NU 725 Unit 4 systems evaluation example

Technology and Nursing Informatics in Advanced Practice Herzing University Free custom sample in 24 to 48h

This NU 725 Unit 4 systems evaluation example judges barcode medication administration as configured on a 30-bed composite surgical unit against criteria set before any evidence was gathered. Herzing NU 725 wants MSN students to evaluate a clinical system on more than whether staff like it, and this sample uses criteria drawn from the DeLone and McLean success model and the BCMA literature. Evidence under each criterion includes a three-month compliance report showing 89 percent medication scanning, observation of 20 oral administrations with a median of seven steps, and a comparison of 40 records with direct observation. Workarounds and alert burden get their own section, citing Koppel and colleagues. A limits section admits that a warning log sample of 60 was small. The overall judgment finds the system meets its purpose but fails three criteria for local reasons, such as an unreadable pharmacy label printer. About 1,020 words.

CourseNU 725 Technology and Nursing Informatics in Advanced Practice
UnitUnit 4
Paper typeInformation system evaluation
LengthAbout 1,020 words, 6 pages
FormatAPA 7 student paper
SchoolHerzing University
ProgramMSN
UpdatedOctober 2026

Free sample paper for NU 725 Unit 4

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Evaluating Barcode Medication Administration as Configured on a 30-Bed Surgical Unit

Student Name

Department of Nursing, Herzing University

NU 725: Technology and Nursing Informatics in Advanced Practice

Instructor Name

February 10, 2026

What this page is doingThe title says the system is judged as configured on one unit, not as the product was sold. A systems evaluation is read for discipline: criteria first, evidence from the installed system second.
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Evaluating Barcode Medication Administration as Configured on a 30-Bed Surgical Unit

Criteria, Committed to Before Looking

The system under review is barcode medication administration (BCMA) as installed on a 30-bed composite surgical unit, where nurses scan the patient's wristband and each medication before administration and the system checks the scan against the active order. Six criteria were set before any data were reviewed, organized loosely around the dimensions of information systems success proposed by DeLone and McLean (2003): system quality, information quality, use, user satisfaction, and net benefits. Each criterion has a definition of what would count as meeting it.

Criterion 1, scanning compliance: at least 95% of administered doses have both the patient and the medication scanned. Criterion 2, error interception: the system produces actionable warnings that stop wrong-patient, wrong-drug, and wrong-dose events, with evidence from the unit's warning log. Criterion 3, override behavior: fewer than 10% of warnings are overridden, and override reasons are documented. Criterion 4, workflow burden: routine administration of a single oral medication takes no more than five discrete steps, and the process does not require workarounds. Criterion 5, information quality: the medication administration record matches what was given, with time stamps accurate to within five minutes. Criterion 6, user acceptance: nurses report that the system helps rather than hinders safe administration.

What this page is doingThe criteria set comes before any description of the system, and each criterion carries a definition of what would count as meeting it. An evaluation that starts by describing the product tends to become a summary of its features, which earns little.
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Evidence Under Each Criterion

Criterion 1, compliance. The system's compliance report for the past three months shows patient wristband scanning at 97% and medication scanning at 89%. Medications not scanned were concentrated in three groups: intravenous piggyback bags, whose labels printed by pharmacy often fail to scan; liquid oral medications drawn up from bulk bottles; and doses given during rapid response events. Verdict: not met for medication scanning.

Criterion 2, interception. The warning log recorded 412 warnings over the three months. A review of a random sample of 60 found 9 that prevented a real error, including 2 wrong-patient scans and 4 doses given too early. That matches published evidence: when a large academic hospital compared units before and after adoption, barcode technology combined with an electronic administration record reduced administration errors unrelated to timing by about 41% and potential adverse drug events by about half (Poon et al., 2010). Verdict: met, with evidence of real interceptions.

Criterion 3, overrides. 138 of 412 warnings, 33%, were overridden. Most overrides were for timing warnings on scheduled doses given within the hospital's allowed window, which the system flags because its timing tolerance is set more narrowly than policy. Verdict: not met, largely because of a configuration choice.

