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Natural language processing application to identify covert administration of medicines: development and pilot audit
Ninoslav Majkic1, Jyoti Sanyal2, Robert Stewart3,4
1South London and Maudsley NHS Foundation Trust, London, UK.
Bjpsych Bulletin
|May 23, 2025
Summary
A new natural language processing (NLP) method accurately identifies covert medicine administration in mental health records. This tool aids in auditing and monitoring medication practices, improving patient safety and ethical compliance.
Area of Science:
- Health Informatics
- Natural Language Processing
- Mental Health Research
Background:
- Covert medicine administration presents significant legal and ethical challenges.
- Effective identification and monitoring are crucial for patient safety and regulatory compliance.
Purpose of the Study:
- To develop a natural language processing (NLP) methodology for identifying covert medicine administration.
- To pilot an audit using the developed NLP method on electronic mental health records.
Main Methods:
- Free-text searching within electronic mental health records using a developed NLP method.
- Precision of the NLP method was evaluated for accuracy in identifying covert administration.
Main Results:
- The NLP method achieved a precision of 72% in identifying covert medicine administration.
- A pilot audit revealed that 95% of patients undergoing covert administration had documented capacity assessments and best interests meetings.
- Pharmacy was consulted for administration details in 77% of audited patient cases.
Conclusions:
- A deployable NLP method for identifying covert medicine administration was successfully developed.
- This method shows potential for broader application in health informatics and real-time health record monitoring.
- The NLP approach can facilitate active monitoring, enhancing oversight of covert medication practices.
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