Coding and classifying GP data: the POLAR project. Issue 1 (10th November 2019)

• Record Type: Journal Article
• Authors: Pearce, Christopher; McLeod, Adam; Patrick, Jon; Ferrigi, Jason; Bainbridge, Michael Michael; Rinehart, Natalie; Fragkoudi, Anna
• Is Part Of: BMJ health & care informatics. Volume 26:Issue 1(2019)
• Publication Date: 2019-11-10
• Subjects: information management -- information science; Medical informatics -- Great Britain -- Periodicals; Information storage and retrieval systems -- Medical care -- Periodicals; Primary care (Medicine) -- Great Britain -- Data processing -- Periodicals; 362.10285
• DOI: 10.1136/bmjhci-2019-100009
• Languages: English
• ISSNs: 2632-1009

Abstract

Abstract : Background: Data, particularly 'big' data are increasingly being used for research in health. Using data from electronic medical records optimally requires coded data, but not all systems produce coded data. Objective: To design a suitable, accurate method for converting large volumes of narrative diagnoses from Australian general practice records to codify them into SNOMED-CT-AU. Such codification will make them clinically useful for aggregation for population health and research purposes. Method: The developed method consisted of using natural language processing to automatically code the texts, followed by a manual process to correct codes and subsequent natural language processing re-computation. These steps were repeated for four iterations until 95% of the records were coded. The coded data were then aggregated into classes considered to be useful for population health analytics. Results: Coding the data effectively covered 95% of the corpus. Problems with the use of SNOMED CT-AU were identified and protocols for creating consistent coding were created. These protocols can be used to guide further development of SNOMED CT-AU (SCT). The coded values will be immensely useful for the development of population health analytics for Australia, and the lessons learnt applicable elsewhere.