Rachel C Ambagtsheer1,2, Justin Beilby3,4, Julia Dabravolskaj5,6, Marjan Abbasi5,6, Mandy M Archibald4,7, Elsa Dent3,8. 1. Torrens University of Australia, 220 Victoria Square, Adelaide, SA, 5000, Australia. rambagtsheer@laureate.net.au. 2. National Health and Medical Research Council Centre of Research Excellence in Trans-Disciplinary Frailty Research to Achieve Healthy Ageing, Adelaide, Australia. rambagtsheer@laureate.net.au. 3. Torrens University of Australia, 220 Victoria Square, Adelaide, SA, 5000, Australia. 4. National Health and Medical Research Council Centre of Research Excellence in Trans-Disciplinary Frailty Research to Achieve Healthy Ageing, Adelaide, Australia. 5. University of Alberta, Edmonton, AB, Canada. 6. Edmonton Oliver Primary Care Network, Edmonton, AB, Canada. 7. College of Nursing and Health Sciences, Flinders University, Adelaide, Australia. 8. Baker Heart and Diabetes Institute, Melbourne, Australia.
Abstract
BACKGROUND: The primary care setting is the ideal location for identifying the condition of frailty in older adults. AIMS: The aim of this pragmatic study was twofold: (1) to identify data items to extract the data required for an electronic Frailty Index (eFI) from electronic health records (EHRs); and (2) test the ability of an eFI to accurately and feasibly identify frailty in older adults. METHODS: In a rural South Australian primary care clinic, we derived an eFI from routinely collected EHRs using methodology described by Clegg et al. We assessed feasibility and accuracy of the eFI, including complexities in data extraction. The reference standard for comparison was Fried's frailty phenotype. RESULTS: The mean (SD) age of participants was 80.2 (4.8) years, with 36 (60.0%) female (n = 60). Frailty prevalence was 21.7% by Fried's frailty phenotype, and 35.0% by eFI (scores > 0.21). When deriving the eFI, 85% of EHRs were perceived as easy or neutral difficulty to extract the required data from. Complexities in data extraction were present in EHRs of patients with multiple health problems and/or where the majority of data items were located other than on the patient's summary problem list. DISCUSSION: This study demonstrated that it is entirely feasible to extract an eFI from routinely collected Australian primary care data. We have outlined a process for extracting an eFI from EHRs without needing to modify existing infrastructure. Results from this study can inform the development of automated eFIs, including which data items to best access data from.
BACKGROUND: The primary care setting is the ideal location for identifying the condition of frailty in older adults. AIMS: The aim of this pragmatic study was twofold: (1) to identify data items to extract the data required for an electronic Frailty Index (eFI) from electronic health records (EHRs); and (2) test the ability of an eFI to accurately and feasibly identify frailty in older adults. METHODS: In a rural South Australian primary care clinic, we derived an eFI from routinely collected EHRs using methodology described by Clegg et al. We assessed feasibility and accuracy of the eFI, including complexities in data extraction. The reference standard for comparison was Fried's frailty phenotype. RESULTS: The mean (SD) age of participants was 80.2 (4.8) years, with 36 (60.0%) female (n = 60). Frailty prevalence was 21.7% by Fried's frailty phenotype, and 35.0% by eFI (scores > 0.21). When deriving the eFI, 85% of EHRs were perceived as easy or neutral difficulty to extract the required data from. Complexities in data extraction were present in EHRs of patients with multiple health problems and/or where the majority of data items were located other than on the patient's summary problem list. DISCUSSION: This study demonstrated that it is entirely feasible to extract an eFI from routinely collected Australian primary care data. We have outlined a process for extracting an eFI from EHRs without needing to modify existing infrastructure. Results from this study can inform the development of automated eFIs, including which data items to best access data from.
Entities:
Keywords:
Aged, 80 and over; Electronic health records; Frailty; Geriatric assessment; Primary health care
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