Quantitative Evaluation of Drug Name Safety Using Close-to-Reality Simulated Pharmacy Practice Sean Hennessy, PharmD, PhD Assistant Professor of Epidemiology & Pharmacology Center for Clinical Epidemiology and Biostatistics University 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

Name Qualitative Evaluation Process Outcome accept reject A Big-Picture View of Drug Name Evaluation

Outcome accept reject A Big-Picture View of Drug Name Evaluation Qualitative Evaluation Process

Name Qualitative 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

Name Quantitative Evaluation Process Outcome accept reject A Big-Picture View of Drug Name Evaluation What underlies this binary (yes/no) decision?

Safe nameUnsafe name Rating

Name Quantitative 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? Bates DW. Drug Safety 1996;15:

Are These Equally Bad? erythromycin  clarithromycin chloramphenicol  chlorambucil

Name Quantitative 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

Name Quantitative 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 Hospitalization Meningitis  recovery Deafness Minor brain damage Severe brain damage Death Benett JE, et al. Arch Ped & Adoles Med 2000;154:43-48.

A Possible Quantitative Rating P error  Consequences error =P error  P AE  error  Disutility of AE

Probability of error Consequences of error Rating

Name Quantitative 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

Potential Model for Name Evaluation: Mock Pharmacy Practice

Name Quantitative 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-Reality Simulated 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, 3 rd 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 Evaluation  Rating 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 Evaluation  Rating 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

Research Agenda Feasibility Cost Reliability Validity Utility