TY - JOUR
T1 - Clinicians’ Perceptions of an Artificial Intelligence–Based Blood Utilization Calculator
T2 - Qualitative Exploratory Study
AU - Choudhury, Avishek
AU - Asan, Onur
AU - Medow, Joshua E.
N1 - Publisher Copyright:
©Avishek Choudhury, Onur Asan, Joshua E Medow.
PY - 2022/10
Y1 - 2022/10
N2 - Background: According to the US Food and Drug Administration Center for Biologics Evaluation and Research, health care systems have been experiencing blood transfusion overuse. To minimize the overuse of blood product transfusions, a proprietary artificial intelligence (AI)–based blood utilization calculator (BUC) was developed and integrated into a US hospital’s electronic health record. Despite the promising performance of the BUC, this technology remains underused in the clinical setting. Objective: This study aims to explore how clinicians perceived this AI-based decision support system and, consequently, understand the factors hindering BUC use. Methods: We interviewed 10 clinicians (BUC users) until the data saturation point was reached. The interviews were conducted over a web-based platform and were recorded. The audiovisual recordings were then anonymously transcribed verbatim. We used an inductive-deductive thematic analysis to analyze the transcripts, which involved applying predetermined themes to the data (deductive) and consecutively identifying new themes as they emerged in the data (inductive). Results: We identified the following two themes: (1) workload and usability and (2) clinical decision-making. Clinicians acknowledged the ease of use and usefulness of the BUC for the general inpatient population. The clinicians also found the BUC to be useful in making decisions related to blood transfusion. However, some clinicians found the technology to be confusing due to inconsistent automation across different blood work processes. Conclusions: This study highlights that analytical efficacy alone does not ensure technology use or acceptance. The overall system’s design, user perception, and users’ knowledge of the technology are equally important and necessary (limitations, functionality, purpose, and scope). Therefore, the effective integration of AI-based decision support systems, such as the BUC, mandates multidisciplinary engagement, ensuring the adequate initial and recurrent training of AI users while maintaining high analytical efficacy and validity. As a final takeaway, the design of AI systems that are made to perform specific tasks must be self-explanatory, so that the users can easily understand how and when to use the technology. Using any technology on a population for whom it was not initially designed will hinder user perception and the technology’s use.
AB - Background: According to the US Food and Drug Administration Center for Biologics Evaluation and Research, health care systems have been experiencing blood transfusion overuse. To minimize the overuse of blood product transfusions, a proprietary artificial intelligence (AI)–based blood utilization calculator (BUC) was developed and integrated into a US hospital’s electronic health record. Despite the promising performance of the BUC, this technology remains underused in the clinical setting. Objective: This study aims to explore how clinicians perceived this AI-based decision support system and, consequently, understand the factors hindering BUC use. Methods: We interviewed 10 clinicians (BUC users) until the data saturation point was reached. The interviews were conducted over a web-based platform and were recorded. The audiovisual recordings were then anonymously transcribed verbatim. We used an inductive-deductive thematic analysis to analyze the transcripts, which involved applying predetermined themes to the data (deductive) and consecutively identifying new themes as they emerged in the data (inductive). Results: We identified the following two themes: (1) workload and usability and (2) clinical decision-making. Clinicians acknowledged the ease of use and usefulness of the BUC for the general inpatient population. The clinicians also found the BUC to be useful in making decisions related to blood transfusion. However, some clinicians found the technology to be confusing due to inconsistent automation across different blood work processes. Conclusions: This study highlights that analytical efficacy alone does not ensure technology use or acceptance. The overall system’s design, user perception, and users’ knowledge of the technology are equally important and necessary (limitations, functionality, purpose, and scope). Therefore, the effective integration of AI-based decision support systems, such as the BUC, mandates multidisciplinary engagement, ensuring the adequate initial and recurrent training of AI users while maintaining high analytical efficacy and validity. As a final takeaway, the design of AI systems that are made to perform specific tasks must be self-explanatory, so that the users can easily understand how and when to use the technology. Using any technology on a population for whom it was not initially designed will hinder user perception and the technology’s use.
KW - artificial intelligence
KW - blood transfusion
KW - complications
KW - decision support
KW - decision-making
KW - human factors
KW - perception
KW - prevention
KW - risk
KW - safety
KW - support
KW - technology acceptance
KW - transfusion overload
KW - usability
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UR - http://www.scopus.com/inward/citedby.url?scp=85144752256&partnerID=8YFLogxK
U2 - 10.2196/38411
DO - 10.2196/38411
M3 - Article
AN - SCOPUS:85144752256
VL - 9
JO - JMIR Human Factors
JF - JMIR Human Factors
IS - 4
M1 - e38411
ER -