Chapter 6. Efficiency

Costs may not be on the minds of patients and providers when health care is being delivered. In fact, patients who have generous health insurance coverage rarely have to consider costs. But some patients are confronted with the costs of health care belatedly when they try to fill prescriptions that they discover they cannot afford or when expensive medical bills arrive. Medical bills contribute to many bankruptcies.¹ In addition, many Americans worry about not being able to afford health care. Quite a few report skipping care because of its cost.² People who buy their own health insurance, employers that provide health insurance coverage to their employees, and governments that fund health programs are made particularly aware of health care costs as they see these costs rising more quickly than wages, inflation, or economic growth.

One approach to containing the growth of health care costs is to improve the efficiency of the health care delivery system. This would allow finite health care resources to be used in a way that best supports highquality care. Recent work examining variations in Medicare spending and quality shows that higher cost providers do not necessarily provide higher quality care, illustrating the potential for improvement.³ It should be possible to maintain appropriate levels of health care provision without large increases in costs each year and to extract more value from each health care dollar. Improving efficiency in the Nation's health care system is an important component of the Department of Health and Human Services' (HHS) mission to support a better health care system. In support of this mission, this year's National Healthcare Quality Report (NHQR) continues to look at potential information sources and findings on efficiency in the U.S. health care system.

This year's NHQR outlines varying perspectives on efficiency and offers potential methods for measuring efficiency that respond to the NHQR's mandate to provide lawmakers in Congress with information on the performance of the U.S. health care system. This chapter does not attempt to provide a definitive framework for efficiency; nor does it provide an exhaustive list of potential measures of efficiency. Rather, the Agency for Healthcare Research and Quality (AHRQ) hopes that this chapter will stimulate productive dialog on health care efficiency. AHRQ intends this chapter to be part of an evolving national discussion on measuring efficiency in the U.S. health care system that will be reviewed, revised, and presented in future reports.

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Measures

Part of the discussion about how to improve efficiency involves the question about how best to measure it. Varying perspectives and definitions of health care efficiency exist, and the lack of consensus on what constitutes appropriate measurement of efficiency has stymied efforts to report on this area.

To improve understanding of efficiency measures, AHRQ commissioned the RAND Corporation to systematically review measures of efficiency and to assess their potential to be tracked and reported at various levels.⁴ The RAND report provides a typology of efficiency measures that emphasizes the multiple perspectives on efficiency. It also points out that measures must be considered from the standpoint of what the measuring organization is and what its goal is in assessing efficiency.

In considering efficiency measures, AHRQ also built on another report that examined the question of efficiency from the cost-of-waste point of view. In that report, the authors outline another common typology for efficiency measurement: the tracking of overuse, underuse, and misuse in the health care system.⁵

This chapter presents measures from the population and provider perspective to provide some insight into health care efficiency. They are:

• Trends in potentially avoidable hospitalizations and costs (population perspective).
• Disparities in potentially avoidable hospitalizations (population perspective).
• Potentially avoidable hospitalizations among Medicare home health and nursing home patients (population perspective).
• Potentially avoidable hospitalizations and emergency department encounters for congestive heart failure (CHF) (population perspective).
• Rehospitalization for CHF for selected States (population perspective).
• Reduction of unnecessary costs (population perspective).
• Trends in hospital efficiency (provider perspective).

Consensus has yet to emerge about the appropriate framework and acceptable measures of efficiency, and the examples provided are designed to stimulate productive ongoing discussion about health care efficiency. We anticipate reporting the trends in potentially avoidable hospitalizations and costs and trends in hospital efficiency measures in future NHQRs. We also plan to include periodic focuses on particular conditions. However, some of the estimates that we are making available in this year's chapter will only appear intermittently in the future.

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Findings

Trends in Potentially Avoidable Hospitalizations and Costs

To address potentially avoidable hospitalizations and costs from the population perspective, data on ambulatory care-sensitive conditions are summarized here using the AHRQ Prevention Quality Indicators (PQIs). Not all hospitalizations that the AHRQ PQIs track are preventable, but ambulatory care-sensitive conditions are those for which good outpatient care can prevent the need for hospitalization or for which early intervention can prevent complications or more severe disease. The AHRQ PQIs track these conditions using hospital discharge data. Hospitalizations for acute conditions, such as dehydration or pneumonia, are distinguished from hospitalizations for chronic conditions, such as diabetes or CHF.

