Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication

Chen, Chun‑You; Chen, Ya-Lin; Scholl, Jeremiah; Yang, Hsuan-Chia; Li, Yu-Chuan (Jack)

doi:10.1016/j.cmpb.2023.107869

Cited by 2 publications

(1 citation statement)

References 53 publications

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“…The topic of over alert is currently subject of further research. 45 The question remains as to whether the adjustment rate stated by the physicians ( Figure 5c ) corresponds to reality. According to our survey, 64% of physicians claim to follow at least 50% of the warning messages provided by the software.…”

Section: Discussionmentioning

confidence: 98%

We Ask and Listen: A Group-Wide Retrospective Survey on Satisfaction with Digital Medication Software

Herrmann,

Giesel-Gerstmeier,

Demuth

et al. 2024

JMDH

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Purpose Computerized physician order entry (CPOE) and clinical decision support systems (CDSS) are used internationally since the 1980s. These systems reduce costs, enhance drug therapy safety, and improve quality of care. A few years ago, there was a growing effort to digitize the healthcare sector in Germany. Implementing such systems like CPOE-CDSS requires training for effective adoption and, more important, acceptance by the users. Potential improvements for the software and implementation process can be derived from the users’ perspective. The implementation process is globally relevant and applicable across professions due to the constant advancement of digitalization. The study assessed the implementation of medication software and overall satisfaction. Methods In an anonymous voluntary online survey, physicians and nursing staff were asked about their satisfaction with the new CPOE-CDSS. The survey comprised single-choice queries on a Likert scale, categorizing into general information, digital medication administration, drug safety, and software introduction. In addition multiple-choice questions are mentioned. Data analysis was performed using Microsoft Office Excel 2016 and GraphPad PRISM 9.5.0. Results Nurses and physicians’ satisfaction with the new software increased with usage hours. The software’s performance and loading times have clearly had a negative impact, which leads to a low satisfaction of only 20% among physicians and 17% among nurses. 53% of nurses find the program’s training period unsuitable for their daily use, while 57% of physicians approve the training’s scope for their professional group. Both professions agree that drug-related problems are easier to detect using CPOE-CDSS, with 76% of nurses and 75% of physicians agreeing. The study provides unbiased feedback on software implementation. Conclusion In conclusion, digitizing healthcare requires managing change, effective training, and addressing software functionality concerns to ensure improved medication safety and streamlined processes. Interfaces, performance optimization, and training remain crucial for software acceptance and effectiveness.

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Section: Discussionmentioning

confidence: 98%

We Ask and Listen: A Group-Wide Retrospective Survey on Satisfaction with Digital Medication Software

Herrmann,

Giesel-Gerstmeier,

Demuth

et al. 2024

JMDH

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Enhancing risk management in hospitals: leveraging artificial intelligence for improved outcomes

Guerra

2024

Ital J Med

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In hospital settings, effective risk management is critical to ensuring patient safety, regulatory compliance, and operational effectiveness. Conventional approaches to risk assessment and mitigation frequently rely on manual procedures and retroactive analysis, which might not be sufficient to recognize and respond to new risks as they arise. This study examines how artificial intelligence (AI) technologies can improve risk management procedures in healthcare facilities, fortifying patient safety precautions and guidelines while improving the standard of care overall. Hospitals can proactively identify and mitigate risks, optimize resource allocation, and improve clinical outcomes by utilizing AI-driven predictive analytics, natural language processing, and machine learning algorithms. The different applications of AI in risk management are discussed in this paper, along with opportunities, problems, and suggestions for their effective use in hospital settings.

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Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication

Cited by 2 publications

References 53 publications

We Ask and Listen: A Group-Wide Retrospective Survey on Satisfaction with Digital Medication Software

We Ask and Listen: A Group-Wide Retrospective Survey on Satisfaction with Digital Medication Software

Enhancing risk management in hospitals: leveraging artificial intelligence for improved outcomes

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