# Presentation: 113472

## 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**

## Outline

**Big-picture view of drug name evaluation**
**Improving the process by making it quantitative**
**Model for measurement in mock pharmacy setting**
**Research agenda**

## A Big-Picture View 

**Qualitative Evaluation**
**Process**

_**Outcome**_
**accept**
**reject **

**A Big-Picture View **
**of Drug Name Evaluation**

## A Big-Picture View 

_**Outcome**_
**accept**
**reject **

**A Big-Picture View **
**of Drug Name Evaluation**

**Qualitative Evaluation**
**Process**

## A Big-Picture View 

**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**

## A Big-Picture View 

**Quantitative Evaluation**
**Process**

_**Outcome**_
**accept**
**reject **

**A Big-Picture View **
**of Drug Name Evaluation**

## Safe name

**Unsafe name**

**Rating**

## A Big-Picture View 

**Quantitative Evaluation**
**Process**

_**Rating**_
**probability of error**

_**Outcome**_
**accept**
**reject**

**A Big-Picture View **
**of Drug Name Evaluation**

## Are All Medication Errors Created Equal?

## Are These Equally Bad?

**erythromycin******** clarithromycin**
** ****chloramphenicol******** chlorambucil**

## A Big-Picture View 

**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**

## A Big-Picture View 

**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****	****	****	****	****	****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.**

## A Possible Quantitative Rating

** ****P****error**** ******** Consequences****error**
**=P****error**** ******** P****AE********error ******** Disutility of AE**

## Rating

**Consequences of error**

**Rating**

## Slide 20

## A Big-Picture View 

**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

## A Big-Picture View 

**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**

## Slide 34