1. 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.

2. Peaky Blinders
David RP Terry

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

4. Hand written drug-charts!
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5. David RP Terry

6. 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 % of medication orders written for hospital inpatients
E-prescribing reduces error rates by 55% and serious medication errors by 88%

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

8. … 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

9. Safety Quality Resources
Prescribing Information and Communications System
Safety
Quality
Resources
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10. BDD
BCH Drug Database
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11. 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: /archdischild
David RP Terry

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

14. Age Drug Indication Route Dose David RP Terry 55 8-12 years 5657
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 years 65
Child 6-18 years 66
Child 8-11 years 67
Children 68
Children (all ages) 69
Children 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

15. 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

16. 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 /archdischild
David RP Terry

17. BCH – current position Pilot ward go live January 2017
Go live over a 6 month period?
Dose ceilings – drug, age, route
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18. 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
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19. SPACER Ethnographic Data Envelopment Analysis
Drugs Data Decisions (3D)
David RP Terry

20. 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

21. 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

22. 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

23. Director – Pharmacy Academic Practice Unit d.terry@aston.ac.uk
Dr David Terry
Director – Pharmacy Academic Practice Unit
David RP Terry