For this analysis, total hospital charges were converted to costs using Healthcare Cost and Utilization Project (HCUP) cost-to-charge ratios based on hospital accounting reports from the Centers for Medicare & Medicaid Services. Therefore, cost estimates in this section refer to hospital costs for providing care, but do not include either payers' costs or costs for physician care that are billed separately.

Figure 6.1. National trends in potentially avoidable hospitalization rates for adults, by type of hospitalization, 2000-2006

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, Nationwide Inpatient Sample, 2000-2006.
Note: Data are for adults age 18 and over. Annual rates are adjusted for age and gender.

• From 2000 to 2006, overall rates of avoidable hospitalizations decreased significantly, from 1,944 per 100,000 to 1,761 per 100,000 (Figure 6.1).
• This decline is largely attributable to avoidable hospitalizations for chronic conditions, which decreased significantly, from 1,213 per 100,000 to 1,078 per 100,000.
• Avoidable hospitalizations for acute conditions did not change significantly from 2000 to 2006.

Figure 6.2. Total national costs associated with potentially avoidable hospitalizations, 2000-2006

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, Nationwide Inpatient Sample, 2000-2006.
Note: Data are for adults age 18 and over. Annual rates are adjusted for age and gender. Costs are adjusted for inflation.

• From 2000 to 2003, total national hospital costs associated with potentially avoidable hospitalizations adjusted for inflationⁱ increased from $27.9 billion to $31.9 billion (Figure 6.2). Costs then declined to $30.1 billion in 2006.
• These changes are largely attributable to avoidable hospitalizations for chronic conditions, with national hospital costs that increased from $17.6 billion to $20.3 billion between 2000 and 2003 and then declined to $19.2 billion in 2006.
• National hospital costs for avoidable hospitalizations for acute conditions did not change significantly from 2000 to 2006.

Disparities in Potentially Avoidable Hospitalizations

Relatively little work has focused on the use of efficiency measures to assess disparities in the delivery of health care. In considering efficiency measures for the NHQR and the National Healthcare Disparities Report (NHDR), we assessed their ability to support analyses by race, ethnicity, and socioeconomic status (SES). Most measures did not allow assessment of disparities, so we have not included a section on efficiency in the NHDR. However, data for one efficiency measure, potentially avoidable hospitalizations, were deemed to be of sufficient quality to assess disparities.

A critical caveat should be noted. Comparatively high rates of potentially avoidable hospitalizations may reflect inefficiency in the health care system. Therefore, groups of patients should not be "blamed" for receiving less efficient care. Instead, examining disparities in efficiency may help make the business case for addressing disparities in care. Investments that reduce disparities in access to high-quality outpatient care may help reduce rates of avoidable hospitalizations among groups that have high rates.

Figure 6.3. Potentially avoidable hospitalization rates for adults, by race/ethnicity and area income, 2006

Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, Nationwide Inpatient Sample, 2006.
Note: Data are for adults age 18 and over. Annual rates are adjusted for age and gender. White, Black, and Asian or Pacific Islander are non-Hispanic. Income quartiles based on median income of Zip Code of patient's residence.

• Rates of avoidable hospitalizations overall and avoidable hospitalizations for chronic conditions were higher among Blacks and Hispanics compared with Whites. Rates were lower among Asians and Pacific Islanders (APIs) compared with Whites (Figure 6.3).
• Rates of avoidable hospitalizations overall and avoidable hospitalizations for chronic conditions were higher among residents of areas in the lowest, second, and third income quartiles compared with residents of areas in the highest income quartile.
• Rates of avoidable hospitalizations for acute conditions were higher among Blacks compared with Whites and among residents of areas in the lowest and second income quartiles compared with residents of areas in the highest income quartile.

Potentially Avoidable Hospitalizations Among Medicare Home Health and Nursing Home Patients

Many patients are hospitalized while receiving care from home health agencies and nursing homes, with resulting high costs and care transition problems. A number of these hospitalizations of nursing home and home health patients are appropriate. However, some hospital admissions could be prevented with better primary care and monitoring in these settings, or the patient could receive appropriate treatment in a less resource-intense setting.

