Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living with Frailty?

• Published in: International journal of population data science Vol. 5; no. 1; p. 1343
• Main Authors: Wong, S T, Katz, A, Williamson, T, Singer, A, Peterson, S, Taylor, C, Price, M, McCracken, R, Thandi, M
• Format: Journal Article
• Language: English
• Published: Wales Swansea University 13-08-2020
• Subjects: Population Data Science
• ISSN: 2399-4908; 2399-4908
• DOI: 10.23889/ijpds.v5i1.1343

Frailty is a complex condition that affects many aspects of patients' wellbeing and health outcomes.
We used available Electronic Medical Record (EMR) and administrative data to determine definitions of frailty. We also examined whether there were differences in demographics or health conditions among those identified as frail in either the EMR or administrative data.
EMR and administrative data were linked in British Columbia (BC) and Manitoba (MB) to identify those aged 65 years and older who were frail. The EMR data were obtained from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) and the administrative data (e.g. billing, hospitalizations) was obtained from Population Data BC and the Manitoba Population Research Data Repository. Sociodemographic characteristics, risk factors, prescribed medications, use and costs of healthcare are described for those identified as frail.
Sociodemographic and utilization differences were found among those identified as frail from the EMR compared to those in the administrative data. Among those who were >65 years, who had a record in both EMR and administrative data, 5%-8% (n=191 of 3,553, BC; n=2,396 of 29,382, MB) were identified as frail. There was a higher likelihood of being frail with increasing age and being a woman. In BC and MB, those identified as frail in both data sources have approximately twice the number of contacts with primary care (n=20 vs. n=10) and more days in hospital (n=7.2 vs. n=1.9 in BC; n=9.8 vs. n=2.8 in MB) compared to those who are not frail; 27% (BC) and 14% (MB) of those identified as frail in 2014 died in 2015.
Identifying frailty using EMR data is particularly challenging because many functional deficits are not routinely recorded in structured data fields. Our results suggest frailty can be captured along a continuum using both EMR and administrative data.