Older adults with multimorbidity and repeat admissions are the single group NU507's Unit 3 profile defines, measuring their conditions, access and breakdown points so later model choices have a target. Searches like "nu 507 unit 3 assignment example", "nu507 unit 3 sample" and "nu507 unit 3 example" land here.
Who Keeps Coming Back: A Population Needs Profile of Older Adults With Multiple Chronic Conditions and Repeat Admissions on a Composite Medical Unit
[Student Name]
Purdue University Global
NU507: Promoting Optimal Models and Systems for Health Care Delivery
Unit 3 Assignment
[Instructor Name]
[Date]
Unit figures in brackets are composite and are the student's own; published studies are cited for comparison.
Definition
The population is defined so a report could reproduce it: adults aged 65 or older, admitted to the medical unit, with two or more chronic conditions from a stated list (heart failure, chronic obstructive pulmonary disease, diabetes, chronic kidney disease stage 3 or higher, atrial fibrillation, dementia), and one or more hospital admissions in the 12 months before the index admission. Last year, [260] patients met the definition, accounting for [410] admissions. Of those admissions, [24] percent were followed by a readmission within 30 days, compared with [14] percent for all other adult admissions to the unit.
How the Figures Were Gathered
The counts come from three sources. The hospital's admission data identified patients by age, diagnosis codes and prior admissions, and the quality department supplied 30-day readmission flags. Demographic items such as living alone, distance to clinic and caregiver were taken from the nursing admission assessment, which is completed for every patient but not always fully; where an item was missing, the patient was counted as unknown rather than guessed. The readmission review drew a random sample of [30] of last year's [98] readmissions in the population and read each record from discharge to return, using a short form that recorded the first documented problem after discharge and when it appeared.
1. Median age: [79] years.
2. Living alone: [41] percent.
3. More than 30 miles from their primary care clinic: [38] percent.
4. Common pairings of conditions: heart failure with chronic kidney disease; chronic obstructive pulmonary disease with heart failure; diabetes with chronic kidney disease.
5. Median daily medications at admission: [11].
6. Cognitive impairment documented at admission: [22] percent.
7. Primary caregiver identified in the record: [63] percent, most often a daughter or spouse.
8. Discharged to home with home health: [34] percent; to home without services: [48] percent; to a skilled nursing facility: [18] percent.
Needs, Grouped by What a Model Must Supply
Needs are grouped by what a delivery model would have to supply rather than by diagnosis. That choice makes the profile usable: continuity and medication management are needs a model meets or fails, while heart failure is not.
1. Continuity. One person who knows the patient's history, home situation and goals across the admission and into discharge. Evidence: record review of [30] readmissions; national data showing that about half of Medicare patients readmitted within 30 days after a medical discharge had no physician office visit billed in between (Jencks et al., 2009).
2. Medication management. Reconciliation, simplification and teaching for a median of [11] medications, with attention to changes made in hospital. Evidence: record review; a prospective study in which 19 percent of medical patients had an injury from medical management within weeks of going home, with drugs the leading cause (Forster et al., 2003).
3. Caregiver support. Teaching the person who will actually manage care at home, often not the patient, and assessing what that person can do. Evidence: record review; unit data on identified caregivers.
4. Access. Getting to follow-up care, and communication with the primary care clinic. Evidence: distance data; published findings that discharge summaries often do not reach the first follow-up visit (Kripalani et al., 2007).
Where the Plans Came Apart
Review of [30] readmissions from last year shows where plans failed, organized by time after discharge.
First 72 hours: [11] readmissions. Most common problems: a new or changed medication not started or taken wrongly; a caregiver who had not been present for teaching; confusion about which old medication to stop.
First week: [9] readmissions. Most common problems: weight gain or symptoms not reported; no home health visit yet; no clinic contact.
First month: [10] readmissions. Most common problems: missed or distant follow-up appointments; worsening that a clinic visit might have caught.
Most failures clustered before the first clinic visit, in the window when the hospital no longer held the patient and the clinic did not yet know about the discharge. That pattern is consistent with national findings that discharge information often fails to reach primary care in time (Kripalani et al., 2007), and it points at the handoff rather than at any single unit or clinic.