Using the AHRQ Prevention Quality Indicators (PQIs), we track potentially avoidable hospitalizations among Medicare patients occurring within 30 days of the start of home health or nursing home care. These patients may differ from patients discussed earlier in this chapter who are predominantly admitted for avoidable conditions from home. At home, some are receiving appropriate primary care and others have not visited a health care provider for years.

In contrast, Medicare home health and nursing home patients have regular contact with health providers, which should reduce rates of avoidable hospitalization. However, these patients are also more acutely ill, may become seriously ill when affected by a new illness, and may have multiple comorbidities. Medicare patients in these settings often have been hospitalized recently. Therefore, an avoidable hospitalization may represent a return to the hospital, perhaps against the expectation that the patient was no longer in need of acute care.

For application to home health and nursing home settings, the potentially avoidable stays are identified within a defined time period, 30 days, from the home health or nursing home admission date. If a patient is hospitalized more than once in that period, only the first stay is recognized for the measure.

Data on home health patients come from Medicare fee-for-service (FFS) home health claims and Outcome and Assessment Information Set (OASIS) patient assessment information. Data on nursing home patients come from Medicare skilled nursing facility (SNF) FFS claims and Minimum Data Set (MDS) patient assessment information. These data are linked with Medicare Part A acute care hospital claims to determine hospitalizations for potentially avoidable conditions.

Figure 6.4. Medicare home health patients with potentially avoidable hospitalizations within 30 days of start of care, 2001-2006

Source: Centers for Medicare & Medicaid Services, Outcome and Assessment Information Set linked with Medicare Part A claims (100%), 2001-2006.
Denominator: Adult nonmaternity patients starting an episode of skilled home health care.
Note: Rates standardized to the 2006 patient population according to Medicare enrollment category.

• Between 2001 and 2006, hospitalizations within 30 days of home health episode start for potentially avoidable conditions declined from 4.6% to 3.9%.

Figure 6.5. Short-stay and long-stay nursing home residents with potentially avoidable hospitalizations within 30 days of admission, 2000-2005

Source: Centers for Medicare & Medicaid Services, Minimum Data Set, 2000-2005 linked with Medicare Part A claims (100%).
Denominator: Short-stay residents are those who met the Medicare skilled nursing facility (SNF) criteria for nursing home admission; long-stay residents are nursing home admissions that did not meet Medicare SNF criteria.

• Between 2000 and 2005, hospitalizations within 30 days of nursing home admission for potentially avoidable conditions increased for short-stay nursing home residents but declined slightly for long-stay nursing home residents.

Potentially Avoidable Hospitalizations and Emergency Department Encounters for Congestive Heart Failure

Potentially preventable, high-cost encounters with the medical system occur not only in hospitals, but also in emergency departments (EDs). There were more than 120 million ED encounters in 2006. ED crowding, boarding (i.e., holding patients until an inpatient bed is available), and ambulance diversion have become more prevalent and have given rise to increasing concerns about the quality of care delivered in EDs.

Congestive heart failure (CHF) is an ambulatory care-sensitive condition. Patients typically need to restrict their intake of salt, take their medications regularly, and monitor their weight. Good primary care can help patients with self-management and make adjustments to treatment before exacerbations in CHF become severe and require emergent attention.

Some hospitalizations and ED encounters cannot be avoided, but appropriate ambulatory care can help keep some patients from having to visit an ED or from being hospitalized. Reducing potentially avoidable ED encounters, in particular, holds promise for reducing cost, improving quality, and enhancing efficiency. For this analysis, the CHF measure from the the AHRQ PQI software was applied to the 2005 HCUP Nationwide Inpatient Sample (NIS) and the Nationwide Emergency Department Sample (NEDS).

Figure 6.6. Potentially avoidable hospitalizations and emergency department encounters for congestive heart failure, national and regional estimates, 2005

Key: ED = emergency department.
Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, Nationwide Inpatient Sample and Nationwide Emergency Department Sample, 2005.
Note: Data are for adults age 18 and over. Annual rates are adjusted for age and gender.

