Data measurement is a fundamentally important part of the Collaborative methodology. The measures should not be ‘time-consuming’ to extract, academic in their nature or too elaborate. At the organisation level measures provide an essential feedback loop giving the organisation the ability to identify success, or challenges, and enable staff to direct and steer system change in small, rapid steps through the Plan, Do, Study, Act (PDSA) cycles. At the program level the measures provide power to make comparisons and to demonstrate improvement at regional and state levels. Baseline data is collected at the beginning of a Collaborative. This provides an important snapshot of the participant’s position before making improvements, and enables participants to see the results of their work.
| Data Analysis and Feedback||Download||Data analysis and feedback|
| Data Analysis and Interpretation||Download||Data interpretation and analysis - why its important to understand data|
| Data Cleansing 101||Download||Why data quality is important in relation to chronic disease management|
| IF Measure Suite – Measure Specifications V5.8 – 31 March 2016||Download||Complete suite of IF measures|
| Medicare Local Quality Improvement Partnership – Manual Measures: A Brief Guide – 2015||Download||Document describing considerations for creating manual measures|