A composite example shows the pattern. A 78-year-old woman with heart failure and diabetes went home on a Friday with a new diuretic dose and instructions to stop one of her blood pressure medications. Her daughter, who fills her pill organizer, was at work during teaching. On Sunday the daughter filled the organizer from the old list. By Tuesday the patient was dizzy and fell, and she was readmitted Wednesday with low blood pressure and a kidney injury. Nothing in her discharge was wrong on paper; the plan simply never reached the person who carried it out.
Strengths
The population and its setting also have strengths a model could use. Most patients have a caregiver who can be taught. The hospital's primary care clinics have registered nurse care managers who already follow some of these patients. Home health agencies serve the county, and [34] percent of the population already receives home health. And many of these patients know the unit well, which gives the unit an unusual chance to know them.
What the Profile Cannot Tell Us
The profile has limits. It counts readmissions to this hospital only, so patients readmitted elsewhere are missed and the true rate is probably higher. The record review sampled [30] cases and read what was documented, which captures problems that reached the chart and misses those that did not. And the population's needs are described from the unit's side; interviews with patients and caregivers, planned for later in the term, may change the order of the four needs.
What the Profile Asks of a Delivery Model
Any model the unit chooses would need to give these patients continuity across the admission, a medication plan they and their caregivers can follow, teaching that reaches the right person before discharge day, and a handoff that reaches the clinic before the first 72 hours are over. Those four needs are the criteria the comparative analysis in the next unit uses, and a model that meets three of them while failing the fourth should be judged by the failure, since each need maps to a documented cause of return.
References
Forster, A. J., Murff, H. J., Peterson, J. F., Gandhi, T. K., & Bates, D. W. (2003). The incidence and severity of adverse events affecting patients after discharge from the hospital. Annals of Internal Medicine, 138(3), 161-167. https://doi.org/10.7326/0003-4819-138-3-200302040-00007
Jencks, S. F., Williams, M. V., & Coleman, E. A. (2009). Rehospitalizations among patients in the Medicare fee-for-service program. New England Journal of Medicine, 360(14), 1418-1428. https://doi.org/10.1056/NEJMsa0803563
Kripalani, S., LeFevre, F., Phillips, C. O., Williams, M. V., Basaviah, P., & Baker, D. W. (2007). Deficits in communication and information transfer between hospital-based and primary care physicians: Implications for patient safety and continuity of care. JAMA, 297(8), 831-841. https://doi.org/10.1001/jama.297.8.831
How this NU507 Unit 3 example is structured
The definition comes first because every figure after it depends on who is counted, and a population described loosely cannot be matched to anything. Demographics and conditions follow in a table, since they are read rather than argued. Needs are grouped by what a delivery model would have to supply, not by diagnosis, the choice that makes the profile usable: continuity and medication management are needs a model meets or fails, while heart failure is not. Each need cites its evidence, a record review, the readmission report or published data on older rural adults, showing which needs rest on local figures. The breakdown narrative is organized by the time after discharge, first 72 hours, first week, first month, and most failures cluster before the first clinic visit. Strengths are included so the eventual model can build on resources that already exist.
Get an NU507 Unit 3 example written to your instructions
Choose a population you have worked with closely and describe it in general terms, along with the Unit 3 instructions and grading criteria. The profile comes back defined reproducibly, needs grouped by what care must supply and each local number held in brackets until real counts replace it, within 24-48h and free as a first custom sample. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
NU507 Unit 3 questions, answered
How narrowly should I define the population?
Narrowly enough that one model could plausibly serve all of it and that the definition could be run as a report. Age plus condition plus a utilization marker, such as a recent admission, is a common pattern. The sample limits itself to older adults with multimorbidity and a prior admission, which excludes stable outpatients whose needs would point toward a different model.
Can I use national data if local figures are unavailable?
Yes, labeled clearly as national or regional, and paired with any local indicator you can obtain. The weakness is presenting outside statistics as if they described your setting. The sample brackets every local figure as a placeholder for real counts and cites published data only for points such as rural distance to care, stated as context rather than local fact.
Why group needs by function rather than by diagnosis?
Because models are compared on what they supply: continuity, coordination, access, support. A list of diagnoses tells a reader what patients have; a list of functional needs tells a reader what care has to do. The sample converts its common condition pairings into four needs, each of which later units use as a comparison criterion.