Development of an automated model to predict the risk of elderly emergency medical admissions within a month following an index hospital visit: a Hong Kong experience.

OBJECTIVES: To develop an automated risk prediction model to identify elderly patients at high risk of emergency admission to medical wards within 28 days following an index hospital visit.
METHODS: A retrospective data analysis of 41 hospitals and 48 specialist outpatient clinics in Hong Kong. The study subjects were elderly patients aged 65 years or above, who had index hospital visit(s) in the year of 2005, which included hospitalizations at medical wards and attendances at the accident and emergency departments or specialist outpatient clinics for medical conditions. Multiple logistic regression was used to estimate the risk of emergency medical admission in 28 days after an index hospital visit. Model validation was performed against the complete cohort in 2006.
RESULTS: Over a million of episodes were included in the derivation cohort. A total of 14 predictor variables included patient socio-demographics, service utilization in the previous year, presence and number of chronic diseases and type of index episode. The model has a good discriminative ability with the area under receiver-operating characteristic curve at 0.819 and 0.824 for the derivation and validation cohorts, respectively. The model has a sensitivity of 70.3 per cent, specificity of 78.4 per cent, positive predictive value of 21.7 per cent and negative predictive value of 96.9 per cent.
CONCLUSION: This simple, accurate and objective risk prediction model has been computerized into an automated screening tool to recruit high-risk elderly patients discharged from all public hospitals in Hong Kong into the Community Health Call Centre service with an aim to prevent avoidable hospitalizations.