Completeness and accuracy of adverse drug reaction documentation in electronic medical records at a tertiary care hospital in Australia.
Gina B McLachlanAirley BroomfieldRohan A ElliottPublished in: Health information management : journal of the Health Information Management Association of Australia (2021)
Background: A large proportion of patients presenting to hospitals have experienced a previous adverse drug reaction (ADR). Electronic medical records (EMRs) present an opportunity to accurately document ADRs and alert clinicians against inadvertent rechallenge where there is a pre-existing reaction. However, EMR systems are imperfect and rely on the accuracy of the data entered. Objective: To ascertain the completeness of ADR documentation and the accuracy of the classification of ADRs as allergy versus intolerance in the EMR at a major metropolitan hospital in Australia. Method: Cross-sectional audit of the ADR field of the EMR for a sample of patients on four different wards over 3 weeks to ascertain the completeness of ADR documentation and the accuracy of classification of ADRs. Results: Of the 264 patients assessed, 102 (38.6%) had a total of 210 ADRs documented in the EMR. Of these, 105 (50%) were considered to have complete documentation; 63/210 (30.0%) were missing a reaction description and 88/210 (41.9%) were missing severity information. For those ADRs with a reaction description ( n = 147), 97 (66.0%) were considered to be appropriately classified as allergy or intolerance. Conclusion: Incomplete and inaccurate ADR documentation was common. These findings highlight a need for optimising ADR documentation to improve appropriate medication use in hospital. Implications: Improved EMR design and education of healthcare workers on the importance of complete and accurate documentation of reactions are needed to improve completeness and accuracy of ADR classification.
Keyphrases
- adverse drug
- electronic health record
- end stage renal disease
- ejection fraction
- newly diagnosed
- clinical decision support
- machine learning
- chronic kidney disease
- healthcare
- emergency department
- advance care planning
- deep learning
- prognostic factors
- peritoneal dialysis
- patient reported outcomes
- mass spectrometry
- high resolution
- palliative care
- social media
- big data
- health information
- gestational age