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Title: A pragmatic method for electronic medical record-based observational studies: developing an electronic medical records retrieval system for clinical research.
Authors: Yamamoto, Keiichi
Sumi, Eriko  kyouindb  KAKEN_id  orcid (unconfirmed)
Yamazaki, Toru
Asai, Keita  kyouindb  KAKEN_id
Yamori, Masashi
Teramukai, Satoshi
Bessho, Kazuhisa  kyouindb  KAKEN_id
Yokode, Masayuki  kyouindb  KAKEN_id
Fukushima, Masanori
Author's alias: 山本, 景一
Issue Date: 31-Oct-2012
Publisher: BMJ Publishing
Journal title: BMJ open
Volume: 2
Issue: 6
Thesis number: e001622
Abstract: [Objective]The use of electronic medical record (EMR) data is necessary to improve clinical research efficiency. However, it is not easy to identify patients who meet research eligibility criteria and collect the necessary information from EMRs because the data collection process must integrate various techniques, including the development of a data warehouse and translation of eligibility criteria into computable criteria. This research aimed to demonstrate an electronic medical records retrieval system (ERS) and an example of a hospital-based cohort study that identified both patients and exposure with an ERS. We also evaluated the feasibility and usefulness of the method. [Design] The system was developed and evaluated. [Participants] In total, 800 000 cases of clinical information stored in EMRs at our hospital were used. [Primary and secondary outcome measures]The feasibility and usefulness of the ERS, the method to convert text from eligible criteria to computable criteria, and a confirmation method to increase research data accuracy. [Results] To comprehensively and efficiently collect information from patients participating in clinical research, we developed an ERS. To create the ERS database, we designed a multidimensional data model optimised for patient identification. We also devised practical methods to translate narrative eligibility criteria into computable parameters. We applied the system to an actual hospital-based cohort study performed at our hospital and converted the test results into computable criteria. Based on this information, we identified eligible patients and extracted data necessary for confirmation by our investigators and for statistical analyses with our ERS. [Conclusions ]We propose a pragmatic methodology to identify patients from EMRs who meet clinical research eligibility criteria. Our ERS allowed for the efficient collection of information on the eligibility of a given patient, reduced the labour required from the investigators and improved the reliability of the results.
Rights: This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license.
DOI(Published Version): 10.1136/bmjopen-2012-001622
PubMed ID: 23117567
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