The Influence of Explainability and Integrability of AI-CDSS on Usage Behavior Among Primary Care Physicians

The Influence of Explainability and Integrability of AI-CDSS on Usage Behavior Among Primary Care Physicians

The goal of this observational experimental study is to determine how system-level features of artificial intelligence clinical decision support systems (AI-CDSS)-specifically explainability and integrability-affect usage behavior among primary care physicians in China. The study focuses on licensed primary care physicians, regardless of gender, age, years of clinical experience, or prior AI exposure.

The main questions it aims to answer are:

• Do specific AI features (e.g., feature attribution, chain-of-thought explanation, seamless workflow integration, automated data input) independently influence physicians' adoption intention, diagnostic accuracy, and their perceptions of the system's usefulness and ease of use?
• Do pairwise combinations of these AI features produce significant interaction effects-either synergistic or antagonistic-on these outcomes? Researchers will compare 32 distinct AI interface configurations generated from a 2⁶-¹ fractional factorial design (Resolution VI), each representing a unique combination of six binary AI features: (A) gradient-based feature importance (0 = absent, 1 = present), (B) chain-of-thought reasoning (0/1), (C) workflow integration (0 = multiple pop-up alerts, 1 = unified sidebar display), (D) automated data extraction (0 = manual entry, 1 = auto-populated from case text), (E) recommendation scope adapted to primary care settings (0 = restricted to essential options, 1 = full range of recommendations), and (F) model confidence display (0 = absent, 1 = present). This design enables unbiased estimation of all six main effects and all 15 two-way interactions.

Participants will:

Complete three standardized clinical case scenarios involving common respiratory infections via a web-based simulation platform; First provide an initial diagnosis and treatment plan without any AI input; Then review an AI-generated recommendation embedded with a randomly assigned combination of the six AI features; Revise their final diagnosis and prescription based on the AI suggestion; Rate their adoption intention, perceived usefulness, and perceived ease of use using validated 7-point Likert-scale items after each case.

Inclusion Criteria:

1. Be currently employed full-time in clinical practice at a primary care facility, including community health centers, community health stations, township hospitals, or village clinics;
2. Hold a clinical medical license with a specialty in general practice or internal medicine, and have experience in diagnosing and managing respiratory tract infections;
3. Have at least one year of clinical work experience;
4. Be proficient in basic computer use (e.g., web browsing and online questionnaire completion), have reliable internet access, and be capable of independently completing the online experimental tasks;
5. Provide voluntary informed consent to participate in the study.

Exclusion Criteria:

1. Non-clinical staff (e.g., administrative personnel, pharmacists, laboratory technicians, or public health workers who do not directly provide outpatient clinical care);
2. Individuals unable to independently complete the online experimental procedure or who demonstrate significant difficulty understanding the task instructions.