Use of Data to Improve Prescribing Dr Paul M. Upton Director of Transformation, Acting CCIO Consultant Anaesthetist

What I will talk about Key make up of the e prescribing team Data that is available Specific pieces of data Example of driving change

Project Team Core Team EPMA Pharmacist EPMA pharmacy technician 2 x EPMA nurse facilitators IT project manager Information Analyst Ad-hoc Additional nurse and pharmacy to assist during specific deployments

Data Outcomes Improved audit trail, use of drugs across the organisation Protocols for prescribing – order sets e.g. anaesthetics Improved surveillance e.g. missed doses data Individual prescriber information Driver for change across the organisation – 3,800 EPR users

Missed Doses

Specific Data Use Improved antibiotic stewardship, measure to improve Reduction in allergy incidents Decrease in pharmacy errors Research opportunity with funding awarded and further research grants submitted, collaboration with Exeter University – use of “big data” sets

The “Clinics” Assumptions: You are conscientious, committed and diligent Acknowledgements: You may feel you should not be here You may not have confidence in the audit You may feel this process is undermining Assurances: This meeting is important for organisational practice change It is not intended to be punitive We want it to be mutually useful

Allergy Medicine Surgery

Safer Dispensing Early data shows error rates have more than halved %age Error Rate/ Month Monthly Dispensing and Error Rate Pre Electronic Non-Stock Implementation (13-14) (one error for every 8,333 items dispensed) Monthly Dispensing and Error Rate Post Electronic Non-Stock Implementation (13-14)0.005 (one error for every 20,000 items dispensed)