Geographic Variation in Surgical Outcomes and Cost Between the United States and Japan

September 21, 2016

Compared with Japan, the United States has substantially less geographic variation in surgical outcomes, but it has higher variation in cost.

ABSTRACT

Unwarranted geographic variation in spending has received intense scrutiny in the United States. However, few studies have compared variation in spending and surgical outcomes between the United States healthcare system and those of other nations. In this study, we compare the geographic variation in postsurgical outcomes and cost between the United States and Japan.

Objectives:

This retrospective cohort study uses Medicare Part A data from the United States (2010-2011) and similar inpatient data from Japan (2012). Patients 65 years or older undergoing 1 of 5 surgeries (coronary artery bypass graft, abdominal aortic aneurysm repair, colectomy, pancreatectomy, or gastrectomy) were selected in the United States and Japan.

Study Design:

Reliability- and case-mix—adjusted coefficient of variation (COV) values were calculated using hierarchical modeling and empirical Bayes techniques for the following 5 outcomes: postoperative mortality, the development of a complication, death after complication (failure to rescue), length of stay, and the cost of the hospitalization. Sensitivity analyses were also performed by calculating patient demographic-and case-mix–adjusted COV values for each outcome using weighted age- and sex-standardized values.

Methods:

The variability of the postsurgical outcomes was uniformly lower in the United States compared with Japan. Cost variation was consistently higher in the United States for all surgeries.

Results:

Although the US healthcare system may be more inefficient regarding costs, the presence of higher geographic variation in postoperative care in Japan, relative to the United States, suggests that the observed geographic variation in the United States—both for health expenditures and outcomes—is not a unique manifestation of its structural shortcomings.

Conclusions:

Am J Manag Care. 2016;22(9):600-607

Take-Away Points

This investigation compares the amount of observed geographic variation in the United States and Japan for the surgical outcomes and costs of 5 common, higher-risk surgeries. Key findings include:

  • The United States has less variation in surgical outcomes compared with Japan, but it has higher geographic cost variation.
  • The surgical outcome variation in the United States is not unique; it exists even in a country that consistently ranks highly on public health quality measures. This variation is likely the result of unique structural inefficiencies in each healthcare system.
  • There is an opportunity to reduce wasteful spending by standardizing the cost of care in the United States.

Countries around the world are attempting to contain increasing healthcare costs without sacrificing quality of care. Relative to other developed countries, the United States fares poorly on measures of both healthcare expenditure and health outcomes. Although the United States spent $7929 per person (16.4% of gross domestic product [GDP]) in 2010—the most of any Organisation for Economic Co-operation and Development (OECD) country—health outcomes, such as life expectancy, are among the worst in the developed world.1

Much research has focused on small-area geographic variation in healthcare spending as evidence of inefficient healthcare in the United States.2 A recent report released by the Institute of Medicine (IOM) has affirmed that higher healthcare expenditures within a region are not related to better outcomes.3 In principle, the lack of relationship between spending and outcomes suggests that any unwarranted variation is potentially wasteful, and, if eliminated, might decrease costs while maintaining quality. Variations in healthcare expenditure have been identified as a potential area for up to a 30% reduction in waste (approximately $650 billion) in the US healthcare system.4,5

Apart from issues of economic inefficiency, geographic variations in treatments and outcomes have important clinical and health policy implications. Outcome differences across regions are evidence for the presence of inequalities in care across regions, which represent a distinct and important problem in itself. Regional differences in quality of care may indicate opportunities to improve care for patients in places with worse outcomes. Identifying centers that have better outcomes than others and promoting the practice styles of those centers may be a practical way to improve patient outcomes.

Researchers have proposed several reasons for the observed geographic variation in the United States. Studies cite fragmentation of care,6 racial and socioeconomic barriers to care, and the diversity of financing and coverage options as reasons that the United States has unwarranted variation.7 Other studies have also investigated physician peer effects and the ways that social influences can affect medical decisions.8

One premise underlying the interpretation of these findings is that the American healthcare system is unique in its inefficient stewardship of healthcare dollars. This naturally leads to a question: If the United States is so singularly ineffective at controlling costs and ensuring quality relative to other countries, then does it also experience singularly high geographic variation in costs and outcomes relative to other countries?

