NURS 4106 Week 10 Assembled Improvement Project Example

Assembled Improvement Project: Reducing Specimen Labeling Errors on a Medical-Surgical Unit

Student Name

College of Nursing, Walden University

NURS 4106: Patient Safety and Quality Improvement

Instructor Name

Month Day, Year

Assembled Improvement Project: Reducing Specimen Labeling Errors on a Medical-Surgical Unit

Problem Statement

Over the past year, on the composite medical-surgical floor this project serves, the laboratory rejected or corrected 38 mislabeled specimens out of about 15,800 specimens collected by nursing staff, a rate of 2.4 labeling errors per 1,000 specimens. Errors included unlabeled tubes, labels with the wrong patient's name, and labels applied to the wrong tube type. For comparison, when 147 laboratories reviewed over 3.3 million labels between them, they found labeling errors in fewer than 1 of every 1,000 labels (0.92) (Wagar et al., 2008). A mislabeled specimen can lead to a missed diagnosis, a wrong treatment, or, for blood bank samples, a potentially fatal transfusion reaction, and each rejected specimen means another needle stick for the patient.

Aim: within 6 months, reduce specimen labeling errors on the unit from 2.4 to 1.0 or fewer per 1,000 specimens, measured monthly from laboratory rejection and correction reports.

Cause Analysis

A review of all 38 events and interviews with eight nurses identified three main causes. First, batching: labels are printed at the nurses' station and carried to rooms, and on busy mornings nurses collect from two or three patients before labeling, so tubes can be matched to the wrong label. Second, labeling away from the bedside: in 24 of the 38 events, the tube was labeled at the station rather than in the patient's presence. Third, no identity check at labeling: the unit's collection process does not require scanning the patient's wristband at the time of collection. Staffing and tube supply were considered and judged minor contributors.

The pattern across events was consistent. Errors clustered between 04:00 and 07:00, when night nurses collect morning laboratory work for several patients in a short window before shift change, and 29 of the 38 events occurred in that period. Nurses described printing a stack of labels at the station, collecting from each patient in turn, and labeling the tubes back at the station, sometimes while answering a call light. Nobody described this as unsafe; it was the fastest way to meet the laboratory's morning cutoff. The cause, in other words, sits in the timing of the work and the location of the printer, not in individual carelessness.

Evidence

The evidence points directly at the causes. Every one of the 17 studies pooled in a Laboratory Medicine Best Practices review favored barcoding for patient specimen and point-of-care identification, with substantial effect sizes, and recommended barcoding as an evidence-based best practice (Snyder et al., 2012). The multi-institution labeling study found that laboratories with ongoing quality monitors for specimen identification had lower labeling error rates (Wagar et al., 2008), which supports measuring errors continuously rather than once.

Intervention

The intervention addresses each cause. Bedside label printing: a small label printer on each workstation on wheels prints labels only after the nurse scans the patient's wristband in the room. One patient at a time: the unit adopts a rule that specimens are labeled in the patient's presence before the nurse leaves the room, and blood is not collected from a second patient until the first is labeled. Continuous monitoring: laboratory labeling error reports for the unit are reviewed monthly at the staff meeting.

Each element maps to a cause: bedside printing with a wristband scan answers both labeling away from the bedside and the missing identity check, and the one-patient rule answers batching. Monthly review answers the evidence on quality monitors.

Measures

Outcome: specimen labeling errors per 1,000 specimens, monthly, from laboratory reports, the same measure used in the problem statement and aim. Process: percentage of specimen collections with a wristband scan at the bedside, from the printer log. Balancing: time from specimen collection to laboratory receipt, to detect whether bedside printing slows specimens, and the number of redraws for any reason. All three are plotted monthly on run charts against the 12-month baseline median (Perla et al., 2011).

Implementation

The first plan-do-study-act cycle tests bedside printing on one hall for 4 weeks on day shift, with two workstations fitted with printers (Langley et al., 2009). Nurses record problems on a card, and the process measure is reviewed weekly. If scanning compliance exceeds 90% and collection-to-receipt time does not rise meaningfully, the second cycle extends to both halls and night shift. The third cycle makes the one-patient-at-a-time rule part of the unit's written specimen collection procedure and adds it to orientation. Stakeholders include the laboratory, which provides error reports; information technology, which configures the printers; and phlebotomy, which covers some morning draws and must follow the same process.

Anticipated Barriers

Three barriers are expected. Night nurses may resist a rule that slows the early morning draws; the response is to ask the laboratory whether the morning cutoff can move 30 minutes later for this unit during the pilot. Printers may jam or run out of labels; the response is a spare label roll on each workstation and a simple fix card. Nurses may scan the wristband and then label at the station anyway; the process measure will not catch that, so the charge nurse will observe five collections a week during the pilot and record where labeling happened. Each barrier connects to one of the three causes, which is how the team will know whether a failure means the intervention was wrong or simply not yet in place.

How the Sections Fit Together

The project holds together because each stage answers the one before it. The problem is 2.4 errors per 1,000 specimens, and the aim is 1.0 or fewer on the same measure. The causes are batching, labeling away from the bedside, and no identity check at labeling. The evidence supports barcoding and continuous monitoring. The intervention combines bedside scanning and printing with a one-patient rule and monthly review, one element for each cause. The measures track the same rate as the aim, a process measure for the scanning, and a balancing measure for delay. If the project is presented in pieces, each piece should still point back to the same number and the same three causes.

References

Langley, G. J., Moen, R. D., Nolan, K. M., Nolan, T. W., Norman, C. L., & Provost, L. P. (2009). The improvement guide: A practical approach to enhancing organizational performance (2nd ed.). Jossey-Bass.

Perla, R. J., Provost, L. P., & Murray, S. K. (2011). The run chart: A simple analytical tool for learning from variation in healthcare processes. BMJ Quality & Safety, 20(1), 46-51. https://doi.org/10.1136/bmjqs.2009.037895

Snyder, S. R., Favoretto, A. M., Derzon, J. H., Christenson, R. H., Kahn, S. E., Shaw, C. S., Baetz, R. A., Mass, D., Fantz, C. R., Raab, S. S., Tanasijevic, M. J., & Liebow, E. B. (2012). Effectiveness of barcoding for reducing patient specimen and laboratory testing identification errors: A Laboratory Medicine Best Practices systematic review and meta-analysis. Clinical Biochemistry, 45(13-14), 988-998. https://doi.org/10.1016/j.clinbiochem.2012.06.019

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