An audit of obesity data and concordance with diagnostic coding for patients admitted to Western Australian country health service hospitals

Document Type

Journal Article

Publication Title

Australian Journal of Advanced Nursing





First Page


Last Page



Australian Nursing & Midwifery Federation


School of Medical and Health Sciences




Western Australian Country Health Service


McClean, K., Cross, M., & Reed, S. (2021). An audit of obesity data and concordance with diagnostic coding for patients admitted to Western Australian country health service hospitals. Australian Journal of Advanced Nursing, 38(1), 45-52.


© 2021 Australian Nursing and Midwifery Federation. Objective: Accurate patient obesity data can be used to identify and mitigate patient manual handling risks to healthcare staff. This study investigates the accuracy of patient obesity data within the Western Australian Country Health Service (WACHS) and examines factors potentially affecting obesity data accuracy. Background: Risk of injuries to healthcare staff are increasing due to rising patient obesity. Consistent increases in the prevalence of obesity in Australia have been recorded since 1995 and Australian obesity projections predict that 42% of the population will be obese in 2035. To manage the increased risks of injuries to healthcare workers due to obese patient management, accurate healthcare data relating to patient obesity is required. Design: Researchers examined records of patients admitted to WACHS hospitals with Type II Diabetes, which has confirmed links with obesity. Manual data extraction and comparison of obesity related data within patient medical records and electronic patient admission data was conducted to determine accuracy. Results: Analysis of the patient data examination demonstrated poor recording of weight (67%), height (24%) and Body Mass Index (BMI) when weight and height measurements were recorded (10%). Poor obesity data accuracy was also determined by low sensitivity results (40%), high false negative results (60%) and a Cohen’s kappa value of 0.44. Discussion: The sensitivity result demonstrates that only 40% of obese patients were coded as obese when obesity is recorded in their medical files, and the false negative result demonstrates that where obesity notations were present in medical files, 60% of these cases were incorrectly coded as ʼnormal weighted’. There was only moderate agreement between the occurrences of coded obesity and the recorded obese patient notations in the medical files. Conclusion: Further research is required to inform enhancements to improve obesity recording and coding accuracy, which will increase the collection of reliable obesity data that could be used to reduce obese patient handling risks to nurses and other healthcare staff. What is already known about the topic? • Increasing Australian population obesity rates have been previously demonstrated, this increase corresponds with increasing numbers of obese patients being admitted into hospitals. • Healthcare staff who care for obese patients are at increased risk of injuries when conducting patient handling tasks. What this paper adds: • A model to measure obesity accuracy utilising 14 data accuracy indicators was used, revealing poor obesity data accuracy and poor completeness of obesity data. • Completeness of obesity data is influenced by time demands and workload of clinicians, breadth of clinical recording requirements, lack of organisational direction for the need of obesity data, and challenges in obtaining height measurements of patients who are mobility impaired, bed-ridden or unable to stand. • Complete and accurate obesity data collections will result in increased ability to mitigate safety risks to healthcare staff who manage obese patients and may improve healthcare funding accuracy.



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