OBJECTIVE: To assess the accuracy of data linkage across the spectrum of emergency care in the absence of a unique patient identifier, and to use the linked data to examine service delivery outcomes in an emergency department (ED) setting. DESIGN: Automated data linkage and manual data linkage were compared to determine their relative accuracy. Data were extracted from three separate health information systems: ambulance, ED and hospital inpatients, then linked to provide information about the emergency journey of each patient. The linking was done manually through physical review of records and automatically using a data linking tool (Health Data Integration) developed by the CSIRO (Commonwealth Scientific and Industrial Research Organisation). Match rate and quality of the linking were compared. SETTING: 10,835 patient presentations to a large, regional teaching hospital ED over a 2-month period (August - September 2007). RESULTS: Comparison of the manual and automated linkage outcomes for each pair of linked datasets demonstrated a sensitivity of between 95% and 99%; a specificity of between 75% and 99%; and a positive predictive value of between 88% and 95%. CONCLUSIONS: Our results indicate that automated linking provides a sound basis for health service analysis, even in the absence of a unique patient identifier. The use of an automated linking tool yields accurate data suitable for planning and service delivery purposes and enables the data to be linked regularly to examine service delivery outcomes.
OBJECTIVE: To assess the accuracy of data linkage across the spectrum of emergency care in the absence of a unique patient identifier, and to use the linked data to examine service delivery outcomes in an emergency department (ED) setting. DESIGN: Automated data linkage and manual data linkage were compared to determine their relative accuracy. Data were extracted from three separate health information systems: ambulance, ED and hospital inpatients, then linked to provide information about the emergency journey of each patient. The linking was done manually through physical review of records and automatically using a data linking tool (Health Data Integration) developed by the CSIRO (Commonwealth Scientific and Industrial Research Organisation). Match rate and quality of the linking were compared. SETTING: 10,835 patient presentations to a large, regional teaching hospital ED over a 2-month period (August - September 2007). RESULTS: Comparison of the manual and automated linkage outcomes for each pair of linked datasets demonstrated a sensitivity of between 95% and 99%; a specificity of between 75% and 99%; and a positive predictive value of between 88% and 95%. CONCLUSIONS: Our results indicate that automated linking provides a sound basis for health service analysis, even in the absence of a unique patient identifier. The use of an automated linking tool yields accurate data suitable for planning and service delivery purposes and enables the data to be linked regularly to examine service delivery outcomes.
Authors: John Paul Kuwornu; Lisa M Lix; Jacqueline M Quail; Xiaoyun Eric Wang; Meric Osman; Gary F Teare Journal: BMC Health Serv Res Date: 2016-10-11 Impact factor: 2.655
Authors: Christopher M Pearce; Adam McLeod; Jon Patrick; Douglas Boyle; Marianne Shearer; Paula Eustace; Mary Catherine Pearce Journal: JMIR Res Protoc Date: 2016-12-20
Authors: S J Clark; M Halter; A Porter; H C Smith; M Brand; R Fothergill; S J Lindridge; M McTigue; H Snooks Journal: Int J Popul Data Sci Date: 2019-08-05