Malnutrition and its contributing factors for older people living in residential aged care facilities: Insights from natural language processing of aged care records
Affiliations: [a] Centre for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Wollongong, Australia | [b] School of Computer Science, Qassim University, Buraydah, Saudi Arabia | [c] School of Nursing and Midwifery, Western Sydney University, Penrith, Australia | [d] School of Nursing, University of Wollongong, Wollongong, Australia | [e] Opal Healthcare, Sydney, Australia | [f] School of Medical, Indigenous and Health Sciences, University of Wollongong, Wollongong, Australia
Correspondence:
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Corresponding author: Ping Yu, Director, Centre for Digital Transformation, School of Computing and Information Technology, Faculty of Engineering and Information Sciences, University of Wollongong, Northfield Ave., Wollongong, NSW 2522, Australia. E-mail: [email protected].
Abstract: BACKGROUND: Malnutrition is a serious health risk facing older people living in residential aged care facilities. Aged care staff record observations and concerns about older people in electronic health records (EHR), including free-text progress notes. These insights are yet to be unleashed. OBJECTIVE: This study explored the risk factors for malnutrition in structured and unstructured electronic health data. METHODS: Data of weight loss and malnutrition were extracted from the de-identified EHR records of a large aged care organization in Australia. A literature review was conducted to identify causative factors for malnutrition. Natural language processing (NLP) techniques were applied to progress notes to extract these causative factors. The NLP performance was evaluated by the parameters of sensitivity, specificity and F1-Score. RESULTS: The NLP methods were highly accurate in extracting the key data, values for 46 causative variables, from the free-text client progress notes. Thirty three percent (1,469 out of 4,405) of the clients were malnourished. The structured, tabulated data only recorded 48% of these malnourished clients, far less than that (82%) identified from the progress notes, suggesting the importance of using NLP technology to uncover the information from nursing notes to fully understand the health status of the vulnerable older people in residential aged care. CONCLUSION: This study identified 33% of older people suffered from malnutrition, lower than those reported in the similar setting in previous studies. Our study demonstrates that NLP technology is important for uncovering the key information about health risks for older people in residential aged care. Future research can apply NLP to predict other health risks for older people in this setting.
Keywords: Natural language processing, malnutrition, electronic health records, residential aged care, nursing home
