Implementing an ePrescribing System: A Journey, Not a Solution

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Implementing an e-Prescribing System A Journey,
Not a Solution

• EB Devine, JL Wilson-Norton, NM Lawless,
• W Hollingworth, RN Hansen, BA Comstock,
• AW Fisk, SD Sullivan
• AHRQ HIT Grant 5-UC1 HS015319 (PI Sullivan)
• AHRQ Training Grant 5-K08-HS014739 (PI
  Devine)

Department of Pharmacy, University of Washington
The Everett Clinic, Everett, Washington
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The Everett Clinic

• Physician owned and managed multi-specialty
  integrated health-system with a 83-year history
• 14 locations 60 clinics
• Ancillary services
• 260 physicians/ 1,500 employees
• 225,000 patients
• 700,000 ambulatory visits annually
• 2.5 million Rx annually

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The Everett Clinics e-prescribing system

• CliniTech? Information Resources
• Internally-developed EHR began in 1995 charts,
  labs and imaging reports
• e-prescribing implemented beginning during
  2003-05
• Features of e-prescribing system
• Web-based
• Write new refill prescriptions
• Output fax/ print
• Optimizes choice of medication generates
  medication list as prescriptions are written
• Pediatric antibiotic dosing by weight
• Utilizes subscription to commercial drug database
  as back end
• Builds patient drug database, improving disease
  management

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Implementation of e-prescribing

• Several months in development
• Accuracy and relevance of drug database
• Screens easy to use and involve minimal
  manipulation
• Basic decision support
• Adopt an icon MedMan
• Implement at pilot site refills first
• Voluntary use
• Goal Implement on platform of wireless laptop
• Switch to hardwired desktops in exam rooms
• 505 exam rooms!
• 51 months to last clinic go-live
• Now 5,000 prescriptions/ day (95 written)
  faxed to 600 pharmacies
• Transition to vendor-purchased EHR in late 2007
  CDS customized use becomes mandatory

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AHRQ HIT GrantSpecific Aims

• Capture lessons learned during implementation
• Evaluate impact on medication errors and adverse
  drug events
• Measure impact on workload / workflow
• Time-motion study
• Process metrics (chart pulls, prescriptions
  written)
• Evaluate impact on human factors
• Focus groups
• Survey assessing readiness to adopt IT

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Implementation Lessons (1)

• Culture
• Visionary leadership safety-oriented positive,
  upbeat work environment
• Two-way communication constant
• Iterative implementation
• Re-engineering / standardization of workflow
  integral to process
• Adequate investment in infrastructure a priori
  speed is essential for adoption
• End-user prior experience variable and influences
  user attitudes
• Adequate testing and feedback
• Just-in-time, one-on-one training 24 / 7 help

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Implementation Lessons (2)

• Use early adopters as trainers
• Users interested in research results as
  motivation for adoption
• Medication safety, workflow / workload
• Focus groups provided opportunity to provide
  feedback
• Retail pharmacies are stakeholders, too
• Patients love looking at their data with their
  physician
• Many exam rooms not large enough for computer
• Need retrofitting
• Maintenance includes ongoing monitoring and
  development of plans for system downtime
• Customization of purchased system
• With great care detailed work

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Medication error study

• Aim
• Evaluate the impact of e-prescribing on
  medication errors and adverse drug events (ADEs)
• Capture error characteristics and severity
• Methods
• Pre-/ post- study
• Retrospective review, 10,000 scripts (3,000 for
  pilot study at 1 internal medicine clinic)
• Adopted definition and severity index of the NCC
  MERP1
• Data sources prescriptions, EHR, laboratory
  values, hospital admit / discharge / emergency
  department notes

1National Coordinating Council for Medication
Error Reporting and Prevention.
http//www.nccmerp.org/aboutMedErrors.html
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Incidence of Potential and Medication Errors
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Severity of Potential and Medication Errors
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Characteristics of Errors
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Time-motion study

• Aim
• Evaluate whether the implementation of
  e-prescribing was at least time-neutral for
  physicians and staff members
• Methods
• Shadowed each user over a 4 hour shift
• 8am -12 noon, or 1pm-5pm
• With consent of clinician patient

• Data collected with Timer ProTM
  (http//performance-measurement.com)
• Categories adapted from Overhage

Overhage. JAMIA 20018361-71
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Time-motion study design

• Controlled Pre- Post Study

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Time-motion results Overall task types
(prescribers)
pclinics all other tasks took 15
Time-motion results Prescribing-related events
(prescribers)
Linear mixed effect model, adjusted for
prescriber and type of prescription (new/renew)
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Focus group study

• Aim
• Explore and describe prescriber staff
  experiences with and perceptions of the
  e-prescribing system
• Methods
• Qualitative research
• Universal sample in 3 clinics / 3 stages of
  implementation
• 30 minutes/ group 3-8 participants oral consent
• Semi-structured elicitation techniques
• 4 domains expectations, impact, fears, barriers
  to adoption
• Analysis with Atlas.ti? software

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Focus group results

• 8 focus group 67 participants (17 prescribers
  52 RNs MAs)
• Computer background influenced perceptions

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Research Conclusions

• The Everett Clinic / University of Washington a
  valuable partnership
• Medication errors
• e-prescribing reduces medication errors
• eliminates types of errors (illegibility,
  abbreviations)
• introduces new types of errors (picking errors)
• standardizes care / reduce unnecessary variation
• data available to optimize quality lower cost
  of prescribing
• Time-motion workload / workflow
• e-prescribing has minimal impact on prescriber
  time (12 seconds per event)
• Workload
• 95 of prescriptions written electronically
• Number of monthly chart pulls decreased from
• 5,800 (2003) to 650 (2007) 9-fold reduction
• Focus groups
• impact beneficial users do not wish to go back
  to paper-based prescribing

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Questions?bdevine_at_u.washington.edu
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Characteristics of Errors