# Development and testing of an electronic frailty index using Canadian electronic medical record data in primary care

## TL;DR

This study developed and tested a Canadian electronic frailty index using primary care data to identify and manage frailty in older adults.

## Contribution

The paper introduces a validated Canadian electronic frailty index adapted from the UK model for use in primary care settings.

## Key findings

- Higher frailty scores were significantly associated with increased primary care visits, polypharmacy, and cognitive impairment.
- Frailty levels correlated with age, material deprivation, and social deprivation.
- Severely frail individuals had nine more annual primary care visits and higher odds of polypharmacy and cognitive impairment.

## Abstract

Frailty is a state of increased vulnerability from physical, social, and cognitive factors and can result in several negative health outcomes at an individual and systemic level. Existing electronic medical record (EMR) data can be optimized to identify patients’ frailty level in primary care to facilitate early intervention and management of frailty in an efficient manner. The purpose of this work was to develop and validate a Canadian electronic frailty index (eFI) using primary care EMR data.

We built a Canadian eFI based on the existing UK 36-factor eFI and tested it using EMR data from British Columbia (BC) primary care practices. We used a retrospective cross-sectional design to examine the concurrent criterion validity of the eFI by testing the hypotheses that increasing frailty is associated with (1) higher numbers of primary care visits, (2) increased presence of polypharmacy, and (3) increased presence of cognitive impairment. Hypotheses were tested using Poisson and Logistic regression modelling. The data source for analysis was the BC-Canadian Primary Care Sentinel Surveillance Network.

Our frailty algorithm was successful in its ability to calculate frailty scores for patients. A total of 15,178 patients met eligibility criteria from 22 primary care practices and 108 care providers. Ages ranged from 65 to 109 (mean 75.7); 54.2% were females. The number of frailty factors detected for patients ranged from 0 to 28 (mean 7.1). Analyses showed significant associations (p < 0.0001) between frailty levels and increasing age, material deprivation, and social deprivation. There were significant associations (p < 0.0001) between increasing frailty scores and our three outcomes. Individuals who were severely frail had nine more annual primary care visits, nine times the odds of concurrent polypharmacy, and approximately double the odds of cognitive impairment than someone who was not frail.

Our study provides evidence for initial implementation of the eFI in primary care. There is significant potential for EMR data to facilitate early detection of frailty and drive care planning with healthcare teams. Integrating the eFI within primary care provides a tremendous opportunity to screen and manage frailty with the long-term goal of reducing negative patient health outcomes and often unnecessary healthcare costs.

The online version contains supplementary material available at 10.1186/s12875-025-03075-7.