A Retrospective and Commentary on FDA’s Bar Code Rule

2018 ◽  
Vol 9 (3) ◽  
pp. 496-518
Author(s):  
Aaron Kearsley ◽  
Nellie Lew ◽  
Clark Nardinelli

Food and Drug Administration (FDA) published a final regulation in 2004 that requires pharmaceutical manufacturers to place linear bar codes on certain human drug and biological products. The intent was that bar codes would be part of a system where healthcare professionals would use bar code scanning equipment and software to electronically verify against a patient’s medication regimen that the correct medication is being given to the patient before it is administered, which could ultimately reduce medication errors. In the 2004 prospective regulatory impact analysis, FDA anticipated that the rule would stimulate widespread adoption of bar code medication administration technology among hospitals and other facilities, thereby generating public health benefits in the form of averted medication errors. FDA estimated that annualized net benefits would be $5.3 billion. In this retrospective analysis, we reassess the costs and benefits of the bar code rule and our original model and assumptions. Employing the most recent data available on actual adoption rates of bar code medication administration technology since 2004 and other key determinants of the costs and benefits, we examine the impacts of the bar code rule since its implementation and identify approaches to improve the accuracy of future analyses. In this retrospective study, we use alternative models of health information technology diffusion to create counterfactual scenarios against which we compare the benefits and costs of the bar code rule. The magnitudes of the costs and benefits of the 2004 rule are sensitive to assumptions about the counterfactual technology adoption rate, with the upper-bound range of calculated annualized net benefits between $2.7 billion and $6.6 billion depending on the baseline scenario considered.Disclaimer: The findings, interpretations, and conclusions expressed in this article are those of the authors in their private capacities, and they do not represent the views of the Food and Drug Administration.

2015 ◽  
Vol 33 (11) ◽  
pp. 502-508 ◽  
Author(s):  
ANNA MARY BOWERS ◽  
KAREN GODA ◽  
VICTORIA BENE ◽  
KATHERINE SIBILA ◽  
RITA PICCIN ◽  
...  

Author(s):  
Dalal Salem Al- Dossari ◽  
Mohammed Ibrahim Alnami ◽  
Naseem Akhtar Qureshi

Background: Drug prescription error is a medication error that most frequently happens in healthcare organizations and adversely affects the healthcare consumers. Most medication errors (MEs) but not all are captured and corrected before reaching the patient by designed system controls. Medication administration errors (MAEs) mostly are made by nurses but frequently reported by clinical pharmacists in hospitals in Saudi Arabia. Objective: This study aimed to analyze exclusively the voluntarily reported drug administration errors in a tertiary care hospital in Riyadh city. Methods: This cross-sectional, retrospective study evaluated consecutively collected medication administration report forms over a period of one year from January 1, 2015 to December 31, 2015. Results: The number of MAEs occurring during stage of drug administration constituted 7.1% (n=971) of total medication errors (n=13677). The maximum number of MEs (n=6838, 50%) and MAEs (n=455, 46.9%) occurred during the 4th quarter of the year 2015. The most common MAE happened to be category C (n=888, 91.5%) which means error occurred, reached the patient but without causing any harm. Concerning MAE types, the most common error included wrong frequency (40%) followed by wrong drug (17%), wrong time of administration (16%) and wrong rate of infusion (10%). Nurses made the most of the errors (92.2%) while the clinical pharmacists reported the most MAEs (75.5%). High alert medications (HAM) errors constituted 32.3% (n=314) of MAEs (n=971) and most common HAM errors included the wrong route of administration of Lanus Insulin (15%) followed by Insulin Aspart (15%), Enoxaparin (13%) and Insulin Protamine-Nvomix (12%). Look-alike and sound-alike (LASA) errors constituted 55.2% of MAEs (971/536) and most common LASA drugs identified were Gentamycin (13%), Insulin Mixtard (11%), NPH Insulin (8%) Intralipid vial (8%) and Insulin regular (6%). Conclusion: This retrospective study provides some important tentative pharmacovigilance insights into MAEs, which are partially comparable with current international trends in drug administration errors. Further studies on MAEs are warranted not only in the Kingdom of Saudi Arabia but also other Gulf countries.


2010 ◽  
Vol 56 (10) ◽  
pp. 1554-1560 ◽  
Author(s):  
Marion L Snyder ◽  
Alexis Carter ◽  
Karen Jenkins ◽  
Corinne R Fantz

BACKGROUND Bar code technology has decreased transcription errors in many healthcare applications. However, we have found that linear bar code identification methods are not failsafe. In this study, we sought to identify the sources of bar code decoding errors that generated incorrect patient identifiers when bar codes were scanned for point-of-care glucose testing and to develop solutions to prevent their occurrence. METHODS We identified misread wristband bar codes, removed them from service, and rescanned them by using 5 different scanner models. Bar codes were reprinted in pristine condition for use as controls. We determined error rates for each bar code–scanner pair and manually calculated internal bar code data integrity checks. RESULTS As many as 3 incorrect patient identifiers were generated from a single bar code. Minor bar code imperfections, failure to control for bar code scanner resolution requirements, and less than optimal printed bar code orientation were confirmed as sources of these errors. Of the scanner models tested, the Roche ACCU-CHEK® glucometer had the highest error rate. The internal data integrity check system did not detect these errors. CONCLUSIONS Bar code–related patient misidentifications can occur. In the worst case, misidentified patient results could have been transmitted to the incorrect patient medical record. This report has profound implications not only for point-of-care testing but also for bar coded medication administration, transfusion recipient certification systems, and other areas where patient misidentifications can be life-threatening. Careful control of bar code scanning and printing equipment specifications will minimize this threat to patient safety. Ultimately, healthcare device manufacturers should adopt more robust and higher fidelity alternatives to linear bar code symbologies.


Sign in / Sign up

Export Citation Format

Share Document