Physicians’ preferences and willingness to pay for artificial intelligence-based assistance tools: a discrete choice experiment among german radiologists

• Published in: BMC health services research Vol. 22; no. 1; pp. 398 - 14
• Main Authors: von Wedel, Philip, Hagist, Christian
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 26.03.2022; BioMed Central; BMC
• Subjects: Algorithms; Artificial Intelligence; Coronaviruses; COVID-19; DCE; Discrete choice experiment; Efficiency; Experiments; Health aspects; Health services; Humans; Income; Medical research; Patients; Physician preferences; Physicians; Quality Improvement; Radiologists; Radiology; Surveys; Technology application; Willingness to pay; Germany; Radiology; Artificial intelligence; Willingness to pay; DCE; Physician preferences; Discrete choice experiment
• ISSN: 1472-6963; 1472-6963
• DOI: 10.1186/s12913-022-07769-x

Artificial Intelligence (AI)-based assistance tools have the potential to improve the quality of healthcare when adopted by providers. This work attempts to elicit preferences and willingness to pay for these tools among German radiologists. The goal was to generate insights for tool providers and policymakers regarding the development and funding of ideally designed and priced tools. Ultimately, healthcare systems can only benefit from quality enhancing AI when provider adoption is considered. Since there is no established market for AI-based assistance tools in radiology yet, a discrete choice experiment was conducted. Respondents from the two major German professional radiology associations chose between hypothetical tools composed of five attributes and a no-choice option. The attributes included: provider, application, quality impact, time savings and price. A conditional logit model was estimated identifying preferences for attribute levels, the no-choice option, and significant subject-related interaction effects. 114 respondents were included for analysis of which 46% were already using an AI-based assistance tool. Average adoption probability for an AI-based tool was 81% (95% CI 77.1% - 84.4%). Radiologists preferred a tool that assists in routine diagnostics performing at above-radiologist-level quality and saves 50% in diagnostics time at a price-point of €3 per study. The provider is not a significant factor in the decisions. Time savings were considered more important than quality improvements (i.e., detecting more anomalies). Radiologists are overall willing to invest in AI-based assistance tools. Development, funding, and research regarding these tools should, however, consider providers' preferences for features of immediate everyday and economic relevance like time savings to optimize adoption.