Workflow, Workarounds, and Alert Burden

Criterion 4, burden. Observation of 20 routine oral administrations counted a median of seven discrete steps, including logging in, opening the record, scanning the wristband, scanning each medication, reviewing a pain reassessment prompt, confirming, and signing. Two workarounds were observed: a nurse carrying pre-printed spare wristband labels for a patient whose band would not scan, and another scanning a medication's barcode from the dispensing cabinet drawer rather than the dose itself. Both are well-described workarounds that defeat the safety check they bypass (Koppel et al., 2008). Verdict: not met.

The override rate and the timing warnings are linked. When more than a third of warnings do not require action, nurses learn to click through them, and the warnings that matter lose force. Alert burden here is not an abstract risk; it is visible in the override pattern.

Information Quality and User Acceptance

Criterion 5, information quality. A comparison of 40 administration records with direct observation found the recorded time within five minutes of the actual time in 36, or 90%. The four discrepancies were doses documented after the fact at the end of a medication pass. Verdict: largely met.

Criterion 6, acceptance. A short survey of 22 nurses on the unit found that 18 agreed that the system catches errors they would otherwise miss, but 15 said it slows them down, and 12 said the timing warnings are "mostly noise." Verdict: partly met; nurses value the safety function and distrust the warnings.

Limits of This Evaluation

Three limits affect how far these verdicts can be trusted. First, the warning log sample of 60 was small, and the count of nine real interceptions could easily be several higher or lower across the full log; the verdict on criterion 2 rests as much on the published evidence as on the local sample. Second, the observations of workflow were done on day shifts, when staffing is highest, and may understate the burden on nights, when one nurse may cover more patients and workarounds are more tempting. Third, the survey reached 22 of the unit's 38 nurses, and those who responded may differ from those who did not.

None of these limits reverses a verdict, but they shape the recommendations. The workflow measures should be repeated on nights before the pain reassessment prompt is moved, and the survey should be repeated after the changes, with a reminder sent to every nurse on the unit, so that the before-and-after comparison uses the same population.

Overall Judgment

The system meets its core purpose: it intercepts real errors, and nurses recognize that. It fails on three criteria, and in each case the cause is local rather than inherent to the product: a pharmacy label printer that produces unscannable barcodes, a timing tolerance set narrower than hospital policy, and a sequence of prompts that adds steps without adding safety. The unit is therefore getting part of the safety benefit reported in the literature and paying a higher workflow cost than necessary.

Recommendations

First, replace or recalibrate the pharmacy label printers and track the intravenous piggyback scan rate weekly until it exceeds 95%. Second, widen the timing tolerance to match the hospital's medication administration policy, and measure the override rate again after one month, with a target below 15%. Third, move the pain reassessment prompt out of the administration sequence and into a separate task, reducing the routine steps from seven to five. Fourth, stock bulk liquids as unit doses where possible. Each recommendation targets a specific failed criterion, and each has a measure that can show whether it worked.

What this page is doingThe judgment separates what the product does from how it was configured locally, and each recommendation targets a specific failed criterion with a measure attached. Evaluations that rely on product literature rather than the installed system tend to miss exactly these local causes.
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References

DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748

Koppel, R., Wetterneck, T., Telles, J. L., & Karsh, B.-T. (2008). Workarounds to barcode medication administration systems: Their occurrences, causes, and threats to patient safety. Journal of the American Medical Informatics Association, 15(4), 408-423. https://doi.org/10.1197/jamia.M2616

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/NEJMsa0907115

NU 725 Unit 4 instructions, in plain terms

The Unit 4 assignment has you judge one clinical technology from your own practice setting. Expect to describe the system and its purpose, select evaluation criteria or a framework, gather evidence for each criterion, and reach a judgment with recommendations. Common frameworks include the DeLone and McLean information systems success model, the technology acceptance model and sociotechnical models. Many versions ask you to address usability, workflow fit, safety, information quality and user satisfaction. Some sections require data, while others accept observation and published evidence. Set your criteria before you look at the system so your conclusions are not built backward. Name the configuration you are evaluating, since the same product can perform very differently on different units.

