S209 - Day 1 - 1200 - Electronic prescribing

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Information about S209 - Day 1 - 1200 - Electronic prescribing
Health & Medicine

Published on March 31, 2014

Author: NHSExpo

Source: slideshare.net

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Health and Care Innovation Expo 2014, Pop-up University

S209 - Day 1 - 1200 - Electronic prescribing

Paul Rice
Ann Slee

#Expo14NHS

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 Hospitals Why ePrescribing? Benefits Challenges

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 7 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 Hospitals Why ePrescribing? Benefits Challenges

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

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… 17

£0 £20 £40 £60 £80 £100 £120 £140 £160 £180 Drug Expenditure per FCE Source Clinical Benchmarking Company Ltd Wirral Hospital

Reduction in Drug Expenditure • 12.5% or £48,734 over first few months 19 0 10,000 20,000 30,000 40,000 50,000 60,000 2012 2013 Royal Cornwall Hospitals

0 5000 10000 15000 20000 25000 30000 35000 40000 £ Time (months) Drug A Drug B 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 ignored 6 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 23 • 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 eprescribingresearch@uhb.nhs.uk 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 25

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 26

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…. 27

Quality Initiatives…..

Non-charted medicines….

0.00 4.00 8.00 12.00 16.00 Percentage System A System B System C Reductions in Missed Doses - Antibiotics CarruthersT,CurtisC,MarriottJ,SleeA.Amultisiteanalysisofmisseddosesofantibiotics administeredinhospitalcare.EurJHospPharm2013;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 %ofMissedNon-Antibiotics Initial Gradient -0.01 Percentage Points per Week p=0.01 % Missed Doses Parsimonious Model Intervention Period of Data Removed Step Change -0.92 Percentage Points p<0.001 Step Change -0.33 Percentage Points p=0.045

NHS | Presentation to [XXXX Company] | [Type Date]32 Safer Hospitals Why ePrescribing? Benefits Challenges

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 33

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… HospitalScoresfordetectionoftestorderscausing anADRaccordingtoproduct

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

The Implementation Lifecycle www.eprescribingtoolkit.com

• 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

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 0 10 20 30 40 50 60 NoItems Hour Commencing Inpatient TTH 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

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