implementation of a computerised decision-support system to reduce the level of off-label paediatric...
TRANSCRIPT
Implementation of a computerised decision-support system to reduce
the level of off-label paediatric prescribing in primary care
Colin Simpson, James Mclay,Christine Bond, Robert Milne, Peter Helms
Centre for Academic Primary Care and Department of Child Health
Background
• Aim: to develop a computerised decision support system for GP use to reduce levels of prescribing outwith BNF or Summary of Product Characteristics (SPC) recommendations
• Objectives: • Influence the rate of prescribing outwith the BNF• Acceptance rate of recommendations • Expectations and experiences of GPs and their
staff
Aims and Objectives
MethodsWe designed a computerised decision support system to:
– Alert prescriber to paediatric prescriptions out with BNFc
– Be easily installable over the web• Activate (in the intervention practices) at a pre-arranged
time (not known to the practices)• For comparison practices to install but never switch-on
– Highlight actual BNFc recommendations for 3 months
– Offer the chance to amend the prescription
Methods (cont.)• Participants
– 4 purposively selected primary care practices– Stratified by previous ‘off label’ prescribing of
antibiotics and number of partners– Randomised to two groups (dummy and intervention)
• Automatic data extraction – Levels of non-BNFc prescribing 3 months pre- and
post- software switch-on date. – Software log of acceptance or rejection of software
recommendations
• Interview data– Acceptability to clinical and non-clinical staff.
Decision support software trigger screen
Group PRE-activation period Number prescriptionsoutwith BNFc
POST- activation Number prescriptions outwith BNFc
Absolute %↓P-value
Practice 1Control
27% (107/397) 21% (57/275) 19%0.060
Practice 2Control
21% (18/85) 19% (18/93) 4%0.763
Control (all) 25.9% 125/482 19.4% (75/368) 6.5% 0.061
Practice 3Intervention
25% (41/165) 10% (16/154) 58%0.001
Practice 4Intervention
26% (63/242) 12% (17/142) 54%<0.001
Intervention (all)
25.6% (104/407) 11.1% (33/296) 14.5% <0.001
Main findings
• 25.7% of all prescriptions were out with BNFc and triggered the software.
• Prescribers amended 56.6% of these prescriptions in line with software recommendations
• Significant rate reduction of about 40%– 0.57 (95% CI 0.37-0.88) (compared with 1.00 in non-
software practices)
Summary
Findings from interviews• 14 Interviews conducted.
– 5 GPs, 2 Practice Mxs, 7 Receptionists
• Six themes– Understanding and concerns regarding off-label
prescribing– Current sources of drug prescribing information– Time pressures– Expectations– Acceptability of software: negative and positive
experiences
Understanding and concerns regarding off-label prescribing & time pressures
• ‘..that’s the sort of thing that maybe people do not realise….I’m interested to know how much of that (off label) prescribing I’m doing. I feel like I’m not, but maybe I am.’ (I5)
• ‘It can be very time consuming (to consult the book (BNF)). Yes . It does not take a lot of time..about two minutes, but two minutes out of ten, it’s a long time’ (I2)
Expectations and Acceptability• ‘and actually, I’m confident this will be
helpful. I hope it will be …I will be happy to use it (smiling)…I like the concept, we’ve not had anything like that’ (I5)
• ‘Yes it is easy to use. It just pops up and tells you..that this dose is not right and it tells you what you should be giving. It’s very straightforward.’ (I4)
• ‘I did not actually find it helpful. I think it could be helpful if it is set up right’ (I2)
Conclusions• This approach to improving paediatric prescribing is
highly effective• The support software:
– Is easy to install – Is easy to use– And is acceptable
• But only a small pilot and only one GP software system: GPASS
• Did not really work for receptionist generated repeats• Future work: Need for a bigger study – other systems.
Software more sophisticated
Acknowledgements
• Funding from Chief Scientist Office, Scottish Government
• Campbell Software
• Statistical advice Professor Amanda Lee
• Research Assistant Nara Tagiyeva-Milne
• Practices and practice staff who took part