NR 542 Week 3 Database Structure Write-Up Example

Course	NR 542 Managing Data and Information
Week	3
Paper type	Database structure write-up
Length	About 1,110 words, 3 pages
Format	APA 7 student paper
School	Chamberlain University
Program	MSN, Nursing Informatics
Updated	September 2026

Free sample paper for NR 542 Week 3

Database Structure Write-Up: The Six Tables an Early ICU Transfer Figure Must Be Built From, the Columns That Hold Each Piece of the Definition, and How Two Departments Reached Different Numbers by Reading Different Tables

Student Name

College of Nursing, Chamberlain University

NR 542: Managing Data and Information

Instructor Name

Month Day, Year

What this page is doingThe title locates every element of the definition in a named column of a named table, which is where most disagreements about a figure turn out to live. Student APA 7 title page.

Why Structure Comes First

Before anything can be counted, it has to exist as a value in a named column of a named table. Most disagreements about a figure are really disagreements about which table someone read. This write-up describes the parts of our hospital's reporting database needed to count early ICU transfers, meaning moves to intensive care inside a patient's first day on a medical ward, as defined in the data question brief. It then explains why the quality department and the hospital medicine service had reached different figures, 8% and 5%, before this work began. Both had read the database correctly. They had read different tables.

The Tables

The reporting database is a nightly copy of the electronic record, restructured into tables for reporting. Six tables are needed.

Encounter. One row per hospital visit, keyed by encounter identifier, with the patient identifier, the patient class (inpatient, observation, emergency), the admission source, the admission order date and time, and the discharge date and time. The admission order time here is when the order was signed, not when the patient reached a bed.

Bed movement. One row per movement of a patient into or out of a bed, keyed by movement identifier and linked to the encounter. Each row has the unit, the bed, the event type (admission, transfer in, transfer out, discharge) and the date and time the event was recorded. This is the only table that shows when a patient physically arrived on a medical floor and when they moved to an intensive care unit.

Unit. One row per unit, with its name and a unit type column (intensive care, step-down, medical-surgical, observation). The bed movement table records only a unit code; the unit table says what kind of unit it is.

Team assignment. One row per period in which a clinical team was responsible for the patient, with the service (hospital medicine, surgery, cardiology) and start and end times. Patients may change teams during an encounter.

Procedure schedule. One row per scheduled procedure, with the planned date and whether the procedure requires intensive care monitoring afterward. This table identifies planned transfers to exclude.

Orders. One row per order, with the order type and the date and time it was signed. Goals-of-care orders limiting treatment to comfort care are found here.

Where Each Piece of the Definition Lives

Table 1 maps each element of the definition to its table and column.

Table 1

Definition Elements Mapped to the Database

Definition element	Table	Column or rule
Adult inpatient	Encounter	Age at admission 18 or older; patient class inpatient, or observation converted to inpatient
Admitted through emergency department	Encounter	Admission source equals emergency department
Hospital medicine team	Team assignment	Service equals hospital medicine at the time of first floor arrival
Time zero: arrival on a medical floor	Bed movement joined to unit	Earliest movement into a unit whose type is medical-surgical
Outcome: ICU within 24 hours	Bed movement joined to unit	Any later movement into a unit whose type is intensive care within 24 hours of time zero
Exclude planned ICU transfers	Procedure schedule	Scheduled procedure requiring ICU monitoring within 24 hours of time zero
Exclude comfort care	Orders	Comfort care order signed before time zero

How the Two Figures Arose

The quality department's 8% came from the encounter table alone. It counted encounters whose discharge unit history, summarized in a text column, contained an intensive care unit, among encounters whose attending physician at discharge belonged to hospital medicine, and it used the admission order time as time zero. Three things inflated the figure. Anyone held in the emergency department for hours had the clock start before reaching a bed, so a transfer at hour 30 after reaching the floor could count. Patients admitted by hospital medicine who moved to intensive care and were discharged by the intensive care team were counted as hospital medicine patients only if their discharging attending belonged to hospital medicine, so the population was defined by who discharged the patient, not who admitted them. And planned transfers were not excluded.

Hospital medicine's 5% came from its own census list, which counted patients on its teams each morning and noted those who left for intensive care. It used the correct population but missed patients who arrived on the floor and went to intensive care before the next morning's census, which is exactly the group the question is about.

Both departments read their tables correctly. Neither had written down which table it used, so each assumed the other had made an error.

What this page is doingThe write-up explains two conflicting figures as two correct readings of different tables, which is more useful than declaring one department wrong.

Known Weaknesses in the Structure

Three features of the structure need care. First, bed movement times are recorded when the nurse or clerk enters the move, which may lag the real movement by minutes or, occasionally, hours. Second, the unit type column was last updated two years ago, and one unit that was converted from medical-surgical to step-down last year is still coded as medical-surgical. Third, observation patients converted to inpatient status may appear as two encounters in older data. Weiskopf and Weng's review of how record data quality is judged names dimensions such as completeness, correctness, concordance, plausibility and currency (Weiskopf & Weng, 2013); each of these weaknesses touches one of them, and the data quality audit later in the course will measure them.

