s209 - day 1 - 1200 - electronic prescribing
DESCRIPTION
Health and Care Innovation Expo 2014, Pop-up University S209 - Day 1 - 1200 - Electronic prescribing Paul Rice Ann Slee #Expo14NHSTRANSCRIPT
Electronic prescribing – an NHS guide
to building the case and implementing
electronic prescribing for improved
patient safety
NHS | Presentation to [XXXX Company] | [Type Date]2
Safer HospitalsWhy ePrescribing?BenefitsChallenges
Background
• Equip Study
• 124,260 medication orders across 19 hospitals
• 8.9% error rate
• FY1 8.4%, FY2 10.3%
• Most errors intercepted before caused harm
• Incorrect dosage most common error
3 An in depth investigation into causes of prescribing errors by foundation trainees in relation to their medical education – EQUIP study. GMC, 2009
Source: Garbutt JM, Highstein G, Jeffe DB, Dunagan WC, Fraser VJ. Safe medication prescribing: training and experience of medical students and housestaff at a large teaching hospital. Acad Med. 2005;80:594-599.
• 23/25 show ↓risk reduction
• One study with increased rate due to disconnection and transcription errors
• Home-grown systems compared better to commercial systems
Ref: Ammenwerth et al. J Am Med Inform Assoc. 2008 15(5): 585–600.
Relative risk reduction of 13% to 99%
Review of the impact of CPOE on medication errors
ePrescribing Uptake• 2011
• 13% NHS trusts use for inpatient prescribing in adult medical and surgical wards
• 11% adult critical care
• 1% paediatric/neonatal critical care
• 3% renal
• 34% chemotherapy
• 48% discharge prescribing
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Ahmed Z, McLeod MC, Barber N, Jacklin A, Franklin BD (2013) The Use and Functionality of Electronic Prescribing Systems in English Acute NHS Trusts: A Cross-Sectional Survey. PLoS ONE 8(11): e80378. doi:10.1371/journal.pone.0080378
NHS | Presentation to [XXXX Company] | [Type Date]8
Safer HospitalsWhy ePrescribing?BenefitsChallenges
ePrescribing – The basics
Reduction in chart annotation125 days per year
Legibility & Completeness• Reduced risk of misinterpretation
• Reduced time for transcription & re-writing
• Medical and pharmacy
• Reduced risk of transcription error
• 50% error rate on some TTO audits
10
Efficiency gains
▪ Improved communication
▪ between different departments and care settings
▪ Reductions in paperwork-related problems
▪ e.g. fewer lost prescriptions
▪ Clearer and more complete audit trails
Medication Error
• Decreased risk of medication errors
▪ more legible and complete prescriptions
▪ guidance for inexperienced prescribers
▪ alerts for contra-indications, allergic reactions and drug-
drug interactions
▪ support for timely and accurate medicines administration
13
The average medicines reconciliation rate pre-implementation was 77%, the average medicines reconciliation rate post implementation is 90%.
Decision Support for Prescribing
• Passive
• Guidance
• Active
• Alerts
Basic Passive Support
• Description
• Set basic field parameters
• E.g. Numeric/text, required fields
• Effective as seen as guidance
• Effect on safety
• Reduce errors due to grossly erroneous information
Structured Orders
• Description
• Templates for orders
• Guide choices with allowable values, defaults
• Dose support for paediatrics
• Effect on safety
• More complete, actionable orders
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Newcastle Benefits• Structured orders
• Standard medicines nomenclature
• Standard doses
• Order sets promoting standardisation
• Pain control
• Antibiotic use
• Minimum mandatory data set for prescribing
• Complete prescriptions
• ….and more…
Wirral Hospital
Reduction in Drug Expenditure
• 12.5% or £48,734 over first few months
19
April
May
June Ju
ly
Augus
t
Septe
mbe
r
Octo
ber
Novem
ber
0
10,000
20,000
30,000
40,000
50,000
60,000
20122013
Royal Cornwall Hospitals
Se-ries1
0
5000
10000
15000
20000
25000
30000
35000
40000
Drug A
Drug B
Time (months)
£
Formulary Changes
Active Decision Support Rules
• Description
• Real time prompts at the time of order entry based on explicit rules
• Effect on safety
• Reduced errors of omission or commission
Password-level warnings ignored6 month period
1113
22039
3453
51805
1854
23773
12323
54935
0%
20%
40%
60%
80%
100%
Contraindication Dose Interaction Dose/Freq
Presc Admin
Carried on
Backed off
lvlcat 2
Count of msgid
qtype catname
state
Lower (red) histograms show the number of times the user ‘backed off’ when presented with a password level warning
Detecting Errors
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• Impact Tool
• List of indicators high severity/high frequency
• Amenable to decision support
• Allergy
• Contra-indications
• Dosing
• Drug-drug interactions
• Frequency
• Route
Detecting Errors• Impact tool
• 4000 orders before and after implementation
• Baseline
• Support configuration
• Post implementation
• Support system optimisation
• Identify benefits
Developing consensus on hospital prescribing indicators of potential harms amenable to decision support. Thomas et al. Br J Clin Pharmacol 201324
Cost of Medication Errors• Annual treatment cost of ADE
• 400 bedded hospital
• £599k direct cost; £17.754 with lost health benefit
• Annual cost of ADE in ePrescribing hospital
• £415k direct cost : saving of £184k
• £10.880 with lost health benefit : saving of £6.874
• Audit Commission 2001 – 5% error rate; +8.5 days
• £2.7m saving
Karnon et al. Modelling the expected net benefits of interventions to reduce the burden of medication errors. Journal of Health Services Research & Policy;13(2):2008:85-91
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Prescribing Decision Support• Not all on day one……
• Lots to learn about specificity and tolerances
• Guidance during prescribing more effective
• Hard stops should be used minimally
• Alerts should be last resort
Start slow and grow
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Administration support• Often overlooked yet just as important
• Taxis et al – 50% errors on administration of IVs
• Missed doses upto 20% in some areas
• Improved information on medicines due
• Increased clarity of orders
• More visibility of omitted or missed doses
• Opportunity to provide IV preparation support – links to Medusa
• Barcode opportunities….
