Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jun 27;6(6):e37028.
doi: 10.2196/37028.

The AI Will See You Now: Feasibility and Acceptability of a Conversational AI Medical Interviewing System

Grace Hong  1 , Margaret Smith  1 , Steven Lin  1
Affiliations

The AI Will See You Now: Feasibility and Acceptability of a Conversational AI Medical Interviewing System

Grace Hong et al. JMIR Form Res. .

Abstract

Background: Primary care physicians (PCPs) are often limited in their ability to collect detailed medical histories from patients, which can lead to errors or delays in diagnosis. Recent advances in artificial intelligence (AI) show promise in augmenting current human-driven methods of collecting personal and family histories; however, such tools are largely unproven.

Objective: The main aim of this pilot study was to evaluate the feasibility and acceptability of a conversational AI medical interviewing system among patients.

Methods: The study was conducted among adult patients empaneled at a family medicine clinic within a large academic medical center in Northern California. Participants were asked to test an AI medical interviewing system, which uses a conversational avatar and chatbot to capture medical histories and identify patients with risk factors. After completing an interview with the AI system, participants completed a web-based survey inquiring about the performance of the system, the ease of using the system, and attitudes toward the system. Responses on a 7-point Likert scale were collected and evaluated using descriptive statistics.

Results: A total of 20 patients with a mean age of 50 years completed an interview with the AI system, including 12 females (60%) and 8 males (40%); 11 were White (55%), 8 were Asian (40%), and 1 was Black (5%), and 19 had at least a bachelor's degree (95%). Most participants agreed that using the system to collect histories could help their PCPs have a better understanding of their health (16/20, 80%) and help them stay healthy through identification of their health risks (14/20, 70%). Those who reported that the system was clear and understandable, and that they were able to learn it quickly, tended to be younger; those who reported that the tool could motivate them to share more comprehensive histories with their PCPs tended to be older.

Conclusions: In this feasibility and acceptability pilot of a conversational AI medical interviewing system, the majority of patients believed that it could help clinicians better understand their health and identify health risks; however, patients were split on the effort required to use the system, and whether AI should be used for medical interviewing. Our findings suggest areas for further research, such as understanding the user interface factors that influence ease of use and adoption, and the reasons behind patients' attitudes toward AI-assisted history-taking.

Keywords: artificial intelligence; clinical; diagnostic errors; feasibility studies; health care; healthcare; medical history; patient acceptance of health care; patient-generated health data.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Screenshot of Genie and the conversational AI medical interviewing system. AI: artificial intelligence.
Figure 2
Figure 2
Patient-reported feasibility and acceptability ratings of the system. AI: artificial intelligence; PE: performance expectancy; EE: effort expectancy; ATT: attitude toward using technology.
See this image and copyright information in PMC

References

    1. National Academies of Sciences, Engineering, and Medicine . In: Improving diagnosis in health care. Balogh EP, Miller BT, Ball JR, editors. Washington, DC: The National Academies Press; 2015. - PubMed
    1. Wang C, Bickmore T, Bowen DJ, Norkunas T, Campion M, Cabral H, Winter M, Paasche-Orlow M. Acceptability and feasibility of a virtual counselor (VICKY) to collect family health histories. Genet Med. 2015;17(10):822–30. doi: 10.1038/gim.2014.198. https://linkinghub.elsevier.com/retrieve/pii/S1098-3600(21)03162-2 S1098-3600(21)03162-2 - DOI - PMC - PubMed
    1. Singh H, Meyer A, Thomas EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf. 2014;23(9):727–31. doi: 10.1136/bmjqs-2013-002627. http://qualitysafety.bmj.com/lookup/pmidlookup?view=long&pmid=24742777 bmjqs-2013-002627 - DOI - PMC - PubMed
    1. Singh H, Schiff GD, Graber ML, Onakpoya I, Thompson MJ. The global burden of diagnostic errors in primary care. BMJ Qual Saf. 2017 Jun 16;26(6):484–494. doi: 10.1136/bmjqs-2016-005401. http://qualitysafety.bmj.com/lookup/pmidlookup?view=long&pmid=27530239 bmjqs-2016-005401 - DOI - PMC - PubMed
    1. Tiase V, Hull W, McFarland M, Sward KA, Del Fiol G, Staes C, Weir C, Cummins MR. Patient-generated health data and electronic health record integration: a scoping review. JAMIA Open. 2020 Dec;3(4):619–627. doi: 10.1093/jamiaopen/ooaa052. http://europepmc.org/abstract/MED/33758798 ooaa052 - DOI - PMC - PubMed

LinkOut - more resources