Siaw-Teng Liaw, MB BS, PhD, FRACGP, FACHI, Professor of General Practice; Huei-Yang Chen, MSc, PhD, Research Fellow; Della Maneze, MB BS, Research Associate; Jane Taggart, MPH, Research Fellow; Sarah Dennis, MSc, PhD, Senior Research Fellow; Sanjyot Vagholkar, MB BS, MPH, FRACGP, Staff Specialist; Jeremy Bunker, MB BS, MMEd, FRACGP Staff Specialist.
Health reform: Is routinely collected electronic information fit for purpose?
Article first published online: 19 SEP 2011
© 2011 The Authors. EMA © 2011 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine
Emergency Medicine Australasia
Volume 24, Issue 1, pages 57–63, February 2012
How to Cite
Liaw, S.-T., Chen, H.-Y., Maneze, D., Taggart, J., Dennis, S., Vagholkar, S. and Bunker, J. (2012), Health reform: Is routinely collected electronic information fit for purpose?. Emergency Medicine Australasia, 24: 57–63. doi: 10.1111/j.1742-6723.2011.01486.x
- Issue published online: 8 FEB 2012
- Article first published online: 19 SEP 2011
- Accepted 2 August 2011
- electronic medical record;
- emergency department information system;
- fitness for purpose;
- information quality;
- routinely collected data;
- system consistency
Objective: Little has been reported about the completeness and accuracy of data in existing Australian clinical information systems. We examined the accuracy of the diagnoses of some chronic diseases in an ED information system (EDIS), a module of the NSW Health electronic medical record (EMR), and the consistency of the reports generated by the EMR.
Methods: A list of ED attendees and those admitted was generated from the EDIS, using specific (e.g. angina) and possible clinical terms (e.g. chest pain) for the selected chronic diseases. This EDIS list was validated with an audit of discharge summaries, and compared with a list generated, using similar specific and possible Systematized Nomenclature of Medicine – Clinical Terms (SNOMED-CT), from the underlying EMR database.
Results: Of the 33 115 ED attendees, 2559 had diabetes mellitus (DM), cardiovascular disease or asthma/chronic obstructive pulmonary disease; of these 2559, 876 were admitted. Discharge summaries were missing for 12–15% of patients. Only three-quarters or fewer of the diagnoses were confirmed by the discharge summary audit, best for DM and worst for cardiovascular disease. Proportion of agreement between the lists generated from the EDIS and EMR was best for DM and worst for asthma/chronic obstructive pulmonary disease. Possible reasons for this discrepancy are technical, such as use of different extraction terms or system inconsistency; or clinical, such as data entry, decision-making, professional behaviour and organizational performance.
Conclusions: Variations in information quality and consistency of the EDIS/EMR raise concerns about the ‘fitness for purpose’ of the information for care and planning, information sharing, research and quality assurance.