# Presentation: 449149

## Use of Laboratory and Other Simulations in Assessing Drug Name Confusion

- Tony Grasha (dec)
- University of Cincinnati
- Kraig Schell
- Angelo State University

## The Current State As We See It...

- Current means of assessing drug
- name confusion are primarily
- rational and reductionistic
- FMEA/RCA
- Computer phonological analysis
- Expert teams and committees

- Careful consideration must be paid to drug name confusion to avoid patient injury and to avoid financial loss by companies

## Our Approach to the Problem

- The problem of name confusability is broader and less rational than might be assumed
- In addition to the physical characteristics of the name, other factors may play a role such as:
- Workplace stress and fatigue
- Outside of the workplace stress and tension
- Time of day
- Frequency of prescriptions
- Workload and work rate
- Complex and conflicting information
- Personality characteristics/individual differences

## Our Approach to the Problem...

- Major assumptions and observations
- Drugs that look similar, sound similar, and are spelled in similar ways are not confused with each other or misfilled 100% of the time
- Phonological and perceptual factors are important contributors to the problem and are “necessary” but are not “necessary and  sufficient” explanations for why the problem exists
- The process of human error is not a rational process and cannot be completely reduced to rules and formulae

## The Use of Simulations

- Simulating all or part of a
- dispensing or drug distribution
- process can yield important
- information about:

-   “human factors” that interact with the 				physical characteristics of a drug name
-   work environments within which				particular drug names are more confusable 		than others

## A Hierarchy of Simulations

- Lab
- Simulation

- Full Scale Pharmacy Simulation
- “Movie Set Metaphor”

- Simulations in Pharmacy Schools

- Error Monitoring
- Stations Around the Country

- CONTROL

- REALISM

- H

- H

- L

- L

## Characteristics of Simulations

- Both objective errors and subjective errors can be observed
- Subjective errors include the drug name’s ability to create “process errors”or “process deviations” that may contribute sporadically to actual errors
- Process errors represent mistakes made and corrected  but also are indicative of our cognitive system moving into an “error mode.”
- A 6:1 average ratio of process errors to those that get by normal verification processes has occurred across a number of settings: laboratory, retail pharmacy, outpatient hospital pharmacy

## Characteristics of Simulations

- Safe environment for assessing effects of drug names on performance
- Allow the use of controlled experimental designs, quasi-experimental designs, case studies of individuals and teams, naturalistic observations of performance
- Provide opportunities to insert drug names to be evaluated into a mix of normal products dispensed

## Three Laboratory Approaches

- Full-scale dispensing task
- Simulated products are dispensed from mock scripts
- Verification task
- Simulated prescriptions are checked for accuracy against “pharmacy records”
- Drug name perception task
- Following the methods of Lambert and colleagues, drug name confusion can be connected to specific individual difference factors

## Examples of Laboratory Simulations

- Simulated Prescription Verification Lab

## Drug Name Confusion Task

- Computer-based simulation (currently under construction)
- Names are presented to participants, who must navigate through a “virtual shelf” to retrieve the correct product
- Many parameters of the task are modifiable (i.e., duration of name presentation, inclusion of informational context such as dosage, feedback conditions, etc.)

## Laboratory Simulation

- _Pros_
- Strict control over IVs/DVs
- Name testing can be tailored as necessary
- Other factors (work pace/workload, etc.) can be varied systematically
- Customizable products

- _Cons_
- Lack of realism
- Shorter versions of the task tend to be overly simplistic
- Some causes of name confusion may be controlled for in the experimental design

## “Movie Set” Simulation

- Pharmacists/technicians would fill/check scripts in a fluid and dynamic environment resembling actual pharmacy or variations on it
- Emphasis on duplicating the workflow and other likely conditions under which prescription filling/checking would occur

- Both objective (errors) and 		subjective (observational) 		data collected

## “Movie Set” Simulation

- The name would be tested in a simulated environment that includes estimable factors that could impact performance:
- Phone calls/interruptions
- Customer complaints
- Insurance paperwork
- Working with multiple scripts at once
- Induced stress/fatigue

## “Movie Set” Simulation

- Name confusion could be tested using several “exercises” based around these potential confounding factors:
- The “insurance fiasco” exercise
- The “multiple script” exercise
- The “similar preceding name” exercise
- The “frequent prescription” exercise
- The “stressed out” exercise
- The “irate customer” exercise, etc.
- Exercise creation would be informed by the drug name confusion laboratory task described previously

## Simulations in Colleges of Pharmacy

- Use of existing simulated environment
- Possible to do some of the things done in the “movie set” simulation”
- May not be as flexible for manipulating some psychosocial factors

## The Error Monitoring Station

- Especially in automated pharmacies, the pharmacist’s role has largely switched from filling to verification
- This test would insert the new drug into an existing pharmacy strategically placed around the United States
- Controls in place to insure that the new drug is not actually dispensed to a customer
- Two types of data: pharmacist/technician self-monitoring, objective (end-result) data

## The Error Monitoring Station

- Advantages:
- No conflicts of interest
- Actual “real-world” environment
- Marketing ramifications
- Disadvantages:
- Slight risk of accidental dispensation but correctable with observers on site
- Use of self-report data
- Possible lack of sample size