scholarly journals Using bar codes to create a robust national vaccine registry

2021 ◽  
Vol 193 (10) ◽  
pp. E344-E344
Author(s):  
Iris Gorfinkel
Keyword(s):  
1988 ◽  
Vol 21 (4) ◽  
pp. i-i
Keyword(s):  

2014 ◽  
Vol 60 (3) ◽  
pp. 463-470 ◽  
Author(s):  
Charles D Hawker ◽  
William McCarthy ◽  
David Cleveland ◽  
Bonnie L Messinger

Abstract BACKGROUND Mislabeled samples are a serious problem in most clinical laboratories. Published error rates range from 0.39/1000 to as high as 1.12%. Standardization of bar codes and label formats has not yet achieved the needed improvement. The mislabel rate in our laboratory, although low compared with published rates, prompted us to seek a solution to achieve zero errors. METHODS To reduce or eliminate our mislabeled samples, we invented an automated device using 4 cameras to photograph the outside of a sample tube. The system uses optical character recognition (OCR) to look for discrepancies between the patient name in our laboratory information system (LIS) vs the patient name on the customer label. All discrepancies detected by the system's software then require human inspection. The system was installed on our automated track and validated with production samples. RESULTS We obtained 1 009 830 images during the validation period, and every image was reviewed. OCR passed approximately 75% of the samples, and no mislabeled samples were passed. The 25% failed by the system included 121 samples actually mislabeled by patient name and 148 samples with spelling discrepancies between the patient name on the customer label and the patient name in our LIS. Only 71 of the 121 mislabeled samples detected by OCR were found through our normal quality assurance process. CONCLUSIONS We have invented an automated camera system that uses OCR technology to identify potential mislabeled samples. We have validated this system using samples transported on our automated track. Full implementation of this technology offers the possibility of zero mislabeled samples in the preanalytic stage.


2003 ◽  
Vol 38 (7) ◽  
pp. 17-45
Author(s):  
Christine Lehmann
Keyword(s):  

2019 ◽  
pp. 1094-1099
Author(s):  
Harpinder Singh ◽  
Dheeraj Gambhir ◽  
Sagar Taneja ◽  
Amardeep Singh

Quick response (QR) codes are two-dimensional codes. They are machine-readable optical labels which contain information of the object on which they are attached. Due to their large storage capacity and faster readability, they are becoming more popular than one-dimensional bar codes. They are widely used in creation of inventories, product tracking, document management, etc. With the widespread use of smart phones, QR codes are becoming more popular. QR code interpreting apps are freely available and all modern smartphones are equipped with a camera and internet to process and interpret these codes. The value of printed maps can also be enhanced by using QR codes. This chapter reviews how the value of printed or hard copy maps can be enhanced by adding QR codes that have multimedia content placed on the Cloud.


2011 ◽  
pp. 1294-1304
Author(s):  
Les Pang

Following technical strides in radio and radar in the 1930s and 1940s, the 1950s were a period of exploration for radio frequency identity (RFID) technology as shown by the landmark development of the long-range transponder systems for the “identification, friend or foe” for aircraft. Commercial use of RFID appeared in the 1960s, such as electronic article surveillance systems in retail stores to prevent theft. The 1970s were characterized by developmental work resulting in applications for animal tracking, vehicle tracking, and factory automation. RFID technology exploded during the 1980s in the areas of transportation and, to a lesser extent, personnel access and animals. Wider deployment of RFID tags for automated toll collection happened in the 1990s. Also, there was growing interest of RFID for logistics and having it work along side with bar codes. In the beginning of the 21st century, the application of RFID technology has been ubiquitous and now it is practically part of everyday life (Landt, 2001).


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.


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