In this study, we compared the variability in postoperative outcomes and cost of hospitalization for 5 major surgeries in the United States and Japan. There are several key similarities and differences between the 2 countries. Japan is a modern OECD country that has access to technologies and treatments similar to those used in the United States; however, in 2010, Japan spent only $3204 per person (9.5% of GDP) on healthcare while attaining the highest average life expectancy at birth of any OECD country. Its system also provides universal access to care, and it is consistently well-graded from a public health standpoint.9 In other words, it does not have many of the shortcomings of the US healthcare system that potentially lead to unwarranted variation.

Although the prevalence of various surgical procedures has been studied, to our knowledge, there have been only few and limited comparisons of the variability in surgical outcomes among countries.10,11 Surgical outcomes as a quality metric are ideal to examine, given their well-established definitions and close association between the surgical process and the postoperative outcome. If the observed variability in Japan is low, the implication would be that the United States is truly unique. If, however, the variability in Japan is high, that would suggest that variability could be an inherent feature of healthcare systems. Our main aim is to study whether the United States is truly uniquely inefficient or whether geographic variation is part of the natural fabric of modern healthcare delivery.

METHODS

Data Source

Our analysis uses 2 data sources: a large Japanese administrative database and the Medicare 5% sample standard analytical file. The Japanese administrative database contains anonymous patient and encounter-level information collected over the 9-month period between April 1, 2012, and December 31, 2012. The data were originally collected from hospital forms that were submitted to the Japanese government as part of an ongoing effort to reform the healthcare reimbursement system in Japan.12 Hospitals that participate in this program are designated as Diagnosis Procedure Combination (DPC) hospitals, and together, they account for more than half of all the acute care beds in Japan. The DPC hospitals represent approximately 20% of all hospitals in Japan; they include all major academic medical centers and most of the nation’s larger hospitals. Our sample encompasses 100% of the patients from roughly half of these DPC hospitals. Please see eAppendix Figure A (eAppendices available at www.ajmc.com) for a comparison of our sample with the full set of DPC hospitals. The data contain information on patient status at admission and discharge; cost of admission; and details on surgeries, procedures, and drugs administered during the patient’s stay at the hospital. Medical clerks and licensed information managers generally oversee the collection and reporting of DPC data at individual hospitals to optimize the accuracy of the data. Studies correlating data from electronic health records and DPC data have shown very high compliance.13 In total, our data encompass information for approximately 5 million patients in more than 600 hospitals located across every region of Japan.

Data on patients in the United States were obtained from the Medicare 5% sample from 2010 to 2011. This is a random sampling of 5% of Medicare beneficiaries across all hospitals in the United States and has been used extensively to analyze different outcome measures.14 In our study, we analyze all inpatient claims by hospitals for this 5% sample. The information about each hospitalization includes dates of hospital admission and discharge, discharge status, disease(s) diagnosed, and cost of hospitalization.

Cohort Selection

Patients undergoing any of 5 relatively common, higher-risk surgeries (abdominal aortic aneurysm repair, coronary artery bypass grafting, colectomy, gastrectomy, or pancreatectomy) were included in the analysis. We follow the definitions outlined in the US Agency for Healthcare Research Quality’s Inpatient Quality Indicator guidelines when available.15-17 These surgeries have been previously used in related investigations studying the variability of surgical quality and outcomes.14,18 Japanese patients were selected using a Japan-specific surgery classification scheme (K-codes). K-codes were individually selected and verified to correspond to their International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) counterparts for this study; they have similarly been used in other studies to create comparable cohorts.19 Comorbidities were defined using Elixhauser classification definitions for both the US and Japanese cohorts. Prior validation studies have shown that both ICD-9-CM and ICD-10 coding algorithms produce similar estimates of comorbidity prevalence in administrative data.20 Patients were restricted to those 65 years or older.

