Root causes of adverse drug events in hospitals and artificial intelligence capabilities for prevention

Barriers and Enablers to Using a Mobile App-Based Clinical Decision Support System in Managing Perioperative Adverse Events Among Anesthesia Providers: Cross-Sectional Survey in China

BACKGROUND

Perioperative adverse events (PAEs) pose a substantial global health burden, contributing to elevated morbidity, mortality, and health care expenditures. The adoption of clinical decision support systems (CDSS), particularly mobile-based solutions, offers a promising avenue to address these challenges. However, successful implementation hinges on understanding anesthesia providers' knowledge, attitudes, and willingness to embrace such technologies.

OBJECTIVE

This study aimed to evaluate the knowledge, attitudes, and willingness of Chinese anesthesia professionals to adopt a mobile CDSS for PAE management, and to identify key factors influencing its implementation.

METHODS

A nationwide cross-sectional survey was conducted among anesthesia providers in China from September 5 to December 31, 2023. Participants included anesthesiologists and nurse anesthetists, who play pivotal roles in perioperative care. A 51-item questionnaire, structured around the Knowledge-Attitude-Practice (KAP) framework, was distributed via WeChat through professional anesthesia associations. The questionnaire covered four domains: (1) demographic characteristics, (2) knowledge assessment, (3) attitude evaluation, and (4) practice willingness. Multivariable regression analyses identified predictors of KAP outcomes, with sensitivity analyses focusing on nurse anesthetists.

RESULTS

The study included 2440 anesthesia professionals (2226 anesthesiologists and 214 nurse anesthetists). Overall, 87.3% (2130/2440) expressed willingness to adopt the CDSS, with 87.5% (1947/2226) of anesthesiologists and 85.5% (183/214) of nurse anesthetists showing readiness. However, only 39.2% (956/2440) were satisfied with existing incident management systems. Key findings indicated that higher knowledge scores were associated with female gender (coefficient=0.19, P=.003), advanced education, and lack of previous informatics experience (coefficient=0.29, P<.001). Nurse anesthetists scored lower than anesthesiologists (coefficient=-0.76, P<.001). Negative attitudes were more prevalent among older practitioners (coefficient=-0.13, P<.001), females (coefficient=-0.66, P<.001), nurse anesthetists (coefficient=-1.12, P=.003), and those without prior PAE exposure (coefficient=-0.97, P<.001). Higher willingness was observed among practitioners in Southwest China (coefficient=0.10, P=.048), those with positive attitudes (coefficient=0.06, P<.001), and those dissatisfied (coefficient=0.32, P<.001) or neutral (coefficient=0.11, P=.02) towards existing systems. Infrequent departmental incident discussions would reduce practice willingness (coefficient=-0.08, P=.01).

CONCLUSIONS

This national study highlights a strong readiness among Chinese anesthesia professionals to adopt mobile CDSS for PAE management. However, critical barriers, including role-specific knowledge disparities and ineffective organizational communication, must be addressed to ensure successful implementation. Collaborative efforts among local authorities, health care facilities, anesthesia departments, and technology developers are essential to design and implement tailored strategies. Key recommendations include interdisciplinary training programs to enhance nurse anesthetists' competencies, institution-level incentives to promote incident reporting, and user-centered CDSS designs that prioritize seamless integration into clinical workflows. These measures are vital for improving perioperative incident reporting systems and ultimately advancing the safety and outcomes of surgical patients.

Evolutionary trend analysis of the pharmaceutical management research field from the perspective of mapping the knowledge domain

Background

Pharmaceutical management is a new frontier subject between pharmacy, law and management, and related research involves the whole process of drug development, production, circulation and use. With the development of medical systems and the diversification of patients' drug needs, research in the field of pharmaceutical management is becoming increasingly abundant. To clarify the development status of this field, this study conducted a bibliometric analysis of relevant literature in the field based on the knowledge graph method for the first time and explored the evolutionary trends of research hotspots and frontiers.

Methods

Literature was obtained from the Web of Science Core Collection database. CiteSpace 6.2.R4 (Advanced), VOSViewer, Scimago Graphica, Pajek and the R programming language were used to visualize the data.

