RESEARCH ARTICLE


Data Mining Approach to Estimate the Duration of Drug Therapy from Longitudinal Electronic Medical Records



Olga Montvida1, 2, Ognjen Arandjelović3, Edward Reiner4, Sanjoy K. Paul5, *
1 Clinical Trials and Biostatistics Unit, QIMR Berghofer Medical Research Institute, Brisbane, Australia
2 School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Faculty of Health, Queensland University of Technology, Brisbane, Australia
3 School of Computer Science, University of St. Andrews, St. Andrews, United Kingdom
4 Smart Analyst Inc., New York, Unites States of America
5 Melbourne EpiCentre, University of Melbourne and Melbourne Health, Melbourne, Australia


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Creative Commons License
© 2017 Montvida et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Melbourne EpiCentre, University of Melbourne and Melbourne Health, Melbourne, Australia; Tel: +61 3 93428433; Fax: +61 3 93428780; E-mails: Sanjoy.Paul@mh.org.au; sambhupaul@hotmail.com


Abstract

Background:

Electronic Medical Records (EMRs) from primary/ ambulatory care systems present a new and promising source of information for conducting clinical and translational research.

Objectives:

To address the methodological and computational challenges in order to extract reliable medication information from raw data which is often complex, incomplete and erroneous. To assess whether the use of specific chaining fields of medication information may additionally improve the data quality.

Methods:

Guided by a range of challenges associated with missing and internally inconsistent data, we introduce two methods for the robust extraction of patient-level medication data. First method relies on chaining fields to estimate duration of treatment (“chaining”), while second disregards chaining fields and relies on the chronology of records (“continuous”). Centricity EMR database was used to estimate treatment duration with both methods for two widely prescribed drugs among type 2 diabetes patients: insulin and glucagon-like peptide-1 receptor agonists.

Results:

At individual patient level the “chaining” approach could identify the treatment alterations longitudinally and produced more robust estimates of treatment duration for individual drugs, while the “continuous” method was unable to capture that dynamics. At population level, both methods produced similar estimates of average treatment duration, however, notable differences were observed at individual-patient level.

Conclusion:

The proposed algorithms explicitly identify and handle longitudinal erroneous or missing entries and estimate treatment duration with specific drug(s) of interest, which makes them a valuable tool for future EMR based clinical and pharmaco-epidemiological studies. To improve accuracy of real-world based studies, implementing chaining fields of medication information is recommended.

Keywords: Electronic medical records, Treatment duration, Data mining, Type 2 diabetes, Rule-based algorithm, Patient-level data aggregation.