Deriving an ICU Subset of SNOMED CT from Clinical Notes
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Professor Jon Patrick Health Information Technology Research Laboratory (HITRL - www.it.usyd.edu.au/~hitru) School of Information Technologies University of Sydney (P38, 16/10/08, Coding stream, 3.30pm)
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Deriving an ICU Subset of SNOMED CT from Clinical Notes
- 1. Deriving an ICU Subset of SNOMED CT from Clinical Notes Professor Jon Patrick Health Information Technology Research Laboratory (HITRL - www.it.usyd.edu.au/~hitru) School of Information Technologies University of Sydney
- 10. Sample of Notes and SCT Mapping
- 18. Screenshot of a CDAL query: ARDS SNIFFER : Find all patients’ medical record number (and the number of records retrieved) for patients with age > 16, [AND] arterial blood gas analysis (PaO2 / FiO2) < 300 AND Tidal Volume Peak Pressures (Paw) > 35 OR Delivered tidal volume (Vt) > 8mL IN the GICU (over the last year). Note that: PaO2 / FiO2 = PF Ratio; Paw = PIP; Delivered Vt = Vt Expired
- 23. THE END Health Information Technology Research Laboratory (HITRL) http://www.it.usyd.edu.au/~hitru
Editor's Notes
- Information Extraction from Clinical Notes Abstract We are in the testing phase of a project at the Royal Prince Alfred Hospital that does information extraction from clinical notes in the Intensive Care Unit. The language processing is part of a system to support clinicians complete their ward rounds more efficiently and ease the burden of administration in record keeping.. In the first stage the NLP demonstrates the automatic computation of SNOMED CT codes as clinicians write their progress notes. The system computes a tailored extract of the patient's clinical record from the ICU's information system, CareVue, relevant to the needs of reviewing the patient's case. The extract is presented to the clinician on a screen who then types in the relevant progress notes they wish to make. The system computes the SNOMED CT codes in real-time after analysing the progress notes and then they are stored back into CareVue.The system will be of significant advantage to the clinician in their ward rounds. The automatic extraction of relevant content will give considerable time savings in not having to manually search the clinical information system, considered to be a saving of up to 10 minutes per patient (up to 50 patients in the ward visited twice per day). Post data entry the conversion of clinical records into a coded system will ensure more efficient and more reliable data analytics. The work is expected to progress in two directions, namely to improve the accuracy of the information extraction process and to develop a restricted data analytics natural language grounded in the SNOMED CT coding scheme.