Delivering high-quality health care at a cost the public can afford is a pressing issue in the United States. Per-person health care spending is much higher in the U.S. than in other industrialized nations, without notably superior quality.1 Pressure to spend more will likely increase as the population grows older and new tests and treatments are developed. Inevitably, the rising costs of care will be passed along to federal and local governments, businesses, and the public.
Health care quality and spending are national concerns, but health care delivery is local. Quality of care and spending vary widely across geographic areas, and more spending is not always associated with higher quality of care or better health outcomes.2
Under the Affordable Care Act, reforms in the way health care is delivered are beginning to sprout up in communities across the country. But without the participation of local leaders in every community, efforts to make high-quality care affordable may fall short. The ability to compare quality and spending in local areas could help motivate and guide these leaders. Identifying peer communities, benchmarking, and learning from those that achieve higher-quality care at lower costs are well-established approaches to improvement.
To help meet the need for easily accessible comparative information, The Commonwealth Fund has developed an online tool, the Quality–Spending Interactive (QSI).
This tool portrays what is sometimes called the “relative value” of care—the amount spent per level of quality achieved. We used publicly available data on quality and spending for the population age 65 and older with traditional fee-for-service Medicare to generate this initial version of the tool. We started with Medicare because the data offer standardized quality measures and a standardized approach to calculating spending. In the future we hope to include data for commercially insured and Medicaid populations.
The tool enables comparison by state or local areas called hospital referral regions (HRRs). The starting point is a graph called a scatter plot that shows overall quality and total Medicare per-capita spending on care relative to the median. (Note that prescription drug spending, which required a distinct dataset, is omitted from our “total spending” estimate.)
The default view is of states, but this can be changed to local areas in “view location type.” Relative quality performance is measured along the vertical axis (Y-axis) and relative spending per person is measured along the horizontal axis (X-axis).
Each dot on the chart represents relative quality and spending within a state or local area, and hovering over the dot reveals the percentage difference (higher or lower) between the state or local area’s quality and spending and the median quality and spending for all states or local areas.
Four different provider settings can be selected: hospitals, doctors’ offices, nursing homes, and home health care. For each setting, per-beneficiary spending estimates have been calculated, adjusting for regional wage differences. Quality measures specific to each setting can be viewed individually. We also created a total composite quality score for each setting, which is the average of the component quality measure scores.
In general, states or local areas located in the upper left quadrant of the scatter plot—shaded green—spend less and achieve higher quality. Those in the lower right quadrant—shaded red—spend more and achieve lower quality. The upper right quadrant represents higher spending for relatively higher quality while the lower left quadrant represents lower spending for relatively lower quality.
To illustrate, let’s start with a look at total spending and overall quality in Tucson, Arizona. To select Tucson, move the region toggle to “HRRs” and type Tucson, or a local zip code, into the search box to the right. You’ll see Tucson, in the middle of the upper left quadrant, performs relatively well, with 17 percent lower spending and 11 percent higher quality than the median for all local areas.
Yet, Tucson could do better. Moving our cursor directly up, we find that Appleton, Wisconsin, achieves 30 percent better quality than the median, for about the same level of spending as Tucson. Residents of Tucson would enjoy a 19 percentage point jump in quality of care, on average, if their local area performed as well as Appleton.
To look at a more extreme example, moving the performance of Miami, Florida, to the level of Honolulu, Hawaii, would reduce spending from $13,363 to $5,419 per person and improve quality of care by 52 percentage points on average for the residents of Miami.
There are caveats to the comparison approach. The tool compares state and local-area quality and spending to the median for all states or local areas. But we know that for the U.S. as a whole, quality is not optimal and spending may be higher than necessary (compared with other countries, for example).3 We know also that populations in some areas are not as healthy on average as those in other areas because of poverty, lower education, and other social and economic factors. For example, should we expect high-poverty areas to achieve the levels of quality per dollar spent that are achieved in wealthier areas? The answer is not clear. Finally, the Institute of Medicine has found that even within local geographic areas there is variation in quality and spending among provider organizations and populations.4 This means that even the highest-quality, lowest-spending areas may have room to improve.
The tool we have developed is a first step. We plan to explore adding quality and spending data on nonelderly populations and expanding the number of quality metrics. We hope to include other categories of spending (such as spending for medications).5 We also plan to highlight examples of the use of the tool. The ultimate goal is to offer leaders comparative information on quality and spending that can be used to motivate and guide efforts to improve quality and affordability in every community.6 We welcome feedback on how you might use the tool in your own work.