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

Methods We 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

GroupPRE-activation period Number prescriptions outwith BNFc POST- activation Number prescriptions outwith BNFc Absolute % ↓ P-value Practice 1 Control 27% (107/397)21% (57/275)19% Practice 2 Control 21% (18/85)19% (18/93)4% Control (all)25.9% 125/ % (75/368)6.5% Practice 3 Intervention 25% (41/165)10% (16/154)58% Practice 4 Intervention 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 ) (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