The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN’s Challenge

Opportunities and barriers for telemedicine in pain management: insights from a SIAARTI survey among Italian pain physicians

BACKGROUND

The integration of telemedicine in pain management represents a significant advancement in healthcare delivery, offering opportunities to enhance patient access to specialized care, improve satisfaction, and streamline chronic pain management. Despite its growing adoption, there remains a lack of comprehensive data on its utilization in pain therapy, necessitating a deeper understanding of physicians' perspectives, experiences, and challenges.

METHODS

A survey was conducted in Italy between January 2024 and May 2024. Specialist center members of the SIAARTI were sent an online questionnaire testing the state of the art of telemedicine for pain medicine.

RESULTS

One-hundred thirty-one centers across Italy reveal varied adoption rates, with 40% routinely using telemedicine. Regional disparities exist, with Northern Italy showing higher adoption rates. Barriers include the absence of protocols, resource constraints, and bureaucratic obstacles. Despite challenges, telemedicine has shown positive impacts on service delivery, with increased service volume reported. Technological capabilities, including image sharing and teleconsultation with specialists, indicate promising interdisciplinary potential.

CONCLUSIONS

The integration of advanced telemedicine software utilizing artificial intelligence holds promise for enhancing telemonitoring and alert systems, potentially leading to more proactive and personalized pain management strategies.

Designing and psychometric evaluation of safe nursing care instrument in intensive care units

BACKGROUND

Providing safe care in a sensitive and high-risk unit such as the ICU is one of the most crucial tasks for nurses. One way to establish the criteria for safe care is by creating a instrument to assess it. Therefore, this study was conducted with the aim of designing and psychometrically evaluating an instrument for safe nursing care in the ICU.

METHODS

The current study employed a sequential-exploratory mixed-method approach with two qualitative and quantitative phases. Based on the results of qualitative phase and the literature review, the primary instrument was designed. In the quantitative phase, the designed instrument underwent psychometric evaluation. Face, content and construct validity were assessed. Face validity was assessed by 20 nurses, and content validity was assessed by 26 experts. In the construct validity stage, the sample size for the exploratory factor analysis (EFA) included 300 nurses, and for the confirmatory factor analysis (CFA) included 200 nurses who work full-time in the ICUs of hospitals affiliated with Kermanshah University of Medical Sciences in western Iran. EFA sampling was conducted in three hospitals, encompassing six ICUs, while CFA sampling was carried out in two hospitals, covering four ICUs. Sampling was done using the convenience method. The reliability of the instrument was also assessed. Finally, the interpretability, feasibility, weighting, and scoring of the instrument were evaluated.

RESULTS

The qualitative phase identified three themes, including professional behavior (with categories: Implementation of policies, organizing communication, professional ethics), holistic care (with categories: systematic care, comprehensive care of all systems), and safety-oriented organization (with categories: human resource management and safe environment). The primary instrument was designed with 107 items rated on a five-point Likert scale. In the quantitative phase, the psychometrics of the instrument were conducted. First, the face and content validity were assessed, and the average scale content validity index (S-CVI) was 0.94. Then, a preliminary test was conducted to assess the initial reliability (α = 0.92) and the correlation of each item with the total score. After completing these steps, the number of items in the instrument was reduced to 52. The results of the EFA explained 58% of the total variance, with 4 factors identified: professional behavior by following guidelines, comprehensive care, accurate documentation, and pressure ulcer care. At the CFA stage, the results of the calculation of indices and goodness of fit showed that the model had a good fit. The reliability of the relative stability by examining the intraclass correlation coefficient (ICC) for the whole instrument in 20 samples was 0.92 with a confidence interval of 0.97 - 0.81. To measure absolute stability and determine the responsiveness of the instrument, the standard error of measurement (SEM) was 4.39 and the minimum detectable change (MDC) was 12.13.

CONCLUSION

The instrument for safe nursing care in the ICU has favorable psychometric properties.

Cardiac arrest and cardiopulmonary resuscitation in the next decade: Predicting and shaping the impact of technological innovations

INTRODUCTION

Cardiac arrest (CA) is the third leading cause of death, with persistently low survival rates despite medical advancements. This article evaluates the potential of emerging technologies to enhance CA management over the next decade, using predictions from the AI tools ChatGPT-4 and Gemini Advanced.

METHODS

We conducted an exploratory literature review to envision the future of cardiopulmonary arrest (CA) management. Utilizing ChatGPT-4 and Gemini Advanced, we predicted implementation timelines for innovations in early recognition, CPR, defibrillation, and post-resuscitation care. We also consulted the AI to assess the consistency and reproducibility of the predictions.

RESULTS

We extrapolate that healthcare may embrace new technologies, such as comprehensive monitoring of vital signs to activate the emergency system (wireless detectors, smart speakers, and wearable devices), use new innovative early CPR and early AED devices (robot CPR, wearable AEDs, and immersive reality), and post-resuscitation care monitoring (brain-computer interface). These technologies could enhance timely life-saving interventions for cardiac arrest. However, there are many ethical and practical challenges, particularly in maintaining patient privacy and equity. The two AI tools made different predictions, with a horizon for implementation ranging between three and eight years.

CONCLUSION

Integrating advanced monitoring technologies and AI-driven tools offers hope in improving CA management. A balanced approach involving rigorous scientific validation and ethical oversight is necessary. Collaboration among technologists, medical professionals, ethicists, and policymakers is crucial to use these innovations ethically to reduce CA incidence and enhance outcomes. Further research is needed to enhance the reliability of AI predictive capabilities.

