Creating a Medication Therapy Observational Research Database from an Electronic Medical Record: Challenges and Data Curation
Observational research has shown its potential to complement experimental research and clinical trials by secondary use of treatment data from hospital care processes. It can also be applied to better understand pediatric drug utilization for establishing safer drug therapy. Clinical documentation p...
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Veröffentlicht in: | Applied clinical informatics 2024-01, Vol.15 (1), p.111-118 |
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creator | Rödle, Wolfgang Prokosch, Hans-Ulrich Neumann, Eva Toni, Irmgard Haering-Zahn, Julia Neubert, Antje Eberl, Sonja |
description | Observational research has shown its potential to complement experimental research and clinical trials by secondary use of treatment data from hospital care processes. It can also be applied to better understand pediatric drug utilization for establishing safer drug therapy. Clinical documentation processes often limit data quality in pediatric medical records requiring data curation steps, which are mostly underestimated.
The objectives of this study were to transform and curate data from a departmental electronic medical record into an observational research database. We particularly aim at identifying data quality problems, illustrating reasons for such problems and describing the systematic data curation process established to create high-quality data for observational research.
Data were extracted from an electronic medical record used by four wards of a German university children's hospital from April 2012 to June 2020. A four-step data preparation, mapping, and curation process was established. Data quality of the generated dataset was firstly assessed following an established 3 × 3 Data Quality Assessment guideline and secondly by comparing a sample subset of the database with an existing gold standard.
The generated dataset consists of 770,158 medication dispensations associated with 89,955 different drug exposures from 21,285 clinical encounters. A total of 6,840 different narrative drug therapy descriptions were mapped to 1,139 standard terms for drug exposures. Regarding the quality criterion correctness, the database was consistent and had overall a high agreement with our gold standard.
Despite large amounts of freetext descriptions and contextual knowledge implicitly included in the electronic medical record, we were able to identify relevant data quality issues and to establish a semi-automated data curation process leading to a high-quality observational research database. Because of inconsistent dosage information in the original documentation this database is limited to a drug utilization database without detailed dosage information. |
doi_str_mv | 10.1055/s-0043-1777741 |
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The objectives of this study were to transform and curate data from a departmental electronic medical record into an observational research database. We particularly aim at identifying data quality problems, illustrating reasons for such problems and describing the systematic data curation process established to create high-quality data for observational research.
Data were extracted from an electronic medical record used by four wards of a German university children's hospital from April 2012 to June 2020. A four-step data preparation, mapping, and curation process was established. Data quality of the generated dataset was firstly assessed following an established 3 × 3 Data Quality Assessment guideline and secondly by comparing a sample subset of the database with an existing gold standard.
The generated dataset consists of 770,158 medication dispensations associated with 89,955 different drug exposures from 21,285 clinical encounters. A total of 6,840 different narrative drug therapy descriptions were mapped to 1,139 standard terms for drug exposures. Regarding the quality criterion correctness, the database was consistent and had overall a high agreement with our gold standard.
