MHA 507 Week 1 Benchmarking and Informatics for Decision Making Example

Seventy-Two Percent Nationally, Sixty-Four Percent Here: Choosing Benchmarks and Building the Data Pipeline Behind a Health System's First Quality Scorecard

[Student Name]

University of Phoenix

MHA/507: Using Informatics in the Health Sector

Week 1 Assignment

[Instructor Name]

[Date]

The health system, its scores, data sources and scorecard are composites written for a model paper; national benchmark values come from the federal data sets cited, and research from the sources listed.

At a leadership meeting of a composite four-hospital regional health system, the chief executive held up the latest patient survey report. At one of the system's hospitals, 64% of patients rated their stay 9 or 10 out of 10. She asked whether that was good or bad. The performance improvement manager, a Master of Health Administration student, answered that it depended on what it was compared with. This paper describes how she chose benchmarks and built the data behind the system's first quality scorecard.

What Benchmarking Is

Benchmarking compares an organization's performance with a reference point, such as a national average, a peer group or a best performer, to identify gaps and learn from those who do better. A review of benchmarking in health care described it as a continuous quality improvement method that involves measuring, comparing and learning from others' practices, and identified types including internal, competitive, functional and generic benchmarking, noting that success depends on leadership commitment, comparable data and a focus on learning rather than ranking (Ettorchi-Tardy et al., 2012).

The National Reference Point

Federal patient survey data provide national benchmarks. For discharges from October 2024 through September 2025, 72% of patients nationally rated their hospital 9 or 10, and 86% reported that they were given information about what to do during their recovery at home (Centers for Medicare & Medicaid Services, 2026). At 64%, the system's hospital was eight points below the national rate.

Choosing Peer Comparisons

A national average is not always the right comparison. The lagging hospital is a 120-bed facility in a small city with many older patients; the manager added a peer benchmark of similar-sized hospitals in the state from the state hospital association, which showed a peer average of 68%. The gap was smaller but still real.

Starting From Decisions

The manager asked leaders which decisions they faced in the coming year: where to invest in patient experience, whether to add staff on medical units, which safety problems to prioritize and how to address nurse turnover. Measures were chosen to inform those decisions.

Twelve Measures

The scorecard includes twelve measures in four groups. Experience: overall rating, communication with nurses and discharge information. Safety: falls with injury, hospital-acquired pressure injuries, central line infections and sepsis bundle compliance. Efficiency: length of stay compared with expected, emergency department boarding time and readmissions. Workforce: registered nurse turnover and overtime hours.

Matching Each Measure to a Benchmark

Each measure has a benchmark, a source and a time period. Experience measures use federal survey national and state data. Infection and readmission measures use federal comparison data. Falls and pressure injuries use a national nursing quality database to which the system subscribes. Turnover uses a state hospital association workforce survey. Where no external benchmark exists, the system uses its own best hospital as an internal benchmark.

Where the Data Come From

Informatics supplies the numbers. Patient survey results come from the system's survey vendor. Falls, pressure injuries and infections come from the electronic record and the infection prevention surveillance system. Length of stay, readmissions and boarding times come from the record's data warehouse. Turnover and overtime come from the human resources and payroll systems. A monthly job pulls these into a single data mart.

Checking the Data

Before any report, analysts check data quality: completeness of survey returns, agreement between the infection system and the record, plausibility of length-of-stay values and consistency of definitions over time. The manager discovered that one hospital counted falls differently, excluding assisted falls; definitions were standardized before the first scorecard.

Comparability

Comparisons are only fair when measures are defined the same way and adjusted for differences in patients where appropriate. The federal survey results are adjusted for patient mix, but internal comparisons of length of stay use expected values from a risk model rather than raw averages.

Presenting the Scorecard

The scorecard shows each measure's current value, benchmark, trend over twelve months and a color only when the difference from benchmark is meaningful rather than random noise. Research on how people read data displays found that simple tables were understood well across audiences, that contextual cues and consistency helped and that less information often worked better than more (Hildon et al., 2012), so the scorecard fits on one page with explanatory notes. A benchmark tells leaders where to look; it cannot tell them why the number is what it is.

Decision One: Patient Experience

The scorecard showed that the lagging hospital's biggest gap was discharge information, not nurse communication. Leaders redirected a planned investment in hourly rounding toward a discharge education nurse and a teach-back program, the gap the data identified, and set a six-month target of closing half the gap.

Decision Two: Staffing

The scorecard showed that the hospital with the highest overtime also had the highest nurse turnover and falls. Leaders approved a float pool rather than more overtime, a decision linked across three measures. Six months later, overtime at that hospital had fallen by a third and nurse turnover had begun to decline.

Who Uses the Scorecard

Different leaders use the scorecard differently. The board's quality committee reviews it quarterly for trends and major gaps. Hospital presidents use it monthly to set priorities with their teams. Unit managers receive unit-level versions for the measures they can influence, such as falls and nurse communication. Designing for each audience meant the same data appeared at different levels of detail rather than one report for everyone.

Avoiding Benchmark Traps

The manager warned leaders about three traps. First, reaching the benchmark is not the same as being good; a national average includes many poor performers. Second, chasing a single measure can harm others, as when shortening length of stay raises readmissions. Third, small hospitals have volatile rates; a few events can move a percentage sharply, so trends over several quarters matter more than a single month.

Learning From Better Performers

Benchmarking is most valuable when it leads to learning. The manager arranged for the lagging hospital's nurse managers to visit a peer hospital in the state with discharge information scores above 90%, where they observed a bedside discharge huddle with the patient and family and brought the practice home. The visit did more to change practice than any report.

Limits

Benchmarks describe performance but not causes, and some national data lag by a year or more, so recent changes may not yet show in national comparisons. The scorecard is a starting point for inquiry, not a verdict.

Conclusion

The chief executive's question, whether 64% was good or bad, could only be answered with benchmarks. National and peer comparisons, measures chosen for decisions, a data pipeline from the record and other systems and quality checks turned the scorecard into a tool that redirected investment and staffing. Benchmarking worked because it led to learning, not only ranking.

References

Centers for Medicare & Medicaid Services. (2026). Patient survey (HCAHPS): National [Data set]. Provider Data Catalog. https://data.cms.gov/provider-data/dataset/99ue-w85f

Ettorchi-Tardy, A., Levif, M., & Michel, P. (2012). Benchmarking: A method for continuous quality improvement in health. Healthcare Policy, 7(4), e101-e119. https://doi.org/10.12927/hcpol.2012.22872

Hildon, Z., Allwood, D., & Black, N. (2012). Impact of format and content of visual display of data on comprehension, choice and preference: A systematic review. International Journal for Quality in Health Care, 24(1), 55-64. https://doi.org/10.1093/intqhc/mzr072