Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Scholte, Marijn; van Dulmen, Simone; Neeleman-Van der Steen, Catherina; van der Wees, Philip; Nijhuis-van der Sanden, Maria; Braspenning, Jozé
• Is Part Of: BMC medical informatics and decision making. Volume 16:Issue 1(2016)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-12
• Subjects: Electronic health records -- Data quality -- Completeness -- Correctness -- Data sources -- Healthcare quality indicators -- Physical therapy; Medical informatics -- Periodicals; Clinical medicine -- Decision making -- Periodicals; 610.285
• DOI: 10.1186/s12911-016-0382-4
• Languages: English
• ISSNs: 1472-6947

Abstract

Abstract 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.