Use of Laboratory and Other Simulations in Assessing Drug Name Confusion

1. Use of Laboratory and Other Simulations in Assessing Drug Name Confusion Tony Grasha (dec) University of Cincinnati Kraig Schell Angelo State University

2. 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

3. 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

4. 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

5. 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

6. A Hierarchy of Simulations H L Lab Simulation Simulations in Pharmacy Schools CONTROL REALISM Full Scale Pharmacy Simulation “Movie Set Metaphor” Error Monitoring Stations Around the Country L H

7. 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

8. 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

9. 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

11. Examples of Laboratory Simulations Simulated Prescription Verification Lab

12. 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.)

13. 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 Laboratory Simulation

14. “Movie Set” Simulation • Pharmacists/technicians would fill/check scripts in a fluid anddynamic 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

15. “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

16. “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

17. 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

18. 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

19. 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