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.
Highlights
The electronic medical records (EMRs) and the administrative data from the primary/ambulatory care systems are increasingly being used in epidemiological [1 - 3], pharmaco-epidemiological [4 - 6], pharmaco-vigilance [7 - 9], clinical outcome [5, 10 - 12], health economic [13, 14] and public health related studies [15 - 18]
In the CentricityTM EMR (CEMR) database 1,861,560 patients were identified as having been diagnosed with type 2 diabetes mellitus, as inferred from the assigned ICD-9 codes
In this work we addressed a number of challenging data mining related issues while extracting patient-level longitudinal information on prescription patterns and medication usages from large relational databases
Summary
The electronic medical records (EMRs) and the administrative data from the primary/ambulatory care systems are increasingly being used in epidemiological [1 - 3], pharmaco-epidemiological [4 - 6], pharmaco-vigilance [7 - 9], clinical outcome [5, 10 - 12], health economic [13, 14] and public health related studies [15 - 18]. Analyses of large primary care based EMRs from various countries, most notably from UK, USA and Sweden, have provided significant insight into the effectiveness of changes in health care practices/polices on overall disease and health management costs [3, 15, 19, 20], in addition to population level evidences on the safety and effectiveness of various therapies and the association of disease-related risk factors on long-term outcomes [5, 6, 18, 21 - 23] Increasing use of such large realworld patient-level data is illustrated well by the sixfold increase in EMR based published studies since 2000 [10, 24]. Electronic Medical Records (EMRs) from primary/ ambulatory care systems present a new and promising source of information for conducting clinical and translational research
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