Utilising a Data Capture Tool to Populate a Cardiac Rehabilitation Registry: A Feasibility Study.

BACKGROUND: Clinical registries are effective for monitoring clinical practice, yet manual data collection can limit their implementation and sustainability. The objective of this study was to assess the feasibility of using a data capture tool to collect cardiac rehabilitation (CR) minimum variables from electronic hospital administration databases to populate a new CR registry in Australia. METHODS: Two CR facilities located in Melbourne, Australia participated, providing data on 42 variables including: patient socio-demographics, risk factors and co-morbidities, CR program information (e.g. number of CR sessions), process indicators (e.g. wait time) and patient outcomes (e.g. change in exercise capacity). A pre-programmed, automated data capture tool (GeneRic Health Network Information for the Enterprise [20]: https://www.grhanite.com/) (GRHANITE™) was installed at the sites to extract data available in an electronic format from hospital sites. Additionally, clinicians entered data on CR patients into a purpose-built web-based tool (Research Electronic Data Capture: https://www.project-redcap.org/) (REDCap). Formative evaluation including staff feedback was collected. RESULTS: The GRHANITE™ tool was successfully installed at the two CR sites and data from 176 patients (median age = 67 years, 76% male) were securely extracted between September-December 2017. Data pulled electronically from hospital databases was limited to seven of the 42 requested variables. This is due to CR sites only capturing basic patient information (e.g. socio-demographics, CR appointment bookings) in hospital administrative databases. The remaining clinical information required for the CR registry was collected in formats (e.g. paper-based, scanned or Excel spreadsheet) deemed unusable for electronic data capture. Manually entered data into the web-tool enabled data collection on all remaining variables. Compared to historical methods of data collection, CR staff reported that the REDCap tool reduced data entry time. CONCLUSIONS: The key benefits of a scalable, automated data capture tool like GRHANITE™ cannot be fully realised in settings with under-developed electronic health infrastructure. While this approach remains promising for creating and maintaining a registry that monitors the quality of CR provided to patients, further investment is required in the digital platforms underpinning this approach.

Improving a Secondary Use Health Data Warehouse: Proposing a Multi-Level Data Quality Framework.

BACKGROUND: Data quality frameworks within information technology and recently within health care have evolved considerably since their inception. When assessing data quality for secondary uses, an area not yet addressed adequately in these frameworks is the context of the intended use of the data. METHODS: After review of literature to identify relevant research, an existing data quality framework was refined and expanded to encompass the contextual requirements not present. RESULTS: The result is a two-level framework to address the need to maintain the intrinsic value of the data, as well as the need to indicate whether the data will be able to provide the basis for answers in specific areas of interest or questions. DISCUSSION: Data quality frameworks have always been one dimensional, requiring the implementers of these frameworks to fit the requirements of the data's use around how the framework is designed to function. Our work has systematically addressed the shortcomings of existing frameworks, through the application of concepts synthesized from the literature to the naturalistic setting of data quality management in an actual health data warehouse. CONCLUSION: Secondary use of health data relies on contextualized data quality management. Our work is innovative in showing how to apply context around data quality characteristics and how to develop a second level data quality framework, so as to ensure that quality and context are maintained and addressed throughout the health data quality assessment process.