Viviana Torres1, Mauricio Cerda2, Petra Knaup3, Martin Löpprich3. 1. Program of Master in Medical Informatics, Faculty of Medicine, Universidad de Chile, Santiago. 2. Program of Anatomy and Developmental Biology, Biomedical Neuroscience Institute (BNI), Center of Medical Informatics and Telemedicine (CIMT), ICBM, Faculty of Medicine, Universidad de Chile, Santiago. 3. Institute of Medical Biometry and Informatics, Heidelberg University, Germany.
Abstract
PURPOSE: An important part of the electronic information available in Hospital Information System (HIS) has the potential to be automatically exported to Electronic Data Capture (EDC) platforms for improving clinical research. This automation has the advantage of reducing manual data transcription, a time consuming and prone to errors process. However, quantitative evaluations of the process of exporting data from a HIS to an EDC system have not been reported extensively, in particular comparing with manual transcription. In this work an assessment to study the quality of an automatic export process, focused in laboratory data from a HIS is presented. METHODS: Quality of the laboratory data was assessed in two types of processes: (1) a manual process of data transcription, and (2) an automatic process of data transference. The automatic transference was implemented as an Extract, Transform and Load (ETL) process. Then, a comparison was carried out between manual and automatic data collection methods. The criteria to measure data quality were correctness and completeness. RESULTS: The manual process had a general error rate of 2.6% to 7.1%, obtaining the lowest error rate if data fields with a not clear definition were removed from the analysis (p < 10E-3). In the case of automatic process, the general error rate was 1.9% to 12.1%, where lowest error rate is obtained when excluding information missing in the HIS but transcribed to the EDC from other physical sources. CONCLUSION: The automatic ETL process can be used to collect laboratory data for clinical research if data in the HIS as well as physical documentation not included in HIS, are identified previously and follows a standardized data collection protocol.
PURPOSE: An important part of the electronic information available in Hospital Information System (HIS) has the potential to be automatically exported to Electronic Data Capture (EDC) platforms for improving clinical research. This automation has the advantage of reducing manual data transcription, a time consuming and prone to errors process. However, quantitative evaluations of the process of exporting data from a HIS to an EDC system have not been reported extensively, in particular comparing with manual transcription. In this work an assessment to study the quality of an automatic export process, focused in laboratory data from a HIS is presented. METHODS: Quality of the laboratory data was assessed in two types of processes: (1) a manual process of data transcription, and (2) an automatic process of data transference. The automatic transference was implemented as an Extract, Transform and Load (ETL) process. Then, a comparison was carried out between manual and automatic data collection methods. The criteria to measure data quality were correctness and completeness. RESULTS: The manual process had a general error rate of 2.6% to 7.1%, obtaining the lowest error rate if data fields with a not clear definition were removed from the analysis (p < 10E-3). In the case of automatic process, the general error rate was 1.9% to 12.1%, where lowest error rate is obtained when excluding information missing in the HIS but transcribed to the EDC from other physical sources. CONCLUSION: The automatic ETL process can be used to collect laboratory data for clinical research if data in the HIS as well as physical documentation not included in HIS, are identified previously and follows a standardized data collection protocol.
Authors: Rachel C Brierley; Maria Pufulete; Jessica Harris; Chiara Bucciarelli-Ducci; John P Greenwood; Stephen Dorman; Richard Anderson; Chris A Rogers; Barnaby C Reeves Journal: BMJ Open Date: 2018-03-01 Impact factor: 2.692