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Updated: Nov 12, 2025
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Pain Information Model and Its Potential for Predictive Analytics: Applicability of a Big Data Science Framework
Aline Tsuma Gaedke Nomura1, Miriam de Abreu Almeida2, Steve Johnson3
1Phd student, School of Nursing, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil, and Radiology Service Charge Nurse, Hospital de Clínicas de Porto Alegre.
Summary
A new data science framework was applied to electronic health records to create a pain management model. This model can predict patients likely to experience pain upon discharge, improving care decisions.
Area of Science:
- Healthcare Data Science
- Big Data Analytics in Medicine
Background:
- Effective pain management is crucial for patient recovery and satisfaction.
- Predictive modeling in healthcare can optimize treatment strategies and resource allocation.
Purpose of the Study:
- To apply a big data science framework for developing a comprehensive pain information model.
- To explore the potential of this model for predictive analytics in pain management.
Main Methods:
- Utilized the Applied Healthcare Data Science Framework, adapted from cross-industry data mining processes.
- Analyzed approximately 51,000 electronic health records from adult patients in clinical and surgical units (July 2015 - June 2019).
Main Results:
- Successfully developed an information model for pain management, encompassing assessment, interventions, goals, and outcomes.
- The model demonstrates potential for predicting patients at high risk of experiencing self-reported pain at discharge.
Conclusions:
- The framework supports healthcare professionals in leveraging data for enhanced pain management decision-making.
- Long-term goals include personalizing treatments, reducing healthcare costs, and improving patient outcomes through data science methodologies.
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