Preferences of patients with chronic obstructive pulmonary disease for online nursing consultation services: a discrete choice experiment

Abstract

Background

Chronic obstructive pulmonary disease (COPD) imposes a significant burden on patients and society, and the majority of COPD patients in China manage their condition at home long-term but often fail to achieve the desired outcomes. Online nursing consultation services (ONCS) are an effective intervention to help patients improve their disease prognosis. In recent years, artificial intelligence (AI) technology has garnered considerable attention. Medical institutions in China are exploring and planning ONCS combined with AI, and this study aims to understand the preferences and willingness to pay for ONCS among COPD patients.

Methods

We surveyed 224 COPD patients in Luoyang City, China, collecting their demographic information and responses to a discrete choice experiment (DCE) involving five attributes: service provider, response time, response accuracy, service content, and service cost.

Findings

The results revealed that COPD patients favoured ONCS provided by a combination of nurses and AI as service providers (β = 0.36), preferred faster response time (β = 3.38), higher response accuracy (β = 1.74), and chronic nursing as the service content (β = 0.92), all while expecting lower service costs. The relative importance (RI) of these attributes was distributed as 18.1%, 21.4%, 19.2%, 28.7%, and 12.6%, respectively. Specifically, participants were willing to pay an additional ¥22.3 for a shift from nurses to a combination of nurses and AI, ¥2.3 more for each minute reduction in response time, ¥1.5 more for every 1% increase in response accuracy, and ¥57.1 more for a shift from health education to chronic nursing.

Interpretation

This study thoroughly investigated COPD patients' preferences for ONCS. The findings offer valuable insights for optimizing these services. The findings suggest that healthcare organizations should actively integrate services that combine nurses and AI in order to reduce response time, enhance accuracy, effectively support chronic disease management, and minimise service costs.