NR 360 Week 8 Barcode Scanner Trust Reflection Example

What the Green Light Confirms: A Reflection on Trusting the Scanner

Student Name

College of Nursing, Chamberlain University

NR 360: Information Systems in Healthcare

Instructor Name

Month Day, Year

What the Green Light Confirms: A Reflection on Trusting the Scanner

What I Believed at the Start

When this term began, I took it for granted that a green light on my barcode scanner meant the medication I was about to give was right for the patient in front of me. I would not have said it that way, and if asked, I could have listed what the scanner checks. But in practice, on a busy medical unit with five or six patients, the green confirmation had become the end of my thinking about a dose. I scanned, I saw green, and I gave. When I felt uneasy about a medication, the green light settled the unease for me. In my first discussion post, I wrote that barcode scanning had taken the worry out of medication administration, and I meant it as praise.

What I Think Now

I now think the green light confirms something much narrower than I had allowed it to. It confirms that the medication in my hand matches an active order for the patient whose wristband I scanned, at a dose and time the order allows. It does not confirm that the order was meant for this patient, that it makes sense for this patient's condition right now, or that the prescriber saw the warnings the system showed them. Those are still my job. The scanner is excellent at catching one kind of error, the wrong package or the wrong wristband, and published evidence shows it reduces administration errors substantially (Poon et al., 2010). It is blind to the kind of error that starts upstream, in the order itself.

What Changed My Mind

The change came from one assignment. In the fourth week I analyzed a case in which a beta-blocker ordered for one patient was entered on another patient's chart, and the nurse gave it after a clean scan despite noticing that the patient's heart rate was low. Writing out the chain of conditions link by link, I reached the point where the nurse paused, and I realized that I would have done exactly what she did. The pause is where my own practice lived. The readings for that week, especially the description of how order entry systems can facilitate errors through their displays and their assumptions (Koppel et al., 2005), gave me the vocabulary for what I had felt at the bedside without naming it: the system had replaced a question with a signal.

A second piece came from the workflow analysis in the third week, where I followed our vital signs routine before and after device integration. Seeing values sit gray and unvalidated in the chart for hours showed me how easily a record can look finished when it is not, and it made me less willing to treat any screen as the final word.

How the Belief Showed Up at Work

Looking back at my own shifts with the new view, I can find the belief at work in small ways. I used to give a newly ordered dose of a medication I had not seen before for a patient without looking up why it was ordered, as long as it scanned. I used to explain to new graduates I was precepting that the scanner was our safety net, and I would tell them that if it went green they could relax. When a scan failed because a wristband was wrinkled, I felt more anxious than when a scan succeeded on a medication that puzzled me, which is exactly backward: a failed scan is usually a label problem, while a puzzling order that scans cleanly is where an upstream error would hide.

None of this was reckless by the standards of my unit, where scanning compliance is tracked and praised. The belief was reinforced by the way the tool is measured: we are audited on whether we scan, never on whether we question. The course did not teach me that scanning is bad; it taught me that the audit measures the part of safety the scanner already covers and ignores the part that is still mine.

What I Will Do Differently

My commitment is specific enough that a colleague would notice if I broke it. When a new order for a high-alert medication, meaning insulin, an anticoagulant, a rate-controlling cardiac drug or an opioid, surprises me because it does not fit what I know about the patient, I will call the prescriber before giving it, even if the scan is green. I will not wait for the scanner to tell me something is wrong, because it cannot. I have told my charge nurse about this commitment and asked her to tell me if she sees me skip it. In the three weeks since the case analysis, I have made two such calls. One order was correct and the prescriber explained the reasoning; the other had been entered on the wrong patient and was corrected before any dose was given.

What It Means for How I Precept

The change has already reached how I teach. With the new graduate I am precepting this month, I no longer describe the scanner as a safety net. I describe it as a check on the package and the wristband, and I ask her, before every new high-alert medication, to tell me in one sentence why the patient is getting it. When she cannot, we look it up together or call. It adds a minute to some doses, and it has already produced one useful conversation with a pharmacist about a duplicate anticoagulant order that the scanner had accepted because both orders were active.

What Is Still Unresolved

I have not resolved how to make this kind of check sustainable on a heavy shift. Two calls in three weeks is manageable. On a night with an admission, a rapid response and six patients, I am not sure I will have the time or the energy to question an order that the system has approved, and I suspect that is exactly the night when an upstream error is most likely. Reason (2000) describes errors as the product of systems with holes in every layer, and my own attention is one of those layers. The fix I would like to see is in the system, a check in order entry that makes wrong-patient orders harder to place, but until it exists, the question I learned to ask this term has to come from me.

References

Koppel, R., Metlay, J. P., Cohen, A., Abaluck, B., Localio, A. R., Kimmel, S. E., & Strom, B. L. (2005). Role of computerized physician order entry systems in facilitating medication errors. JAMA, 293(10), 1197-1203. https://doi.org/10.1001/jama.293.10.1197

Poon, E. G., Keohane, C. A., Yoon, C. S., Ditmore, M., Bane, A., Levtzion-Korach, O., Moniz, T., Rothschild, J. M., Kachalia, A. B., Hayes, J., Churchill, W. W., Lipsitz, S., Whittemore, A. D., Bates, D. W., & Gandhi, T. K. (2010). Effect of bar-code technology on the safety of medication administration. New England Journal of Medicine, 362(18), 1698-1707. https://doi.org/10.1056/NEJMoa0907115

Reason, J. (2000). Human error: Models and management. BMJ, 320(7237), 768-770. https://doi.org/10.1136/bmj.320.7237.768