Cases & Commentaries

A Troubling Amine

Commentary By Elizabeth A. Flynn, PhD

The Case

A 43-year-old woman was admitted to the intensive
care unit for symptoms of heart and respiratory failure. She was
found to have severe mitral and tricuspid valve regurgitation. She
responded well to medical therapy, and surgical valve repair was
scheduled. During her initial evaluation, a jaw fracture was
incidentally noted. Given the jaw fracture and her valvular
disease, an oromaxillofacial surgeon recommended prophylactic
antibiotic coverage prior to surgery. Penicillin, 500 mg orally
four times daily, was ordered. On the second day of antibiotics,
when the nurse compared the drug with the medication administration
record (MAR), she noticed that the patient was receiving
penicillamine (a non-antibiotic medication used in the treatment of
Wilson’s disease and severe rheumatoid arthritis) instead of
penicillin and alerted the pharmacy.

A pharmacist reviewed the original handwritten
order and saw that penicillin was clearly prescribed. The
pharmacist who entered the order into the pharmacy computer system
had typed in the code “PENIC” and had received a
drop-down box that displayed all formulations and dosages of both
penicillin and penicillamine. That pharmacist had incorrectly
selected penicillamine as the drug to be given. The final check of
the medication (at the time the drug left the pharmacy) compared
the drug product against the information in the pharmacy computer
system but not against the original handwritten order. The patient
suffered no ill effects from the error and received the course of
penicillin as originally prescribed.

The Commentary

Penicillamine and penicillin are listed as
look-alike/sound-alike drugs by the United States Pharmacopeia,
along with approximately 1,100 other drug pairs.(1) The literature describes an incident similar to this
case, in which a general practitioner wanted to prescribe
penicillin V, 250 mg, but selected penicillamine, 250 mg instead.
That case was a near miss—a
pharmacist detected the error and intervened.(2) Similar incidents related to incorrect selection of a
medication in a computer system have been reported.(3,4)

This case raises the issue of how well pharmacy
computer systems aid in the prevention of medication selection
errors. In one study, the pharmacy technician order entry error
rate was measured at 2.5%. Most of these were missed orders, but 20
errors out of 246 involved incorrect medication
selection.(5) In
a study in 50 community pharmacies, order entry errors were
responsible for 48 of 63 (76%) dispensing errors on new
prescriptions.(6)

Pharmacy computer systems have been used to
improve the medication system for decades. Thirty years ago, Means
and colleagues (7)
used direct observation to study the effect of a pharmacy computer
system on medication administration errors detected but found
little impact. This may be because early pharmacy computer systems
were developed with a focus on operational, financial, and data
management functions, rather than on preventing medication
errors.(8)
Modern pharmacy computer systems include order entry work flow,
medication database information reliability, availability of
enhanced drug name lettering, order verification method, clinical
decision support (eg, drug interaction screening), and format of
the MAR generated for the nurses, all of which may improve
medication safety.(8) The
Institute for Safe Medication Practices (ISMP) conducted a survey
of safety features with pharmacy computer systems in hospitals in
1999 and 2005. The survey included a series of test orders with
problems such as therapeutic duplication, drug-drug interactions,
and overdosage based on age or body surface area. In the 2005
survey, only 4 of the 182 systems detected all of the unsafe
orders, indicating little improvement since 1999 in systems’
ability to detect potential medication errors.(9)

Some, but not all, systems include advanced
decision support such as dose-range checking, drug-laboratory
results checking, and comparison to diagnoses. The value of linking
pharmacy and laboratory data in order to reduce errors was
well-described by Schiff and colleagues.(10) In the latest ISMP survey, 72% of respondents stated
that their systems were linked to the laboratory systems, but only
42% alerted staff based on drug-related problems due to lab
results.(11)
Issues with noise or “alert fatigue,” can be dealt with
by setting the alert sensitivity level at a point in which only the
events with the potential for the most serious harm would
appear.(12)
ISMP reported that 71% of modern pharmacy systems generate screen
alerts that have little or no clinical significance, but 60% of
these alerts can be eliminated easily (ie, removing them with a
single click).(11)

What can be done to improve the accuracy of
medication selection? A study of the use of tall man lettering (eg,
vinCRIStine and vinBLAStine) found that it was helpful in improving
the accuracy of the selection of drugs that can be confused
(sound-alikes and look-alikes).(13)
The ISMP survey found that 57% of responding hospitals had computer
systems that allowed the use of tall man lettering.(11) ISMP recommends tall man lettering for 33
medications, but penicillamine and penicillin are not currently
included on this list.(14)
Providing a link between the drug name and indication as a double
check of correct medication selection likely would have prevented
the error in this case, because penicillamine is used for the
treatment of rheumatoid arthritis and as a chelating agent in heavy
metal poisoning, but would not have been a recommended medication
to treat an infection. A report from a hospital in Taiwan
demonstrated that the addition of a link between certain drug names
and indications for antiretrovirals and insulin secretagogues
successfully prevented wrong drug errors.(15) An expert panel recommended displaying a list of
medications indicated for a diagnosis on entry of the diagnosis.
However, this feature has been recommended for prescriber order
entry systems, not pharmacy systems, but it should be explored for
pharmacy systems as well.(16) At
this time, I am not aware of such diagnoses-medication links in
commercially available pharmacy computer systems.

