2. Improving data collection efficiency: Minimising the burden of data collection processes is crucial, to maximise data submission. Centres with advanced informatics are able to organise their clinical workflows to record data needed for registries in ways that reduce effort and so improve the completeness of data collection.c this kind of structured data capture minimises the number of staff needed for data collection and the time they need to spend. agreements about the core vocabulary and corresponding technical (database) representation allow integration of high-quality data into the processes of care; promotion of automated collection; lowering the burden of data collection; minimization of human error; and reduction of resource requirements. Efforts to reduce the burden of data collection and improve the quality of data include scanned capture of UDI on device labels and auto- population of key device attributes from AccessGUDID. AccessGUDID offers means to auto-populate fields such as manufacturer, brand, device size, and other standard fields needed for analysis. Finally, soliciting patient input and collecting data through innovative patient-facing applications enables inclusion of endpoints of interest, addressing patient preferences and gaining further efficiencies in data collection.
Efficiency domain describes the extent to which the registry is embedded in the healthcare quality improvement system so that data collection occurs as part of care delivery (ie, not overly burdensome, not highly complicated, not overly costly) and integrated with workflow of clinical teams. A key pre-condition for this domain is that the core minimum data process with key stakeholders is developed in order to define the CRF and the elements are clinically relevant and harmonised. This will ensure that reliable and relevant data elements with proper definitions are included in the data collection effort.	Level 1 Early Learner	Heavy burden of data collection with ad hoc data elements on a project basis but without agreement on clinically relevant core minimum data elements.
	Level 2 Making Progress	Clinically relevant core minimum data elements are established with key stakeholder input. Data collection is started but there is a heavy burden on data collectors (manual data entry with no automation).
	Level 3 Defined Path to Success	In addition to level two achievements, technologies are in place (eg, structured data extraction from EHRs; mobile apps) to reduce burden on data collectors, and a pilot project is completed on adoption of data and terminology standards that will enable exchanges between data information ecosystems (interoperability).
	Level 4 Well Managed	Technologies are in place (eg, structured data extraction from EHRs; mobile apps) to reduce burden on data collectors, and a multisite demonstration project is completed on adoption of data and terminology standards that will enable exchanges between data information ecosystems (interoperability).
	Level 5 Optimised	Technologies are in place (eg, structured data extraction from EHRs; mobile apps) for all core minimum data elements and a fully automated data collection for most core minimum data elements, and there is a full adoption and integration of data and terminology standard (assumes complete interoperability).
cSanborn TA, Tcheng JE, Anderson HV, et al. ACC/AHA/SCAI 2014 health policy statement on structured reporting for the cardiac catheterization laboratory: a report of the American College of Cardiology Clinical Quality Committee. J Am Coll Cardiol. 2014;63(23):2591-2623.