Outcomes

We investigated 5 outcomes: in-hospital mortality, postsurgical complications, failure to rescue, length of stay, and cost of hospitalization. Postsurgical complications are defined using ICD-9-CM codes in the US Medicare dataset, and corresponding ICD-10 codes for the Japanese dataset. Complications included pulmonary failure, pneumonia, myocardial infarction, deep vein thrombosis/pulmonary embolism, acute renal failure, postoperative hemorrhage, surgical site infection, and gastrointestinal bleeding. The specific ICD-9-CM codes for these complications have been previously validated as having reasonable agreement when comparing administrative data with the medical record.21,22 Failure to rescue is defined as death after any of the previously listed complications. Variations in cost in the United States were calculated using the Medicare Part A reimbursement to hospitals.

We subtracted disproportionate share payments, indirect medical education payments, and payments classified under total prospective payment system capital from United States cost totals, because these represent intended systemic cost variation and would inflate our variation estimates for the United States. Regions of comparison were defined for each country so that they were similar; variability was compared among states in the United States and among prefectures in Japan, graphically depicted in eAppendix Figure B.

Statistical Analysis

In this study, we calculated outcome-specific coefficients of variation (COVs), defined as the sample standard deviation divided by the mean. The COV is a unitless measure that allows for a comparison of variation between a random variable with different means from 2 samples. For example, when used to measure the consistency of an elite athlete’s performance across multiple races, the COV will usually be between 1% and 2%.23

Potential confounders, such as patient characteristics (age and gender for both countries’ cohorts, and additionally race for the US cohort), comorbidities, and urgent versus scheduled admissions, were included in all regression models. We additionally used hierarchical modeling techniques to adjust our COV estimates for reliability.24 Using random effects logistic regression models, we generated empirical Bayes predictions of the outcomes that were used to calculate our final COV values. This technique minimizes the impact of statistical outliers such as those from small-volume hospitals whose estimates can be considered less reliable than those of larger-volume hospitals. By variably shrinking the contribution of small-volume hospital estimates toward the average mortality rate across all hospitals, we are able to better estimate systematic, nonrandom variation as opposed to variation due to chance.

We additionally performed a sensitivity analysis by calculating age- and sex-standardized COV values for both countries to the 2000 US population, weighting all estimates by the number of procedures performed in each state and prefecture. These demographic- and case-mix—adjusted COVs were evaluated by performing multivariate regressions for each outcome of interest. We then calculated the adjusted COV (adjusted for age, gender, race, comorbidities, and urgency of admission) based on these regression estimates.

RESULTS

We assessed the geographic variation of in-hospital mortality, complication rate, failure to rescue rate, length of stay, and the cost of hospitalization for 5 major surgical procedures in the United States and Japan.

The Japanese surgical cohort had higher rates of colectomy and gastrectomy, together composing 74% of the analyzed surgeries versus 31% for the United States. The US surgical cohort was composed of a higher number of coronary artery bypass graft (CABG) procedures, representing 52% of the surgical cohort (Table 1). Pancreatectomies were the least frequent surgery performed in both groups. Table 1 shows the raw descriptive statistics of each cohort, but they should not be used for comparison against each country as they do not take into account differing patient demographics and comorbidities between each country.

In comparing the United States and Japan’s reliability-adjusted COV values (Table 2 and Figure 1), the observed geographic variations among regions in Japan were higher than those of the United States for all 20 surgical outcome measures (4 outcomes for 5 surgeries). Our sensitivity analyses (Table 3 and Figure 2) reiterated these results, as the variation was higher in all 20 outcomes except for 1: there was lower variation in length of stay for colectomy in Japan. Of note, is that colectomy was the most frequently performed surgery in our analysis (41% of the total cohort) in Japan, but not in the United States (26% of the total cohort).

