Risk-Informed Interventions in Community Pharmacy: Implementation and Evaluation

On September 14, 2009, Michael Cohen made this presentation at the 2009 Annual Conference. Select to access the PowerPoint® presentation (2.55 MB) (Plugin Software Help).

Slide 1

Risk-Informed Interventions in Community Pharmacy: Implementation and Evaluation

Michael R. Cohen (Principal Investigator)
Judy L. Smetzer (Project Manager)
Institute for Safe Medication Practices
September 14, 2009

Slide 2

Current Research Project

Risk-informed Interventions in Community Pharmacy: Implementation and Evaluation

Three interventions

• Scripted mandatory patient counseling for targeted high-alert medications
• Readiness assessment for bar-coding technology
• Risk assessment/intervention scorecard

Slide 3

Prior Study Aims

Using Risk Models to Identify and Prioritize Outpatient

High-Alert Medications

• Identify a list of high-alert medications dispensed from community pharmacies
  • Available error data, ISMP surveys, review of literature, litigation data,
• Develop comprehensive risk models for four high-alert medications (ST-PRA) using model building teams with facilitators
  • Warfarin, fentanyl transdermal, insulin analogs, methotrexate oral
• Identify error pathways that have the highest probability of causing harm (fault trees)
• Identify and determine the impact of approaches for eliminating or reducing the risk of harm

Slide 4

High-Alert Medications

Drug Class/Category

• Antiretroviral agents
• Chemotherapy, oral
• Hypoglycemic agents, oral
• Immunosuppressant agents
• Insulin
• Opioids, all formulations
• Pregnancy category X drugs
• Pediatric liquid medications that require measurement

Individual Drugs

• Carbamazepine
• Chloral hydrate liquid
  • Sedation of children
• Heparin
  • Unfractionated/low-molecular weight
• Methotrexate
  • Non-oncologic use
• Midazolam liquid
  • Sedation of children
• Propylthiouracil
• Warfarin

Slide 5

Socio-Technical Probabilistic Risk Assessment (ST-PRA)

• Models combinations of failures that lead to undesirable consequence (Relex software)
• Used in other industries
• Differs from FMEA, which analyzes each failure separately, never in combination (pharmacy dispensing process)
• Begins by defining the "top-level event" (PADE)
  • Medication dispensed to wrong patient at point of sale
  • Patient given wrong dose of warfarin
• Uses experienced modeling team to yield probability estimates of "basic events"

Slide 6

Socio-Technical Probabilistic Risk Assessment (ST-PRA)

• Risk model includes effects of:
  • Human error
  • Socio-technical aspects
    • At-risk behaviors and procedural deviations
    • Mechanical/technology failures
• Some data readily available in community pharmacies
  • Rx volume, exposure rates, technologies and percent of use, computer alerts followed, presence of certain steps or processes like use of drive through window, availability of 24 hr pharmacy, opening bag at P.O.S.

Slide 7

Example of ST-PRA Fault Tree Risk Model

An image of the ST-PRA Fault Tree Risk Model is shown.

Slide 8

Human Error Probabilities

ST-PRA uses probability estimates to quantify risk

• Unfamiliar task performed at speed/no idea of consequences 5:10
• Task involving high stress levels 3:10
• Complex task requiring high comprehension and skill 15:100
• Select ambiguously labeled control/package 5:100
• Failure to perform a check correctly 5:100
• Error in routine operation when care required 1:100
• Well designed, familiar task under ideal conditions 4:10,000
• Human performance limit 1:10,000

Slide 9

Performance shaping factors that impact on probability of error in community pharmacy

• Task complexity
• Information complexity
• Work environment
• Stress
• Time urgency
• Training/experience
• Familiarity with task
• Design of labels
• Clarity of handwritten prescriptions
• Look-alike drug names or packages

Slide 10

Insulin Analog Data Entry Error (wrong drug)

• Start
  • 1 data entry error per 100 prescriptions
• Capture
  • 96% errors captured
• Risk (PADEs that reach patients)
  • 3 wrong drug errors per 10,000 prescriptions
    • 2,200 errors annually (chains in study)
    • 6,400 errors annually (national)