Results

A total of 12,771 publications were included in the study. The publications in the field of pharmaceutical management show an overall increasing trend. In terms of discipline evolution, early research topics tended to involve the positioning of pharmacists and pharmaceutical care and the establishment of a management system. From 2000 to 2005, this period tended to focus on clinical pharmacy and institutional norms. With the development of globalization and the market economy, research from 2005 to 2010 began to trend to the fields of drug markets and economics. From 2010 to 2015, research was gradually integrated into health systems and medical services. With the development of information technology, after 2015, research in the field of pharmaceutical management also began to develop in the direction of digitalization and intelligence. In light of the global pandemic of COVID-19, research topics such as drug supply management, pharmaceutical care and telemedicine services under major public health events have shown increased interest since 2020.

Conclusion

Based on the knowledge mapping approach, this study provides a knowledge landscape in the field of pharmaceutical management research. The results showed that the reform of pharmacy education, the challenge of drug management under the COVID-19 pandemic, digital transformation and the rise of telemedicine services were the hot topics in this field. In addition, the research frontier also shows the broad prospects of the integration of information technology and pharmaceutical management, the practical value of precision pharmaceutical services, the urgent need of global drug governance, and the ethical and legal issues involved in the application of artificial intelligence technology in drug design, which points out the direction for the future development of pharmaceutical practice.

Burden of death and disability due to adverse effects of medical treatment in India: An analysis using the global burden of disease 2019 study data

Unsafe patient care can result in an adverse event that may lead to hospitalization, disability, or death. India has a vast and diverse population with varying degrees of access to tertiary healthcare. However, there is a lack of studies analyzing the burden of healthcare-related adverse events. We aimed to determine the burden of adverse effects of medical treatment (AEMT) in India from 2010 to 2019 using the global burden of disease (GBD) 2019 study database. Using the GBD data, we computed estimates for deaths and disability-adjusted life years (DALY) due to AEMT at the national level and stratified them based on age and gender. AEMT contributed to less than 0.01 % of death and DALY rates due to all causes in India. From 2010 to 2019, there was a decrease in the death rate from 2.34 (1.75-2.66) to 2.33 (1.73-2.86) per 100000 population. The number of deaths and DALYs was highest in the 50-74-year age group and in females. There has been a decrease in the death and DALY rates in India over the past decade. AEMT accounts for only a small percentage of deaths due to all causes; however, the potential underreporting and the impact of medical treatment-related adverse events on the public perception regarding healthcare services need to be studied.

Trends in artificial intelligence in nursing: Impacts on nursing management

OBJECTIVE

To investigate the academic use of artificial intelligence (AI) in nursing.

BACKGROUND

A bibliometric analysis combined with the VOSviewer software quantification method has been utilized for a literature analysis. In recent years, this approach has attracted the interest of scholars in various research fields. Thus far, there is no publication using bibliometric analysis combined with the VOSviewer software to analyse the applications of AI in nursing.

METHOD

A bibliometric analysis methodology was used to search for relevant articles published between 1984 and March 2022. Six databases, Embase, Scopus, PubMed, CINAHL, WoS and MEDLINE, were included to identify relevant studies, and data such as the year of publication, journals, country, institutional source, field and keywords were analysed.

RESULTS

Most relevant articles were published from institutions in the United States. The League of European Research Universities has published most research studies that use AI and nursing. Scholars have mainly focused on nursing, medical informatics, computer science AI, healthcare sciences services and physics particles fields. Commonly used keywords were machine learning, care, AI, natural language processing, prediction and nurse.

CONCLUSION

Research articles were mainly published in Nurse Education Today. Research topics such as AI-assisted medical recording and medical decision making were also identified. According to this study, AI in nursing has the potential to attract more attention from researchers and nursing managers. Additional high-quality research beyond the scope of medical education, as well as on cross-domain collaboration, is warranted to explore the acceptability and effective implementation of AI technologies.

IMPLICATIONS FOR NURSING MANAGEMENT

This study provides scholars and nursing managers with structured information regarding the use of AI in nursing based on scientific and technological developments across different fields and institutions. The application of AI can improve nursing management, nursing quality, safety management and team communication, as well as encourage future international collaboration.