The appropriate ordering project uses data extracted from Electronic Medical Record to create dashboards to inform and engage clinicians in ordering practices. This presentation looks at the techniques used to create answers for the clinicians questions and discusses the purpose behind 12 dashboards. It looks at the change management approaches and challenges. The initial pilot project has been embraced by a number of local health districts in NSW and templates have been made available along with training tools.

1. Ronan Herlihy
Clinical Excellence Commission & eHealth
NSW
Engaging clinicians with
data on their ordering
practices

2. Agenda
1. Background for Clinical Analytics
2. Project 1 – Coffs Harbour Hospital
3. Project 2 – Port Macquarie Hospital
4. Findings
5. Questions

3. Background
 Clinical Excellence Commission and
eHealth NSW
 Identified the need for clinical analytics to
support quality, safety, efficiency and
research
 Papers from overseas on the role of Big
Data in changing the delivery of
healthcare.
 CEC Program initiative looking diagnostic
test management with very little data to
guide it.

4. Today, plenty of data, but not used
 48 million electronic
orders
 66 million times the
electronic medical record
is opened
 4 million alerts support
clinical decisions
 8 million clinical notes
 7 million patient
registrations

5. Positioning Clinical Analytics

6. Getting started with Clinical Analytics
 Pilot projects - use available data
 Diagnostic orders
 Rapid response
 Clinician eMR usability metrics
 Demonstrate that clinicians will engage
with information they are interested in.
 Demonstrate that they will use the data
to make change happen.
 Deliver tools that they will use.

7. Project 1 - Coffs Harbour
Hospital
 Worked with the Director of Medical
Services
 Extracted 6 months of diagnostic test
orders
 Several questions
 A number of dashboards emerged to
answer questions

8. Information from data extracts
8
PowerPivot used for self service dashboards

9. Data items per order
Patient details: Order details
Hospital Service dept Path / MI
MRN Order name
Patient name Order date
Encounter type Order status
Admission date Order cancelled date
Discharge date Order complete date
Length of stay Order turnaround hours
Clinical diagnosis Order to result total time
Attending medical officer
Who placed the order: Result process
Ordering clinician Result sign date
Entering clinician Result endorse time
Priority
Ward

10. Transforming the data
 Time of day and shifts
 Day of discharge orders
 Teams and Specialties
 Focus order flag
 Denominator – average orders per
patient

11. Dashboards developed

12. Headlines view
12

13. PRESENTATION NAME – MONTH YYYY
PRESENTER NAME13

14. Top x patients, by diagnosis
PRESENTATION NAME – MONTH YYYY
PRESENTER NAME14

15. Use of order sets

16. Who is using order sets

17. Who is placing orders

18. Why are orders cancelled

19. Duplicate orders checking

20. Orders of the day of discharge

21. Discharged without signoff

22. Incomplete orders

23. Project 2 - Port Macquarie Hospital
Emergency Department

24. Approach
 Data extracted
 Reviewed the data
 Picked specific tests to focus on
 Selected CRP
 Commenced reporting

25. How the change was implemented
 Provided presentations to the junior doctors about
evidence supporting the approach to test ordering
regarding specific tests.
 Made Emergency Department Consultants aware of the
approach and goals to targeted test.
 Encouraged questions, curiosity, and challenges from all
ED staff about the evidence and opinions regarding the
targeted tests and their indications.
 Maintained patient centred care approach.

26. Emergency Department poster presentation in prominent
position, reinforcing the evidence based approach

27. 27
Focus on CRP

28. Trend over time – Coags vs INR
CBG – new equipment in ED

29. Outcome from project 2
 Improvement sustained
 More focus orders are being targeted

30. Findings
 Too few resources on the ground to
support and extend this work.
 Alert fatigue with the duplicate orders alert.
 Better decision support required at the time
of ordering – cost, alternatives and better
alerting.
 System has not been optimised to meet
today’s process needs – problem based
ordering.
 The data gave us the information to help
bring about the change and provide
feedback.

31. Sharing the experience
 State-wide workshop (over 100
attendees) to discuss the issues of
appropriate ordering.
 A number of Local Health Districts are
using the dashboard logic in their clinical
analytics databases.
 Templates and training materials
developed.
 Interest is growing based on the

32. Beyond the horizon for Clinical Analytics
 This project helped promote the need for
clinical analytics
 Clinical Analytics Task Force established
– looking at clinicians priorities.
 NSW Health Analytics Strategy has
been developed and is about to publish
priorities for action.
 Analytics requirements are now
embedded in all programs – Clinical
systems, Quality, Safety and Outcomes

33. Acknowledgements
 Dr Peter Kennedy, DCEO Clinical Excellence
Commission
 Dr Paul Travnicek, Port Macquarie Hospital
 Dr Sergio Diez Alvarez, Coffs Harbour Hospital
 Gavin Watson, Account Manager, Microsoft
 The Electronic Medical Record team at eHealth
NSW
 CEC Diagnostic Test Management Program

34. Questions