| Course | NURS 8210 Transforming Nursing and Healthcare Through Technology |
|---|---|
| Week | Week 3 |
| Paper type | Technology outcome rationale paper |
| Length | About 1,260 words, 7 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | Doctor of Nursing Practice (DNP) |
| Updated | September 2026 |
Free sample paper for NURS 8210 Week 3
From Sensor to Skin: An Outcome Rationale for Wearable Position Sensors and Hospital-Acquired Pressure Injuries in a Five-Hospital System
Student Name
College of Nursing, Walden University
NURS 8210: Transforming Nursing and Healthcare Through Technology
Instructor Name
Month Day, Year
From Sensor to Skin: An Outcome Rationale for Wearable Position Sensors and Hospital-Acquired Pressure Injuries in a Five-Hospital System
The Outcome
Technology proposals that name a benefit but no outcome give leadership nothing to hold them to. This rationale names one: pressure injuries that develop in the hospital (HAPIs), counted at stage 2 and above, among adults in the intensive care and step-down units of a composite five-hospital system, with the rate figured against every thousand days of patient stay. The count comes from the system's existing skin surveillance, in which wound care nurses confirm each injury and its stage. The technology under consideration is a wearable patient sensor, a small device placed on the chest that detects the patient's position and movement and sends that information to a display at the nurses' station, flagging patients who have not been repositioned within the ordered interval.
The outcome matters to leadership for three reasons. Pressure injuries cause pain, infection and longer stays for patients. They are costly: one national analysis estimated that HAPIs cost US hospitals billions of dollars each year, driven mostly by the most severe injuries (Padula & Delarmente, 2019). And they are widely treated as a nursing-sensitive quality indicator, so a rising rate is read as a nursing problem whether or not its causes lie with nursing alone.
The Logic Model
Figure 1 presents the causal chain in Donabedian's terms. Donabedian (1988) treated the three as a sequence of probabilities: adequate resources raise the odds of good practice, which in turn raises the odds of good results. The chain below makes each link explicit so that each can be argued and, later, measured.
Table 1
Figure 1, Described as a Table: The Proposed Causal Chain From Sensor to Outcome
| Level | Element | Measure a later evaluation would use |
|---|---|---|
| Structure | Wearable sensor on each eligible patient; central display; turn interval set from the patient's ordered schedule; nurse training | Share of eligible patients wearing a working sensor |
| Process, link 1 | Nurses see which patients are overdue for a turn | Time overdue patients spend unflagged or unaddressed |
| Process, link 2 | Patients are repositioned on schedule and held off the at-risk area long enough | Turning compliance: share of turn intervals met, from sensor data |
| Outcome | Fewer tissue injuries from sustained pressure | HAPI rate (stage 2 and above) for each thousand patient days |
Each arrow in the chain is a claim, and the sections that follow argue each one with evidence.
Link 1: From Sensor to Recognition
The first link claims that the sensor improves nurses' knowledge of when each patient was last repositioned. Under current practice, nurses rely on memory, documentation after the fact and a turning schedule posted in the room. In a busy intensive care unit, turns are often delayed by procedures, admissions and emergencies, and documentation may record a turn that was partial or brief. The sensor replaces memory with a continuous record, and the display shows which patients are overdue. This link is the most plausible of the three, because it concerns information, and the device's purpose is to provide it.
Link 2: From Recognition to Repositioning
The second link claims that knowing a patient is overdue leads to timely repositioning. This is less certain. A flag on a display does not create time or staff, and nurses who are already stretched may see the flag and still be unable to act. The best direct evidence comes from a pragmatic randomized trial conducted in the adult ICUs of one university hospital, which compared care with the wearable sensor against usual care (Pickham et al., 2018). Patients in the sensor group had higher turning compliance than those receiving usual care, which supports this link at least in a well-resourced academic setting. Whether it holds in the system's smaller hospitals, with different staffing, is an open question the evaluation will need to answer.