Despite large amounts of freetext descriptions and contextual knowledge implicitly included in the electronic medical record, we were able to identify relevant data quality issues and to establish a semi-automated data curation process leading to a high-quality observational research database. Because of inconsistent dosage information in the original documentation this database is limited to a drug utilization database without detailed dosage information.</description><identifier>ISSN: 1869-0327</identifier><identifier>EISSN: 1869-0327</identifier><identifier>DOI: 10.1055/s-0043-1777741</identifier><identifier>PMID: 38325408</identifier><language>eng</language><publisher>Germany: Georg Thieme Verlag KG</publisher><subject>Child ; Data Accuracy ; Data Curation ; Databases, Factual ; Documentation ; Electronic Health Records ; Humans</subject><ispartof>Applied clinical informatics, 2024-01, Vol.15 (1), p.111-118</ispartof><rights>The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).</rights><rights>The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( ) 2024 The Author(s).</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c429t-c30abbc42fbde652ad9cd0b9e6bc87fcde945a9dea16dc048233114c68e04a583</citedby><cites>FETCH-LOGICAL-c429t-c30abbc42fbde652ad9cd0b9e6bc87fcde945a9dea16dc048233114c68e04a583</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38325408$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Rödle, Wolfgang</creatorcontrib><creatorcontrib>Prokosch, Hans-Ulrich</creatorcontrib><creatorcontrib>Neumann, Eva</creatorcontrib><creatorcontrib>Toni, Irmgard</creatorcontrib><creatorcontrib>Haering-Zahn, Julia</creatorcontrib><creatorcontrib>Neubert, Antje</creatorcontrib><creatorcontrib>Eberl, Sonja</creatorcontrib><title>Creating a Medication Therapy Observational Research Database from an Electronic Medical Record: Challenges and Data Curation</title><title>Applied clinical informatics</title><addtitle>Appl Clin Inform</addtitle><description>Observational research has shown its potential to complement experimental research and clinical trials by secondary use of treatment data from hospital care processes. It can also be applied to better understand pediatric drug utilization for establishing safer drug therapy. Clinical documentation processes often limit data quality in pediatric medical records requiring data curation steps, which are mostly underestimated.
The objectives of this study were to transform and curate data from a departmental electronic medical record into an observational research database. We particularly aim at identifying data quality problems, illustrating reasons for such problems and describing the systematic data curation process established to create high-quality data for observational research.
Data were extracted from an electronic medical record used by four wards of a German university children's hospital from April 2012 to June 2020. A four-step data preparation, mapping, and curation process was established. Data quality of the generated dataset was firstly assessed following an established 3 × 3 Data Quality Assessment guideline and secondly by comparing a sample subset of the database with an existing gold standard.
The generated dataset consists of 770,158 medication dispensations associated with 89,955 different drug exposures from 21,285 clinical encounters. A total of 6,840 different narrative drug therapy descriptions were mapped to 1,139 standard terms for drug exposures. Regarding the quality criterion correctness, the database was consistent and had overall a high agreement with our gold standard.
Despite large amounts of freetext descriptions and contextual knowledge implicitly included in the electronic medical record, we were able to identify relevant data quality issues and to establish a semi-automated data curation process leading to a high-quality observational research database. Because of inconsistent dosage information in the original documentation this database is limited to a drug utilization database without detailed dosage information.