NSG/577 Week 5: Variables Affecting Quality, sample paper

The Patients Who Live Alone on the County Line: Clinical, Social and Organizational Variables That Shape Home Health Hospitalization and How to Account for Them Fairly

[Student Name]

University of Phoenix

NSG/577: Continuous Quality Monitoring and Outcomes Improvement

Week 5 Assignment

[Instructor Name]

[Date]

The agency, its data and all figures are a composite written for a model paper.

In Week 4, the agency's control chart showed a stable hospitalization rate averaging 18.1%. A stable average hides differences. When the analyst divided the data by team, the rate ranged from 14.2% in the city team to 22.6% in the team covering the two rural counties. Before concluding that one team gives worse care, this paper examines the variables that shape the rate.

Clinical Variables

Rosati and Huang (2007) found that a combination of demographic, financial, clinical and health status factors at admission predicted hospitalization within 60 days among 46,366 home health patients, sorting them into seven risk groups. In the agency's data, clinical factors at admission most associated with hospitalization are a hospitalization in the previous six months, heart failure or chronic obstructive pulmonary disease, a wound requiring skilled care, more than nine medications and dependence in several activities of daily living. The rural team's patients have a higher average risk score, which explains part of its higher rate.

Social Variables

A national committee identified five domains of social risk factors relevant to Medicare payment and quality: socioeconomic position; race, ethnicity and cultural context; gender; social relationships; and residential and community context (National Academies of Sciences, Engineering, and Medicine, 2016). Several appear in the agency's data. Patients who live alone are hospitalized at 23.1% compared with 16.4% of those with a caregiver at home. Patients with dual Medicare and Medicaid eligibility, a marker of low income, are hospitalized at 21.8%. And patients living more than 40 minutes from the nearest hospital have longer delays before emergencies are addressed, which may change whether early problems become admissions.

Living alone on a county line is not a clinical diagnosis, yet in our data it predicts hospitalization almost as strongly as heart failure does.

Organizational Variables

Organizational factors belong to the agency and are within its control. They include nurse caseload, which is highest on the rural team because of travel time; nurse turnover, which interrupts continuity; after-hours response time, which is longer in rural areas because the on-call nurse may be an hour away; and the availability of telehealth monitoring, currently offered only in the city.

Interactions Between Variables

The variables do not act alone. A patient with heart failure who lives alone forty minutes from a hospital faces a combined risk greater than any one factor suggests: no one at home notices swelling in the ankles, the on-call nurse cannot arrive quickly and by the time the patient calls 911 the problem requires admission. In the agency's data, rural patients with heart failure who live alone were hospitalized at nearly 31%. Interventions designed for one variable, such as heart failure teaching, may fail if they ignore another, such as the absence of someone to help weigh the patient each morning.

Variables the Data Do Not Capture

Some important variables are not recorded. Health literacy, the patient's confidence in managing at home, food insecurity and how well the patient knows and trusts a primary care provider all plausibly affect hospitalization but are not systematically measured. Nurses often know them informally. The agency will pilot two short screening questions, one on health literacy and one on food insecurity, at the start of care so that these factors can be examined in future analyses.

Risk Adjustment

Comparing the teams fairly requires accounting for differences in their patients. Using the admission risk score, the analyst calculated each team's expected rate and compared it with its observed rate. The rural team's observed rate of 22.6% compares with an expected rate of 20.4%; the city team's 14.2% compares with an expected 15.1%. After adjustment, the rural team is still somewhat worse than expected, but the gap between teams shrinks from 8.4 points to about 3.1 points of observed-to-expected difference.

Risk adjustment has limits. It can only account for variables that are measured, and assessment items vary in reliability (O'Connor & Davitt, 2012), so some real differences in risk remain hidden. A model that adjusts for too much, including factors the agency could address, may excuse poor care.

The Social Risk Question

Adjusting for living alone and dual eligibility would further reduce the rural team's apparent gap. The committee noted that accounting for social risk can protect providers serving disadvantaged populations from unfair penalties but can also mask disparities that deserve attention (National Academies of Sciences, Engineering, and Medicine, 2016). The agency will not adjust for social risk when evaluating teams. Instead, it will report results stratified by living situation and dual eligibility, so that disparities stay visible and can be addressed with targeted interventions, such as more frequent telephone check-ins for patients who live alone.

Do the Process Measures Matter?

Week 2 chose three process measures because they were thought to affect hospitalization. The analyst tested this over 24 months. Among high-risk patients, those who received front-loaded visits were hospitalized at 19.8% compared with 26.1% of those who did not. Patients whose first visit came within 48 hours had a modestly lower rate. Medication reconciliation with follow-up showed no clear relationship, possibly because nearly all patients received it. These are observational comparisons, and sicker patients may have been treated differently, so the results support but do not prove a causal link (Donabedian, 1988).

Checking for Differences by Race and Ethnicity

The NASEM committee's framework includes race, ethnicity and cultural context. The agency's data showed that Black patients had a hospitalization rate about two points higher than White patients, a difference that narrowed but did not disappear after accounting for clinical risk and dual eligibility. The numbers are small and the difference is within chance for a single year, but it will be monitored as a separate stratum rather than dismissed. A difference too small to prove is not the same as a difference too small to watch.

Staffing as a Variable Over Time

Organizational variables change month to month. When the analyst lined up the rural team's monthly rates against its vacancy rate, the three months with the most open nurse positions were also the three highest hospitalization months, though all remained within control limits. Vacancies raise caseloads and send unfamiliar nurses into homes, which may reduce the chance that a subtle change in a patient is noticed. Staffing will therefore be displayed beside the team's outcome chart so the relationship can be watched.

What the Variables Mean for Action

The rural team's higher rate reflects sicker patients, more patients living alone, greater distance from hospitals and organizational factors the agency controls, notably caseload and after-hours response. The first three call for accounting and targeted support; the last calls for change.

Conclusion

Clinical, social and organizational variables together shape the agency's hospitalization rate. Risk adjustment narrows the apparent gap between the rural and city teams but does not close it, and the agency will report social risk as strata rather than adjusting it away. Its own data support front-loaded visits for high-risk patients, which Week 6 will build into the monitoring and improvement plan.

References

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

National Academies of Sciences, Engineering, and Medicine. (2016). Accounting for social risk factors in Medicare payment: Identifying social risk factors. The National Academies Press. https://doi.org/10.17226/21858

O'Connor, M., & Davitt, J. K. (2012). The Outcome and Assessment Information Set (OASIS): A review of validity and reliability. Home Health Care Services Quarterly, 31(4), 267-301. https://doi.org/10.1080/01621424.2012.703908

Rosati, R. J., & Huang, L. (2007). Development and testing of an analytic model to identify home healthcare patients at risk for a hospitalization within the first 60 days of care. Home Health Care Services Quarterly, 26(4), 21-36. https://doi.org/10.1300/J027v26n04_03