Quality Initiatives…..
Non-charted medicines….
0.00
4.00
8.00
12.00
16.00
Apr-10
Jun-10
Aug-10
Oct-10
Dec-10
Feb-11
Percen
tage System A
System B
System C
Reductions in Missed Doses - Antibiotics
Carruthers T, C
urtis C, M
arriott J, Slee A
. A m
ultisite analysis of missed doses of antibiotics
administered in hospital care. E
ur J Hosp P
harm 2013;20:207-211
Non-antibiotics - % Missed Doses
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
15%
16%
17%
18%
19%
20%
21%
Jan 08 Mar 08 May 08 Jul 08 Sep 08 Nov 08 Jan 09 Mar 09 May 09 Jul 09 Sep 09 Nov 09 Jan 10 Mar 10 May 10 Jul 10 Sep 10 Nov 10
% o
f Mis
sed
No
n-A
ntib
iotic
s
Initial Gradient -0.01 Percentage Points per Weekp=0.01
% Missed Doses
Parsimonious Model
Intervention
Period of Data Removed
Step Change -0.92 Percentage Pointsp<0.001
Step Change-0.33 Percentage Pointsp=0.045
NHS | Presentation to [XXXX Company] | [Type Date]32
Safer HospitalsWhy ePrescribing?BenefitsChallenges
Where now with ePrescribing?• One of, if not, the most complex area to implement
• Culturally challenging
• Realising benefits requires change
• Involves three key professional groups
• All want it for something different
• Unrealistic expectations
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Expectations management….• No system will meet all needs
• Technically challenging
• Different types of prescription
• Inpatient, discharge, outpatient, A/E, theatres etc
• High levels of complexity
• Infusions, insulin, warfarin, etc
• Speciality specific needs
• Paediatrics, anaesthetics, critical care, chemotherapy etc
34
Possible harms
• New sorts of problems
• Key stroke errors
• Picklist errors
• Sociotechnical issues
• Human-computer interactions
• Alert fatigue
• ‘Workarounds’
Unanticipated or unintended consequences of IT
“To Err is System”. Aarts and Gorman. Int J Med Informatics 2007;76: S1
Metzger et al. Mixed Results In The Safety Performance of Computerized Physician Order Entry. Health Affairs 2010 29(4): 655-663
Not all implementations are equal…H
ospi
tal S
core
s fo
r de
tect
ion
of te
st o
rder
s ca
usin
g an
AD
R a
ccor
ding
to p
rodu
ct
Successful implementation
• Requires
• Support for change from leaders and staff
• Development of a gradual and flexible implementation approach
• Adequate resources
• Equipment, staff, infrastructure
• Acceptance that setbacks will occur and will need managing
38
Spetz et al. What determines successful implementation of inpatient IT systems?Am J Manag Care. 2012;18(3):157-162
• What are the drivers?
• Clinical or Financial
• Local requirements?
• Helps drive needs assessment
• Create a vision of a HEPMA-based hospital environment
• Allows a focus
• Establish short and longer term goals
Conceptualisation
“Spot your ‘little
diamonds’ who have
computer skills and are
willing to try
new things”
NHS Connecting for Health: Electronic
Prescribing in Hospitals Challenges and
Lessons Learned
Preparing for the journey…• Leadership
• Executive buy-in
• Clinical champion(s)
• Change management
• Resources
• Infrastructure
• People
• Training
• Rollout plan
• Project team continue evaluation and improvement over early terms of project
• Pause for System Stabilisation before Optimisation
• A driver for medicines optimisation
• Ensuring best use through expert users and ongoing training
• Increased and ‘tweaked’ clinical decision support
• Studying and remedying unintended consequences
System Optimisation
Average Number of Items Prescribed by Hour, 1996
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 80
10
20
30
40
50
60
Inpatient
TTH
Hour Commencing
No
Ite
ms
Slee AL, Farrar KT. Pharm J 1998;260:923-925
When do things happen in 2011?
ePrescribing Toolkit
www.eprescribingtoolkit.comwww.eprescribingtoolkit.com
Summary
• ePrescribing does deliver benefits
• It is not a panacea
• No single system can deliver everything
• Implementation is challenging but NOT impossible
• We know what should be done
• It is a journey….
48
Acknowledgement: Dr J Coleman, NIHRePrescribing research team,Iain Richardson, Neil Watson and others for sharing their work