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Stratification of telehealthcare for patients with chronic obstructive pulmonary disease using a predictive algorithm as decision support: A pilot study

Introduction The number of patients needing care who suffer from chronic obstructive pulmonary disease (COPD) is expected to increase in the future. The consequences thereof will increase the socio-economic burden for both patients and society. Telehealthcare technologies have shown potential in reducing hospitalisation-related costs and in improving health-related quality of life (HRQOL) for some COPD patients, but not all. The aim of this study was to investigate the potential of predictive algorithms for helping the general practitioner to stratify telehealthcare for COPD patients in a way that maximises HRQOL and minimises COPD-related costs. Methods Data from 553 COPD patients based in the North Denmark Region were analysed and used as predictors for four multiple linear regression models. The models were trained and evaluated for their abilities to predict individual patient’s future health- and cost-related developments, with and without telehealthcare. Results The average root-mean-square error (RMSE) of the health and cost models was 5.265 HRQOL scores and US dollars (US$)5430.49, respectively. The accuracy regarding the polarity of the predicted changes ranged from 61–65% for the health models and 74–75% for the cost models. While differences in the magnitude of predictions with and without telehealthcare were statistically significant (p < 0.01), the polarity of predictions was similar across models in 82.05% of all cases. Discussion Our results indicate that it may be possible to predict the magnitude and polarity of a COPD patient’s future health- and cost-related developments with and without telehealthcare. Predictive algorithms may provide a useful decision support tool in stratifying telehealthcare for COPD patients.