• The South had a rate of inpatient stays for CHF that was significantly higher than the rate for the Northeast. The West had a lower rate. The rate for the Midwest was statistically indistinguishable from the rate for the Northeast (Figure 6.6).
• The South had a rate of ED visits for CHF that was significantly higher than the rate for the Northeast. The rates for the Midwest and the West were statistically indistinguishable from the rate for the Northeast.
• The South had a rate of ED visits that resulted in an inpatient admission for CHF that was significantly higher than the rate for the Northeast. The West had a lower rate. The rate for the Midwest was statistically indistinguishable from the rate for the Northeast.
• The South, West, and Midwest had rates of ED visits that did not result in an inpatient admission for CHF that were significantly higher than the rate for the Northeast.

Rehospitalization for Congestive Heart Failure

To gain further insight into the population perspective of potentially avoidable hospitalizations and costs, data on rehospitalization rates for CHF for 14 States in 2006 are summarized here. Rehospitalization for CHF signals a worsened state of illness for patients and is more resource intensive than outpatient treatment. Although some rehospitalizations for CHF cannot be prevented, CHF is a condition for which good outpatient care and early intervention can help prevent rehospitalization.

The estimates below are derived from data for 14 States participating in the HCUP State Inpatient Databases. They are based on all CHF admissions from January 1 to November 30, 2006. Rehospitalizations are defined as admissions to any hospital in that State with a principal diagnosis of CHF within 30 days of the discharge date of an index CHF admission. For this analysis, total hospital charges were converted to costs using HCUP cost-to-charge ratios based on hospital accounting reports from the Centers for Medicare & Medicaid Services. Therefore, cost estimates in this section refer to hospital costs.

Table 6.1. Rehospitalizations for congestive heart failure, 14 States, 2006

Age category	State	Hospitalized CHF patients with rehospitalization for CHF	Average cost of rehospitalization
Ages 18-64	State A	3.6%	DSU
	State B	6.1%	$11,030
	State C	8.3%	$8,753
	State D	8.3%	$9,925
	State E	8.5%	$12,424
	State F	8.6%	$10,809
	State G	9.1%	$10,865
	State H	9.9%	$10,055
	State I	9.9%	$11,049
	State J	10.1%	$9,583
	State K	11.4%	$9,488
	State L	11.5%	$8,599
	State M	11.7%	$12,908
	State N	11.8%	$8,058
Age 65+	State A	4.4%	$8,907
	State D	6.8%	$9,867
	State I	6.9%	$9,800
	State C	8.4%	$8,139
	State E	8.8%	$7,901
	State B	8.8%	$9,633
	State J	9.3%	$9,390
	State N	9.4%	$7,631
	State F	9.4%	$9,926
	State M	9.5%	$12,692
	State K	9.8%	$9,047
	State H	10.0%	$8,821
	State L	10.0%	$7,351
	State G	10.6%	$8,891

Key: DSU = data statistically unreliable.
Source: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project, State Inpatient Databases, 2006.
Note: Data are for adults age 18 and over. The data for State A were insufficient for determining the average cost of rehospitalization.

• The percentage of State-level CHF hospitalizations resulting in rehospitalization for CHF ranged from a low of 3.6% to a high of 11.8% for patients ages 18-64 and from a low of 4.4% to a high of 10.6% for patients age 65 and over. State A had the lowest percentage for both age groups (Table 6.1).
• Costs for a rehospitalization for CHF where the index hospitalization was for CHF ranged from a low of $8,058 to a high of $12,908 for patients ages 18-64 and from a low of $7,351 to a high of $12,692 for patients age 65 and over.
• Costs for a rehospitalization for CHF where the index hospitalization was for CHF were generally lower in the Medicare-eligible population than in those ages 18-64.

It is important to note that the figures reported above are not national estimates and that no conclusions about national trends should be inferred. The States in the analysis account for about 32% of all adult discharges for CHF in the Nation and provide an indication of the general trend that readmissions for CHF may be following.