The variation in the cost of hospitalization was uniformly lower for Japan across all 5 surgeries for reliability-adjusted COVs. This was true in our sensitivity analysis as well. The level of cost variation between surgeries was similar across surgeries in Japan (adjusted COV values of 7.3 to 7.6), indicating that the specific type of surgery did not have much influence on the cost variability between regions. This is mostly true for the United States as well, as the adjusted COV values ranged from 21.7 to 22.8 with the exception of CABG with a COV of 25.3.

The level of variation for a given postsurgical outcome was similar across different surgeries in each country. For example, the approximate reliability-adjusted COVs for mortality across the 5 surgeries ranged from 15 to 17 in the United States, while ranging from 29 to 33 in Japan. The failure-to-rescue COV ranges from 16 to 19 in the United States and between 47 and 50 in Japan. One notable deviation from this pattern was length of stay in the United States. Four of 5 surgeries in the United States had a length-of-stay COV between 42 and 55, with the exception of CABG, which had a much lower COV at 29.

Our sensitivity analysis endorses the conclusions from our reliability-adjusted models, and it also demonstrates the importance of using the reliability-adjusted estimates. Though our conclusions remain the same, the estimates of variability are much higher and dispersed in our sensitivity analysis, especially for less frequently performed procedures such as pancreatectomy where small differences in outcomes could have large statistical manifestations (Table 3 and Figure 2).

In summary, the United States has lower variability for all postsurgical outcomes, but it has higher cost variability relative to Japan. The United States and Japan have low variability between surgeries for a given postsurgical outcome, suggesting that the differences in overall variation between the countries are not dependent on the specific surgery performed.

DISCUSSION

Much of the discussion around geographic variation in US healthcare expenditure and quality has focused singularly on the United States, with little work comparing the geographic variation in healthcare outcomes among other countries and the United States, especially for resource-intensive surgical procedures. Although previous studies have investigated whether the US healthcare system as a whole is uniquely inefficient compared with other countries,7 much of the literature around geographic variation does not address this topic, instead citing the inefficiency of the US healthcare system as a cause for geographic variation. This context is important when evaluating the causes and consequences of variation.

Healthcare efficiency has previously been framed as a combination of productive and allocative efficiency. Productive efficiency refers to the impact of healthcare given a series of inputs such as physicians and hospitals, and allocative efficiency measures the benefits from the marginal cost of healthcare versus the cost of other goods. In this analysis, we characterize both the variation in healthcare outcomes (outputs) and the variation in procedure costs (inputs) across each country, therefore addressing both aspects of efficiency described above. Higher outcome variations reflect lower productive efficiency—that is, given the same level of healthcare inputs, there are differences in achieving desirable outcomes. In the same manner, higher cost variations reflect lower allocative efficiency—that is, in order to achieve the desirable outcome, the amount and therefore the value of each healthcare dollar spent varies greatly.

In this study, we demonstrate that although cost varies more in the United States compared with Japan, all postsurgical outcome measures show less variability in the United States. This suggests that geographic variation in outcomes is not a manifestation of US-specific inefficiencies, but is more likely an inherent part of healthcare delivery and a shared characteristic among healthcare systems.25

Our conclusions are in line with the results of previous studies comparing the United States, United Kingdom, and Germany that show that among a group of medical conditions, no single country consistently spent less money and achieved better health outcomes across different indications.26 Our finding suggests that every country struggles in its own way to achieve productive efficiency in healthcare. In other words, building a healthcare system where the least expenditures result in the best outcomes is a universal problem.

The fact that the United States had higher observed cost variation than Japan is also consistent with previous analyses. In 2008, a report by the Congressional Budget Office concluded that the geographic variation in healthcare spending was higher in the United States than in Canada and the United Kingdom, citing the fact that the financing of healthcare is more centralized in those countries.27 In Japan, the government centrally controls healthcare costs by budgeting and setting standard reimbursement rates for procedures. Although our results show that there is more cost variation in the United States compared with Japan, this is unlikely to represent US-specific unwarranted variation in healthcare resource use. A recent report by the IOM found that if geographic variation were eliminated for postacute care services, variation in Medicare spending would fall by 73%.28 Much of the geographic variation in expenditure in the United States is actually attributable to postacute care and not to excess use of care during hospitalizations.28

