Human factors: bridging artificial intelligence and patient safety

Choudhury, Avishek; Asan, Onur

doi:10.1177/2327857920091007

Cited by 8 publications

(4 citation statements)

References 25 publications

(22 reference statements)

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“…Similarly, poor AI design in health care can lead to patient harm, where clinicians can misinterpret AI information or click on the wrong option on the AI display. Inadequate maintenance, that is, not retraining the AI with new patient data, can compromise its prediction accuracy, and thus hinder patient safety [ 43 ]. In addition, sometimes, bedside care providers make clinical decisions that do not necessarily fall within the standard guideline (for specific patient types) or skip the prescribed clinical steps (under excessive workload) to accomplish a particular clinical goal promptly [ 44 ].…”

Section: Framework Developmentmentioning

confidence: 99%

Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians

Choudhury¹

2022

JMIR Hum Factors

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The health care management and the medical practitioner literature lack a descriptive conceptual framework for understanding the dynamic and complex interactions between clinicians and artificial intelligence (AI) systems. As most of the existing literature has been investigating AI’s performance and effectiveness from a statistical (analytical) standpoint, there is a lack of studies ensuring AI’s ecological validity. In this study, we derived a framework that focuses explicitly on the interaction between AI and clinicians. The proposed framework builds upon well-established human factors models such as the technology acceptance model and expectancy theory. The framework can be used to perform quantitative and qualitative analyses (mixed methods) to capture how clinician-AI interactions may vary based on human factors such as expectancy, workload, trust, cognitive variables related to absorptive capacity and bounded rationality, and concerns for patient safety. If leveraged, the proposed framework can help to identify factors influencing clinicians’ intention to use AI and, consequently, improve AI acceptance and address the lack of AI accountability while safeguarding the patients, clinicians, and AI technology. Overall, this paper discusses the concepts, propositions, and assumptions of the multidisciplinary decision-making literature, constituting a sociocognitive approach that extends the theories of distributed cognition and, thus, will account for the ecological validity of AI.

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Section: Framework Developmentmentioning

confidence: 99%

Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians

Choudhury¹

2022

JMIR Hum Factors

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“…While studies have investigated factors such as ease of use, privacy and security of personal information focusing on the technical and clinical aspects of AI adoption in healthcare, studies exploring factors influencing AI use from the patients' perspective are still limited. Patients may have concerns about the accuracy and reliability of the information provided by AI and the privacy and security of their personal information usage and storage (Choudhury and Asan, 2020). Moreover, patients may also face challenges related to the use of AIbased home care devices, such as difficulty in understanding the information provided by the device or difficulty in using the device (Esmaeilzadeh, 2020).…”

Section: Role Of Trustworthinessmentioning

confidence: 99%

“…Moreover, in addition to the perceived usefulness and efficiency, patients may also perceive the risks and barriers associated with the uncertainty and unknowns of AI (Choudhury and Asan, 2020;Elkefi et al, 2020). Considering the vulnerability and sensitivity of patients regarding the AI technology used in medical treatment, in addition to the UTAUT model that addresses patients' perception of new technology, we incorporate risk perception enlightened by the Health Belief Model (HBM) (Rosenstock et al, 1988).…”

Section: Steps To Trustworthy Ai In Healthcarementioning

confidence: 99%

“…Ease of use Ease of use is important in the implementation of AI in healthcare because it affects the ability of healthcare professionals and patients to access and utilize the technology effectively (Esmaeilzadeh, 2020). Complicated and hard-touse AI healthcare systems will cause reluctance to adopt or use as intended (Choudhury and Asan, 2020), leading to potential missed opportunities for improved patient outcomes.…”

Section: Variablesmentioning

confidence: 99%

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What May Impact Trustworthiness of AI in Digital Healthcare: Discussion from Patients’ Viewpoint

Asan

Mansouri

2023

Proceedings of the International Symposium on Human Factors and

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The healthcare industry is undergoing a transformation of traditional medical relationships from human-physician interactions to digital healthcare focusing on physician-AI-patient interactions. Patients’ trustworthiness is the cornerstone of adopting new technologies expounding the reliability, integrity, and ability of AI-based systems and devices to provide an accurate and safe healthcare environment. The main objective of this study is to investigate the various factors that influence patients’ trustworthiness in AI-based systems and devices, taking into account differences in patients’ experiences and backgrounds. First, an exploratory conceptual framework inspired by the United Theory of Acceptance and Use of Technology (UTAUT) and Health Belief Model (HBM) is established to further explain the patients’ trust to support the adoption willingness of AI. Then a case study that includes 218 samples from chronic patients is conducted. The results of the study indicate that factors such as accountability, risk perception, facilitating conditions, and social influence play a significant role in determining a patient’s trust in AI-based healthcare devices, while ease of ease may not have a direct impact to trust. And among the demographic factors, only race showed a strong correlation with the level of patient trust in AI. The contributions of this study can provide a comprehensive understanding of patients’ trustworthiness and inform the development and deployment of the technology in a way that prioritizes patients’ interests.

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