A Target Worth Reaching and Six Ways to Hit It Without Improving: Benchmarks, Targets and the Ways a Hospitalization Measure Can Be Misread or Gamed
[Student Name]
University of Phoenix
NSG/577: Continuous Quality Monitoring and Outcomes Improvement
Week 3 Assignment
[Instructor Name]
[Date]
The agency, its data and all figures are a composite written for a model paper.
The Week 2 definition, unplanned admission to a hospital in the first two months of home care, produced a first full-quarter result of 17.8%. Before setting a goal, this paper asks what the number should be compared with, what target is reasonable and how the measure could mislead.
What a Benchmark Is For
A benchmark answers the question of how good is good. Without one, the agency could celebrate a small improvement while remaining well behind comparable agencies, or feel discouraged by a rate that is actually typical. A useful benchmark must count the same events by the same rules in a comparable group of patients. The Institute of Medicine (2001) argued that care should be effective and efficient, which in practice means comparing results with what similar providers achieve, not with an abstract ideal.
External Benchmarks
The most visible benchmark is the national and state home health hospitalization data published for Medicare-certified agencies. Those public measures use their own specifications, including risk adjustment and specific definitions of the episode, so they are not identical to the agency's internal measure. Comparing the internal 17.8% directly with a public rate would be comparing two different measures. The agency will track its public results separately and use them as context rather than as a direct benchmark.
Internal and Peer Benchmarks
Two internal benchmarks are more useful. The agency's own trend, calculated with the same definition for the past four quarters, shows rates of 18.4%, 17.1%, 19.0% and 17.8%, with no clear direction. And the agency participates in a regional home health collaborative whose members calculate a hospitalization measure with a shared definition close to ours; the collaborative median is 15.6%, and the top quartile starts at 13.2%.
Setting a Target
Targets should be ambitious enough to require change and realistic enough to be believed. A target set from a hope rather than a benchmark tends to be abandoned the first time a quarter misses it. The agency sets a target of 15.0% within 18 months, slightly better than the collaborative median, with a stretch goal of 13.2%, the top quartile. The target is paired with process targets from Week 2: timely initiation at 95%, medication reconciliation with follow-up at 90% and 85% of the highest-risk patients receiving concentrated early visits.
Six Ways the Measure Could Improve Without Better Care
Every measure creates incentives. Six risks are foreseeable.
Risk 1: patient selection. The agency could decline referrals for high-risk patients, such as those with advanced heart failure, lowering its rate while harming access. Risk models such as the one developed by Rosati and Huang (2007) show that admission characteristics strongly predict who will be hospitalized, which is precisely what makes selection tempting. Safeguard: track the referral acceptance rate by risk group and the average admission risk score each quarter; a falling risk score alongside a falling hospitalization rate would prompt review.
Risk 2: early discharge. Discharging patients before 60 days removes them from observation if hospitalization is detected only through agency records. Safeguard: the definition follows each patient for the full two months whether or not home care has ended, identified through the health information exchange, and the agency tracks length of service.
Risk 3: reclassification. Unplanned admissions could be recorded as planned to meet an exclusion. Safeguard: planned admissions must be documented in the plan of care before they occur, and the quality team audits a sample of excluded admissions each quarter.
Risk 4: diverting to observation stays. If hospitals place more patients in observation status, which is not an inpatient admission, hospitalizations could fall while patients still return to the hospital. Safeguard: track emergency department visits and observation stays as balancing measures.
Risk 5: shifting to emergency visits. Improvement efforts might treat problems in the emergency department rather than prevent them. Safeguard: the emergency visit measure described above.
Risk 6: under-reporting. Hospitalizations could be missed if hospitals do not notify the agency. Safeguard: reconciliation of agency records with health information exchange notifications and a monthly check of patients whose visits stopped abruptly.
Misreading Without Gaming
Measures can also mislead honestly. A small agency's quarterly rate varies by chance: with about 360 admissions a quarter, the rate could move two points without any change in care. Rates also depend on case mix; an agency that accepts more patients discharged after heart failure hospitalizations will have higher rates even with excellent care. Week 4 will use control charts to distinguish chance from real change, and Week 5 will address risk adjustment.
Case Review as a Check
Numbers cannot tell whether hospitalizations were avoidable. Each month, two nurses will review five hospitalizations using a structured tool that asks whether early signs were present, whether they were recognized and communicated and whether a different response might have prevented admission. Donabedian (1988) described the use of clinical judgment in reviewing care as a complement to numerical measures, and the reviews will keep the measure tied to clinical reality.
Who Reviews the Safeguards
The safeguards only work if someone looks at them. The quality committee, which includes a board member, two clinical managers, a field nurse and the quality analyst, will review the balancing measures and audit results every quarter alongside the lead measure. Any safeguard that signals a problem will be discussed before the lead result is presented to the board, so that an apparent improvement is never reported without its context.
When the Target Should Change
The target is not permanent. If the collaborative's median improves, if Medicare changes how the public measure is defined or if the agency's patient population changes significantly, for example by taking on a new hospital's discharges, the target will be reviewed. Changing a target for these reasons is appropriate; lowering it because progress is slow is not, and the committee will record its reasons for any change.
Celebrating the Right Things
When the rate improves, recognition will go to teams whose process measures improved and whose balancing measures stayed steady, not simply to the team with the lowest rate. A team serving sicker rural patients may do excellent work and still post a higher rate than the city team. Recognizing process gains and honest reporting, including the reporting of hospitalizations that would otherwise have been missed, rewards the behavior the agency wants to see.
Transparency With Staff
Staff will see the measure, the target, the balancing measures and the safeguards together. Explaining openly that the agency will watch for patient selection and reclassification signals that improvement must come from better care, not better numbers.
Conclusion
The agency's 17.8% hospitalization rate is best compared with its own trend and a regional collaborative using a similar definition, not directly with public rates built on different specifications. A target of 15.0% in 18 months is ambitious and grounded. Six foreseeable ways to improve the number without improving care, from selecting healthier patients to shifting events to emergency visits, each have a safeguard, and case reviews keep the measure honest.
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
Institute of Medicine. (2001). Crossing the quality chasm: A new health system for the 21st century. National Academies Press. https://doi.org/10.17226/10027
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
How this NSG 577 Week 3 example is structured
The NSG/577 description asks learners to consider the many variables that affect quality. This paper examines the measure from Week 2 critically: where benchmarks come from and whether they fit, how targets should be set and how measures can be misread or gamed, with a specific safeguard for each risk. Students search this week as NSG 577 Week 3, NSG577 Wk 3 or NSG/577 Wk 3; all three are the same assignment.
NSG/577 Week 3 questions, answered
What does NSG/577 Week 3 usually ask for?
Many sections ask students to identify benchmarks and set targets for their performance measures and to consider how measures might be misinterpreted or manipulated.
What is gaming a measure?
Changing behavior or data to improve a measure's result without improving the quality it is meant to reflect, such as reclassifying events or avoiding difficult patients.
What is a balancing measure?
A measure that watches for unintended harm from an improvement effort, such as rising emergency visits when hospitalizations fall.
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