Exploring factors influencing user perspective of ChatGPT as a technology that assists in healthcare decision making: A cross sectional survey study.
Choudhury, A.; Elkefi, S.; Tounsi, A.
Show abstract
As ChatGPT emerges as a potential ally in healthcare decision-making, it is imperative to investigate how users leverage and perceive it. The repurposing of technology is innovative but brings risks, especially since AIs effectiveness depends on the data its fed. In healthcare, where accuracy is critical, ChatGPT might provide sound advice based on current medical knowledge, which could turn into misinformation if its data sources later include erroneous information. Our study assesses user perceptions of ChatGPT, particularly of those who used ChatGPT for healthcare-related queries. By examining factors such as competence, reliability, transparency, trustworthiness, security, and persuasiveness of ChatGPT, the research aimed to understand how users rely on ChatGPT for health-related decision-making. A web-based survey was distributed to U.S. adults using ChatGPT at least once a month. Data was collected from February to March 2023. Bayesian Linear Regression was used to understand how much ChatGPT aids in informed decision-making. This analysis was conducted on subsets of respondents, both those who used ChatGPT for healthcare decisions and those who did not. Qualitative data from open-ended questions were analyzed using content analysis, with thematic coding to extract public opinions on urban environmental policies. The coding process was validated through inter-coder reliability assessments, achieving a Cohens Kappa coefficient of 0.75. Six hundred and seven individuals responded to the survey. Respondents were distributed across 306 US cities of which 20 participants were from rural cities. Of all the respondents, 44 used ChatGPT for health-related queries and decision-making. While all users valued the content quality, privacy, and trustworthiness of ChatGPT across different contexts, those using it for healthcare information place a greater emphasis on safety, trust, and the depth of information. Conversely, users engaging with ChatGPT for non-healthcare purposes prioritize usability, human-like interaction, and unbiased content. In conclusion our study findings suggest a clear demarcation in user expectations and requirements from AI systems based on the context of their use.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities 94%
- Benefits and Challenges of Using Virtual Primary Care During the COVID-19 Pandemic: From Key Lessons to a Framework for Implementation 94%
- Defining Destigmatizing Design Guidelines for Use in Sexual Health-Related Digital Technologies: A Delphi Study 94%
Similar papers in this journal
- Improving Patient Engagement in Phase 2 Clinical Trials with a Trial-specific Patient Decision Aid (tPDA): A Development and Usability Study 95%
- How the COVID-19 pandemic is favoring the adoption of digital technologies in healthcare: a literature review 95%
- Artificial Intelligence (AI)-based Chatbots in Promoting Health Behavioral Changes: A Systematic Review 94%
Similar papers in this journal
- Validating a Clinical Decision Support System for Palliative Care using healthcare professionals’ insights 96%
- Enhancing Uploads of Health Data in the Electronic Health Record - The Role of Framing and Length of Privacy Information 94%
- How suitable are clinical vignettes for the evaluation of symptom checker apps? A test theoretical perspective 93%
Similar papers in this journal
- A Web-based, Mobile Responsive Application to Screen Healthcare Workers for COVID Symptoms: Descriptive Study 95%
- The potential for digital patient symptom recording through symptom assessment applications to optimize patient flow and reduce waiting times in Urgent Care Centers: a simulation study 95%
- Is virtual care the new normal? Evidence supporting Covid-19’s durable transformation on healthcare delivery 93%
Similar papers in this journal
- Synthetic Data Generation in Healthcare: A Scoping Review of reviews on domains, motivations, and future applications 94%
- Development and Evaluation of MADDIE: Method to Acquire Delivery Date Information from Electronic Health Records 94%
- Digital applications to support self-management of multimorbidity: A scoping review 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.