Elizabeth Plant, Taranaki District Health Board: Integrated Medication Management Implementation And Project At TDHB (Taranaki District Health Board)
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Elizabeth Plant, Taranaki District Health Board: Integrated Medication Management Implementation And Project At TDHB (Taranaki District Health Board)

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Elizabeth Plant, Director of Medication Management, Taranaki District Health Board, NZ delivered this presentation at the 2013 Electronic Medication Management conference. It is Australia’s only......

Elizabeth Plant, Director of Medication Management, Taranaki District Health Board, NZ delivered this presentation at the 2013 Electronic Medication Management conference. It is Australia’s only conference to look solely at electronic prescribing and electronic medication management systems. For more information on the annual event, please visit the conference website: http://www.healthcareconferences.com.au/emedmanagement

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  • 1. Integrated Medication Management Implementation Project at Taranaki District Health Board Elizabeth Plant Director of Medication Management HIQ 2nd Annual Electronic Medication Management Conference Melbourne 2013
  • 2. Admission to Acute Care • Adverse drug events occur in 0.7% of all patients admitted. (Harvard Medical Practice Study, N Eng J Med.1991) • Using the IHI ADE Trigger Tool 28.9/100 Admissions were identified as having an ADE (12mths / 3 hospital). (Seddon,Jackson et al, NZMedJ. 25 Jan 2013, Vol 126 No1368:ISSN 1175 8716) Extrapolated to New Zealand • 50,000 patients per year admitted to acute care hospitals will experience an adverse drug event each year. • 120 people will die from an adverse drug event. • Preventable annual hospital drug cost = $411 million.
  • 3. Where It Can Go Wrong Medication Errors – Percentage, Type and Intervention Process Prescribing % of Error Type of error 49% Incomplete list Wrong medication Wrong dose Wrong route Wrong time Intervention - Transcribing Dispensing 11% 14% - Wrong medication - Wrong dose - Look alike Sound alike Wrong medication Wrong dose - System Interoperability - All Med Systems - Common Allergy Information - Robotics automation - Bar coding - Automated dispensing - All Med Systems - Common Allergy Information Illegible writing Wrong patient Contraindications Wrong medication Wrong dose - Medicine Reconciliation - e-Prescribing - Decision Support Bates D, Cullen D, Laird N, Peterson L, Small S, Servi D, et al. Incidence of Adverse Drug Events and Potential Adverse Drug Events. JAMA 1995; 274(1):29-34. Administering 26% - Wrong patient - Wrong medication - Wrong dose - Wrong route - Wrong time - Missed doses - e-Administration - Bar code at point of care (BPOC)
  • 4. North Western slopes of Mt Taranaki with the Central Plateau Peaks in the distance
  • 5. Map of Taranaki
  • 6. National Health IT Board Vision To achieve high quality healthcare and improve patient safety by 2014, New Zealanders will have a core set of personal health information available electronically to them and their treatment providers regardless of the setting as they access health services.
  • 7. Medication Safety Programme • Partnership between National Health Board, National Health IT Board and Health Quality & Safety Commission • Aligns to NZ Medicines Strategy and National IT Plan – Reduce harm through medication error – Improve quality of care and patient outcomes – Improve productivity and reduce waste – Contribute to enabling integrated healthcare
  • 8. Taranaki Base Hospital and Mt Egmont / Taranaki
  • 9. Taranaki District Health Board (TDHB) • Small provincial hospital • 245 beds • General medical / surgical and full range of services • Pharmacy Department operating Monday to Friday 8am – 5pm; Saturday and Sunday 10am – 12noon • One small remote hospital (Hawera) - one hour away, 20 beds, as well as an ED • TDHB serves a population of 104,000 people – or 2.8% of New Zealand’s population (4,430,689)
  • 10. e-Pharmacy Strategy - Vision TDHB e-Pharmacy Vision “…Clinicians and other stakeholders will be able to prescribe, dispense, and review medications reliably via online electronic tools accessed through the TDHB Clinical Portal or their local system of choice (such as their GP practice management system).” “Patients will have appropriate online access to their medication history.” “Bedside verification will be used within the hospital setting.”
