1. WMQI
Neonates
10 November 2011

2. “We can only be sure to improve what we can actually measure”
Lord Darzi, High Quality Care for All, June 2008
February 19

3. Origins of the quality observatories
“There is clear local support for quality improvement. A new ‘Quality Observatory’ will be established in every NHS region to inform local quality improvement efforts.”
“We will also ask each SHA to establish a formal Quality Observatory, building on existing analytical arrangements, to enable local benchmarking, development of metrics and identification of opportunities to help frontline staff innovate and improve the services they offer. “
High Quality Care For All
February 19

4. Approach and principles
Clinically championed Indicators
No measure about me, without me
Developmental not regulation
Metrics not targets
Helpful benchmarking and comparison
Helping clinicians to set their aspirations
Helping patients to compare different providers
February 19

5. Journey to date Lead by a group of clinicians
Global search for metrics
Literature and internet
Refined in a series of meetings
List on your table

6. Paediatric Metric Development
This presentation will highlight the following
The various datasets available at disposal of the WMQI
The possibility of linking varying datasets to create a better picture of healthcare services
The usefulness of the datasets in creating clinically relevant metrics to monitor quality and clinical outcomes
It will also serve as an introduction to the datasets used by the WMQI , which include but not exclusively
Hospital episode statistics HES
Badger neonatal datasets
Office for national statistics mortality datasets
Unify 2

7. HES Outpatient
The next few slides will show triangulation of the services being rendered in the West Midlands
I have selected a cohort of patients (68) from the financial year (FY 0809)
The patients are between the ages of 14 and 17
Show their outpatient activity from the year of selection up to only a few months ago
Illustrating the ability we have to look at transitions in care

8. DNA rates of the cohort in the reference year

9. Cohort DNA rate 3yrs later

10. DNA rate of cohort 3 years from initial visit

11. Hospital cancellation rate of cohort 3 years from initial visit

12. HES Inpatient
This data set has good data quality in terms of diagnoses and procedures
Gives the opportunity to examine hospital quality outcomes
Is very robust with data going back 10 years
The next few slides look at emergency admissions for asthma and neonatal pneumothorax

15. Badger Dataset Your Clinical dataset
Opportunity to track most interventions on a neonate
Drawbacks are
in the ability to track patients once they are discharged from hospital
No access to complete regional or national dataset

20. Office for National Statistics: Mortality
This dataset is also robust
All Deaths
Gives opportunity to track mortality by cause post discharge
This is possible because of the linkage between HES and ONS dataset

21. Summary WMQI is able to access a wide variety of datasets
Use the datasets to create clinically relevant quality metrics
Ensures clinical engagement to facilitate the selection of best indicators to improve quality
Limited by access to certain datasets
Limitations of certain datasets reduce the capability to triangulate the quality of care effectively e.g. currently unable to assess long term complications from neonatal intervention as there is no way to link data after discharge

22. Securing the data Data downloaded by analyst from Badger
Database administrator uploads data into SQL database
Restricted by access accounts
Database administrator does a hard delete from server
Removes all trace from back ups etc
Only copy of download present on analyst work station which is passworded
Analyst deletes NHS Number from data
Date of birth not fully present in dataset (month and year only)
Anonymised patient details provided by badger used to create metrics

23. Today’s questions Are these indicators clinically useful?
Are there any issues with the data required to measure these indicators? If so what are they?
Data quality
How timely are they
How would you like these reported back?
How would you use these to improve quality
Are there other things that you would like to measure?