Reduction of Unnecessary Costs

This section of the chapter highlights waste and opportunities to reduce unnecessary costs. Waste can include overuse, underuse, or misuse of health care services. An example of overuse is prostate-specific antigen (PSA) screening among men age 75 and over, which the U.S. Preventive Services Task Force (USPSTF) recently recommended against.⁶ Our analyses of the 2005 National Health Interview Survey indicate that there were approximately 1.7 million men age 75 and over with no history of prostate cancer who reported having a routine PSA test in the past year. This makes up 42.8% of all men age 75 and over.

There is concern that administration of the PSA test in men age 75 and over will lead to false positives and subsequent unnecessary treatments. Reductions in costs and improvements in quality should result from reductions in unnecessary PSA screening. Patient and provider education is regarded as the key to reducing the overutilization of PSA screening.

Another overused treatment that can be reduced through education is the use of antibiotics to treat the common cold. Taking antibiotics does not treat or relieve symptoms of the common cold and may lead to the development of antibiotic-resistant bacterial infections. Although antibiotic prescribing patterns are slowly improving, inappropriate use of antibiotics for the common cold is still a concern.⁷ Children have the highest rates of antibiotic use and the highest rates of bacterial infection with antibiotic-resistant bacterial pathogens.⁸

Figure 6.7. Visits with antibiotics prescribed for a diagnosis of common cold per 10,000 population, 1998-2007.

Source: Centers for Disease Control and Prevention, National Center for Health Statistics, National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey, 1998-2007.
Denominator: U.S. noninstitutionalized population.

• In 2006-2007, the overall rate of antibiotics prescribed at visits with a diagnosis of the common cold stood at 59.9 per 10,000 population, which is below the Healthy People 2010 target of reducing rates to no more than 126.8 per 10,000 (Figure 6.7).
• From 1998-1999 to 2006-2007, the rate of antibiotic prescription at visits with a diagnosis of common cold decreased overall and for people of all age groups.
• In 2006-2007, all age groups were below the Healthy People 2010 target.

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Trends in Hospital Efficiency

Significant attention has been paid to cost variations across providers and across the country. Yet it is often difficult to separate out costs that reflect differences among providers in outputs, patient burden of illness,ⁱⁱ or care quality. To address the provider perspective, hospital cost efficiency is examined using a technique from the field of econometrics that can account for such differences.ⁱⁱⁱ This analysis uses data from the American Hospital Association Annual Survey and from Medicare Cost Reports, as well as data derived from the application of AHRQ Quality Indicators software to HCUP data and the application of comorbidity software to HCUP data.

Here, hospital efficiency is defined as the ratio of best practice costs to total observed costs. For example, given the types and quantities of outputs a hospital produces, the input prices it pays, its case mix, its quality, and its market characteristics, a theoretical best practice hospital might incur expenses amounting to $90 million. A comparison hospital in an identical situation with total expenses of $100 million would have an estimated cost efficiency of 90%.

Cost-efficiency estimates have been converted to index numbers with a base of 100 for the year 2002 as a way to place less emphasis on the specific magnitude of estimated hospital efficiency than on its general trend.

Figure 6.8. Average estimated relative hospital cost-efficiency index for a selected sample of urban general community hospitals, 2002-2006

Source: Agency for Healthcare Research and Quality. Analysis based on 1,382 urban general community hospitals with data in the Healthcare Cost and Utilization Project, State Inpatient Databases. Refer to Chapter 1, Introduction and Methods, for further details.

• Estimated urban hospital cost efficiency increased slightly from 2002 to 2004 but decreased slightly in 2005 for a selected sample of urban general community hospitals. It increased again in 2006 (Figure 6.8).
• The most cost-efficient hospitals (i.e., hospitals in the highest quartile of estimated cost efficiency) compared favorably with the least cost-efficient hospitals (i.e., hospitals in the lowest quartile of estimated cost efficiency) on a number of important variables. The most cost-efficient hospitals had lower costs and fewer full-time-equivalent employees per case mix-adjusted admission compared with the least cost-efficient hospitals. The most cost-efficient hospitals also had a shorter average length of stay, although the difference was not statistically significant (Table 6.2).
• The most cost-efficient hospitals had a higher operating margin than the least cost-efficient hospitals (Table 6.2).

