1. #CCIO

2. #CCIO
Using prescribing and medicines
management data to improve patient safety
Andrew Heed
Lead Clinical Informatics Pharmacist
The Newcastle upon Tyne Hospitals NHS Foundation Trust

3. “Healthcare at its very best - with a personal touch”
Using prescribing and medicines
management data to improve
patient safety
Andrew Heed
Lead Clinical Informatics Pharmacist.
The Newcastle upon Tyne Hospitals NHS
Foundation Trust

4. Introduction
• A spoonful of sugar. Medicines management
in NHS hospitals. 2001
– Optimising the use of medicines in hospitals is
central to the quality of patient care in hospitals.
But many hospitals face significant service
pressures that prevent them improving the quality
of care given to patients.

5. Computer technology
Errors are mainly caused because the prescriber does not have
immediate access to accurate information either about the
medicine or the patient. Handwritten prescriptions also
contribute to errors as they may be illegible, incomplete and
subject to transcription errors. Electronic prescribing reduces
medicine errors significantly.

6. The Prescribing data cycle
• Background data (patient, SCR)
• Data input (prescribing)
• Data use (administration and supply)
• Data supply (GP letter)
• Process and review (audit)

7. The data cycle
PAPER PRESCRIBING ePRESCRIBING
Little control on input Ability to standardise
Single user access to data Multiple user access to data
Difficult to retrieve Easy to retrieve
Requires manual extract Automated extract
Selective extract White noise
Audit cycle Audit cycle
Little control on input Ability to standardise

8. ePrescribing and errors?
• Error reduction?
–Yes?
–No?
–Maybe?

9. Error reduction - Yes?
• Bates DW, Leape LL, Cullen DJ, et al. Effect of computerized physician order entry and a
team intervention on prevention of serious medication errors. JAMA 1998 ; 280 : 1311 – 16 .
– 55% reduction in errors
• Bates DW, Teich JM, Lee J, et al. The impact of computerized physician order entry on
medication error prevention. J Am Med Inform Assoc 1999 ; 6 : 313 – 21 .
– 86% reduction in errors with clinical decision
support (CDS)

10. Error Reduction – No?
• Yong Y. Han et al. Unexpected Increased Mortality After Implementation of a Commercially Sold
Computerized Physician Order Entry System Pediatrics Vol. 116 No. 6 December 1, 2005
pp. 1506 -1512
– mortality rate significantly increased from 2.80% to 6.57%
after CPOE implementation.
• Mark A. Del Beccaro, et al. Computerized Provider Order Entry Implementation: No Association With
Increased Mortality Rates in an Intensive Care Unit. Pediatrics Vol. 118 No. 1 July 1, 2006 pp. 290 -295
– a nonsignificant reduction in the risk of mortality in the
post-implementation period (4.22% vs 3.46%)

11. Error Reduction – Maybe?
• Jani YH, Barber N, Wong IC. Paediatric dosing errors before and after electronic
prescribing. Qual Saf Health Care 2010 ; 19 : 337 – 40 .

12. What do I think?
• Standardisation.
– The single biggest benefit of ePx?
– Difficult to quantify
• Data.
– What do you want to do with it?
– Facilitate change: audit, education, design.
• Decision support and alerts.
– Some are effective, others are a nuisance

13. Do Alerts do this?
• Improve the training / test competence
• Control the environment, standardise it, greater
controls on riskier drugs, use technology to
provide decision support.
• Change organisational cultures, which do not
support the belief that prescribing is a complex,
technical, act, and that it is important to get it
right. . . and an open process of sharing and
reviewing prescribing decisions.
• Barber N, Rawlins M, Dean Franklin B. Qual Saf Health Care. 2003 Dec;12 Suppl 1 :i29-32. Reducing
prescribing error: competence, control, and culture.

14. Do alerts do this?
React. Get in the way
Inform. Annoy
Advise. Bamboozle
Facilitate action Self-destruct

15. NUTH experience
• Disable interaction checking
• Retain allergy checking
• Bespoke system alerts:
– Housekeeping, Antibiotic stewardship
• Regular Audit cycles
• Targeted working

16. Standard alert experience
• Take last Monday for instance:
– 357 people ordered 6794 prescriptions.
– There would have been 57,472 multum alerts
• 157 allergy
• 38,323 Drug interactions
• 17,005 duplications
• 1987 drug food interactions.

17. Bespoke Alert experience
• Take last Monday for instance:
– 2413 Discern Alerts.
• 446 helped stop premature paracetamol doses.
• 108 gave advice on prescribing Antibiotics
• 1124 were prompts to review drug chart.
• Other alerts
– MHRA YellowCard alerts
– Drug shortage alerts
– Formulary switches

18. Audit cycles
• Missed doses:
– Medicines not available, high risk medication.
• Chemotherapy prescribing:
– Interventions to eliminate this.
• Scheduled vs actual administration times:
– Intervention to promote adherence.

19. Hidden Alerts
• Flumazenil / Naloxone
• Lithium
• Clozapine
• Warfarin
• Tazocin

20. Using targeted alerts
• Use the underlying logic of alerts to notify
someone to do something:
• Define high risk patients, medication,
scenarios and auto generate (NPSA)
– Message to message centre
– E-mail
– Tasks in task list

24. Potential of using targeted alerts
• Unable to cover every ward.
• High risk patients can be anywhere.
– Identifies risks quicker.
– Enables targeting of resources.
• Improve communication:
– Promote education / discussion.
• Performance tracking.