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

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

• 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

The Everett Clinic’s e-prescribing system

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

• 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

AHRQ HIT Grant:Specific Aims

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

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

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

0%

5%

10%

15%

20%

25%

30%

Total Number of Prescriptionswith (Potential) Errors

27.4% 9.3%

Pre-Implementation Post-Implementation

Incidence of Potential and Medication Errors

Severity of Potential and Medication Errors

0%

5%

10%

15%

20%

25%

30%

Pre-Implementation 25.1% 2.2% 0.1%

Post-Implementation 6.9% 2.3% 0.1%

Potential Errors (Level A)

Errors, No Harm = Potential ADEs

(Levels B-D)

Errors, with Harm = Preventable ADEs

(Level E)

Characteristics of Errors

0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%

Missing Information

Incorrect Directions

Illegible Prescriptions

Administrative (Refill)

Inappropriate Abbreviations

Geriatric Contraindication

Drug-Disease Interaction

Wrong Drug

Laboratory Monitoring

Wrong Prescriber

Pre-Implementation

Post-Implementation

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 2001;8:361-71

Time-motion: study design

Pre Post

Clinic Date Rx System Date Rx System

Silver Lake Feb-Mar 05 Paper 3rd/ 4th Quarter

2006

Exam Room

Desktop

Harbour Pointe

Aug 05 MD office Desktop

3rd/ 4th Quarter

2006

Exam Room

Desktop

Snohomish Nov 05 – Jan 06

Wireless Laptop

3rd/ 4th Quarter

2006

Exam Room

Desktop

Controlled Pre- Post Study

Time-motion results: Overall task types (prescribers)

Task Silver Lake

(paper)

n=8

(min/hr)

Harbour Pointe

(desktop)

n=11

(min/hr)

Snohomish

(laptop)

n=8

(min/hr)

Computer tasks 3.8* 7.4 8.1

Writing Tasks 8.7* 5.5 5.9

Computer & writing 12.4 12.9 14.0

Talking to patient 19.0 17.8 20.3

Talking to colleague 6.6 11.6 8.2

Examining patient 8.9* 4.9 5.3

Examining chart 6.1 5.7 5.3

*p<0.05; chi-squared test Silver Lake vs. other clinics; all other tasks took < 7.5% of total time

Time-motion results: Prescribing-related events (prescribers)

Clinic Hand-written event

(sec/ event)

(number of events)

E-Prescription

event

(sec/event)

(number of events)

Adjusted mean

difference

(sec/event)*

(95% CI)

Silver Lake (paper) 47.6 (68) None -

Harbour Pointe (desktop)

38.1 (26) 43.6(79) 9.5

(-9.8, 28.8)

Snohomish (laptop) 63.1 (10) 72.5(59) 9.8

(-23.4, 43.1)

All sites 46.7 (104) 56.0 (138) 12.0

(-1.6, 25.6)

* Linear mixed effect model, adjusted for prescriber and type of prescription (new/renew)

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

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

10 Themes - Perceptions of e-prescribing system

Benefits Accuracy, transparency, integration

Downsides Programming errors; lack of wireless reliability

Efficiency Increased after training period

Expectations Streamline work

Fears Yes, at paper-based clinic

Desired features Prescribers – CDS alerts and favorites lists

Impact Prescribe from home – an unexpected impact

Impressions “I love it!”

Leadership Training, feedback, support

Safety Improved

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

Questions?

bdevine@u.washington.edu

Characteristics of Errors

0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%

Drug-Drug Interaction

Therapeutic Duplication

Wrong Dose

Wrong Strength

Wrong Dosage Form

No Chart Note

Allergy

Multiple Prescribers

Wrong Patient

Wrong Route

Pre-Implementation

Post-Implementation