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NCT Number: NCT07401979

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.

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Key information

Conditions

Respiratory Tract Infections (RTI) Infections Respiratory Tract Diseases Respiratory Tract Infections

Age range

18 year–65 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Tongji Medical College of Huazhong University of Science & Technology School of Medicine and Health Management

Wuhan, Hubei, China

Location contact

Yushu Liu

CONTACT

[email protected]

+86 18772101551

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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;
  • Hold a clinical medical license with a specialty in general practice or internal medicine, and have experience in diagnosing and managing respiratory tract infections;
  • Have at least one year of clinical work experience;
  • 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;
  • Provide voluntary informed consent to participate in the study.

Exclusion criteria

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

Treatment and study plan

AI-CDSS with Stepwise Medication and Auto-extraction

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Stepwise Medication; Auto-extraction. Inactive features: Sidebar Display; Chain-of-Thought Reasoning; Confidence Display; Feature Importance. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Feature Importance and Auto-extraction

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Feature Importance; Auto-extraction. Inactive features: Sidebar Display; Chain-of-Thought Reasoning; Confidence Display; Stepwise Medication. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Feature Importance and Stepwise Medication

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Feature Importance; Stepwise Medication. Inactive features: Sidebar Display; Chain-of-Thought Reasoning; Confidence Display; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Confidence Display and Auto-extraction

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Confidence Display; Auto-extraction. Inactive features: Sidebar Display; Chain-of-Thought Reasoning; Feature Importance; Stepwise Medication. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Confidence Display and Stepwise Medication

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Confidence Display; Stepwise Medication. Inactive features: Sidebar Display; Chain-of-Thought Reasoning; Feature Importance; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Confidence Display and Feature Importance

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Confidence Display; Feature Importance. Inactive features: Sidebar Display; Chain-of-Thought Reasoning; Stepwise Medication; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 8)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Confidence Display; Feature Importance; Stepwise Medication; Auto-extraction. Inactive features: Sidebar Display; Chain-of-Thought Reasoning. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Chain-of-Thought Reasoning and Auto-extraction

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Chain-of-Thought Reasoning; Auto-extraction. Inactive features: Sidebar Display; Confidence Display; Feature Importance; Stepwise Medication. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Chain-of-Thought Reasoning and Stepwise Medication

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Chain-of-Thought Reasoning; Stepwise Medication. Inactive features: Sidebar Display; Confidence Display; Feature Importance; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Chain-of-Thought Reasoning and Feature Importance

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Chain-of-Thought Reasoning; Feature Importance. Inactive features: Sidebar Display; Confidence Display; Stepwise Medication; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 12)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Chain-of-Thought Reasoning; Feature Importance; Stepwise Medication; Auto-extraction. Inactive features: Sidebar Display; Confidence Display. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Chain-of-Thought Reasoning and Confidence Display

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Chain-of-Thought Reasoning; Confidence Display. Inactive features: Sidebar Display; Feature Importance; Stepwise Medication; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 14)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Chain-of-Thought Reasoning; Confidence Display; Stepwise Medication; Auto-extraction. Inactive features: Sidebar Display; Feature Importance. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 15)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Chain-of-Thought Reasoning; Confidence Display; Feature Importance; Auto-extraction. Inactive features: Sidebar Display; Stepwise Medication. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 16)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Chain-of-Thought Reasoning; Confidence Display; Feature Importance; Stepwise Medication. Inactive features: Sidebar Display; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Sidebar Display and Auto-extraction

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Auto-extraction. Inactive features: Chain-of-Thought Reasoning; Confidence Display; Feature Importance; Stepwise Medication. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Sidebar Display and Stepwise Medication

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Stepwise Medication. Inactive features: Chain-of-Thought Reasoning; Confidence Display; Feature Importance; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Sidebar Display and Feature Importance

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Feature Importance. Inactive features: Chain-of-Thought Reasoning; Confidence Display; Stepwise Medication; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 20)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Feature Importance; Stepwise Medication; Auto-extraction. Inactive features: Chain-of-Thought Reasoning; Confidence Display. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Sidebar Display and Confidence Display

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Confidence Display. Inactive features: Chain-of-Thought Reasoning; Feature Importance; Stepwise Medication; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 22)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Confidence Display; Stepwise Medication; Auto-extraction. Inactive features: Chain-of-Thought Reasoning; Feature Importance. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 23)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Confidence Display; Feature Importance; Auto-extraction. Inactive features: Chain-of-Thought Reasoning; Stepwise Medication. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 24)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Confidence Display; Feature Importance; Stepwise Medication. Inactive features: Chain-of-Thought Reasoning; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with Sidebar Display and Chain-of-Thought Reasoning

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Chain-of-Thought Reasoning. Inactive features: Confidence Display; Feature Importance; Stepwise Medication; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 26)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Chain-of-Thought Reasoning; Stepwise Medication; Auto-extraction. Inactive features: Confidence Display; Feature Importance. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 27)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Chain-of-Thought Reasoning; Feature Importance; Auto-extraction. Inactive features: Confidence Display; Stepwise Medication. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 28)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Chain-of-Thought Reasoning; Feature Importance; Stepwise Medication. Inactive features: Confidence Display; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 29)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Chain-of-Thought Reasoning; Confidence Display; Auto-extraction. Inactive features: Feature Importance; Stepwise Medication. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 30)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Chain-of-Thought Reasoning; Confidence Display; Stepwise Medication. Inactive features: Feature Importance; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

AI-CDSS with 4 Features (Config 31)

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Chain-of-Thought Reasoning; Confidence Display; Feature Importance. Inactive features: Stepwise Medication; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

Full-Featured AI-CDSS

Behavioral

An artificial intelligence-based Clinical Decision Support System for respiratory tract infections. Active features: Sidebar Display; Chain-of-Thought Reasoning; Confidence Display; Feature Importance; Stepwise Medication; Auto-extraction. The system provides diagnosis suggestions and antibiotic recommendations for primary care physicians.

