121 PICTURE – a tool for personalised paediatric informatics consultation using real-world evidence. (23rd February 2023)

• Record Type: Journal Article
• Authors: Bowyer, Stuart A; Pissaridou, Eleni; Booth, John; Briggs, Lydia; Bryant, William A; Key, Daniel; Shah, Mohsin; Spiridou, Anastassia; Sebire, Neil J
• Is Part Of: Archives of disease in childhood. Volume 108(2023)Supplement 1
• Page Start: A45
• Page End: A45
• Publication Date: 2023-02-23
• Subjects: Children -- Diseases -- Periodicals; Infants -- Diseases -- Periodicals; 618.920005
• DOI: 10.1136/archdischild-2023-gosh.121
• Languages: English
• ISSNs: 0003-9888

Abstract

Abstract : An 'Informatics Consultation' is an approach to supporting clinical decision making by providing on-demand evidence based on data from other patients. While high quality trials and academic publications currently provide the best evidence for clinical practice, these trials are inherently generalised, often exclude complex comorbidities and subsequently do not necessarily translate well to the realities of patients with even slightly atypical characteristics or multimorbidities. We present PICTURE, a web-application and data science platform developed at the Great Ormond Street Hospital (GOSH). PICTURE provides informatics consultation support to the hospital, based on the real-world evidence from routinely acquired data stored in the electronic health record. The application allows clinical teams to select characteristics of their individual patient to define tailored cohorts of historical patients upon which evidence-producing analyses, and models are run. For example, the application can generate a personalised prognosis report for a specific patient (in both interactive and static formats) by comparing historical patients with similar demographics and comorbidities. The PICTURE proof-of-concept application has been built in the R programming language with a Shiny based user interface and is hosted on the Aridhia Digital Research Environment. The tool makes use of the existing data 'extract, transform, load' pipeline and data-model in the GOSH 'Data Research, Innovation, and Virtual Environments' (DRIVE) unit to allow analyses across a full range of patient data, including, diagnoses, procedures, laboratory tests, medications, and admissions. The current application has demonstrated how simple analytics methods can be configured to run across user-defined patient cohorts to provide informative prognostic real-world evidence. Future work will develop the PICTURE tool, in collaboration with clinicians from a range of specialities, to provide more effective and detailed evidence reports. Additionally, the tool will be expanded to incorporate methods from machine learning and allow data linkage with external data sources.