Puggle16

The 3 bottle drainage system for chest tubes. White dresses, white hose, white SAS shoes, nurses cap secured with 50 bobby pins Scrubbing, washing and starching your white nurses cap every night. Rounding with the doctors meant you as the nurse followed along carrying the pt. charts. The doctor examined the patient verbalizing his findings and the nurse was the scribe writing it all down for the doctor to sign. Heaven help you if you asked the doctor to repeat something!

The floowing link has some good references on the topic.

While I'm not sure where the OP is located many of us who are bedside nurses work in areas that are "right to work" states and unions are persona non grata. Especially in the South, hospitals will go to great extents to keep unions out.

Biometric fingerprint systems are used in a wide array of applications. But the system is not without problems. Many scanners dont recognize liveness. So it is very easy to fool the system with a forged fingerprint from a latent sample taken from somewhere else the user touched. Firthermore, fingerprint quality degrades with age, lower ambient temperatures, wet or very dry (chapped) fingerprints, the amount of pressure of the finger on the sensor and if the sensor isn't kept clean. Optical readers are the most common type of sensor. Theses utilize a digital camera that acquires a visual image of the fingerprint. These sensors are very impacted by a dirty sensor surface, or marked fingers. These readers are the easiest to fool. There are 3 different levels of fingerprint patterns. With Minutiae being the most commonly used level in biometric systems for user verification and identification purposes. The major Minutiae features of fingerprints are ridge endings, bifucations and short ridges. The representation of a fingerprint by the Minutiae is not only the type and position of these features, but also the angle and direction of the ridge; the distance between 2 consecutive ridges. Minutiae matching relies on recognition of these Minutiae points. Fingerprint samples are acquired from the subject by a sensor. The sensor's output is sent to a processor which extracts the distinctive but repeatable measures of the samples and discards all other components. The resulting features can then be stored in the database as a template, or compared to a specific template, many templates or all templates already stored in a database to determine if there is a match. A decision regarding the identity chain is made based on the similarity between the sample's features and those of the template or templates compared. Verification - the sample features are considered to match a compared template when the similairity score exceeds a specific threshold. Identification - the enrolled identifier or template is a potential candidate for the subject when the similairity score exceeds a specific threshold, and/or when the similairity score is among the highest k values generated for a specific k value. FVC-onGoing initiative is a web based automated evaluation system for fingerprint recognition algorithms, managed by the FVC Biometric System Lab of the University of Bologna. Their testing is carried out on a set of sequestered datasets. The very best state of the art fingerprint recognition algorithms have an error rate of 0.7% while others can be as high as 20%. According to Carefusion, the BioID fingerprint recognition algorithm system utilized by in their Pyxis Medstation 4000 unit is Lumidigm technology. A biometrics system utilizing multispectral imaging technology manufactured by HD Global. However Carefusion dues not identify which series of Limidigm they are using nor do they provide any actual data on what their FRR - false reject rate, FTE - failure to enroll, FTA - failure to acquire and FAR - false acceptance rate*** are for their various Pyxis versions. ***FAR - the false acceptance rate; is the percentage of times the system will mistake one user for another user.

It really makes me made when hospitals purchase equiptment and are not familiar with how it works. The Pyxis Medstation establishes a BioID for each user, i.e. it scans your fingerprint. HOWEVER, It is not storing the user's fingerprint. The mechanism measures a mathmatical distance between specific ridges in the user's fingerprint and converts these into a mathmatical algorithm. It is possible for two users' finger scan to produce identical ridge measurements and therefore the Pyxis will register the same mathematical algorithm for more than one user. Your employer should have known of this possibility when they purchased the Pyxis as well as the issues from hand sanitizers etc. Good luck. See the newletter regarding how the scanner works below: