| Course | NR 361 RN Information Systems in Healthcare |
|---|---|
| Week | 4 |
| Paper type | Literature review matrix |
| Length | About 1,090 words, 6 pages |
| Format | APA 7 student paper |
| School | Chamberlain University |
| Program | RN-to-BSN |
| Updated | October 2026 |
Free sample paper for NR 361 Week 4
Remote Intensive Care Monitoring and Patient Outcomes: A Literature Matrix
Student Name
College of Nursing, Chamberlain University
NR 361: RN Information Systems in Healthcare
Instructor Name
Month Day, Year
Remote Intensive Care Monitoring and Patient Outcomes: A Literature Matrix
The Question and the Columns
The question behind this matrix is whether intensive care telemedicine, in which a remote team of critical care physicians and nurses monitors patients in several intensive care units through cameras, electronic records and physiologic data feeds, is associated with lower mortality for critically ill adults. The writer works in a community hospital intensive care unit that has no overnight intensivist and is considering joining a health system's remote monitoring program, so the matrix is built to inform that decision. Nurses in the unit have a particular stake in the answer, because the remote team's nurses would watch the same monitors overnight and could call the bedside nurse at any hour.
The column headings were set before the reading started, so that every row answers the same questions: population, setting, what the service actually consisted of, what was measured, what was found and the limit that bounds the finding. The service column matters more than usual here, because intensive care telemedicine ranges from a camera that a remote physician checks when called to a program that reviews every patient on a schedule and enforces best-practice protocols, and those are different services. The measure column was restricted to mortality and length of stay because those are the outcomes reported consistently enough to compare; staff satisfaction and alarm response times, though relevant to nurses, are reported too unevenly to line up across rows.
Row 1: Thomas et al. (2009)
Population and setting: About 4,100 adults in six intensive care units in five hospitals of one health system in the United States.
Service: Remote monitoring by intensivists and critical care nurses, with alerts from physiologic data, mostly at night; local physicians kept primary responsibility.
Measure: Hospital and intensive care mortality, complications and length of stay, before and after the service began.
Finding: No significant association with mortality or length of stay overall.
Limit: Before-and-after design; local physicians often limited the remote team's role, so many patients received little active remote care.
Row 2: Lilly et al. (2011)
Population and setting: About 6,300 adults in the intensive care units of one academic medical center in the United States.
Service: Remote monitoring combined with reengineered care processes, including enforcement of best-practice protocols, earlier intensivist review of new admissions and faster response to alarms.
Measure: Hospital mortality, length of stay and preventable complications, before and after implementation.
Finding: Lower adjusted hospital mortality, shorter length of stay and fewer preventable complications after implementation.
Limit: Single center, before-and-after design; the effect cannot be separated between the remote monitoring and the process changes introduced with it.
Row 3: Young et al. (2011)
Population and setting: 13 studies of intensive care telemedicine covering tens of thousands of adult patients.
Service: Varied across studies, from passive monitoring to active programs.
Measure: Intensive care and hospital mortality and length of stay, pooled by meta-analysis.
Finding: Lower intensive care unit mortality and shorter intensive care stay with telemedicine; the reduction in hospital mortality was not statistically significant.
Limit: Most included studies were before-and-after designs with heterogeneous services, so the pooled estimate mixes very different programs.
Row 4: Kahn et al. (2016)
Population and setting: Medicare beneficiaries admitted to intensive care units in hospitals across the United States that adopted telemedicine, compared with matched hospitals that did not.
Service: Whatever telemedicine each adopting hospital implemented; not standardized.
Measure: Ninety-day mortality.
Finding: A small overall reduction in mortality associated with telemedicine, with large variation between hospitals; some programs were associated with benefit and others with none.
Limit: Observational, national data; cannot identify which program features produced benefit.
How the Rows Were Selected
The four rows were selected from a search of PubMed and CINAHL for intensive care telemedicine, tele-ICU and remote critical care monitoring, limited to adult populations and to studies reporting mortality. Studies were preferred when they were large, reported mortality as a primary outcome and described the service in enough detail to fill the service column. Two frequently cited single-hospital reports were read and not included because they described only nurse satisfaction or alarm response times, which the matrix does not track. A study that described its service only as telemedicine, with no detail about what the remote team did, would have been marked as such in the service column rather than guessed at, since the service is the variable the matrix most needs to compare.
The search also showed how the literature has changed over time. Early studies asked whether telemedicine worked at all; later work, such as the national study in Row 4, asks which programs work and why. That shift matters for a hospital deciding now, because the question is no longer whether to buy a camera system but what kind of program to join and what to require of it.
What the Matrix Shows
Read down the finding column, the studies disagree: one found no effect, one found a clear benefit, the meta-analysis found benefit on intensive care mortality but not on hospital mortality, and the national study found a small average benefit with wide variation. Read down the service column, the disagreement makes sense. The program with the clearest benefit changed care processes and gave the remote team authority to act (Lilly et al., 2011), while the program with no effect had a remote team whose role was often limited by local physicians (Thomas et al., 2009). The national study's variation between hospitals is consistent with the same explanation (Kahn et al., 2016): the camera is not the intervention; what the remote team is allowed and expected to do is.
For the writer's hospital, the matrix supports joining a remote monitoring program only if the agreement specifies what the remote team will do: scheduled review of every patient, authority to enter orders overnight and a defined response time to alerts. A contract for monitoring alone is the version the evidence supports least. None of the rows studied a community hospital unit without overnight intensivist coverage specifically, which is the gap that bounds any conclusion for this setting.