Link 3: From Repositioning to Fewer Injuries
The third link claims that more consistent repositioning reduces pressure injuries. This is the link with the most complicated evidence. Repositioning is a long-standing element of prevention, and trials have examined different combinations of turning intervals and support surfaces; one found that the combination of turning every four hours on a pressure-redistributing foam mattress was associated with fewer serious injuries than other schedules (Defloor et al., 2005). Yet a Cochrane review of repositioning for prevention in adults found the evidence on repositioning frequency to be of low certainty, with no clear difference between schedules (Gillespie et al., 2020). The trial of the wearable sensor did report fewer HAPIs in the sensor group (Pickham et al., 2018), but it was conducted in one center, and a single trial cannot settle how much of the effect would carry to other settings.
The honest summary is that the evidence supports the chain more strongly at the process level than at the outcome level. The system can expect the sensor to improve turning compliance. It can hope, with some support, that injuries will fall, but it should not promise that they will.
Why the Outcome Is Not a Process Measure
It would be easy to evaluate the sensor by counting what it produces: flags fired, turns logged, sensors applied. Those are process and structure measures, and they could all rise without any patient benefiting. A sensor that fires more alerts might even worsen care, if nurses begin to ignore them. Turning compliance is closer to the outcome, since it measures what nurses actually do, but it is still a process. The rationale therefore commits the system to the outcome, the rate of HAPIs at stage 2 and above, as the measure of success, with turning compliance tracked alongside it to show whether the chain is working.
Keeping the two separate also protects the evaluation from a common trap. If injuries fall but turning compliance does not change, something other than the sensor is probably responsible, such as a new mattress contract or a change in the patient mix. If compliance rises but injuries do not fall, the third link is weaker in this system than hoped. Either result is useful, but only if both measures are collected.
Rival Explanations the Evaluation Must Address
Any change in the injury rate after the sensor is introduced could have other causes. Three rival explanations are most likely. First, other prevention measures, such as support surfaces, nutrition consults or moisture management, may change at the same time. Second, surveillance itself may change: training nurses to use the sensor may make them look more closely at skin, which could raise the count of injuries found even as true injuries fall. Third, the patient mix may shift, for example if one hospital adds a cardiac surgery program with sicker, longer-staying patients.
An evaluation should address these by recording other prevention changes, keeping skin surveillance methods constant across the period and adjusting comparisons for patient acuity where possible. A staggered rollout, introducing the sensor to one unit or hospital at a time, would also help, because a real effect should appear on each unit when it begins using the sensor and not before.
What the Rationale Asks Leadership to Accept
The rationale asks leadership to accept three things. The outcome is pressure injuries, not sensor use. The evidence is stronger for the process link than for the outcome link. And the rollout should be staged so that its effect can be seen. On those terms, the wearable sensor is a reasonable investment to test in the intensive care and step-down units, with a decision about wider use made only after the outcome data are in.
References
Defloor, T., De Bacquer, D., & Grypdonck, M. H. F. (2005). The effect of various combinations of turning and pressure reducing devices on the incidence of pressure ulcers. International Journal of Nursing Studies, 42(1), 37-46. https://doi.org/10.1016/j.ijnurstu.2004.05.013
Donabedian, A. (1988). The quality of care: How can it be assessed? JAMA, 260(12), 1743-1748. https://doi.org/10.1001/jama.1988.03410120089033
Gillespie, B. M., Walker, R. M., Latimer, S. L., Thalib, L., Whitty, J. A., McInnes, E., & Chaboyer, W. P. (2020). Repositioning for pressure injury prevention in adults. Cochrane Database of Systematic Reviews, 2020(6), Article CD009958. https://doi.org/10.1002/14651858.CD009958.pub3
Padula, W. V., & Delarmente, B. A. (2019). The national cost of hospital-acquired pressure injuries in the United States. International Wound Journal, 16(3), 634-640. https://doi.org/10.1111/iwj.13071
Pickham, D., Berte, N., Pihulic, M., Valdez, A., Mayer, B., & Desai, M. (2018). Effect of a wearable patient sensor on care delivery for preventing pressure injuries in acutely ill adults: A pragmatic randomized clinical trial (LS-HAPI study). International Journal of Nursing Studies, 80, 12-19. https://doi.org/10.1016/j.ijnurstu.2017.12.012
NURS 8210 Week 3 instructions, in plain terms
Outcome rationale papers in this course usually ask you to select a technology, name the outcome it should change and argue why the change is plausible. Expect several pages in APA format with a logic model or figure. State the outcome so it can be counted, with its data source, before discussing the technology. Lay out the causal chain from technology to outcome, and argue each link with evidence, saying where the evidence is strong and where it is thin. Keep process measures, such as alerts fired, separate from the outcome. Close with rival explanations an evaluation would need to rule out and what you are asking leadership to accept.