  • 11. TDHB New Hospital Build • TDHB is embarking on a hospital redesign and build programme over the next three years • New models of care to reduce length of inpatient stays are envisaged to coincide with the new facilities • e-Medication Management Project seen as key to drive change and is aligned with the IT build principles for the new facility
  • 12. ADMISSION TO HOSPITAL Key Messages DISCHARGE FROM HOSPITAL Allergy Warning + ADR Medicine and allergy information from: Key Messages Medicine and allergy information to: e-MEDICATION RECONCILIATION e-Prescribing • Patient (+ family, caregivers) Dx summary (inc DMCS) Patient e-Administration • GP/specialist Dx scripts e-Dispensing • Community pharmacy • Rest homes • Other hospitals • Ambulance GP/specialist DMCS ▪ Input by pharmacists Pyxis (Automated Drug Distribution System) Yellow cards ▪ 3+ sources used ▪ Discrepancies listed as unintentional / intentional Patient info leaflets Discrepancies must be resolved by a doctor within 24 hours of arriving in ED Patients own medicines into “green bag” DMCS = discharge medicines changes summary Community pharmacy Rest homes Other hospitals
  • 13. Integration Schematic
  • 14. Proposed End to End Medicines Management System e-Medication Reconciliation System MHF MRF (Medication History Form) (Medication Reconciliation Form) Current eRx (e-Prescribing) e-Administration e-Pharmacy (Dispensing System) Pyxis (Automated Drug Distribution System) Yellow cards EDS Rx (Electronic Discharge Summary with Discharge Medication Changes Summary [DMCS]) DS – DMCS PILS eRx
  • 15. Challenges • There are existing e-Prescribing systems but none are integrated with electronic Medication Reconciliation (eMR), dispensing and automated drug distribution in Australasia • Led to requirement for three different software vendors to partner in project • Software vendors not able to keep to deadline commitments • Integration complexity slows the project timelines: – required TDHB to change the order of implementation • stand alone components • subsequent joining up • Evaluation framework not defined in time for baseline data to be collected pre-go live
  • 16. Challenges cont’d • The different professional domains involved adds to the complexity of needing to find a solution that meets the requirements of all groups eg: – requirements for generic prescribing for doctors, compared to dispensing of specific brands for pharmacists or administering specific doses for nurses – requirements for prescribing without strength (only dose) for doctors compared to administering specific strength / dose for nurses • Bringing all the professional domains together: – technically – professionally • Integration into the Clinical Portal environment • Challenge of future need for linking into GP / community pharmacy e-Prescribing and centralised data repositories, to enable true end-to-end medication management for Taranaki
  • 17. Benefits of an Integrated System • No transcribing between systems • Reduction in error • Improved efficiency • Reduced time for clinicians on data entry • New Model of Care - will change the way clinicians deliver care to patient • Medication information and decision support available at patient bedside
  • 18. Problems with trying to Integrate • Need to ensure productivity gains in one area don’t translate to inefficiencies in another • Universal Medicines List (NZUML) - drug categorisation essential for true integration between e-Meds systems (master list of all medicines in New Zealand - brings together drug information, regulatory status, safety information, funding information, medicines terminology - Snowmed CT based, into a unique identifying number) • Decision support needs to be standardised across various systems - ideally should all operate off same decision support structure: • eg allergy management, dose ranges, smart pump library-infusion pumps in ePA
  • 19. Project Phases to Date • Pyxis implemented at TDHB in November 1999 • November 2010 – e-Pharmacy (CSC) Dispensing System implemented with improved integration to Pyxis • June 2011 – electronic medication reconciliation system (Orion SMT) implemented with associated clinical change management • April 2012 – allergy project went live in MedChart (CSC e-Prescribing system) hospital wide for all patients; allergy status and process of verification to be built around this
  • 20. Project Phases to Date cont’d • 6 June 2012 – MedChart (CSC) electronic prescribing / administration to go-live – beginning of staged rollout • February 2013 – integrated solution between eMR and ePA to be incorporated as soon as available from software vendors • Pre July 2013 – rollout for e-PA scoped and plan to be fully electronic in three wards • End of 2013 – full hospital rollout of integrated eMeds
  • 21. Pyxis MedStation - ED
  • 22. Medication Reconciliation (MR)
  • 23. Electronic Prescribing (ePA)
  • 24. Benefits at Discharge with eMR Discharge Medication Changes Summary (DMCS) What medication patient came in on What was changed and why? What was stopped and why? What was started and why? - What the patient has been discharged on • Colour coded • Part of discharge summary
