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Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data

BACKGROUND: With the emergence of the electronic health records (EHRs) as a pervasive healthcare information technology, new opportunities and challenges for use of clinical data for quality measurements arise with respect to data quality, data availability and comparability. The objective of this s...

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Autores principales: Scholte, Marijn, van Dulmen, Simone A., Neeleman-Van der Steen, Catherina W. M., van der Wees, Philip J., Nijhuis-van der Sanden, Maria W. G., Braspenning, Jozé
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5101697/
https://www.ncbi.nlm.nih.gov/pubmed/27825333
http://dx.doi.org/10.1186/s12911-016-0382-4
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author Scholte, Marijn
van Dulmen, Simone A.
Neeleman-Van der Steen, Catherina W. M.
van der Wees, Philip J.
Nijhuis-van der Sanden, Maria W. G.
Braspenning, Jozé
author_facet Scholte, Marijn
van Dulmen, Simone A.
Neeleman-Van der Steen, Catherina W. M.
van der Wees, Philip J.
Nijhuis-van der Sanden, Maria W. G.
Braspenning, Jozé
author_sort Scholte, Marijn
collection PubMed
description BACKGROUND: With the emergence of the electronic health records (EHRs) as a pervasive healthcare information technology, new opportunities and challenges for use of clinical data for quality measurements arise with respect to data quality, data availability and comparability. The objective of this study is to test whether data extracted from electronic health records (EHRs) was of comparable quality as survey data for the calculation of quality indicators. METHODS: Data from surveys describing patient cases and filled out by physiotherapists in 2009-2010 were used to calculate scores on eight quality indicators (QIs) to measure the quality of physiotherapy care. In 2011, data was extracted directly from EHRs. The data collection methods were evaluated for comparability. EHR data was compared to survey data on completeness and correctness. RESULTS: Five of the eight QIs could be extracted from the EHRs. Three were omitted from the indicator set, as they proved too difficult to be extracted from the EHRs. Another QI proved incomparable due to errors in the extraction software of some of the EHRs. Three out of four comparable QIs performed better (p < 0.001) in EHR data on completeness. EHR data also proved to be correct; the relative change in indicator scores between EHR and survey data were small (<5 %) in three out of four QIs. CONCLUSION: Data quality of EHRs was sufficient to be used for the calculation of QIs, although comparability to survey data was problematic. Standardization is needed, not only to be able to compare different data collection methods properly, but also to compare between practices with different EHRs. EHRs have the option to administrate narrative data, but natural language processing tools are needed to quantify these text boxes. Such development, can narrow the comparability gap between scoring QIs based on EHR data and based on survey data. EHRs have the potential to provide real time feedback to professionals and quality measurements for research, but more effort is needed to create unambiguous and uniform information and to unlock written text in a standardized manner. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-016-0382-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-51016972016-11-10 Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data Scholte, Marijn van Dulmen, Simone A. Neeleman-Van der Steen, Catherina W. M. van der Wees, Philip J. Nijhuis-van der Sanden, Maria W. G. Braspenning, Jozé BMC Med Inform Decis Mak Research Article BACKGROUND: With the emergence of the electronic health records (EHRs) as a pervasive healthcare information technology, new opportunities and challenges for use of clinical data for quality measurements arise with respect to data quality, data availability and comparability. The objective of this study is to test whether data extracted from electronic health records (EHRs) was of comparable quality as survey data for the calculation of quality indicators. METHODS: Data from surveys describing patient cases and filled out by physiotherapists in 2009-2010 were used to calculate scores on eight quality indicators (QIs) to measure the quality of physiotherapy care. In 2011, data was extracted directly from EHRs. The data collection methods were evaluated for comparability. EHR data was compared to survey data on completeness and correctness. RESULTS: Five of the eight QIs could be extracted from the EHRs. Three were omitted from the indicator set, as they proved too difficult to be extracted from the EHRs. Another QI proved incomparable due to errors in the extraction software of some of the EHRs. Three out of four comparable QIs performed better (p < 0.001) in EHR data on completeness. EHR data also proved to be correct; the relative change in indicator scores between EHR and survey data were small (<5 %) in three out of four QIs. CONCLUSION: Data quality of EHRs was sufficient to be used for the calculation of QIs, although comparability to survey data was problematic. Standardization is needed, not only to be able to compare different data collection methods properly, but also to compare between practices with different EHRs. EHRs have the option to administrate narrative data, but natural language processing tools are needed to quantify these text boxes. Such development, can narrow the comparability gap between scoring QIs based on EHR data and based on survey data. EHRs have the potential to provide real time feedback to professionals and quality measurements for research, but more effort is needed to create unambiguous and uniform information and to unlock written text in a standardized manner. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-016-0382-4) contains supplementary material, which is available to authorized users. BioMed Central 2016-11-08 /pmc/articles/PMC5101697/ /pubmed/27825333 http://dx.doi.org/10.1186/s12911-016-0382-4 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Scholte, Marijn
van Dulmen, Simone A.
Neeleman-Van der Steen, Catherina W. M.
van der Wees, Philip J.
Nijhuis-van der Sanden, Maria W. G.
Braspenning, Jozé
Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data
title Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data
title_full Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data
title_fullStr Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data
title_full_unstemmed Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data
title_short Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data
title_sort data extraction from electronic health records (ehrs) for quality measurement of the physical therapy process: comparison between ehr data and survey data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5101697/
https://www.ncbi.nlm.nih.gov/pubmed/27825333
http://dx.doi.org/10.1186/s12911-016-0382-4
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