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Initial Experience with a COVID-19 Screening Chatbot Before Radiology Appointments.
Wang, Winston T; Tan, Nelly; Hanson, James A; Crubaugh, Courtney A; Hara, Amy K.
  • Wang WT; Mayo Clinic Alix School of Medicine - Arizona Campus, Mayo Clinic College of Medicine and Science, Scottsdale, AZ, USA.
  • Tan N; Department of Radiology, Mayo Clinic Hospital, Phoenix, AZ, USA. tan.nelly@mayo.edu.
  • Hanson JA; Department of Radiology, Mayo Clinic Hospital, Phoenix, AZ, USA.
  • Crubaugh CA; Division of Community Internal Medicine, Mayo Clinic, Scottsdale, AZ, USA.
  • Hara AK; Department of Radiology, Mayo Clinic Hospital, Phoenix, AZ, USA.
J Digit Imaging ; 35(5): 1303-1307, 2022 10.
Article in English | MEDLINE | ID: covidwho-1844399
ABSTRACT
Guidelines for COVID-19 issued by the Centers for Disease Control and Prevention prompted state and local governments to mandate safety measures for screening high-risk patient populations and for institutions to look for ways to limit human contact when possible. The aim of this study was to determine the feasibility of an automated communication system (chatbot) for COVID-19 screening before patients' radiology appointments and to describe patient experiences with the chatbot. We developed a chatbot for COVID-19 screening before outpatient radiology examination appointments and tested it in a pilot study from July 6 to August 31, 2020. The chatbot assessed the presence of any symptoms, exposure, and recent testing. User experience was assessed via a questionnaire based on a 5-point Likert scale. Multivariable logistic regression was performed to predict response rate. The chatbot COVID-19 screening SMS message was sent to 4687 patients. Of these patients, 2722 (58.1%) responded. Of the respondents, 46 (1.7%) reported COVID-19 symptoms; 34 (1.2%) had COVID-19 tests scheduled or pending. Of the 1965 nonresponders, authentication failed for 174 (8.8%), 1496 (76.1%) did not engage with the SMS message, and 251 (12.8%) timed out of the chatbot. The mean rating for the chatbot experience was 4.6. In a multivariable logistic regression model predicting response rate, English written-language preference independently predicted response (odds ratio, 2.71 [95% CI, 1.77-2.77]; P = .007). Age (P = 0.57) and sex (P = 0.51) did not predict response rate. SMS-based COVID-19 screening before scheduled radiology appointments was feasible. English written-language preference (not age or sex) was associated with higher response rate.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Radiology / COVID-19 Type of study: Observational study / Prognostic study / Qualitative research / Randomized controlled trials Limits: Humans Language: English Journal: J Digit Imaging Journal subject: Diagnostic Imaging / Medical Informatics / Radiology Year: 2022 Document Type: Article Affiliation country: S10278-022-00650-7

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Radiology / COVID-19 Type of study: Observational study / Prognostic study / Qualitative research / Randomized controlled trials Limits: Humans Language: English Journal: J Digit Imaging Journal subject: Diagnostic Imaging / Medical Informatics / Radiology Year: 2022 Document Type: Article Affiliation country: S10278-022-00650-7