datasets. Structured data can be analysed at a regional or national level to gain powerful insights into clinical trajectories at scale [5,6]. This largely manual process uses the ICD10, OPCS ontologies and follows rules around conciseness. Due to the laborious nature of this process and lack of an automation-assisted process, most organisations only perform this ’structuring and standardising’ process on inpatient episodes and the text generated from the large proportion of outpatient activity are ignored. This ’lacune’ means that certain populations with conditions that do not result in hospitalisations (or where clinical pathway transformations migrate to ambulatory or outpatients routes) would be under-represented systematically; dependency on only manually-derived coded data potentially incorporates a hidden ‘inclusion bias’ in many datasets. + +Natural Language Processing (NLP) combined with rich clinical terminologies such as SNOMED have the potential to automate a large portion of the ‘structure and standardise’ process to make the full clinical record accessible to computational analysis [7–9]. Previous attempts have focused on specific cohorts (e.g. critical care patients only [10], patients with a certain disease only [11–13], discharge letters only [14]). Doing this across a whole hospital’s record has not previously been attempted, and produces the opportunity to automate a laborious manual process for healthcare delivery, and also to enrich any structured registries or databases (like HES [15], SUS [16], CPRD [17], Caliber [18], CVD-Covid-UK [19]) with greater phenotypic and narrative expressiveness. Any downstream data-dependent activity, including population health and research, or trial recruitment [20], would potentially benefit. + +In this paper we present the first descriptive summary of the entire text record of a large UK secondary and tertiary healthcare system in London, King’s College Hospital NHS Foundation Trust over a period of about 9 years. To our knowledge this is the first study of a large-scale EHR dataset derived from NLP, although there are several other descriptive analyses of large-scale structured EHR data (Kuan et al. 2019; Thygesen et al. 2022; Kuan et al. 2023). Compared to structured data, the free-text portion of the EHR captures a more detailed clinical narrative. The description of this data provides three useful resources: + +Detailed description of the scale and nature of the available data within a UK hospital +Analysis of disease prevalence and comorbidity patterns with comparison to national prevalence data in the NHS Quality and Outcomes Framework +Description of the data in the UK Health Data Research Innovation Gateway [21] to support open research and collaboration. + +Summarize the above in few lines.
Sure, here is a summary of the above in a few lines:
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- Electronic Health Records (EHRs) are now widely deployed and contain a wealth of clinical data. +
- However, most analytical quantitative research has focused on the structured elements of EHRs, as the unstructured free text has traditionally been difficult to access and analyse. +
- Natural Language Processing (NLP) combined with rich clinical terminologies have the potential to automate the process of structuring and standardising the free text in EHRs, making it accessible to computational analysis. +
- This paper presents the first descriptive summary of the entire text record of a large UK secondary and tertiary healthcare system in London, King’s College Hospital NHS Foundation Trust over a period of about 9 years. +
- The description of this data provides three useful resources:
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- Detailed description of the scale and nature of the available data within a UK hospital +
- Analysis of disease prevalence and comorbidity patterns with comparison to national prevalence data in the NHS Quality and Outcomes Framework +
- Description of the data in the UK Health Data Research Innovation Gateway [21] to support open research and collaboration. +
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