  • 25. Upgrade GP Messaging – PDF
  • 26. Clinical Change Process Key Principles for Success • “Clinical Change Champion” on the floor • Branding for the project • Training needs to be targeted and preferably one-on-one • Development of training scenarios • Initial training, followed by audit of six interventions, then specific one-on-one training to fix deviations • Using data to improve process and feedback to staff – make sure it is relevant and timely (also use for functional enhancement requirements) • Identify the barriers to using the system and fix as soon as possible
  • 27. Key Principles for Training • Super user - to cascade training • Training scenarios with audit and feedback, and follow-up training • Flipchart • Interactive video training tool • Team based on ward pre and post Go-Live
  • 28. Use Measurement Tools and Make Relevant Goals • Use a dashboard to report results regularly – Must be visible in the ward • Make the targets relevant to the clinician group – ie use targets that they can influence • Have positive incentives – “Go for Gold” Coffee Cups (something they like!) – regular newsletter with names of excellent achievers – presentations and morning teas to thank the team – praise and encouragement goes a long way
  • 29. Photos of Dashboard (eMR)
  • 30. “Going for Gold” Coffee Cup
  • 31. National Pilot Programme – TDHB GOING FOR GOLD (eMR) ☺ eMR Doctor Targets ☺ Target Measure Aim TDHB Target (interim) Pre-Go Live February 2012 Differences Target 1 Resolved 100% > 75% 54% 85% ☺ Target 2 Intentional 0% < 5% 8% 2% ☺ MR Completion Target 3 Completion rate (initiated) 100% 90% 79% 90% ☺ Target 4 Completed in 24 hrs (admitted) 40% 25% 22% 18% x
  • 32. Clinical Change Management Lessons • International literature indicates the following factors are important for the success of e-Prescribing systems: – This is a complex redesign of clinical processes, which will change virtually all processes around medication management and thus will be challenging for clinicians. 1, 2, 3 – Workflow analysis is essential. – Barriers to the use of new technology need to be identified and addressed before and after implementation, to ensure appropriate use of electronic medication management systems. 1, 18
  • 33. Clinical Change Management Lessons cont’d – e-Prescribing systems can lead to increased mortality if implemented poorly ie too rapidly, low efficiency and with a lack of consideration to changes in workflow processes. 4 • Two paediatric hospitals implemented same system - one well, one poorly (in six days), with different mortality outcomes.
  • 34. Sittig et al 5 Significant Body of Experts in the USA Principles: • If the system is not available then you won’t get the benefit • If users choose not to use it then you won’t get the benefit • If the system is not efficient then you won’t get the benefit • So you need to get workflow, structure and process right first before measuring the outcome measures • The measures need to be ones that can reasonably be measured in hospital organisations Reference : AIMA 2007 Symposium Proceedings
  • 35. Nursing Workflow Impacts • Pyxis –”batching” process for withdrawal • Time delay between withdrawal from Pyxis and administration to patient at bedside • Need to capture both activities • MedChart only recorded administration at bedside • Needed enhancement to “park” and “retrieve for administration” medication
  • 36. Nursing Workflow Impacts (cont’d) • Checked nationally and found even sites without Pyxis recorded withdrawal of drug from drug room by annotating on paper chart and then recorded the actual time of admin at bedside • MedChart Version 8 now has “two step administration” functionality
  • 37. Batch Administration
  • 38. Batch Retrieval
  • 39. Incremental Dosing • Identified requirement to record incremental doses of medication eg morphine pain protocol • Needed to build in check with requirement for signature without making it too restricting in terms of workflow • Needed to have ability to waste and change dose (requiring extra signature)
  • 40. Incremental Dosing cont’d • Co-signature needed to be flexible after administration to cater for the various scenarios: – controlled drug increments – enrolled nursing – I.V. infusion administration • Version 8 MedChart will have this functionality
  • 41. Clinical Change Management Lessons – Administrative type errors (ie documentation and “completeness”) will decrease with electronic solutions due to forced compliance. 6, 7 – To decrease clinical errors and “patient harm”, some degree of decision support is essential but this should be introduced slowly as clinicians adjust to the changes and to avoid “alert fatigue”. 8, 9, 1, 10 – Customisation of decision support is important for ownership and adherence. 11, 12
