Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
1454 participants
INTERVENTIONAL
2023-10-31
2023-12-11
Brief Summary
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Detailed Description
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* The surveys will show fictional situations where patients ask questions and get answers from either real people or computer programs, with or without a disclosure about how the response was written.
* The investigators will ask the people taking the survey to share what they think about these situations using tools like rating scales, comparison scales, or written responses.
* If patients want to, they can provide their contact information to be part of future discussion groups. Participants do not have to give any personal information to complete the survey.
Conditions
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Keywords
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Study Design
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RANDOMIZED
PARALLEL
* Over the course of the study period, there will be multiple rounds of surveys, which will consist of a clinical scenario, a response (human or AI generated), and a disclosure statement (none, human, or AI).
* All respondents within an arm will be assigned to a sequence of response-disclosure pairs prior to the first round of surveys.
OTHER
SINGLE
Study Groups
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Arm A
Each arm will receive 3 clinical scenarios spaced over time across 3 Sends. The 6 groups (Arms A-F) will be arranged with naming conventions as such:
* First letter = A, B, or C where A = scenario 1, B = scenario 2, and C = scenario 3.
* Second letter(s) = H or AI, where H = human response and AI = AI-written response to the patient question posed.
* Third letter(s) = N, C, or H, where N = no disclosure, C = computer disclosure, and H = human disclosure. This refers to the disclosure at the bottom of the response message whereby the author is or is not disclosed.
Arm A receives AHN in Send 1, BAIC in Send 2, and CHH in Send 3
Generative AI for electronic communication and disclosure
We will use a large language model such as GPT 3.5 to automatically generate responses to fictional messages to a physician. We will disclose whether the message was generated using this technology or not. There are 3 clinical scenarios and 6 pairs of human/AI response and human disclosure/AI disclosure/not disclosed that will test patient attitudes toward this technology.
Arm B
Each arm will receive 3 clinical scenarios spaced over time across 3 Sends. The 6 groups (Arms A-F) will be arranged with naming conventions as such:
* First letter = A, B, or C where A = scenario 1, B = scenario 2, and C = scenario 3.
* Second letter(s) = H or AI, where H = human response and AI = AI-written response to the patient question posed.
* Third letter(s) = N, C, or H, where N = no disclosure, C = computer disclosure, and H = human disclosure. This refers to the disclosure at the bottom of the response message whereby the author is or is not disclosed.
Arm B receives BHC in Send 1, CAIH in Send 2, and AAIN in Send 3
Generative AI for electronic communication and disclosure
We will use a large language model such as GPT 3.5 to automatically generate responses to fictional messages to a physician. We will disclose whether the message was generated using this technology or not. There are 3 clinical scenarios and 6 pairs of human/AI response and human disclosure/AI disclosure/not disclosed that will test patient attitudes toward this technology.
Arm C
Each arm will receive 3 clinical scenarios spaced over time across 3 Sends. The 6 groups (Arms A-F) will be arranged with naming conventions as such:
* First letter = A, B, or C where A = scenario 1, B = scenario 2, and C = scenario 3.
* Second letter(s) = H or AI, where H = human response and AI = AI-written response to the patient question posed.
* Third letter(s) = N, C, or H, where N = no disclosure, C = computer disclosure, and H = human disclosure. This refers to the disclosure at the bottom of the response message whereby the author is or is not disclosed.
Arm C receives CHC in Send 1, AHH in Send 2, and BAIN in Send 3
Generative AI for electronic communication and disclosure
We will use a large language model such as GPT 3.5 to automatically generate responses to fictional messages to a physician. We will disclose whether the message was generated using this technology or not. There are 3 clinical scenarios and 6 pairs of human/AI response and human disclosure/AI disclosure/not disclosed that will test patient attitudes toward this technology.
Arm D
Each arm will receive 3 clinical scenarios spaced over time across 3 Sends. The 6 groups (Arms A-F) will be arranged with naming conventions as such:
* First letter = A, B, or C where A = scenario 1, B = scenario 2, and C = scenario 3.
* Second letter(s) = H or AI, where H = human response and AI = AI-written response to the patient question posed.
* Third letter(s) = N, C, or H, where N = no disclosure, C = computer disclosure, and H = human disclosure. This refers to the disclosure at the bottom of the response message whereby the author is or is not disclosed.
Arm D receives AAIH in Send 1, BHN in Send 2, and CAIC in Send 3
Generative AI for electronic communication and disclosure
We will use a large language model such as GPT 3.5 to automatically generate responses to fictional messages to a physician. We will disclose whether the message was generated using this technology or not. There are 3 clinical scenarios and 6 pairs of human/AI response and human disclosure/AI disclosure/not disclosed that will test patient attitudes toward this technology.
Arm E
Each arm will receive 3 clinical scenarios spaced over time across 3 Sends. The 6 groups (Arms A-F) will be arranged with naming conventions as such:
* First letter = A, B, or C where A = scenario 1, B = scenario 2, and C = scenario 3.
* Second letter(s) = H or AI, where H = human response and AI = AI-written response to the patient question posed.
* Third letter(s) = N, C, or H, where N = no disclosure, C = computer disclosure, and H = human disclosure. This refers to the disclosure at the bottom of the response message whereby the author is or is not disclosed.
Arm E receives BAIH in Send 1, CHN in Send 2, and AHC in Send 3
Generative AI for electronic communication and disclosure
We will use a large language model such as GPT 3.5 to automatically generate responses to fictional messages to a physician. We will disclose whether the message was generated using this technology or not. There are 3 clinical scenarios and 6 pairs of human/AI response and human disclosure/AI disclosure/not disclosed that will test patient attitudes toward this technology.
Arm F
Each arm will receive 3 clinical scenarios spaced over time across 3 Sends. The 6 groups (Arms A-F) will be arranged with naming conventions as such:
* First letter = A, B, or C where A = scenario 1, B = scenario 2, and C = scenario 3.
* Second letter(s) = H or AI, where H = human response and AI = AI-written response to the patient question posed.
* Third letter(s) = N, C, or H, where N = no disclosure, C = computer disclosure, and H = human disclosure. This refers to the disclosure at the bottom of the response message whereby the author is or is not disclosed.
Arm F receives CAIN in Send 1, AAIC in Send 2, and BHH in Send 3
Generative AI for electronic communication and disclosure
We will use a large language model such as GPT 3.5 to automatically generate responses to fictional messages to a physician. We will disclose whether the message was generated using this technology or not. There are 3 clinical scenarios and 6 pairs of human/AI response and human disclosure/AI disclosure/not disclosed that will test patient attitudes toward this technology.
Interventions
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Generative AI for electronic communication and disclosure
We will use a large language model such as GPT 3.5 to automatically generate responses to fictional messages to a physician. We will disclose whether the message was generated using this technology or not. There are 3 clinical scenarios and 6 pairs of human/AI response and human disclosure/AI disclosure/not disclosed that will test patient attitudes toward this technology.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
ALL
Yes
Sponsors
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Duke University
OTHER
Responsible Party
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Principal Investigators
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Anand Chowdhury, MD, MMCi
Role: PRINCIPAL_INVESTIGATOR
Duke University
Locations
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Duke University Health System
Durham, North Carolina, United States
Countries
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References
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Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, Faix DJ, Goodman AM, Longhurst CA, Hogarth M, Smith DM. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Intern Med. 2023 Jun 1;183(6):589-596. doi: 10.1001/jamainternmed.2023.1838.
Other Identifiers
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Pro00113587
Identifier Type: -
Identifier Source: org_study_id