Operational data collected for care and billing are not designed for research questions, and their structure reflects the purposes for which they were collected (Hersh et al., 2013). Knowing the structure is the first defense against misreading it. A harmonized framework for data quality in secondary use of record data recommends documenting the data's origin and structure alongside any measure built from it (Kahn et al., 2016), which is what this write-up does for the early transfer figure.

Mapping each element to a table also exposed a choice nobody had noticed. The team assignment table records teams by period, so a patient admitted by hospital medicine and handed to cardiology after an hour appears under both. The report uses the team at the moment of floor arrival, which is when the placement decision takes effect, and the documentation says so.

References

Hersh, W. R., Weiner, M. G., Embi, P. J., Logan, J. R., Payne, P. R., Bernstam, E. V., Lehmann, H. P., Hripcsak, G., Hartzog, T. H., Cimino, J. J., & Saltz, J. H. (2013). Caveats for the use of operational electronic health record data in comparative effectiveness research. Medical Care, 51(8, Suppl. 3), S30-S37. https://doi.org/10.1097/MLR.0b013e31829b1dbd

Kahn, M. G., Callahan, T. J., Barnard, J., Bauck, A. E., Brown, J., Davidson, B. N., Estiri, H., Goerg, C., Holve, E., Johnson, S. G., Liaw, S.-T., Hamilton-Lopez, M., Meeker, D., Ong, T. C., Ryan, P., Shang, N., Weiskopf, N. G., Weng, C., Zozus, M. N., & Schilling, L. (2016). A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. eGEMs, 4(1), Article 18. https://doi.org/10.13063/2327-9214.1244

Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. https://doi.org/10.1136/amiajnl-2011-000681

The Chamberlain NR 542 Week 3 instructions

The Week 3 assignment asks you to describe the data structures behind a measure. Prompts usually request the relevant tables or data elements, their relationships, how the measure's components map to them, and implications for data use. Name each table and what one row represents. List the columns you need and note what each actually records, such as when a time is entered rather than when an event happens. Map every element of your definition to a specific table and column. If different figures exist, explain which tables produced them. Identify weaknesses in the structure. Data quality literature belongs in the references, formatted in APA 7. Present the mapping as a table and describe the tables in prose.

How the NR 542 Week 3 example is put together

The write-up opens by stating that disagreements about figures are usually disagreements about tables, and introduces two conflicting figures. It describes six tables, what a row represents in each and the columns that matter, noting that the admission order time is not floor arrival. Table 1 maps seven definition elements to their tables and rules. A section explains how the quality department's figure came from the encounter table, with order time as time zero and the discharging attending as team, and how hospital medicine's came from a morning census that missed overnight transfers. Weaknesses in recorded times, an outdated unit type and duplicate encounters close the paper, linked to data quality frameworks.

Where the points sit in the NR 542 Week 3 rubric

Structure write-ups are usually graded on accurate description of data sources and relationships, correct mapping of measure components, insight into how structure affects results and attention to data quality. Graders favor write-ups that explain discrepancies between figures through the tables used rather than blaming analysts. A mapping table that locates every definition element demonstrates rigor. Noting what fields actually record, such as entry time rather than event time, shows attention to the details that alter measures. Identifying structural weaknesses prepares for the audit. Citations to data quality frameworks support the analysis. A reader should be able to rebuild the measure from the write-up alone, which is the practical test of a good one.

Common NR 542 Week 3 mistakes, and how to avoid them

Write-ups often list tables without explaining what a row represents or how tables connect. Describe both. Another weakness is assuming a column means what its name suggests; check what it actually records and when. Students also skip the mapping step, leaving the reader to guess where each element comes from. Map everything. Avoid judging the analysts who produced earlier figures; explain their choices. Do not ignore reference tables, such as unit types, which can quietly go out of date. Name structural weaknesses honestly. Keep technical terms clear for a nursing audience. A good test is to ask a colleague to find one element of the definition using only your write-up.

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NR 542 Week 3 questions, answered

Do I need to write actual SQL?

Sections differ, and many accept a written path in place of syntax. Where code is wanted, short and readable beats clever, because the marker is checking whether the joins match the structure you described rather than admiring the query. Where code is not wanted, name the tables in the order they would be joined and state the field each join uses.

What if I cannot see my employer's database?

Most people cannot, and the week does not require it. A published sample schema, a vendor's documented structure, or a teaching dataset supplies real tables and real keys to reason about. Working from documentation is also safer, since a live system holds material that belongs to the organization and its patients rather than to a classroom assignment.

How many tables should the map include?

Only those the measure actually needs, which is usually fewer than students expect. Four or five well described entities read as controlled; fifteen drawn without explanation read as copied. If a table appears in the diagram, the write-up should be able to say which part of the count depends on it, and if it cannot, the table does not belong.

Where can I find a free NR 542 Week 3 sample paper?

The six-table structure write-up shown above is the whole NR 542 Week 3 sample, with nothing locked. If your measure draws on a different database, describe its tables to us and a write-up mapping your definition to them can be written.

Why did two NR 542 figures disagree in the sample?

Because each department read a different table correctly. One used the encounter table with the admission order as time zero; the other used a morning census that missed overnight transfers.