Applying Intelligent Algorithms to Automate the Identification of Error Factors

Jin, Haizhe; Qu, Qing-Xing; Munechika, Masahiko; Sano, Mizuka; Kajihara, Chisato; Duffy, Vincent G.; Chen, Han

doi:10.1097/pts.0000000000000498

Cited by 9 publications

(6 citation statements)

References 33 publications

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“…Mathematical/text data To identify, extract, and minimize medical error factors in the medication administration process Jin et al [37] Machine-learning algorithms identified error rates in imaging, lab, and medication orders…”

Section: Patients and Healthy Participantsmentioning

confidence: 99%

Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review

Asan¹,

Choudhury²

2021

JMIR Hum Factors

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Background Despite advancements in artificial intelligence (AI) to develop prediction and classification models, little research has been devoted to real-world translations with a user-centered design approach. AI development studies in the health care context have often ignored two critical factors of ecological validity and human cognition, creating challenges at the interface with clinicians and the clinical environment. Objective The aim of this literature review was to investigate the contributions made by major human factors communities in health care AI applications. This review also discusses emerging research gaps, and provides future research directions to facilitate a safer and user-centered integration of AI into the clinical workflow. Methods We performed an extensive mapping review to capture all relevant articles published within the last 10 years in the major human factors journals and conference proceedings listed in the “Human Factors and Ergonomics” category of the Scopus Master List. In each published volume, we searched for studies reporting qualitative or quantitative findings in the context of AI in health care. Studies are discussed based on the key principles such as evaluating workload, usability, trust in technology, perception, and user-centered design. Results Forty-eight articles were included in the final review. Most of the studies emphasized user perception, the usability of AI-based devices or technologies, cognitive workload, and user’s trust in AI. The review revealed a nascent but growing body of literature focusing on augmenting health care AI; however, little effort has been made to ensure ecological validity with user-centered design approaches. Moreover, few studies (n=5 against clinical/baseline standards, n=5 against clinicians) compared their AI models against a standard measure. Conclusions Human factors researchers should actively be part of efforts in AI design and implementation, as well as dynamic assessments of AI systems’ effects on interaction, workflow, and patient outcomes. An AI system is part of a greater sociotechnical system. Investigators with human factors and ergonomics expertise are essential when defining the dynamic interaction of AI within each element, process, and result of the work system.

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Section: Patients and Healthy Participantsmentioning

confidence: 99%

Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review

Asan¹,

Choudhury²

2021

JMIR Hum Factors

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“…3,18 MAE is the deviation between the medication content pre-provided to nurses in the medication operation and the actual medication content. 16…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the complexity and diversity of medical errors make it impossible to generalize them, and each type must be categorized and discussed in depth. 10,16,17 There are many types of medical errors, including MAEs, falls, blood collection and transfusion errors, and surgical errors, with MAEs accounting for a relatively high proportion at more than 50% of overall medical errors during medication nursing delivery. 3,18 MAE is the deviation between the medication content pre-provided to nurses in the medication operation and the actual medication content.…”

Section: Introductionmentioning

confidence: 99%

“…3,18 MAE is the deviation between the medication content pre-provided to nurses in the medication operation and the actual medication content. 16 Nursing is characterized by various types of work, high work intensity, coordination of multiple types of work, irregular work times, and complex operation situations. [19][20][21] It is accompanied by a high workload.…”

Section: Introductionmentioning

confidence: 99%

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Effects of workload on medication administration errors in nursing: an analysis based on system dynamics modeling

et al. 2023

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Medication administration errors account for a relatively high proportion of medical errors, with more than 50% occurring at the nursing administration stage. Nursing is characterized by a large amount of work, rigid working hours, high information cognitive intensity, and frequent information updates. The high workload of nurses is a significant cause of medication administration errors. In this study, a literature analysis was used to determine the elements of the system dynamics model, and the causal loop diagram was used to draw the relationship framework among the elements. Vensim personal learning edition and interview surveys were then used for model validation and simulation. First, 302 case analyses of medication administration errors collected from the three metropolitan area hospitals were used to construct the causal loop diagram, the stock and flow map of the medication administration error system, and the dynamics model; second, the model was tested from theoretical and historical data simulation perspectives; finally, the system dynamics model proposed in this study was used to simulate a medical institution from overtime and policy perspectives. Through system dynamics modeling, the inducing mechanism of workload on medication administration errors in nursing operations was elucidated, and corresponding suggestions for prevention were provided. In addition, ideas and basis for optimizing the medication administration process, improving workload, and preventing medication administration errors considering workload were provided.

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“… 13 Due to the diversity, urgency, complexity, and variability of medical procedures, there is a large amount of information processing content and associated cognitive processes involved in the daily operations of medical staff, which easily causes mental fatigue. 14 , 15 Mental fatigue is defined as a psychobiological state caused by long-term and/or intense cognitively demanding activities, and is characterized by subjective feelings of “tiredness” and “lack of energy”. 16 In the medical field, mental fatigue is the primary manifestation of fatigue.…”

Section: Introductionmentioning

confidence: 99%

Influence of Different Protection States on the Mental Fatigue of Nurses During the COVID-19 Pandemic

Jin¹,

Xiao²,

Gong³

et al. 2022

RMHP

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Background COVID-19 has brought greater workload pressures to the medical field, such as medical staff being required to wear personal protective equipment (PPE). While PPE can protect the safety of staff during the pandemic, it can also accelerate the accumulation of fatigue among operators. Objective This study explores the influence of different protection states on the mental fatigue of nurses. Methods In this study, 10 participants (5 males and 5 females) were randomly selected among applicants to monitor mental fatigue during the nurses’ daily work in four different PPE states (low temperature and low protection; low temperature and high protection; high temperature and low protection; high temperature and high protection). The NASA subjective mental fatigue scale was used for subjective evaluation. Reaction time, attention concentration, attention distribution, memory, and main task completion time were used for objective evaluation. Results The results demonstrated a significant difference in the effects of different protection states on mental fatigue. The state of high temperature and high protection had the greatest influence on mental fatigue, the state of low temperature and low protection had the least, and states of high (low) temperature and low (high) protection had intermediate effects on mental fatigue. Furthermore, the correlation between the subjective and objective fatigue indices was analyzed using a multiple regression model. Conclusion This study clarified the influence of different protection states on the mental fatigue of nurses, and verified that nurses require more time and energy to complete the same work as before under high protection states. It provides a basis for evaluating the mental fatigue of nurses in the unique period of the COVID-19 pandemic and specific ideas for optimizing the nursing process.

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Applying Intelligent Algorithms to Automate the Identification of Error Factors

Cited by 9 publications

References 33 publications

Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review

Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review

Effects of workload on medication administration errors in nursing: an analysis based on system dynamics modeling

Influence of Different Protection States on the Mental Fatigue of Nurses During the COVID-19 Pandemic

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