Abstract
Predictive risk models using general practice (GP) data to predict the risk of hospitalisation have the potential to identify patients for targeted care. Effective use can help deliver significant reductions in the incidence of hospitalisation, particularly for patients with chronic conditions, the highest consumers of hospital resources. There are currently no published validated risk models for the Australian context using GP data to predict hospitalisation. In addition, published models for other contexts typically rely on a patient’s history of prior hospitalisations, a field not commonly available in GP information systems, as a predictor. We present a predictive risk model developed for use by GPs to assist in targeting coordinated healthcare to patients most in need. The algorithm was developed and validated using a retrospective primary care cohort, linked to records of hospitalisation in Victoria, Australia, to predict the risk of hospitalisation within one year. Predictors employed include demographics, prescription history, pathology results and disease diagnoses. Prior hospitalisation information was not employed as a predictor. Our model shows good performance and has been implemented within primary care practices participating in Health Care Homes, an Australian Government initiative being trialled for providing ongoing comprehensive care for patients with chronic and complex conditions.
Highlights
The growing burden of chronic conditions, is responsible for 70% of deaths globally[1]
Patients eligible for the 22-month trial are identified in the general practice (GP) using an algorithm that predicts the risk of a patient being hospitalised over the 12 months
This study describes the development and validation of a prediction model using Australian hospital and GP data to identify patients at risk of hospitalisation for the Health Care Homes trial
Summary
These diagnosis group variables were the primary means to include the occurrence of relevant diagnosed conditions/ diseases in the models Note that these predictors correspond to diagnosis, not occurrence; they provide no way to capture additional risk from undiagnosed conditions/diseases of interest. Comorbidity associated with each patient was included in modelling as the total number of distinct diagnosis families, either as a continuous predictor (range 0–35) or as a categorical variable with nine categories. Normal ranges for pathology results were defined in consultation with the Australian Government Department of Health and used to calculate morbidity risk flags representing three categories of risk – Low, Medium and High (see Supplementary Table S3). The code used for model development and validation is available from the corresponding author on reasonable request for non-commercial purposes
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