An autonomous mobile system for the management of COPD

• The design of a novel mobile disease management system for COPD is described in detail.
• Patient data is interpreted by a Bayesian network model for COPD-exacerbation detection.
• The model is evaluated and shown to be able to reliably detect COPD exacerbations.
• A pilot study with COPD patients has shown that the system is useful and usable.

IntroductionManaging chronic disease through automated systems has the potential to both benefit the patient and reduce health-care costs. We have developed and evaluated a disease management system for patients with chronic obstructive pulmonary disease (COPD). Its aim is to predict and detect exacerbations and, through this, help patients self-manage their disease to prevent hospitalisation.MaterialsThe carefully crafted intelligent system consists of a mobile device that is able to collect case-specific, subjective and objective, physiological data, and to alert the patient by a patient-specific interpretation of the data by means of probabilistic reasoning. Collected data are also sent to a central server for inspection by health-care professionals.MethodsWe evaluated the probabilistic model using cross-validation and ROC analyses on data from an earlier study and by an independent data set. Furthermore a pilot with actual COPD patients has been conducted to test technical feasibility and to obtain user feedback.ResultsModel evaluation results show that we can reliably detect exacerbations. Pilot study results suggest that an intervention based on this system could be successful.

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