NUR 683 Module 2 Safety Science Paper Example

Why Nurses Err at the Medication Cart: A Work System Analysis Using SEIPS and Reason's Model

[Student Name]

Southern New Hampshire University

NUR 683: Patient Safety and Quality Capstone

Module Two Safety Science Paper

[Instructor Name]

[Date]

Why Nurses Err at the Medication Cart: A Work System Analysis Using SEIPS and Reason's Model

When a medication error reaches a patient, the usual question is who made it. Safety science asks a different question: what about the work made the error likely? This paper applies two established frameworks to medication administration on the 34-bed cardiac telemetry floor at Ridgeview Memorial, a composite hospital, where direct observation found errors in close to one in ten administrations. SEIPS, a human factors model from the Systems Engineering Initiative for Patient Safety, lays out the elements of the work system and how they shape care. Reason's account of error explains how failures at the bedside connect to weaknesses designed into the system. A third perspective, the high reliability path described by Chassin and Loeb, explains what an organization must build to keep errors rare over time.

The SEIPS Work System Model

Carayon et al. (2006) developed the SEIPS model to bring human factors engineering into health care. Its core is the idea that five parts of the work system act on one another: the person doing the work, the tasks involved, the tools and technologies used, the physical environment and the organizational conditions such as staffing, scheduling and policy. How those parts fit together determines how care is actually delivered, and delivery determines what happens to patients, to staff and to the organization. Because the elements interact, a change in one, such as a new scanner, can create problems in another, such as extra steps that tempt workarounds.

The model's value for a capstone is that it forces a complete inventory. An analysis that looks only at the nurse, or only at the technology, will miss the interactions that produce most errors.

Applying SEIPS to the Unit

Observation on the unit over two weeks, with the charge nurse and a pharmacist, showed how each element contributes. Nurses carry five patients on days and six at night, many on cardiac drips that need frequent titration. The 9 a.m. medication pass is the densest task of the day, with an average of eleven medications per patient. Barcode scanners are wall-mounted in only half the rooms, so nurses often scan at the doorway or carry wristbands to the scanner. The medication room sits beside the nurses' station, where phone calls, alarms and questions converge. Policy requires two-nurse checks for insulin and heparin, which pulls a second nurse away from her own patients.

These conditions produce predictable process failures: scanning skipped when the scanner is out of reach, doses prepared while answering questions and double checks done hurriedly in the hallway. Table 1 maps them.

Table 1. SEIPS Elements and Observed Conditions

Note. Conditions are drawn from two weeks of observation on the composite unit.

Reason's Model: Active Failures and Latent Conditions

Reason (2000) argued that errors can be seen in two ways. The person approach treats mistakes as the product of forgetfulness, inattention or carelessness and responds with retraining, warnings and discipline. The system approach accepts that people are fallible and asks why defenses failed. He distinguished active failures, the slips and rule-bending of the clinician holding the syringe, from latent conditions, the weaknesses created by design, staffing and management decisions that may lie dormant for years until they combine with an active failure. His image of stacked slices of cheese, each with holes that shift, conveys how harm occurs only when gaps in several defenses line up.

On the unit, a nurse who gives the wrong dose after being interrupted three times has committed an active failure. But the latent conditions are all visible in Table 1: the medication room's location, the missing scanners, the staffing pattern and the absence of protected time. Retraining that nurse would change nothing about the next nurse's chances. The system approach instead targets the latent conditions, which is where the capstone intervention must aim.

From Analysis to High Reliability

Analysis alone does not make a unit safe. Chassin and Loeb (2013) describe high reliability organizations as those that operate hazardous processes for long periods with remarkably few failures, and they argue that health care can approach this only by building three capacities together. The first is leadership committed to eliminating harm, not just reducing it. The second is a safety culture in which staff report hazards without fear and leaders respond. The third is robust process improvement, meaning disciplined methods for finding causes and testing solutions rather than one-time fixes.

For this capstone, the implication is that an intervention aimed at interruptions and scanning must also address culture and leadership. A quiet-zone policy for the medication room will fail if managers interrupt nurses there, and a scanning target will backfire if staff fear punishment for reporting that a wristband would not scan.

Implications for the Capstone

The combined analysis narrows the capstone's focus to two latent conditions that are both modifiable and strongly linked to errors: interruptions during medication preparation and gaps in barcode scanning. SEIPS explains how these conditions arise from interacting elements, Reason explains why fixing them matters more than retraining individuals and the high reliability perspective explains why the fix must include leadership behavior and reporting culture. The frameworks also shape measurement: process measures, such as interruptions per administration and scanning compliance, will be tracked alongside the observed error rate.

Limitations

SEIPS is descriptive and does not rank which elements matter most, so the choice of focus relies on judgment and the literature. Reason's model has been criticized for implying neat, linear layers of defense when real failures are messier. The high reliability literature draws heavily on industries such as aviation, whose conditions differ from nursing units. These limits do not undermine the analysis but remind the project to test assumptions with data.

Conclusion

Medication errors on the unit are not mainly the product of careless nurses but of a work system that interrupts, rushes and undersupplies them. Applying SEIPS and Reason's model shifts the capstone's attention from people to conditions, and the high reliability perspective ensures that the intervention reaches the culture and leadership needed to sustain it.

References

Carayon, P., Schoofs Hundt, A., Karsh, B.-T., Gurses, A. P., Alvarado, C. J., Smith, M., & Flatley Brennan, P. (2006). Work system design for patient safety: The SEIPS model. Quality and Safety in Health Care, 15(Suppl. 1), i50-i58. https://doi.org/10.1136/qshc.2005.015842

Chassin, M. R., & Loeb, J. M. (2013). High-reliability health care: Getting there from here. The Milbank Quarterly, 91(3), 459-490. https://doi.org/10.1111/1468-0009.12023

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