Maladaptive personality traits are associated with burnout risk in Italian anesthesiologists and intensivists: a secondary analysis from a cross-sectional study

BACKGROUND

Burnout is a maladaptive response to chronic stress, particularly prevalent among clinicians. Anesthesiologists are at risk of burnout, but the role of maladaptive traits in their vulnerability to burnout remains understudied.

METHODS

A secondary analysis was performed on data from the Italian Association of Hospital Anesthesiologists, Pain Medicine Specialists, Critical Care, and Emergency (AAROI-EMAC) physicians. The survey included demographic data, burnout assessment using the Maslach Burnout Inventory (MBI) and subscales (emotional exhaustion, MBI-EE; depersonalization, MBI-DP; personal accomplishment, MBI-PA), and evaluation of personality disorders (PDs) based on DSM-IV (Diagnostic and Statistical Manual of Mental Disorders Fourth Edition) criteria using the assessment of DSM-IV PDs (ADP-IV). We investigated the aggregated scores of maladaptive personality traits as predictor variables of burnout. Subsequently, the components of personality traits were individually assessed.

RESULTS

Out of 310 respondents, 300 (96.77%) provided complete information. The maladaptive personality traits global score was associated with the MBI-EE and MBI-DP components. There was a significant negative correlation with the MBI-PA component. Significant positive correlations were found between the MBI-EE subscale and the paranoid (r = 0.42), borderline (r = 0.39), and dependent (r = 0.39) maladaptive personality traits. MBI-DP was significantly associated with the passive-aggressive (r = 0.35), borderline (r = 0.33), and avoidant (r = 0.32) traits. Moreover, MBI-PA was negatively associated with dependent (r =  - 0.26) and avoidant (r =  - 0.25) maladaptive personality features.

CONCLUSIONS

There is a significant association between different maladaptive personality traits and the risk of experiencing burnout among anesthesiologists. This underscores the importance of understanding and addressing personality traits in healthcare professionals to promote their well-being and prevent this serious emotional, mental, and physical exhaustion state.

Envisioning gamification in anesthesia, pain management, and critical care: basic principles, integration of artificial intelligence, and simulation strategies

Unlike traditional video games developed solely for entertainment purposes, game-based learning employs intentionally crafted approaches that seamlessly merge entertainment and educational content, resulting in captivating and effective learning encounters. These pedagogical methods include serious video games and gamification. Serious games are video games utilized as tools for acquiring crucial (serious) knowledge and skills. On the other hand, gamification requires integrating gaming elements (game mechanics) such as points, leaderboards, missions, levels, rewards, and more, into a context that may not be associated with video gaming activities. They can be dynamically (game dynamics) combined developing various strategic approaches. Operatively, gamification adopts simulation elements and leverages the interactive nature of gaming to teach players specific skills, convey knowledge, or address real-world issues. External incentives stimulate internal motivation. Therefore, these techniques place the learners in the central role, allowing them to actively construct knowledge through firsthand experiences.Anesthesia, pain medicine, and critical care demand a delicate interplay of technical competence and non-technical proficiencies. Gamification techniques can offer advantages to both domains. Game-based modalities provide a dynamic, interactive, and highly effective opportunity to learn, practice, and improve both technical and non-technical skills, enriching the overall proficiency of anesthesia professionals. These properties are crucial in a discipline where personal skills, human factors, and the influence of stressors significantly impact daily work activities. Furthermore, gamification can also be embraced for patient education to enhance comfort and compliance, particularly within pediatric settings (game-based distraction), and in pain medicine through stress management techniques. On these bases, the creation of effective gamification tools for anesthesiologists can present a formidable opportunity for users and developers.This narrative review comprehensively examines the intricate aspects of gamification and its potentially transformative influence on the fields of anesthesiology. It delves into theoretical frameworks, potential advantages in education and training, integration with artificial intelligence systems and immersive techniques, and also addresses the challenges that could arise within these contexts.

Crossing the AI Chasm in Neurocritical Care

Despite the growing interest in possible applications of computer science and artificial intelligence (AI) in the field of neurocritical care (neuro-ICU), widespread clinical applications are still missing. In neuro-ICU, the collection and analysis in real time of large datasets can play a crucial role in advancing this medical field and improving personalized patient care. For example, AI algorithms can detect subtle changes in brain activity or vital signs, alerting clinicians to potentially life-threatening conditions and facilitating rapid intervention. Consequently, data-driven AI and predictive analytics can greatly enhance medical decision making, diagnosis, and treatment, ultimately leading to better outcomes for patients. Nevertheless, there is a significant disparity between the current capabilities of AI systems and the potential benefits and applications that could be achieved with more advanced AI technologies. This gap is usually indicated as the AI chasm. In this paper, the underlying causes of the AI chasm in neuro-ICU are analyzed, along with proposed recommendations for utilizing AI to attain a competitive edge, foster innovation, and enhance patient outcomes. To bridge the AI divide in neurocritical care, it is crucial to foster collaboration among researchers, clinicians, and policymakers, with a focus on specific use cases. Additionally, strategic investments in AI technology, education and training, and infrastructure are needed to unlock the potential of AI technology. Before implementing a technology in patient care, it is essential to conduct thorough studies and establish clinical validation in real-world environments to ensure its effectiveness and safety. Finally, the development of ethical and regulatory frameworks is mandatory to ensure the secure and efficient deployment of AI technology throughout the process.