Physicians' Perceptions and Expectations of an Artificial Intelligence-Based Clinical Decision Support System in Cancer Care in an Underserved Setting

Rubina F. Rizvi; Srinivas Emani; Hermano A. Lima Rocha; Camila Machado de Aquino; Pamela M. Garabedian; Angela Rui; Carlos André Moura Arruda; Megan Sands-Lincoln; Ronen Rozenblum; Winnie Felix; Gretchen P. Jackson; Sérgio F. Juacaba; David W. Bates

doi:10.1055/s-0042-1751088

ACI Open, Table of Contents

CC BY 4.0 · ACI open 2022; 06(02): e66-e75
DOI: 10.1055/s-0042-1751088

Original Article

Physicians' Perceptions and Expectations of an Artificial Intelligence-Based Clinical Decision Support System in Cancer Care in an Underserved Setting

Authors

Rubina F. Rizvi^*

¹IBM Watson Health, Cambridge, Massachusetts, United States
Srinivas Emani^*

²Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States

³Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States
Hermano A. Lima Rocha

⁴Department of Community Health, Federal University of Ceará, Fortaleza, Ceará, Brazil
Camila Machado de Aquino

⁵Department of Maternal and Child Health, Faculty of Medicine, Federal University of Ceará, Fortaleza, Ceará, Brazil
Pamela M. Garabedian

⁶Clinical and Quality Analysis, Partners HealthCare, Somerville, Massachusetts, United States
Angela Rui

⁶Clinical and Quality Analysis, Partners HealthCare, Somerville, Massachusetts, United States
Carlos André Moura Arruda

⁵Department of Maternal and Child Health, Faculty of Medicine, Federal University of Ceará, Fortaleza, Ceará, Brazil
Megan Sands-Lincoln

¹IBM Watson Health, Cambridge, Massachusetts, United States
Ronen Rozenblum

²Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
Winnie Felix

¹IBM Watson Health, Cambridge, Massachusetts, United States
Gretchen P. Jackson

⁷Departments of Surgery, Pediatrics, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
Sérgio F. Juacaba

⁸Departments of Surgery, Cancer Institute of Ceara, Fortaleza, Ceará, Brazil
David W. Bates

²Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States

Abstract

Objectives Artificial intelligence (AI) tools are being increasingly incorporated into health care. However, few studies have evaluated users' expectations of such tools, prior to implementation, specifically in an underserved setting.

Methods We conducted a qualitative research study employing semistructured interviews of physicians at The Instituto do Câncer do Ceará, Fortaleza, Brazil. The interview guide focused on anticipated, perceived benefits and challenges of using an AI-based clinical decision support system tool, Watson for Oncology. We recruited physician oncologists, working full or part-time, without prior experience with any AI-based tool. The interviews were taped and transcribed in Portuguese and then translated into English. Thematic analysis using the constant comparative approach was performed.

Results Eleven oncologists participated in the study. The following overarching themes and subthemes emerged from the analysis of interview transcripts: theme-1, “general context” including (1) current setting, workload, and patient population and (2) existing challenges in cancer treatment, and theme-2, “perceptions around the potential use of an AI-based tool,” including (1) perceived benefits and (2) perceived challenges. Physicians expected that the implementation of an AI-based tool would result in easy access to the latest clinical recommendations, facilitate standardized cancer care, and allow it to be delivered with greater confidence and efficiency. Participants had several concerns such as availability of innovative treatments in resource-poor settings, treatment acceptance, trust, physician autonomy, and workflow disruptions.

Conclusion This study provides physicians' anticipated perspectives, both benefits and challenges, about the use of an AI-based tool in cancer treatment in a resource-limited setting.

Keywords

clinical decision support - artificial intelligence - oncology - treatment - physicians - underserved setting - implementation - perceptions

Full Text

References

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