Primary outcomes

  1. Correction Event Rate

    Time frame: Immediately after intervention

    Proportion of cases where the physician's initial decision was incorrect, but was corrected to the right decision after viewing AI recommendations. Calculated as: (Number of cases where initial decision was wrong AND final decision was correct) / (Total number of cases where initial decision was wrong). This measures the positive corrective influence of AI-CDSS on physician decision-making. Continuous variable ranging from 0 to 1.

  2. Misleading Event Rate

    Time frame: Immediately after intervention

    Proportion of cases where the physician's initial decision was correct, but was changed to an incorrect decision after viewing AI recommendations. Calculated as: (Number of cases where initial decision was correct AND final decision was wrong) / (Total number of cases where initial decision was correct). This measures the potential negative influence of AI-CDSS on physician decision-making. Continuous variable ranging from 0 to 1.

  3. Diagnostic Accuracy (Top-1)

    Time frame: Immediately after intervention

    Proportion of cases where the physician's final primary diagnosis (first-ranked diagnosis) matches the correct diagnosis (gold standard). Measured separately for initial diagnosis (before AI) and final diagnosis (after AI) to assess AI impact on diagnostic accuracy. Continuous variable ranging from 0 to 1.

  4. Diagnostic Accuracy (Top-3)

    Time frame: Immediately after intervention

    Proportion of cases where the correct diagnosis (gold standard) appears within the physician's top 3 differential diagnoses. Measured separately for initial diagnosis (before AI) and final diagnosis (after AI). This captures whether the correct diagnosis was considered even if not ranked first. Continuous variable ranging from 0 to 1.

  5. Appropriateness of Antibiotic Use Decision

    Time frame: Immediately after intervention

    Proportion of cases where the physician's final decision on whether to prescribe antibiotics (yes/no) aligns with evidence-based guidelines (gold standard). This measures the appropriateness of the binary decision to use or withhold antibiotics, regardless of the specific antibiotic chosen. Measured separately for initial decision (before AI) and final decision (after AI). Continuous variable ranging from 0 to 1.

  6. Appropriateness of Antibiotic Selection

    Time frame: Immediately after intervention

    Among cases where antibiotics were prescribed, proportion of cases where the physician's final antibiotic selection (specific drug choice) aligns with evidence-based guidelines (gold standard). This measures the appropriateness of the specific antibiotic chosen, conditional on the decision to prescribe. Measured separately for initial selection (before AI) and final selection (after AI). Continuous variable ranging from 0 to 1.

Secondary outcomes

  1. Decision Certainty

    Time frame: Immediately after intervention

    Physician's self-reported confidence level in their final clinical decision after viewing AI recommendations. Measured using a 5-point Likert scale: 1 = Very Uncertain, 2 = Uncertain, 3 = Neutral, 4 = Certain, 5 = Very Certain. This reflects the cognitive impact of AI-CDSS on decision confidence.

  2. Intention to Adopt AI-CDSS

    Time frame: Immediately after intervention

    Measured using a 3-item scale adapted from the Technology Acceptance Model (TAM). Items assess: (1) willingness to apply AI-CDSS in daily clinical practice when available, (2) intention to use AI-CDSS based on actual patient care needs, and (3) willingness to frequently use AI-CDSS when conditions permit. Each item is rated on a 5-point Likert scale (1=Strongly Disagree to 5=Strongly Agree). Total score ranges from 3 to 15.

  3. Perceived Usefulness of AI-CDSS

    Time frame: Immediately after intervention

    Measured using a 6-item Perceived Usefulness scale adapted from TAM (Davis, 1989). Items assess perceived improvements in: (1) task completion speed, (2) job performance, (3) productivity, (4) work effectiveness, (5) ease of work, and (6) overall usefulness. Each item is rated on a 7-point Likert scale (1=Strongly Disagree to 7=Strongly Agree). Total score ranges from 6 to 42.

  4. Perceived Ease of Use of AI-CDSS

    Time frame: Immediately after intervention

    Measured using a 6-item Perceived Ease of Use scale adapted from TAM (Davis, 1989). Items assess: (1) ease of learning, (2) ease of getting desired functions, (3) clarity of interaction, (4) flexibility of interaction, (5) ease of becoming proficient, and (6) overall ease of use. Each item is rated on a 7-point Likert scale (1=Strongly Disagree to 7=Strongly Agree). Total score ranges from 6 to 42.

Study contacts

Contact information is provided by the study sponsor or research team.

Yushu Liu

CONTACT

[email protected]

18772101551

Sponsors and collaborators

Lead sponsor

Huazhong University of Science and Technology

Other

Registry information

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Feb 11, 2026
Registry last updated
Feb 11, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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