</description><subject>Child</subject><subject>Data Accuracy</subject><subject>Data Curation</subject><subject>Databases, Factual</subject><subject>Documentation</subject><subject>Electronic Health Records</subject><subject>Humans</subject><issn>1869-0327</issn><issn>1869-0327</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkU1LxDAQhoMoKurVo-TopZqvtokXkfoJiiB6DtNkulvpNmvSFTz43-2uq-hcMkmeeRJ4CTnk7ISzPD9NGWNKZrwcS_ENsst1YTImRbn5p98hBym9srHygmtdbpMdqaXIFdO75LOKCEPbTyjQB_StGzehp89TjDD_oI91wvi-OoOOPmFCiG5KL2GAGhLSJoYZhZ5edeiGGPrWrS1L2IXoz2g1ha7DfoJpBP1qlFaLuHLuk60GuoQH63WPvFxfPVe32f3jzV11cZ85JcyQOcmgrse-qT0WuQBvnGe1waJ2umycR6NyMB6BF94xpYWUnCtXaGQKci33yPm3d76oZ-gd9kOEzs5jO4P4YQO09v9N307tJLxbzrQyWpSj4XhtiOFtgWmwszY57DroMSySFUZIw5WQS_TkG3UxpBSx-X2HM7vMzSa7zM2ucxsHjv7-7hf_SUl-AXKulfs</recordid><startdate>20240101</startdate><enddate>20240101</enddate><creator>Rödle, Wolfgang</creator><creator>Prokosch, Hans-Ulrich</creator><creator>Neumann, Eva</creator><creator>Toni, Irmgard</creator><creator>Haering-Zahn, Julia</creator><creator>Neubert, Antje</creator><creator>Eberl, Sonja</creator><general>Georg Thieme Verlag KG</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20240101</creationdate><title>Creating a Medication Therapy Observational Research Database from an Electronic Medical Record: Challenges and Data Curation</title><author>Rödle, Wolfgang ; Prokosch, Hans-Ulrich ; Neumann, Eva ; Toni, Irmgard ; Haering-Zahn, Julia ; Neubert, Antje ; Eberl, Sonja</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c429t-c30abbc42fbde652ad9cd0b9e6bc87fcde945a9dea16dc048233114c68e04a583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Child</topic><topic>Data Accuracy</topic><topic>Data Curation</topic><topic>Databases, Factual</topic><topic>Documentation</topic><topic>Electronic Health Records</topic><topic>Humans</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rödle, Wolfgang</creatorcontrib><creatorcontrib>Prokosch, Hans-Ulrich</creatorcontrib><creatorcontrib>Neumann, Eva</creatorcontrib><creatorcontrib>Toni, Irmgard</creatorcontrib><creatorcontrib>Haering-Zahn, Julia</creatorcontrib><creatorcontrib>Neubert, Antje</creatorcontrib><creatorcontrib>Eberl, Sonja</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Applied clinical informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rödle, Wolfgang</au><au>Prokosch, Hans-Ulrich</au><au>Neumann, Eva</au><au>Toni, Irmgard</au><au>Haering-Zahn, Julia</au><au>Neubert, Antje</au><au>Eberl, Sonja</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Creating a Medication Therapy Observational Research Database from an Electronic Medical Record: Challenges and Data Curation</atitle><jtitle>Applied clinical informatics</jtitle><addtitle>Appl Clin Inform</addtitle><date>2024-01-01</date><risdate>2024</risdate><volume>15</volume><issue>1</issue><spage>111</spage><epage>118</epage><pages>111-118</pages><issn>1869-0327</issn><eissn>1869-0327</eissn><abstract>Observational research has shown its potential to complement experimental research and clinical trials by secondary use of treatment data from hospital care processes. It can also be applied to better understand pediatric drug utilization for establishing safer drug therapy. Clinical documentation processes often limit data quality in pediatric medical records requiring data curation steps, which are mostly underestimated.
The objectives of this study were to transform and curate data from a departmental electronic medical record into an observational research database. We particularly aim at identifying data quality problems, illustrating reasons for such problems and describing the systematic data curation process established to create high-quality data for observational research.
Data were extracted from an electronic medical record used by four wards of a German university children's hospital from April 2012 to June 2020. A four-step data preparation, mapping, and curation process was established. Data quality of the generated dataset was firstly assessed following an established 3 × 3 Data Quality Assessment guideline and secondly by comparing a sample subset of the database with an existing gold standard.
The generated dataset consists of 770,158 medication dispensations associated with 89,955 different drug exposures from 21,285 clinical encounters. A total of 6,840 different narrative drug therapy descriptions were mapped to 1,139 standard terms for drug exposures. Regarding the quality criterion correctness, the database was consistent and had overall a high agreement with our gold standard.
Despite large amounts of freetext descriptions and contextual knowledge implicitly included in the electronic medical record, we were able to identify relevant data quality issues and to establish a semi-automated data curation process leading to a high-quality observational research database. Because of inconsistent dosage information in the original documentation this database is limited to a drug utilization database without detailed dosage information.</abstract><cop>Germany</cop><pub>Georg Thieme Verlag KG</pub><pmid>38325408</pmid><doi>10.1055/s-0043-1777741</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Child Data Accuracy Data Curation Databases, Factual Documentation Electronic Health Records Humans |
title | Creating a Medication Therapy Observational Research Database from an Electronic Medical Record: Challenges and Data Curation |
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