Inside the NU 725 Unit 4 example

The paper opens by stating the criteria and the standard for each before any finding appears, which is the move that makes it an evaluation instead of a description. Evidence follows criterion by criterion, numbered so a reader can match each verdict to its standard. Compliance draws on the scanning report and breaks missed scans down by medication type. The burden section uses observed steps and alert counts, and connects workarounds to Koppel's typology. Information quality is tested by comparing recorded times with what was observed. The limits section is specific, giving the sample sizes and how they affect confidence in each verdict. The overall judgment separates the product from its local setup. Recommendations are tied to the failed criteria, including printer replacement and a timing tolerance that matches policy.

Where the points sit in the NU 725 Unit 4 rubric

Herzing rubrics for a systems evaluation typically assign points to the system description, the evaluation framework and criteria, the evidence and analysis, conclusions and recommendations, and writing. Framework points require a named model or clearly sourced criteria. The evidence criterion is usually weighted most and gives full credit when each criterion has data, observation or literature behind it rather than impressions. Analysis points reward recognizing unintended consequences such as workarounds and alert fatigue. Conclusions earn credit when they follow from the evidence and distinguish fixable local problems from inherent limits. Each recommendation needs a direct link to a failed criterion. Writing points cover a logical order, a measured voice and APA 7 references to the framework, the safety evidence such as Poon and colleagues' trial, and any vendor documentation used.

NU 725 Unit 4 help before you submit

The most common Unit 4 problem is an evaluation that describes the system's features and never judges them against anything. Criteria chosen after looking at the system, or not stated at all, cost heavily. Students also rely on their own experience as the only evidence, which reads as opinion. Workarounds are frequently left out because they feel like staff failures, yet the rubric treats them as evidence about the system. Another gap is blaming the product for problems caused by local configuration or hardware. Recommendations such as upgrade the system are too vague to earn points. If your data is limited, say so in a limits section rather than overstating certainty. Finally, cite the evaluation framework you use, because many papers name a model without a reference.

Get a NU 725 Unit 4 example written to your instructions

Send the Unit 4 instructions and the rubric from your NU 725 classroom, plus the system you are evaluating and any framework your instructor named. We write a custom example against those instructions, criteria declared first and the verdict carrying its tradeoff, and return it in 24 to 48 hours. The first one is free.

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NU 725 Unit 4 questions, answered

Where do criteria come from if none are given?

From published evaluation frameworks and from what the system was installed to accomplish. Informatics literature offers established dimensions covering safety, quality, efficiency, usability and cost, and any of those can be adapted with a citation. Whatever set you choose, define each criterion so a reader knows what would count as meeting it before you present any evidence against it.

What if I cannot get performance data for the system?

Say what you could not obtain and evaluate on what remains. Published studies of comparable installations, observable characteristics of the local build, documented override behavior and what staff report doing all constitute evidence when they are labeled honestly. What damages the piece is filling a gap with a vendor claim and presenting it as though it described the system in front of you.

Can I evaluate a system I use every day at work?

In many sections yes, and familiarity helps, provided the writing stays at the level of configuration and process. Keep patient data, screenshots carrying identifiers and internal documents you cannot share out of the paper. Describe the setting generically, and be careful that criticism of a build stays criticism of the build rather than of named colleagues who maintain it.

Where can I find a free NU 725 Unit 4 sample paper?

This page carries the full NU 725 Unit 4 barcode evaluation free, with each criterion, its evidence and the overall judgment. If you are evaluating a different system, such as a smart pump or a patient portal, ask for a custom paper built on it.

What framework should I use to evaluate a clinical information system?

The DeLone and McLean success model is common because it covers system quality, information quality, use, satisfaction and net benefits. This sample borrows its criteria from that model and adds burden and workarounds from the barcode literature.