The use of enhanced lettering and links between
drug name and indication will not prevent all errors associated
with information systems, however. Research applying cognitive
science and usability engineering methods (17) to identify design features that enhance order entry
accuracy are needed, particularly for pharmacy systems. In the
future, pharmacy systems may respond to the penicillamine order
entered in this case with an alert such as “This surgeon has
never prescribed penicillamine, but frequently orders penicillin.
Are you sure you want to enter penicillamine?”

Take-Home Points

  • Pharmacists inspecting filled orders
    should use the original order as the basis for comparison and take
    the opportunity to verify correct entry of the order into the
    computer system.(18)
  • Verify information at each step in the
    process. This case illustrates the value of including double checks
    by everyone using the medication system–the nurse detected
    the error by doing a routine medication check.
  • Incorporate enhanced lettering in
    computer drug lists if not already available.
  • Physicians can help improve patient
    safety by providing an indication for each medication prescribed.
    Pharmacists assigned to enter orders into computers may not be
    familiar with the patient involved and appreciate having the
    indication available as they process the order.

Elizabeth A. Flynn, PhD
Associate Research Professor
Auburn University Harrison School of Pharmacy

References

1. USP Center for the Advancement of Patient
Safety. Use caution–avoid confusion. USP QualityReview. April
2004. Available at: http://www.usp.org/pdf/EN/patientSafety/qr792004-04-01.pdf.
Accessed September 12, 2006.

2. White A, Russ C. Recent prescribing near
misses in practices. Bolton, England: Bolton NHS Primary Care
Trust; September 6, 2005. Available at: http://www.bolton.nhs.uk/clinical/med_manage/circletters/formulary%20letter.doc.
Accessed September 12, 2006.

3. Ash JS, Berg M, Coiera E. Some unintended
consequences of information technology in health care: the nature
of patient care information system-related errors. J Am Med Inform
Assoc. 2004;11:104-112. [go to PubMed]

4. Kushniruk A, Triola M, Stein B, Borycki E,
Kannry J. The relationship of usability to medical error: an
evaluation of errors associated with usability problems in the use
of a handheld application for prescribing medications. Medinfo.
2004;11:1073-1076. [go to PubMed]

5. Tierney M, McLurg D, Macmillan C. Transferring
medication order entry from pharmacists to pharmacy technicians.
Can J Hosp Pharm. 1999;52:240-243.

6. Flynn EA, Barker KN, Carnahan BJ. National
observational study of prescription dispensing accuracy and safety
in 50 pharmacies. J Am Pharm Assoc (Wash). 2003;43:191-200.
[go to PubMed]

7. Means BJ, Derewicz HJ, Lamy PP. Medication
errors in a multidose and a computer-based unit dose drug
distribution system. Am J Hosp Pharm. 1975;32:186-191. [go to PubMed]

8. Daniels R. Pharmacy information systems. In:
Brown TR, ed. Handbook of Institutional Pharmacy Practice. 4th ed.
Bethesda, MD: American Society of Health-System Pharmacists;
2006:313-327.

9. Safety still compromised by computer
weaknesses. ISMP Medication Safety Alert! Acute Care Edition. April
25, 2005. Available at: http://www.ismp.org/Newsletters/acutecare/articles/20050825.asp

10. Schiff GD, Klass D, Peterson J, Shah G, Bates
DW. Linking laboratory and pharmacy: opportunities for reducing
errors and improving care. Arch Intern Med. 2003;163:893-900.
[go to PubMed]

11. Institute for Safe Medication Practices.
National survey of safety features with pharmacy computer systems.
2005. Available at: http://www.ismp.org/survey/survey200505R.asp. Accessed
September 12, 2006.

12. Stiening KK, Trimble JM, Merryfield DW.
Computerized clinical alerts--reducing the noise. Abstract of
meeting presentation at: American Society of Health-System
Pharmacists Midyear Clinical Meeting; December 7-11, 2003; New
Orleans, LA.

13. Filik R, Purdy K, Gale A, Gerrett D. Labeling
of medicines and patient safety: evaluating methods of reducing
drug name confusion. Hum Factors. 2006;48:39-47. [go to PubMed]

14. Name differentiation project. US Food and
Drug Administration, Center for Drug Evaluation and Research Web
site. Available at: http://www.fda.gov/cder/drug/MedErrors/nameDiff.htm.
Accessed August 18, 2006.

15. Yu KH, Su SS, Kuo CC, Tsao HL. Reducing
medication errors by linking drug names with patients' diagnostic
codes. Am J Health Syst Pharm. 2006;63:808-809. [go to PubMed]

16. Bell DS, Marken RS, Meili RC, et al.
Recommendations for comparing electronic prescribing systems:
Results of an expert consensus process. Health Aff (Millwood).
2004; Suppl Web Exclusives:W4-305-317. [go to PubMed]

17. Kushniruk AW, Patel VL. Cognitive and
usability engineering methods for the evaluation of clinical
information systems. J Biomed Inform. 2004;37:56-76. [go to PubMed]

18. Kuyper AR. Patient counseling detects
prescription errors. Hosp Pharm. 1993;28:1180-1181, 1184-1189.
[go to PubMed]