1. Safe IT systems? Safe Patients? Professor Bryony Dean Franklin October 2012CMSSQ Centre for Medication Safety & Service Quality

2. Why are you still studying medication errors? There wont be any soon, once we have electronic prescribing…

3. Automation and IT in pharmacy...

4. Examples Electronic prescribing (+/- electronic medication administration records in hospital and care home) – with various levels of decision support Automated dispensing –Pharmacy based (robots) –Ward based (vending machines) –Aseptic compounding robots –Automated CD storage Barcode verification of medication and/or patients Smart IV pumps

5. A quiz Inpatient electronic prescribing with prescriber order entry – is it more prevalent in: A. USA ? B. UK ?

6. UK hospital electronic prescribing 101 (61%) of 165 hospital trusts responded in survey of English hospitals –70 (70%) had at least one EP system in place –56% of sites with EP had more than one system in place. Four sites had more than 4 systems. –63 different systems Nearly half of respondents had EP systems supporting in-patient prescribing (47.5%, n=48). Discharge prescribing in 65.3% (n=66) of sites. Outpatients was the least catered for (5.9%, n=6). Ahmed, Franklin and Barber, 2012

7. UK hospital electronic prescribing 101 (61%) of 165 hospital trusts responded in survey of English hospitals –70 (70%) had at least one EP system in place –56% of sites with EP had more than one system in place. Four sites had more than 4 systems. –63 different systems Nearly half of respondents had EP systems supporting in-patient prescribing ( 47.5%, n=48). Discharge prescribing in 65.3% (n=66) of sites. Outpatients was the least catered for (5.9%, n=6). Ahmed, Franklin and Barber, 2012

8. US hospital CPOE ASHP national survey of pharmacy practice in hospital settings 2011 Stratified random sample of 1401 hospitals 40.1% response rate (n=562) 34% of hospitals had computerised prescriber order entry 67% using electronic medication administration records

9. US hospital CPOE ASHP national survey of pharmacy practice in hospital settings 2011 Stratified random sample of 1401 hospitals 40.1% response rate (n=562) 34% of hospitals had computerised prescriber order entry 67% using electronic medication administration records

10. A quiz Inpatient electronic prescribing with prescriber order entry – is it more prevalent in: A. USA ? B. UK ?

11. A quiz Inpatient electronic prescribing with prescriber order entry – is it more prevalent in: A. USA ? B. UK?

12. Automation of dispensing in hospitals Automated dispensing systems –Pharmacy based (robots) –Aseptic compounding robots –Ward based (vending machines) 6 (7%) of 91 UK respondents (cf 89% in USA) –Automated CD storage 2 (2%) of 91 UK respondents McLeod, Barber and Franklin, 2012

13. Aseptic compounding robot Verifies bags using barcode Verifies vials using photo recognition

14. Ward-based automated storage Verifies product on loading, using barcode

15. Automated CD storage

16. Are our IT systems safe?

17. Are our patients safe?

18. Whats the evidence?

19. International literature Studies of CPOE generally show benefits (17- 81% reduction in errors) –But increasing realisation that new types of error

20. Smart pumps Used in 68% US hospitals Drug libraries to permit checking of doses and infusion rates Require standardisation of concentrations etc Bypassing of the safety software is common Nuckols et al: Only 4% of preventable IV ADEs would be preventable with smart pumps

21. UK evaluations Electronic prescribing in hospitals –Most (but not all) evaluations show a modest reduction in prescribing error Closed loop ward based automated dispensing system with barcode verification –More dramatic reduction in administration errors Dispensing robots –Reduction in wrong content errors Smart pumps? Ward-based automated dispensing?

22. Why?

23. What is technology good at? Repetitive tasks, same every time Follows the rules Forcing functions –Cant proceed until youve completed all the fields More legible than handwriting Reminders Supporting formularies, protocols, standardisation of treatment Audit trail

24. But… Can be inflexible New error types –Selection errors from menus –Menus often present very long lists of options which prescribers not familiar with –Assumptions - the computer must be right New work processes may be required, which can themselves increase or decrease errors –Development of workarounds Alert overload

25. Unintended consequences

26. Selection errors Selection of penicillamine, instead of penicillin Menu arranged alphabetically in hospital system –Paracetamol soluble tablets –Paracetamol suspension –Paracetamol tablets Many patients prescribed paracetamol soluble tablets –At risk of hypernatraemia

27. Selection errors Selection of penicillamine, instead of penicillin Menu arranged alphabetically in hospital system –Paracetamol suspension –Paracetamol tablets –Paracetamol tablets soluble

28. Assumptions Human-computer interaction causes most deaths of all IT induced fatalities –Eg a UK hospital: ~1000 cancer patients under- dosed with radiotherapy over 9 years. Decision support software incorporated in machine, staff did not know and applied a second, manual dose reduction calculation –McKenzie Knowing machines 1996 –Assumption that EP system would include allergy checking, when it didnt...

29. Workarounds

30. Increased patient identification from 17% of doses with manual system, to 81% with barcode system Why only 81%? Staff sometimes found the wristband hard to scan, and so stuck the barcode to the patients table…

32. Alert overload If you have too many warnings from the computer then that makes you tend to override them, you become a bit more cavalier and that's a danger. (Practice Study, PR6-GP3)

33. How do we maximise error reduction and minimise new errors?

34. 1. Health warning Do not assume that benefits in other health systems / other countries will extrapolate to your own context

35. 2. Systems arent plug and play

38. 3. Local evaluation essential

39. When do we measure the effectiveness of the system?

40. With thanks to Nick Barber

41. Conclusions Huge potential patient safety benefits Success in achieving these is dependent on many other contextual and organisational factors Local evaluation is essential –Need some form of ongoing monitoring and refining of the system. And listening to users Need a good relationship with suppliers Embedding systems into everyday practice is a long-term project