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Quantitative Evaluation of Drug Name Safety Using Close-to-Reality Simulated
Pharmacy Practice
Sean Hennessy, PharmD, PhDAssistant Professor of Epidemiology & Pharmacology
Center for Clinical Epidemiology and BiostatisticsUniversity of Pennsylvania School of Medicine
CCEB
Outline• Big-picture view of drug name
evaluation• Improving the process by making
it quantitative• Model for measurement in mock
pharmacy setting• Research agenda
NameQualitative Evaluation
Process
Outcome•accept•reject
A Big-Picture View of Drug Name Evaluation
Quantitative
Outline• Big-picture view of drug name
evaluation• Improving the process by making
it quantitative• Model for measurement in mock
pharmacy setting• Research agenda
Advantages of Quantitative Approach
• Explicit and systematic
• Uses fuller range of information
• Transparency of data & assumptions
• Acknowledges uncertainty
• Identifies knowledge gaps
NameQuantitative Evaluation
Process
Outcome•accept•reject
A Big-Picture View of Drug Name Evaluation
What underlies this binary
(yes/no) decision?
NameQuantitative Evaluation
Process
Rating• probability of error
Outcome•accept•reject
A Big-Picture View of Drug Name Evaluation
Is this enough?
Are All Medication Errors Created Equal?
99%
1%
No observable ADE
Observable ADE
Bates DW. Drug Safety 1996;15:303-10.
NameQuantitative Evaluation
Process
Rating• probability of error• consequences of
error•probability of AE
Outcome•accept•reject
A Big-Picture View of Drug Name Evaluation
Probability of Adverse Event
• Includes adverse outcomes from not getting intended drug
– From placebo-controlled trials
• ADE depends on identity of drug that is mistakenly substituted
– Measured empirically, as discussed later
• Frequency of ADEs in recipients of mistakenly substituted drug
– From pharmacoepidemiologic studies
NameQuantitative Evaluation
Process
Rating• probability of error• consequences of
error•probability of AE•disutility of AE
Outcome•accept•reject
A Big-Picture View of Drug Name Evaluation
Disutility
•The value of avoiding a particular health state, usually expressed on a scale from 0 to 1
•Measured empirically by asking patients standardized questions
Disutility of Outcomes for Occult Bacteremia
• Blood draw 0.0026
• Hospitalization 0.0079
• Meningitis recovery 0.0232
• Deafness 0.1379
• Minor brain damage 0.2607
• Severe brain damage 0.6097
• Death 0.9823
Benett JE, et al. Arch Ped & Adoles Med 2000;154:43-48.
NameQuantitative Evaluation
Process
Rating• probability of error• consequences of
error•probability of AE•disutility of AE
Outcome•accept•reject
A Big-Picture View of Drug Name Evaluation
What settings?• outpatient pharmacy• inpatient pharmacy• physician office• inpatient unit• nursing administration • patient home administration• etc.
Outline• Big-picture view of drug name
evaluation• Improving the process by making
it quantitative• Model for measurement in mock
pharmacy setting• Research agenda
NameQuantitative Evaluation
Process
Rating• probability of error• consequences of
error•probability of AE•disutility of AE
Outcome•accept•reject
A Big-Picture View of Drug Name Evaluation
Close-to-RealitySimulated Pharmacy Practice
• New or existing simulated pharmacies
• Use per diem practicing pharmacists or late-year pharmacy students
– Cost vs. realism
• List test drugs in computerized drug info source
• List test drugs in prescription entry program
• Put test drugs on pharmacy shelf
Pharmacy Practice Lab for Testing Drug Names
• Simulate pharmacy practice by presenting Rx’s (phone, hand-written, computer-generated) for real and test drug
• Add Rx volume, noise, interruptions, 3rd party reimbursement issues, Muzak, etc.
• Pharmacist enters and fills prescription• Measure the rate of name mix-ups at all stages
of filling process, and which drug was mistakenly substituted
Getting from EvaluationRating
• For probability of error, use point estimate or upper confidence limit (CL)?
Maximum value statistically compatible with data; function of measured rate & sample size
Getting from EvaluationRating
• For probability of error, use point estimate or upper confidence limit (CL)?–Using upper CL encourages bigger studies
–What coverage for CLs? (95%? 90%? 80%?)
»Base on what seems reasonable using real data?
Potential Advantages vs. Expert Opinion
• Yields empiric estimates of error rate, and of which drugs are mistakenly substituted
• Better face validity• Validity can be tested by examining
known “bad” names• Makes knowledge & assumptions
explicit
Obstacles & Limitations-1• Hawthorne effect?
–Initial improvement in a process of production caused by the obtrusive observation of that process
• Technical challenges
Obstacles & Limitations-2• Need large sample sizes
• Use routinely, or just to validate qualitative approaches?
• Worth the added cost?
Outline• Big-picture view of drug name
evaluation• Improving the process by making
it quantitative• Model for measurement in mock
pharmacy setting• Research agenda