Slide 11

Insulin Analog Data Entry Error (wrong drug)

• Interventions
  • Use of tall man letters to distinguish products
    • 50% improvement
  • Increase patient counseling from 30% to 80%
    • 67% improvement
  • Conduct a second redundant data entry verification during product verification
    • 50% improvement
  • All three interventions
    • 95% improvement
    • 3/10,000 to 1/1 million errors that reach patients

Slide 12

Fentanyl Patches Prescribing Errors (wrong dose)

• Start
  • 1 dose error per 1,000 prescriptions
• Capture
  • 27% errors
  • Lack of information about opioid tolerance, indication
• Risk (PADEs that reach patients)
  • 7 dose errors per 10,000 prescriptions
    • 1,000 errors annually (chains in study)
    • 3,400 errors annually (national)

Slide 13

Fentanyl Patches Prescribing Errors (wrong dose)

• Interventions
  • Increase in patient counseling from 10% to 80% and increase ability to detect inappropriate doses during counseling session
    • 64% improvement
  • Conduct an intake history of opioid use at drop-off
    • 40% improvement (tested with 20% implementation)
  • Both interventions
    • 78% improvement
      • 7/10,000 to 1/10,000 errors that reach patients

Slide 14

Warfarin Filling Errors (drug/dose)

• Start
  • 1 drug selection error per 1,000 prescriptions
  • 1 dose selection error per 10 prescriptions
• Capture
  • 99.9% errors
  • Consistent use of bar-coding technology
• Risk (PADEs that reach patients)
  • 9 wrong drug errors/1 billion prescriptions
    • 1 error every 14 years (chains in study only)
  • 9 wrong dose errors/10 million prescriptions
    • 7 errors annually (chains in study only)

Slide 15

Warfarin Filling Errors (drug/dose)

• Interventions
  • Increase patient counseling from 30% to 80%
    • 67% improvement
      • 9/1 billion to 3/1 billion errors reach pt (drug)
      • 9/10 million to 3/10 million errors reach pt (dose)
  • Eliminate bar-coding technology
    • (95,340%) reduction in safety
  • Eliminate pill image on the product verification screen
    • (334%) reduction in safety
  • Eliminate bar-coding and pill image
    • (445,000%) reduction in safety
      • 9/1 billion to 4/100,000 errors that reach pt (drug)
      • 9/10 million to 4/1,000 errors that reach pt (dose)

Slide 16

All Medications Point of Sale Error (wrong patient)

• Start
  • Due to bagging error (4 per 10,000 prescriptions)
  • Due to misidentification of bag or patient (3 per 1,000 prescriptions)
• Captured
  • 64% errors captured
• Risk (PADEs that reach patients)
  • 1 error per 1,000 prescriptions
    • 1.3 million errors annually (chains in study)
    • 4 million errors annually (national)

Slide 17

All Medications Point of Sale Error (wrong patient)

• Interventions
  • Increase patient counseling from 30% to 50%
    • 27% improvement
  • Open the bag at the POS
    • 56% improvement
  • Increase compliance with ID process from 50% to 80%
    • 34% improvement
  • All three interventions together
    • 86% improvement
    • 1/1,000 to 2/10,000 errors that reach patients

Slide 18

Current Research Project

• Risk-informed Interventions in Community Pharmacy: Implementation and Evaluation
• Scripted mandatory patient counseling
  • Warfarin
  • Fentanyl patches
  • Methotrexate
  • Insulin analogs
  • Low-molecular weight heparin*
  • Hydrocodone and oxycodone (with acetaminophen) - top 200*
• Readiness assessment for bar-coding technology
• Risk assessment/intervention scorecard using risk models from first study: HAMERS tool

* Added to increase frequency of observation of counseling sessions

Slide 19

Intervention 3: HAMERS (High-Alert Medication Error Risk Scorecard)

• ST-PRA models translated into practical assessment tool and scorecard
• Tool Kit will include:
  • Introduction
  • Key learning from risk models (prior study)
  • User instructions
  • HAMERS tool
    • Scorecard with qualitative (distribution of risk) and quantitative (PADE rates) information
    • Tool calculations driven by reports from original risk models