The most likely reason for Japan’s increased outcomes variability compared with that of the United States is the fact that Japan has many more hospitals per capita than the United States. In 2010, Japan had 68 hospitals per million individuals compared with 19 hospitals per million in the United States—a more than 3-fold difference.1 This translates into surgeons performing high-risk procedures, such as those in this study, relatively infrequently. Similarly, hospital staff may have less experience caring for these postoperative patients and are likely to be less prepared for both preventing and resolving postoperative complications. There is an extensive literature, both in the United States and in Japan, on the benefits of centralized surgical centers, where increased surgical and postoperative care experience, as well as differences in staffing, can translate to better outcomes for patients.29-31

Other reasons that explain the high variability in Japan include the less-centralized regulation of hospitals and medical care, and the lower level of regulation over physicians’ training and development.32 In 1 study, adherence to clinical practice guidelines for cholangitis were measured in hospitals across Japan. Results showed markedly varied compliance that correlated directly with in-hospital mortality rates.33 Patients in Japan have universal access to any facility, and in general they do not require referrals to visit a specialist. Furthermore, physicians and nurses are currently licensed for life; they do not have requirements for license renewal or continuing medical education.34 Both of these factors could contribute to higher variability in quality of care that patients could receive, especially for higher-risk, lower-volume procedures.

Previous studies demonstrate that clinical decision making is affected by the supply of specialists, local training frameworks, and regulatory factors, all of which are more fragmented in Japan than in the United States. In addition, geographic variation in rates of surgery reflect physician beliefs about the level of appropriateness of the surgery and the extent to which patients have a say in their treatment.35 The decentralized nature of Japan’s healthcare system, with its many hospitals and unstructured referral system, presumably exacerbates variation by creating more physician “micro-communities” that lead to variable care.36

Potential drivers for more homogeneous outcome performance in the United States may arise from the fact that the United States has greater standardization of the surgical training curriculum relative to Japan, as there is no standardized national surgical curriculum in Japan or even the equivalent of the Accreditation Council for Graduate Medical Education.37 The fact that the United States has a multiplicity of accrediting organizations that all work toward achieving a higher quality of care also undoubtedly plays a role.38 The existence of managed care in the United States is unique, and the role that national payers have in emphasizing quality of care also encourages more uniform quality of care across the country. Public disclosure of surgical outcomes in the United States may also influence quality, as public reporting has been extensively studied as an effective quality improvement strategy by encouraging changes in the behavior of providers.39 However, with respect to surgical outcomes the evidence is mixed.40 The higher variation in United States costs could be due to regional adjustments and variation in diagnoses billed for the same procedure.

Limitations

There are several limitations to our analysis. First, similar to other studies that have made international comparisons, we were not able to capture the full variation in care and expenditures due to the limited nature of the datasets. We studied a small number of high-risk surgical procedures for each country, and we limited our observations to patients 65 years or older. In particular, we used a subset of the full universe of DPC hospitals; however, if we performed the same analysis on the full dataset, the observed variation in Japan would likely be even higher. In addition, DPC hospitals are generally larger and considered to be more elite hospitals with better patient care, and so our estimates are conservative relative to a larger dataset. Most investigations concerning geographic variation in the United States have also used the Medicare population due to the wealth of information that is contained in the dataset. Our analysis uses the Medicare Part A reimbursement to institutions as the basis for our cost analysis, thereby underestimating the true variation in costs across all sectors, but this errs on the side of conservatively estimating variation. Other limitations include the fact that the ICD-10 codes used to define comorbidities and complications from the DPC database have not been rigorously validated. However, since the definitions that were used in this study were consistently applied across regions in Japan, estimates for variability should be unaffected.

