Electronic Pain Assessment and the Role of Nurses in Pain Management
Year: 2025
Type: article
Abstract: Background: Pain management is a fundamental aspect of clinical care, yet traditional pain assessment methods rely heavily on subjective self-reporting, which can lead to inconsistencies and inadequate treatment. The integration of electronic pain assessment tools, including artificial intelligence (AI), wearable sensors, and electronic health records (EHRs), has shown promise in improving pain measurement accuracy, clinical workflow, and patient outcomes. Objective: This study examines the impact of electronic pain assessment technologies on pain measurement accuracy, nursing workflow efficiency, and patient satisfaction. Additionally, it explores barriers to implementation and provides recommendations for integrating digital pain management into routine clinical practice. Methods: A systematic literature review and quantitative analysis were conducted to assess the effectiveness of electronic pain assessment tools. Data were collected from clinical trials, observational studies, and real-world applications, focusing on accuracy, workflow efficiency, and patient-reported outcomes. Statistical analyses included descriptive statistics, t-tests, ANOVA, and regression models to evaluate differences between traditional and electronic pain assessment methods. Results: The findings suggest that electronic pain assessment tools significantly improve accuracy, with AI-based and wearable sensor technologies demonstrating the highest reliability compared to conventional self-report scales. Additionally, the adoption of digital pain assessment reduced documentation time by 32% and improved adherence to standardized pain protocols. Patients assessed using electronic methods reported higher satisfaction rates and lower incidence of undertreated pain. However, barriers to adoption include interoperability challenges, data security concerns, and insufficient provider training. Conclusion: Electronic pain assessment enhances clinical decision-making, workflow efficiency, and patient outcomes. Addressing barriers such as system interoperability, cybersecurity, and provider training is essential for successful implementation. Future research should focus on AI-driven predictive pain management and cost-effectiveness evaluations to support large-scale adoption. MeSH Keywords: Pain assessment, Artificial intelligence, Nursing informatics, Wearable technology, Digital health, Electronic health records, Pain management, Machine learning, Healthcare technology.
Author: Milic, Marko Kimi
Language: English
Subfield: Anesthesiology and Pain Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6930618936
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