Slide 20

Intervention 3: HAMERS

• Inputs
• Set-up questions
  • Relevance: Would the step provide capture opportunity?
  • System attributes: Require data entry verification for pharmacists?
  • Availability: Use bar-coding technology? Specific computer alerts?
  • Prescription volumes?
• Exposure rates
  • Frequency of pharmacists/technicians entering prescriptions?
• Capture opportunities
  • What percent of errors will be caught during this step?
• At-risk behaviors
  • Frequency of choosing not to ask patient for second identifier?
• Human errors
  • Frequency of forgetting to read back an oral prescription?

Slide 21

Intervention 3: HAMERS

• Outputs
• Scorecard that quantifies the risk of specific PADEs
• Bar-graph that shows distribution of risk
  • Which elements contribute most to the PADE?
• Menu of interventions to reduce risk
  • Pharmacy chooses from the menu of interventions
  • Pharmacy makes changes to inputs based on the planned interventions
  • Pharmacy receives a revised scorecard that quantifies improvements based on planned interventions
    • "If (intervention) is implemented, then risk that the PADE will reach the patient is ___%."
    • If risk factor is (increased/decreased) by __%, risk that the PADE will reach the customer is reduced to __%."

Slide 22

Intervention 3: HAMERS

• Tool can be used to measure risk within dispensing system for any medication or most types of errors/ PADEs
  • Focus on high-alert medications
  • Can measure risk of not capturing prescribing errors
  • Cannot measure risk of patient self-administration
• Limited menu of interventions
  • General in nature
  • Specific to high-alert medications
  • Include all tested interventions from prior study and others

Slide 23

Intervention 1: Patient Counseling

• Pre-intervention observation in pharmacies
  • 50 observations completed
  • 4 states
    • 2 states with mandatory counseling
    • 2 states with mandatory offer to counsel
  • Preliminary findings
    • No counseling in states with "offer" to counsel
      • Counseling for OTCs more common than for prescription drugs
    • More frequent counseling in states with mandatory counseling
      • Differences between state enforcement of counseling
      • Not covering information linked to PADEs

Slide 24

Intervention 1: Patient Counseling

• Implementation Tool Kit
  • Scripted counseling materials, checklists, health questions
  • Consumer handouts about targeted drugs
    • Specifically targets known causes of PADEs
  • Consumer outreach materials to promote counseling
    • Availability on http://www.consumermedsafety.org
  • Model state regulations for requiring/limiting mandatory counseling for high-alert drugs

Slide 25

An image of a drug brochure for Fentanyl is shown.

Slide 26

An image of a drug brochure for Warfarin is shown.

Slide 27

An image of a drug brochure for Humalog is shown.

Slide 28

An image of a drug brochure for Methotrexate is shown.

Slide 29

Intervention 1: Patient Counseling

• Measures
• Self-administered surveys to patients
  • Perception of counseling encounter/value of handouts
  • Increase understanding?
  • Result in new information?
  • Result in changed behavior?
  • Reduce risk of self-administration error?
  • Treatment for PADE?
  • Toll-free number to call research team
    • Incentives to send back survey
• Self-administered surveys to pharmacists
  • Perceived value and impact of counseling

Slide 30

Intervention 1: Patient Counseling

• Measures (cont'd)
• Post-implementation observation
  • Detection of prescribing or dispensing errors
  • Detection of potential self-administration errors
  • Barriers to counseling
  • Factors that facilitate counseling
  • Quality of counseling sessions

Slide 31

Intervention 2: Bar-coding Readiness Assessment

• 46-50% of community pharmacies in the US do not use barcode technology for product verification
• 100 pharmacies participating in the study
• Survey to determine why non-users are still non-users
• Phase 1
  • 100 pharmacies will complete the assessment and submit findings
  • Pharmacies will complete survey to measure perceived value
• Phase 2
  • Pharmacies from Phase 1 that have since implemented bar-coding will complete survey to measure actual value