NUR4083 Module 6 technology adoption plan example

Scan the Wristband, Print at the Bedside: An Adoption Plan for Barcode Specimen Collection, From Superusers to the Night the Network Goes Down

Student Name

American College of Education

NUR4083: Nursing Informatics

Module 6 Assignment

Instructor Name

February 15, 2027

Why This Technology

On the three adult medical units of the hospital in this plan, an invented setting, nurses draw most morning blood specimens during the 0400 medication and assessment round. Labels are printed in advance at the nurses' station from a list of pending orders, carried to the rooms and applied after the draw. The laboratory rejected or corrected 41 mislabeled or unlabeled specimens from these units last year, and in two further cases a blood bank sample turned out to contain another patient's blood, discovered only because the type disagreed with an earlier result on file. Specimen labeling errors are not rare nationally; a study of more than 3.3 million specimen labels in 147 laboratories found errors at a rate of 0.92 per 1,000 labels (Wagar et al., 2008).

Barcode positive patient identification removes the step that makes these errors likely. The nurse scans the patient's wristband, the handheld device displays the pending orders for that patient only, and a label printed at the bedside is applied immediately. Morrison et al. (2010) evaluated a barcode positive patient identification system for inpatient phlebotomy in a before-and-after design and found that labeling errors fell from 5.45 to 3.2 per 10,000 specimens, an estimated 108 mislabeling events prevented in a year, and that the system removed a preprinting step that had carried potential labeling errors. The technology works by removing the moment when a label and a patient can be separated, which is the moment preprinting creates.

Training

Training is planned for the 96 registered nurses and 30 nursing assistants on the three units, since assistants also collect urine and some point-of-care specimens. Each person will complete a 20-minute online module on the reason for the change and the steps of the new process, followed by a 45-minute hands-on session in the unit's conference room using practice wristbands, a handheld scanner and a portable label printer. The hands-on session ends with a return demonstration of three scenarios: a routine morning draw, a draw for two orders placed at different times and a patient whose wristband will not scan.

Sessions will run on every shift, including 0300 sessions for night staff, during the three weeks before go-live, and training hours will be paid and scheduled into shifts rather than added to them. For 126 staff at sixty-five minutes each, the total is about 137 staff hours, plus 18 hours for the superusers' additional session, and the plan budgets all of it explicitly. No one will be able to use the device until they have completed the return demonstration, which the educator records in the learning system.

Superusers and Go-Live Support

Each unit will have at least one superuser on every shift for the first four weeks, which means nine superusers across the three units, chosen from nurses who volunteered and who are respected by their peers rather than from those who are simply comfortable with technology. Superusers receive an additional two hours of training on troubleshooting, printer maintenance and escalation, and they carry a red badge clip during the first month so staff know who to ask. For the first week, a superuser will be relieved of a patient assignment on the 0000 to 0800 shift, when most specimens are drawn.

The informatics team and laboratory will round on each unit at 0500 for the first two weeks, and a daily fifteen-minute huddle among the superusers, the unit educator and the laboratory supervisor will review every rejected specimen and every scanning problem from the previous twenty-four hours. Problems will be logged in a shared tracker with an owner and a date. Superusers make adoption social: the question a nurse will not ask the help desk at 0400, she will ask the colleague across the hall.

Downtime: What the Floor Does Without the System

Every new dependency on technology creates a new way to fail. Larsen et al. (2020) reviewed downtime events and news reports and found that 166 United States hospitals experienced 701 days of electronic record downtime across 43 events between 2012 and 2018, that almost half of the published events involved some form of cyberattack, and that contingency planning is still mostly designed from the top down. They recommended involving the front-line staff who will carry out the downtime procedure. The federal SAFER guide on contingency planning likewise recommends written downtime procedures, paper forms kept ready and regular practice (Office of the National Coordinator for Health Information Technology [ONC], 2016).

The downtime procedure for specimen collection has four steps, written by the superusers with the laboratory. First, if handheld devices or printers fail but the electronic record is available, nurses print labels for one patient at a time at the station, take only that patient's labels to the room and verify two identifiers against the wristband aloud before labeling. Second, if the electronic record is unavailable, nurses use pre-printed downtime requisitions and handwrite two identifiers, date, time and initials on each tube at the bedside, with a second nurse verifying any blood bank specimen. Third, in a power failure, the portable printers run on batteries charged at the start of every shift, and the procedure moves to the handwritten process when batteries fail. Fourth, all downtime specimens are delivered to the laboratory in a marked bag so they are checked with extra care.

The procedure will be tested in an unannounced drill on each unit within two months of go-live, at 0400, with the devices taken offline. The drill will be scored on whether staff found the downtime forms within five minutes and whether every specimen collected during the drill was labeled correctly.

Measures of Adoption

Adoption will be measured over six months. The primary outcome is mislabeled or unlabeled specimens from the three units per 10,000 specimens collected, compared with the baseline year. The process measure is the proportion of specimens collected with a wristband scan, drawn from device logs, with a target of 95 percent by month two. A workaround measure will track the number of specimens labeled using the manual override function, since barcode systems commonly produce workarounds such as scanning a spare wristband kept at the station. Any override rate above 3 percent on a unit will trigger a superuser review of the reasons. A staff survey at one and six months will ask nurses how long a typical morning draw takes compared with the old process and what still gets in the way, because a system that adds time at 0400 will be worked around no matter how well it is trained. The results of each measure will be posted on the unit's quality board monthly, so that staff see the effect of the change they were asked to make.

Conclusion

Barcode specimen collection addresses a real and measurable source of error by making it hard to separate a label from the patient it belongs to. Adopting it well depends less on the device than on the plan around it: paid training on every shift, respected superusers where the work happens, daily review in the first weeks, measures that include the workarounds and a downtime procedure that nurses wrote, practiced and can carry out at 0400 when the network goes down.

References

Larsen, E. P., Rao, A. H., & Sasangohar, F. (2020). Understanding the scope of downtime threats: A scoping review of downtime-focused literature and news media. Health Informatics Journal, 26(4), 2660-2672. https://doi.org/10.1177/1460458220918539

Morrison, A. P., Tanasijevic, M. J., Goonan, E. M., Lobo, M. M., Bates, M. M., Lipsitz, S. R., Bates, D. W., & Melanson, S. E. F. (2010). Reduction in specimen labeling errors after implementation of a positive patient identification system in phlebotomy. American Journal of Clinical Pathology, 133(6), 870-877. https://doi.org/10.1309/AJCPC95YYMSLLRCX

Office of the National Coordinator for Health Information Technology. (2016). SAFER guides: Contingency planning. U.S. Department of Health and Human Services.

Wagar, E. A., Stankovic, A. K., Raab, S., Nakhleh, R. E., & Walsh, M. K. (2008). Specimen labeling errors: A Q-Probes analysis of 147 clinical laboratories. Archives of Pathology & Laboratory Medicine, 132(10), 1617-1622. https://doi.org/10.5858/2008-132-1617-SLEAQA