Factors influencing health practitioners' cognitive processing and decision-making style

Decision-Making Styles in Medical Students and Healthcare Professionals: The Roles of Personality Traits and Socio-Emotional Intelligence Factors

Intuitive and deliberative styles can be considered the best-known decision-making styles, which are thought to be linked to actual workplace performance. However, there is a limited research on individual differences in these styles among individuals who provide healthcare. Therefore, adopting the self-report approach, this study examines the roles of the Big Five personality traits and socio-emotional intelligence factors in intuitive and deliberative decision-making styles among medical students and healthcare professionals. The research sample consists of 203 participants (50 medical students, 153 healthcare professionals) who completed the Big Five Inventory, the Trait Meta-Mood Scale, the Tromsø Social Intelligence Scale, and the Preference for Intuition and Deliberation Scale. The regression analyses revealed that attention to one's emotions and social information processing were positively related to intuitive decision-making style, while the clarity of one's emotions and social awareness were negatively related to intuitive decision-making style. It was further shown that conscientiousness, neuroticism, repair of one's emotions, and social information processing were positively related to deliberative decision-making style. The findings highlight the importance of personality and socio-emotional intelligence in understanding decision-making. Specifically, they point out that Big Five personality traits better explain deliberative style, while socio-emotional intelligence factors better explain intuitive style.

Inter-expert agreement on indications for antibiotic therapy in older adults admitted to French hospital through an emergency department

Background

Around one third of older adults with infections have an atypical presentation upon admission to an emergency department (ED).

Objective

To evaluate the level of agreement between experts from several disciplines on the indication for antibiotic therapy for a bacterial infection in older patients presenting at an ED, and to describe the characteristics of the infections.

Methods

Based on comprehensive medical records, three experts (a geriatrician, an emergency physician (EP), and an infectious disease specialist (IDS)) determined independently and then jointly whether a patient presenting at the ED had a bacterial infection requiring antibiotic therapy. Inter-expert agreement was expressed as a fixed-marginal Fleiss’ kappa (κ).

Results

Of the 444 medical records included, the consensus meeting found that 114 (25.7%) had an indication for antibiotics, 327 (73.6%) did not have an indication, and 3 could not be classified. The overall level of agreement was 85.2%, and κ[95%CI] was 0.64 [0.57–0.72] (p < 0.001). The level of agreement between the geriatrician and the IDS (89.41%, κ0.73, 95%CI [0.62–0.85] (p < 0.001)) was higher than that between the geriatrician and the EP (83.56%, κ0.62, 95%CI [0.51–0.73] (p < 0.001)) and between the IDS and the EP (82.66%, κ0.59, 95%CI [0.48–0.70] (p < 0.001)). The levels of agreement between the final adjudication, was higher for the geriatrician, and IDS respectively 94.1% (κ0.85, 95%CI [0.74–0.97] (p < 0.001) and 94.4% (κ0.86, 95%CI [0.74–0.97] (p < 0.001)). 114 (25.7%) patients had a bacterial infection (mostly lung infections (n = 55, 48.2%) and urinary tract infections (n = 25, 21.9%)), and 28 patients (6.3%) had a viral infection.

Conclusion

Our results highlighted substantial agreement between members of a multidisciplinary expert panel.

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Experts from different disciplines showed substantial agreement in deciding on the requirement of antibiotics

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The level of inter-expert agreement depended on the physicians' medical specialties

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Most of the bacterial infections were lung infections and urinary tract infections

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This study is the first step towards to better identification of infections with an atypical presentation of infections

Clinicians' Perceptions of an Artificial Intelligence-Based Blood Utilization Calculator: Qualitative Exploratory Study

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.

Intuition and rationality in intrapreneurship and innovation outputs: The case of health professionals in primary health care

The aim of this article is to explore the role of cognitive styles and intrapreneurship in health professionals’ innovation outputs, as well the mediated effect of intrapreneurship between cognitive styles and innovation output. This study used the survey method of data collection, through a self-administered questionnaire. Partial least square structural equation modelling method was used to analyse the result of the sample of 209 professionals of primary health care providers in Northern Portugal. Our findings reveal that cognitive style plays a significant role in intrapreneurship and innovation outputs, which are mediated by intrapreneurship. In particular, health care professionals with the rational cognitive style are likely to be more of a intrapreneur and innovative as compared to those with a intuitive cognitive style. Promoting intrapreneurship is crucial for successful innovation outputs. This study reveals that an understanding of the cognitive style of the health care professionals can help managers allocate appropriate individuals to different healthcare tasks. Our primary contribution to theory has been to highlight the importance of cognitive styles in intrapreneurship and innovation within the context of primary health care organizations.

The predict of metacognitive awareness of nursing students on self-confidence and anxiety in clinical decision-making

PURPOSE

This study was conducted to investigate the affect that metacognitive awareness in nursing students has on self-confidence and anxiety with respect to clinical decision-making.

DESIGN AND METHODS

The sample for this descriptive, correlational, and cross-sectional study consisted of 186 nursing students who voluntarily participated. Data were collected using the Metacognitive Awareness Inventory and Nursing Anxiety and Self-Confidence with Clinical Decision-Making Scale. Correlation and regression analyses were then performed on the data.

FINDINGS

Nursing students' metacognitive awareness level explained the three subdimensions of self-confidence in clinical decision-making by 26.7% (r²  = 0.267, p < 0.01), 24.6% (r²  = 0.246, p < 0.01), and 26.8% (r²  = 0.268, p < 0.01), respectively. Nursing students' metacognitive awareness level explained the three subdimensions of anxiety in clinical decision-making by 3.7% (r²  = 0.037, p < 0.01), 3.2% (r²  = 0.03, p < 0.05), and 2.4% (r²  = 0.024, p < 0.05), respectively.

IMPLICATIONS FOR NURSING PRACTICE

Clinical decision-making skills can be supported by increasing students' metacognitive awareness.

The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World

Artificial intelligence (AI) algorithms have been shown to diagnose skin lesions with impressive accuracy in experimental settings. The majority of the literature to date has compared AI and dermatologists as opponents in skin cancer diagnosis. However, in the real-world clinical setting, the clinician will work in collaboration with AI. Existing evidence regarding the integration of such AI diagnostic tools into clinical practice is limited. Human factors, such as cognitive style, personality, experience, preferences, and attitudes may influence clinicians' use of AI. In this review, we consider these human factors and the potential cognitive errors, biases, and unintended consequences that could arise when using an AI skin cancer diagnostic tool in the real world. Integrating this knowledge in the design and implementation of AI technology will assist in ensuring that the end product can be used effectively. Dermatologist leadership in the development of these tools will further improve their clinical relevance and safety.