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Related Experiment Video

Updated: Jun 10, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

The prescribed duration algorithm: utilising 'free text' from multiple primary care electronic systems.

Caroline J Brooks¹, Ronan A Lyons, Kerina H Jones

¹Health Information Research Unit (HIRU), Centre for Health Information Research & Evaluation (CHIRAL), School of Medicine, Swansea University, Wales, UK. c.brooks@swansea.ac.uk

Pharmacoepidemiology and Drug Safety

|July 24, 2010

Summary

This summary is machine-generated.

A new algorithm accurately calculates prescribed medication duration from free-text prescriptions, improving efficiency for large-scale analysis of antibiotic and antidepressant treatments.

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Last Updated: Jun 10, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

Medical Informatics
Pharmacovigilance
Health Data Science

Background:

Accurate calculation of prescribed treatment duration is crucial for medication adherence and effectiveness.
Manual review of prescriptions is time-consuming and prone to errors, especially with large datasets.

Purpose of the Study:

To develop and validate a computational algorithm for extracting numerical treatment durations from free-text prescription entries.
To assess the algorithm's accuracy and efficiency in processing antibiotic and antidepressant prescriptions.

Main Methods:

Algorithm development using a large dataset of 711,714 antibiotic prescriptions.
Validation through comparison with manual review by a blinded researcher on independent antibiotic and antidepressant prescription samples.
Statistical analysis of agreement using confidence intervals for differences in proportions.

Main Results:

The algorithm processed 98.5% of antibiotic prescriptions with 99.8% accuracy, closely matching manual review.
For antidepressant prescriptions, the algorithm achieved 96.6% accuracy on 91.5% of scripts, demonstrating generalizability.
High accuracy and efficiency were observed for both medication types, with minimal differences compared to manual review.

Conclusions:

The developed algorithm is effective and efficient for automated calculation of prescribed medication durations.
It offers a rapid and scalable solution for analyzing treatment durations from large volumes of electronic health records.
The algorithm demonstrates applicability across different drug classes, enhancing its utility in clinical research and practice.