1. Data for Improvement

2. Clinical Review Tool
It consists of a set of actions that, when performed collectively and reliably has the potential to improve patient outcomes.
The elements of the tool are not intended to be prescriptive.
Provides a snapshot of where care is in line with best practice and where improvements need to be made either to practice processes or patient care.
Clinicians are expected to continue to utilise their clinical judgement and customise actions to patients as appropriate.

3. Using Data to drive change in systems and processes…..Understand
Step 1: Analysis of Clinical Review Tool data
Here you ask the question why?
Negative Part: Why was there a decrease in foot checks
Positive: What did you do right in these cases?
Is this what you do with every patients

4. Using Data to drive change in systems and processes…..Understand
Step 1: Analysis of Clinical Review Tool data
Here you ask the question why?
Negative Part: Why was there a decrease in foot checks
Positive: What did you do right in these cases?
Is this what you do with every patients

5. Using Data to drive change in systems and processes….. Diagnose
Step 2: Review of Practice Level Diabetes data to understand the magnitude of your findings from the Clinical Review Tool data
MMA eg:
Developed a query builders to identify Diabetic patients without Foot check in last 12 months
312 patients were due for the foot check

6. Using Data to drive change in systems and processes…
Using Data to drive change in systems and processes…..Generate Ideas & Test
Step 3: Generate ideas for Change.
Eg: TXT msg sent to patients
Step 4: Plan-Do-Study-Act (PDSA) cycles to test the change.
Learning:
PDSA data were collected in a span of 2 & 4 weeks post the TXT messages
66 foot checks were completed in the 2 weeks- An increase of 44 foot checks compared to the 2 weeks prior.
111 foot checks were completed in total in 4 weeks.

7. Measurement for Improvement
The purpose of measurement in quality improvement is learning not judgement
All measures have limitations, but the limitations do not negate their value for learning

8. “Errors using inadequate data are much less than those using no data at all”
- Charles Babbage