A useful next step would be to add rows describing program features directly, for example studies that compare remote teams with and without order-writing authority, so that the service column can carry more of the conclusion than it can now.
References
Kahn, J. M., Le, T. Q., Barnato, A. E., Hravnak, M., Kuza, C. C., Pike, F., & Angus, D. C. (2016). ICU telemedicine and critical care mortality: A national effectiveness study. Medical Care, 54(3), 319-325. https://doi.org/10.1097/MLR.0000000000000485
Lilly, C. M., Cody, S., Zhao, H., Landry, K., Baker, S. P., McIlwaine, J., Chandler, M. W., & Irwin, R. S. (2011). Hospital mortality, length of stay, and preventable complications among critically ill patients before and after tele-ICU reengineering of critical care processes. JAMA, 305(21), 2175-2183. https://doi.org/10.1001/jama.2011.697
Thomas, E. J., Lucke, J. F., Wueste, L., Weavind, L., & Patel, B. (2009). Association of telemedicine for remote monitoring of intensive care patients with mortality, complications, and length of stay. JAMA, 302(24), 2671-2678. https://doi.org/10.1001/jama.2009.1902
Young, L. B., Chan, P. S., Lu, X., Nallamothu, B. K., Sasson, C., & Cram, P. M. (2011). Impact of telemedicine intensive care unit coverage on patient outcomes: A systematic review and meta-analysis. Archives of Internal Medicine, 171(6), 498-506. https://doi.org/10.1001/archinternmed.2011.61
NR 361 Week 4 instructions, in plain terms
For Week 4 you lay out a literature review matrix on a telehealth or informatics topic. The checklist generally covers a focused question, a set number of scholarly studies, consistent columns for each study, a search description and a written synthesis, in APA. Fix your columns before you start reading so every row answers the same questions. Include a column for what the service or intervention actually was, since programs with the same name can differ. Keep cells as short parallel phrases. Name a specific limit for each study, not a generic comment about design. Describe how you searched. Write the synthesis from the columns, not by restating each row.
Inside the NR 361 Week 4 example
The opening section states the question and explains why the service column matters. Row 1 summarizes Thomas and colleagues: about 4,100 adults in six ICUs, mostly night monitoring, no significant link to mortality, and a limit tied to local physicians restricting the remote team. Row 2 summarizes Lilly and colleagues: about 6,300 adults, monitoring combined with protocol changes, lower mortality, but effects that cannot be separated. Row 3 gives Young and colleagues' pooled analysis of 13 studies. Row 4 places Kahn and colleagues' national study last because it shows variation between programs. The search section lists PubMed and CINAHL terms. The synthesis reads down the finding column and sets conditions for joining a program.
Reading the NR 361 Week 4 grading rubric
Literature matrix rubrics usually award points for a focused question, appropriate scholarly sources, consistent and accurate extraction, critical appraisal of limits, a clear search strategy, synthesis and APA. Synthesis is the strongest row, and this matrix meets it by drawing a conclusion from the columns about which program features matter. Extraction is credited because every row answers the same five questions in parallel phrases. Appraisal scores when each limit names the specific reason a finding is bounded. The question row is met by a clear outcome and service. Sources earn points for mixing single-center studies, a meta-analysis and a national study. The search description and four complete references cover the rest.
NR 361 Week 4 help: mistakes that cost points
The expensive failure on this assignment is an annotated bibliography placed in a table, with a paragraph in each cell and no way to compare rows. Another is choosing columns after reading, so the grid is built around the first study found. Students also write "small sample" in every limit cell, or include studies that do not answer the question. A synthesis that repeats each row in prose earns little. Four to six studies are typical, and a word processing table is fine if the columns stay consistent. All studies should address your question, even if some are not about telehealth alone. If your matrix topic is different, send the question and an NR 361 matrix can be set up with columns fixed first.
Get a NR 361 Week 4 example written to your instructions
Send the Week 4 template if your section supplies one, along with the topic you have been given, and a custom example is filled to those exact columns and returned inside 24 to 48 hours. The first one is free. If the studies are already chosen, name them and the matrix is built from those instead.
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NR 361 Week 4 questions, answered
How many studies belong in the table?
Whatever the section sets, and the number is usually smaller than students expect because each row has to be filled completely. Six well entered studies beat twelve with blank cells, since an empty column is exactly the thing a grader notices. If a study cannot be reduced to phrases across your headings, that is often a sign it does not belong in this table rather than a sign the headings are wrong.
Do the studies all have to be about telehealth?
They have to be about the same question, which is narrower than a topic. A matrix mixing remote monitoring in heart failure with video visits in behavioral health has two questions in it and the synthesis will show the strain. Pick the population and the outcome first, then let the search fill rows that speak to that pair, and say in the synthesis what you excluded.
Can the matrix be built in a document table rather than a spreadsheet?
Yes, and most sections expect exactly that, often with a supplied file. What matters is that the table stays readable at page width, which forces the compression the assignment wants anyway. Turning the page sideways and dropping the font size are ordinary fixes. If your section provides the file, use it unchanged, because altered headings are a quiet way to lose points nobody warned you about.
Where can I find a free NR 361 Week 4 sample paper?
This page has the complete Week 4 literature matrix on tele-ICU outcomes, all four rows, the search description, the synthesis and the reference list.
Why does the NR 361 Week 4 matrix include a column for the service itself?
Because programs called ICU telemedicine range from passive night monitoring to teams with protocols and order authority, and that difference explains why the studies disagree.