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

BMC Medical Informatics and Decision Making

Research Article

Catherina W. M. Neeleman-Van der Steen¹  Marijn Scholte²  Philip J. van der Wees³  Simone A. van Dulmen³  Maria W. G. Nijhuis-van der Sanden³  Jozé Braspenning³ 

[1] ROS Caransscoop, Beekbergen, The Netherlands;Radboud Institute for Health Sciences, Scientific Institute for Quality of Healthcare, Radboud University Medical Center, Geert Grooteplein 21, 6525 EZ, Nijmegen, The Netherlands;Present address: Faculty of Social Sciences, Department of Sociology, Radboud University, Thomas van Aquinostraat 6, 6525 GD, Nijmegen, The Netherlands;Research Institute for Health Sciences, Scientific Institute for Quality of Healthcare, Radboud University Medical Center, Nijmegen, The Netherlands;

关键词: Electronic health records;    Data quality;    Completeness;    Correctness;    Data sources;    Healthcare quality indicators;    Physical therapy;   

DOI  :  10.1186/s12911-016-0382-4

 received in 2016-04-18, accepted in 2016-11-02,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundWith 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.MethodsData 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.ResultsFive 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.ConclusionData 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.

【 授权许可】

CC BY   
© The Author(s). 2016