Abstract TP417: Improving Stroke Rehabilitation Intensity Data Collection: Collaborative Implementation of a Quality Assurance Framework
Background and Issues: A minimum of 3 hours of rehabilitation intensity (RI) is a stroke best practice standard of care that has been established in Ontario. As collection of RI time has been mandated in Ontario, the Ontario Stroke Network has been working with stakeholders to capture RI data using workload measurement systems (WMS). Given that this is a new indicator and variability exists in use of WMS across Toronto, there is a need for standardization in RI data collection.
Purpose: To develop a quality assurance (QA) framework for RI data collection in Toronto that can be used locally and provincially to ensure consistent and accurate data collection and reporting.
Methods: The Toronto Stroke Networks formed a working group comprised of clinical and decision support leads from 6 rehabilitation centres. This group developed standard approaches to capturing RI data. They also examined factors that influence the quality of the data, ranked these factors by level of influence on data quality, and established mitigation strategies for the top ranked factors. Additionally, a reporting and monitoring plan was determined.
Results: Through collaborative sharing of information, a QA framework was developed and adopted by the group. The top ranked factors affecting data quality included: 1) When the RI data field is left blank; 2) Inaccurate data entry by staff; 3) Variations in service interruptions and alternate level of care; and 4) Timeliness in entering data. Mitigation strategies included use of lock out periods, clear definitions, processes for staff feedback and education, and establishment of a quarterly reporting structure for organizational comparison. This QA framework has been shared with provincial stakeholders to inform system planning in other regions.
Conclusions: As data accuracy in capturing RI data is important for current state analysis and benchmarking, a comprehensive QA framework was developed to support data accuracy and has been used for local monitoring and reporting of RI data. Identified factors that influence data quality related to processes for data capture. As mitigation strategies will further support these processes, this QA framework ensures accurate data collection and confidence in using RI data to support system planning.
Author Disclosures: E. Linkewich: None. D. Cheung: None. J. Willems: None. S. Sharp: None. S. Quant: None.
- © 2016 by American Heart Association, Inc.