Inside the NURS 8210 Week 3 example
The first section defines the injury rate, names wound nurses' surveillance as its source and cites Padula and Delarmente on national cost. Table 1 lays out the chain from sensor to recognition, repositioning and injury, with a measure for each level. Three sections argue the links: the sensor informs nurses, recognition leads to turning, supported by the randomized trial of Pickham and colleagues, and turning reduces injury, where the Cochrane review found low-certainty evidence on frequency. A section explains why turning compliance is still a process measure. Rival explanations include new mattresses, sharper surveillance and sicker patients. The close recommends a staged rollout with a decision after the outcome data arrive.
Reading the NURS 8210 Week 3 rubric
Outcome rationales tend to be judged on how clearly the outcome is stated, the logic of the causal chain, the quality of evidence for each link and awareness of alternative explanations. A counted outcome with a data source earns more than a general benefit. The chain is judged on whether each link is explicit and testable. Evidence credit rewards honest grading of strength, including where results are mixed. Faculty usually credit papers that separate process from outcome measures. Rival explanations show readiness for evaluation. A readable figure or table, APA style and peer-reviewed support make up the rest. Papers that promise a fixed percentage reduction without local data tend to lose credibility.
NURS 8210 Week 3 help from the desk
Rationale papers lose marks when the outcome is vague, such as better safety, or when the technology is chosen first and the outcome attached afterward. Another common loss is skipping links in the chain, going straight from device to outcome. Some papers cite vendor claims as evidence. Others treat alerts fired or devices used as proof of benefit. Define a counted outcome, argue each link with peer-reviewed evidence, separate process from outcome and name rival explanations. The desk can draft a rationale for the technology and outcome your organization is weighing. Check how your organization already counts the outcome before proposing a new measure. If the count depends on staff noticing and reporting, say so, because a technology that improves detection can make the outcome look worse at first.
Get a NURS 8210 Week 3 example written to your instructions
Send the outcome your prompt targets, or the technology if that is what your section assigned, along with the rubric. A custom rationale arrives in 24-48h, the first free, with the logic figure delivered as a file you can redraw around your own organization and its weakest link already named.
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NURS 8210 Week 3 questions, answered
Is Donabedian required, or could another framework work?
Any framework that separates means from ends would serve, and some sections point to a logic model or a specific implementation framework instead. Donabedian's structure, process and outcome triad is used here because it is familiar to nurse leaders and maps cleanly onto technology decisions. The framework matters less than using it to organize the argument rather than naming it in passing.
Where can I find a free NURS 8210 Week 3 sample paper?
The full outcome rationale, with its logic table, evidence for each link and rival explanations, is free to read on this page. A rationale for your own technology can be requested.
What outcome does the NURS 8210 Week 3 paper hold the sensor to?
Pressure injuries of stage 2 and above that develop in the hospital, per thousand patient days, confirmed by wound care nurses, not the number of alerts or turns logged.
Does the NURS 8210 Week 3 paper promise that injuries will fall?
No. It says the evidence is stronger for better turning than for fewer injuries, and asks leadership to decide on wider use only after local outcome data are in.
What rival explanations does the paper name?
Other prevention changes such as new mattresses, sharper skin surveillance after training that finds more injuries, and a shift toward sicker patients.