  • 42. Lessons from the Westbrook Studies Comparison of two e-Prescribing Systems on Prescribing Error Rates • Both systems were associated with a statistically significant reduction in total prescribing error rates (>55%): – mainly attributed to a reduction in administrative prescribing errors {incomplete, illegal and unclear prescriptions} NOT clinical prescribing errors – overall little effect on clinical error reduction however did show decrease in serious error (44% cp 17% in control ward) • The rate of system related errors can be significant (35% error rate introduced) • The different prescribing systems (Cerner and MedChart) introduced different types of system related error (e-iatrogenesis)
  • 43. Lessons from the Westbrook Studies cont’d 13 • Clinicians’ greatest concern was the associated work practice changes • Qualitative and observational studies may identify the nature of these changes • The results highlight the need to continually monitor and refine the design of these systems to increase their effectiveness, appropriateness and safety • The same system can produce different outcomes in different wards – don’t assume that you can just go from one ward type to another without considering the different variables • The complexity of undertaking real world studies should not be underestimated
  • 44. Decision Support (Baysari / Westbrook Studies) • Allergy Alerts found to be most significant and most useful 14 • Clinicians don’t take notice of many (most) alerts 14 • Prescribing decisions are usually already made by senior consultants so alerts valued less by junior doctors at time of prescribing into the system 15 • There are unintended consequences often due to system design 14 • Most common alert to cause errors was the duplicate therapy warning –not helped by system configuration- need to look at training and system redesign 14
  • 45. Evaluation Considerations – Limited evidence for ePx reducing patient harm. 1, 16, 17 • Most evidence centres around indirect measures of harm such as medication errors – Because of the complexity of ePx implementation, a variety of evaluation methods need to be employed to measure success: • Safety Outcomes – including e-iatrogenesis (ie errors caused by ePx system introduction) • Quality Outcomes • Communication, Efficiency and Workflow Outcomes • Financial Outcomes (cost / resources) • User Outcomes (satisfaction, usability, availability, uptake)
  • 46. References 1 Ammenwerth E et al. The effect of electronic prescribing on medication errors and adverse drug events: a systematic review. JAMIA 2008; 15:585-600. 2 Gay T. Report - A Case Study on Computerized Physician Order Entry - A Blueprint for a Beginning. E-Health A division of the Massachusetts Technology Collaborative 2006; December. 3 Ormond C. Discussion paper: CPOE. Institute for Health Policy 2005;1-22. 4 Han YY et al. Unexpected increased mortality after implementation of a commercially sold computerised physician order entry system. Paediatrics 2005; 116(6):1506-12. 5 Sittig D F et al. Recommendations for Monitoring and Evaluation of In-Patient Computer-based Provider Order Entry Systems: Results of a Delphi Survey. AMIA .Annu Symp Proc 2007; 671-675. 6 Colpaert K et al. Impact of computerised physician order entry on medication prescription errors in the intensive care unit: a controlled cross-sectional trial. Critical Care2006; 10(1): R21. 7 Kaushal R et al. Effects of CPOE and CDSS on medication safety: a systematic review. Arch Intern Med 2003; 163(12):1409-1416. 8 Nebeker J et al. High rates of adverse events in a highly computerised hospital. Arch Int Med 2005; 165:1111-1116. 9 Semple S J et al. Medication safety in acute care in Australia: where are we now? Part 2: a review of strategies and activities for improving medication safety 2002-2008. Australia and NZ Health Policy 2009; 6 (24): 1-14. 10 Kuperman G et al. CPOE: benefits, costs and issues. Annals Int Medicine 2003; 139:31-39. 11 Garg A et al. Effects of computerised CDSS on practitioner performance and patient outcomes: a systematic review. JAMA 2005; 293(10): 1223-1238. 12 Kawamoto K et al. Improving clinical practice using CDSS: a systematic review of trials to identify features critical to success. BMJ 2005; 330(7494): 765. 13 Westbrook J, Reckmann M, et al www.plosmedicine.org January 2012 Vol 9 Issue 1 e1001164. 14 Baysari M, Reckmann M, Westbrook J, et al. Failure to utilise functions of an electronic prescribing system and the subsequent generation of “technically preventable” computerised alerts. Downloaded from jamia.bmj.com June 26 2012. 15 Baysari M, Westbrook J, et al. The influence of computerised decision support on prescribing during ward rounds: are the decision makers targeted? jamia.bmj.com October 18 2011. 16 Shamliyan T et al. Just what the doctor ordered. Review of the evidence of the impact of CPOE system on medication errors. Health Services Research 2008; 43(1): 32-52. 17 Wolfstadt J et al. The effect of CPOE with clinical decision support on the rates of adverse drug events: a systematic review. J Gen Intern Med. 2008; 23(4): 451-458. 18 Classen D et al. Meaningful use of CPOE. J Patient Saf 2010; 6(1): 15-23.