Methods: The medical records of 95 older patients (age ≥ 65) who attended the GMC from 16 December 2019 to 10 January 2020 were reviewed. Frailty was identified using the FRAIL scale and the CFS. Patient characteristics were investigated for their association with frailty and their difference in the prevalence of frailty by the FRAIL scale and CFS.
Results: The CFS identified nonsignificant higher prevalence of frailty compared to the FRAIL scale (21/95; 22.1% vs. 17/95; 17.9%, ratio of prevalence = 1.235, p=0.481). Minimal agreement was found between the FRAIL scale and the CFS (Kappa = 0.272, p < 0.001). Three out of 5 components of the FRAIL scale (resistance, ambulation, and loss of weight) were associated with frailty by the CFS. Higher prevalence of frailty was identified by the CFS in those above 70 years of age. The FRAIL scale identified more patients with frailty in ischaemic heart disease patients.
Conclusion: Patient characteristics influenced the choice of the frailty assessment tool. The FRAIL scale and the CFS may complement each other in providing optimized care to older patients who attended the GMC.
METHODS: We utilised systematic random sampling by recruiting every 25th patient registered in our clinic during data collection. Participants answered a self-administered printed questionnaire regarding their smartphone usage and familiarity with QR code scanning at the patients' waiting area. Data were analysed using the Statistical Package for the Social Sciences version 26.
RESULTS: A total of 323 patients participated (response rate=100%). The participants' median age was 57 years (interquartile range=4l-67). Most participants were women (63.1%). Approximately 90.4% (n=282) used smartphones, with 83.7% (n=261) reporting average or good usage proficiency. More than half (58.0%) accessed medical information via their smartphones, and 67.0% were familiar with QR codes. Multiple logistic regression analyses revealed that familiarity with QR codes was linked to age of <65 years [adjusted odds ratio (AOR)=4.593, 95% confidence interval (CI)=2.351-8.976, P<0.001], tertiary education (AOR=2.385, 95% CI=1.170-4.863, P=0.017), smartphone proficiency (A0R=4.703, 95% CI= 1.624-13.623, P=0.004) and prior smartphone usage to access medical information (AOR=5.472, 95% CI=2.790-10.732, P<0.001).
CONCLUSION: Since smartphones were accessible to most primary care patients, and more than half of the patients were familiar with QR code scanning, QR code-based quality improvement projects can be used to improve services in our setting.