Table 6.2. Correlates of hospital cost efficiency

Measure	Estimate	Standard deviation
Cost per case mix-adjusted admission:
Top quartile of hospital cost efficiency	$4,740	$1,321
Bottom quartile of hospital cost efficiency	$6,581	$2,612
Full-time equivalent employees per case mix-adjusted admission:
Top quartile of hospital cost efficiency	.042	0.01
Bottom quartile of hospital cost efficiency	.053	0.02
Average length of stay (days):
Top quartile of hospital cost efficiency	5.08	1.47
Bottom quartile of hospital cost efficiency	5.14	1.79
Operating margin:
Top quartile of hospital cost efficiency	.008	0.12
Bottom quartile of hospital cost efficiency	-.072	0.23

Source: American Hospital Association Annual Survey of Hospitals and Medicare Cost Reports, 2002-2006.

It is important to note that the figures reported above are not national estimates and that no conclusions about national trends should be inferred. However, the hospitals in the analysis represent about 55% of all non-Federal urban general community hospitals and therefore provide an indication of the general trend that cost efficiency may be following.

References

1.Himmelstein DU, Thorne D, Warren E, et al. Medical bankruptcy in the United States, 2007: results of a national study. Am J Med 2009 Aug;122(8):741-6.

2.Kaiser health tracking poll. Washington, DC: The Henry J. Kaiser Family Foundation; February 2009. Available at: http://www.kff.org/kaiserpolls/upload/7866.pdf. Accessed on August 12, 2009.

3.Fisher ES, Wennberg DE, Stukel TA, et al. The implications of regional variations in Medicare spending. Part 1: the content, quality, and accessibility of care. Ann Intern Med 2003 Feb 18;138(4):273-87.

4.McGlynn EA. Identifying, categorizing, and evaluating health care efficiency measures: . Rockville, MD: Agency for Healthcare Research and Quality; 2008. AHRQ Publication No. 08-0030. Available at: http://www.ahrq.gov/qual/efficiency. Accessed on December 8, 2009.

5.James B, Bayley KB. Cost of poor quality or waste in integrated delivery system settings (Final report prepared under Contract No. 290-00-0018-11). Rockville, MD: Agency for Healthcare Research and Quality; 2006. Available at: http://www.ahrq.gov/research/costpqids/. Accessed on August 13, 2009.

6.Screening for prostate cancer. Rockville, MD: Agency for Healthcare Research and Quality; 2008. Available at: http://www.ahrq.gov/clinic/uspstf/uspsprca.htm. Accessed on August 13, 2009.

7.Nash DR, Harman J, Wald ER, et al. Antibiotic prescribing by primary care physicians for children with upper respiratory tract infections. Arch Pediatr Adolesc Med 2002 Nov;156(11):1114-9.

8.Perz JF, Craig AS, Coffey CS, et al. Changes in antibiotic prescribing for children after a community-wide campaign. JAMA 2002 Jun 19;287(23):3103-9.

9.Elixhauser A, Steiner C, Harris R, et al. Comorbidity measures for use with administratiove data. Med Care 1998; 36:8-27.

10.Mutter R, Rosko M, Wang H. Measuring hospital inefficiency: the effects of controlling for quality and patient burden of illness. Health Serv Res 2008;43:1992-2013.

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ⁱ The inflation adjustment was done using the gross domestic product implicit price deflator.
ⁱⁱ This analysis controls for the following components that Elixhauser, et al. (1998) contend are part of patient burden of illness: (1) primary reason for admission to the hospital, (2) severity of the principal diagnosis, (3) iatrogenic complications, and (4) comorbidities that are unrelated to the primary diagnosis but have a substantial impact on both the resources used to treat the patient and the outcomes of the care provided.^9
iii Stochastic frontier analysis (SFA) is the technique used in this analysis. SFA can estimate best practice costs as the value total costs would be if full efficiency were attained. The hospital-level "cost efficiency" estimates that SFA produces measure whether output is obtained using the fewest inputs (i.e., technical efficiency), as well as whether output is produced using the optimal mix of inputs, given prices (i.e., allocative efficiency), the size of a hospital's operations (i.e., scale efficiency), and the range of a hospital's operations (i.e., scope efficiency), including possible overspecialization or overdiversification.¹⁰

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