Paediatric Electronic Prescribing Dr David Terry Director – Pharmacy Academic Practice Unit Electronic Prescribing in Hospitals Moving Forward Thursday 6th October 2016 The Studio Conference Centre, Birmingham Prescribing for children in hospitals is complex and complicated. Dosages change with age and often involves the use of off-label or unlicensed drugs. Some children cannot swallow oral solid-dosage forms. Birmingham Children's Hospital is pursuing a paediatric EPMA system in collaboration with colleagues at UHB and expects to implement the PAED-PICs system in the next 6 months. Defining decision support tools for prescribers and others is difficult. Drug regimens may be usefully described in DIRADs - drug, indication, route, age and dose. However, there is a lack of standardisation of terms e.g. over 400 age ranges are described in a well know paediatric formulary. Ensuring that computer algorithms can identify dosage regimen errors is difficult yet is fundamental to risk reduction through electronic prescribing. What is the right strategy for maximum benefit? Different strategies will be described in the presentation. Identifying benefit may also be a requirement of successful business cases which may run into millions of pounds. How can benefits be identified? One quantitative technique includes Data Envelopment Analysis. DEA is used in numerous industries – including energy, manufacturing, airlines etc – to define efficiency and identify changes that can lead to further efficiency. By defining discrete areas of practice within the three major aspects of the medication process – prescribing, supply and administration of drugs –DEA techniques can be used to observe changes as the organisation implements EPMA. Can benefits attributable to EPMA be identified within the ‘noise’ of routine variability? The presentation will describe the experience of Birmingham Children’s Hospital on their journey to develop a paediatric EPMA system, refine decision support strategies and define flags where expected limits are exceeded. Future identification of benefits using the SPACER research programme will also be described.

Peaky Blinders David RP Terry

over 270,600 patient visits every year 361 beds 43,151 inpatient admissions each year David RP Terry

Hand written drug-charts! David RP Terry

David RP Terry

Dispensing errors are identified in 0.02% of dispensed items Drug Errors Dispensing errors are identified in 0.02% of dispensed items Medication administration errors occur in 3-8% of non-intravenous doses and about 50% of all intravenous doses Prescribing errors occur in 1.5-9.2% of medication orders written for hospital inpatients E-prescribing reduces error rates by 55% and serious medication errors by 88%

Swallowing / formulations Fewer decision support tools Changing doses Unlicensed products Variable strengths Swallowing / formulations Fewer decision support tools David RP Terry

… built at UHB, but now to include children. Developing eRx’ing EPMA – 5yrs PICs … built at UHB, but now to include children. David RP Terry

Safety Quality Resources Prescribing Information and Communications System Safety Quality Resources David RP Terry

BDD BCH Drug Database David RP Terry

BDD Drug Indication Route Age Dose Terry D, Junaid E, Reynolds F, Sinclair A, Bugg N, Terry A, Terry J, Hussain A, Caffrey J, Burridge A. Electronic prescribing: the development of a paediatric drug database. Arch Dis Child 2015;100:e1 doi:10.1136/archdischild-2015-308634.2 David RP Terry

Age Age ranges! Drug Indication Route Dose Over 400 in a single text No consistency in terms Developed our own rules David RP Terry

Drug Indication Route Age Dose David RP Terry

Age Drug Indication Route Dose David RP Terry 55 8-12 years 56 57 Adult 58 All ages 59 All ages (under 15 years) 60 All ages except neonates 61 All ages except premature babies 62 All children > 1 month 63 Birth-2 years 64 Child 11-18 years 65 Child 6-18 years 66 Child 8-11 years 67 Children 68 Children (all ages) 69 Children 13-16 years 70 Children over 12 years 71 Children over 2 years 72 Children over 4 weeks 73 Children over 5 years 75 Infants & Children Drug Indication Route Age Dose David RP Terry

Age ranges! age ranges should be mutually exclusive and coterminous; age ranges should have precise upper and lower limits; time units should be used in the expression of all age ranges; acceptable time units are days, weeks, months and years; the birth date is ‘day 0’ whatever time of day the child is born; a ‘transition day’ is the day on which the child attains the next significant age and is included in the upper limit of an age range; when a child reaches 18 years old the adult drug dictionary will apply. David RP Terry

Age ranges! a neonate is a child of 0 days to 28 days; the first month of life is always 28 days, 2 months is 56 days, 3 months is 13 weeks (91 days); other than for the first 3 months of life in the period up to one year ‘a month’=30 days; other than for children in the first 3 months of life age ranges in the period up to 5 years are expressed in months; for ages over 5 years the limits are expressed in years. RATIONALISATION OF PAEDIATRIC DRUG DOSING AGE RANGES: REDUCING CONFUSION Alice Burridge, John Caffrey, Fiona Reynolds, David Terry, Akhmed Hussain,Emma Pring, Basheer Tharayil. 10.1136/archdischild-2015-308634.2 David RP Terry

BCH – current position Pilot ward go live January 2017 Go live over a 6 month period? Dose ceilings – drug, age, route David RP Terry

SPACER Before and after study 3 years 3 strands Aims: To identify the benefits and disbenefits of EPMA compared to paper-based system Before and after study 3 years 3 strands David RP Terry

SPACER Ethnographic Data Envelopment Analysis Drugs Data Decisions (3D) David RP Terry

Data Envelopment Analysis Drugs Data Decisions (3D) Ethnographic Data Envelopment Analysis Drugs Data Decisions (3D) Safety Quality Resources Culture Technology Processes Organisation structure David RP Terry

Strand A Ethnographic strand Strand B Efficiency – “DEA model” strand Safety Quality Resources Strand A Ethnographic strand Mixed method – Qualitative and Quantitative study Observe the organisational change, explore staff perspectives of doctors, nurses and pharmacists as e- prescribing is implemented Strand B Efficiency – “DEA model” strand DEA – Data Envelopment Analysis What is the impact of e- prescribing on the efficiency of the services? Strand C – 3D study – Drugs, Data, Decisions What Key Performance Measures does the hospital measure before implementation of e-prescribing, how much resources are used to generate it? What will be measured during and after implementation? Culture Technology Processes Structure Pre-implementation (year 1) Peri-implementation (year 2) Post-implementation (year 3) David RP Terry

Identify inputs & outputs Determine ‘efficiency’ Strand B Efficiency – “DEA model” strand DEA – Data Envelopment Analysis What is the impact of e- prescribing on the efficiency of the services? Benchmarking tool? Define areas – DMU Identify inputs & outputs Determine ‘efficiency’ Identify changes over time David RP Terry

Director – Pharmacy Academic Practice Unit d.terry@aston.ac.uk Dr David Terry Director – Pharmacy Academic Practice Unit d.terry@aston.ac.uk David.terry@bch.nhs.uk 0121-204-3941 David RP Terry