CONCLUSIONS

Geographic variation in quality and expenditures has been the target of intense investigation in the United States in recent years. Though the US healthcare system is often indicted for being inefficient, fragmented, and generally wasteful in spending, our analysis shows that the US healthcare system has less variation in postsurgical outcomes relative to Japan’s healthcare system. This suggests that geographic variation, whether in postsurgical outcomes or healthcare expenditures, is an attribute that is likely inherent to healthcare systems and not unique to the US healthcare system. However, our analysis shows that that there is an opportunity to reduce unintentional cost variation in the United States by increasing efforts to standardize the cost of care.

Acknowledgments

The authors would like to acknowledge the help and support of the following members from Global Health Consulting Japan for their assistance with the Japanese data: Makie Furuya, MD; Masa Horie, BS; Taku Miura, MD; Keisuke Suzuki, MBA; Tamotsu Yagi, BS; and Moe Yanatori, MBA.

Author Affiliations: Department of Health Research and Policy (MPH), Center for Health Policy/Center for Primary Care and Outcomes Research (LS, JB), and Department of Surgery (JMM, SMW), Stanford University School of Medicine, Stanford University, CA; Department of Economics, Terry College of Business, University of Georgia (WBV), Athens; Global Health Consulting Japan (SW, AY), Tokyo, Japan.

Source of Funding: Stanford University School of Medicine Medical Scholars Program, Nishioka-JMSA Scholarship from the Nishioka Charitable Foundation.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. The content is solely the responsibility of the authors and does not necessarily represent the official views of Global Health Consulting Group or any of its affiliated or subsidiary entities.

Authorship Information: Concept and design (JB, MPH, JMM, LS, WBV, SMW); acquisition of data (MPH, JMM, AY); analysis and interpretation of data (JB, MPH, LS, WBV, SMW, AY); drafting of the manuscript (MPH); critical revision of the manuscript for important intellectual content (JB, MPH, JMM, LS, WBV, SMW, AY); statistical analysis (JB, MPH, LS, WBV); obtaining funding (JB, MPH); administrative, technical, or logistic support (JB, AY); and supervision (JB).

Address Correspondence to: Michael P. Hurley, MD, MS, UCSF Department of Internal Medicine, 1382 8th Ave, Apt 1, San Francisco, CA 94122. E-mail: Michael.Hurley@ucsf.edu.

REFERENCES

1. Health expenditure and financing: health expenditure indicators. Organisation for Economic Co-operation and Development website. http://www.oecd-ilibrary.org/social-issues-migration-health/data/oecd-health-statistics/system-of-health-accounts-health-expenditure-by-function_data-00349-en. Accessed August 27, 2016.

2. Wennberg JE, Fisher ES, Skinner JS. Geography and the debate over Medicare reform. Health Aff (Millwood). 2002;suppl web exclusives:W96-W114.

3. MaCurdy T, Bhattacharya J, Perlroth D, et al. Geographic variation in spending, utilization and quality: Medicare and Medicaid beneficiaries. National Academy of Sciences website. https://www.nationalacademies.org/hmd/~/media/Files/Report%20Files/2013/Geographic-Variation2/Subcontractor-Reports/Updated%20Acumen%20Report.pdf. Published May 2013. Accessed August 27, 2016.

4. Farrell D, Jensen E, Kocher B, et al. Accounting for the cost of US health care: a new look at why Americans spend more. McKinsey & Company website. http://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/accounting-for-the-cost-of-us-health-care. Published December 2008. Accessed August 27, 2016.

5. Wennberg JE. An agenda for change: improving quality and curbing health care spending: opportunities for the congress and the Obama Administration. The Dartmouth Atlas of Health Care website. http://www.dartmouthatlas.org/downloads/reports/agenda_for_change.pdf. Published December 2008. Accessed August 27, 2016.

6. Cebul RD, Rebitzer JB, Taylor LJ, Votruba ME. Organizational fragmentation and care quality in the U.S healthcare system. J Econ Perspect. 2008;22(4):93-113.

7. Garber AM, Skinner J. Is American health care uniquely inefficient? J Econ Perspect. 2008;22(4):27-50. doi: 10.1257/jep.22.4.27.

8. Burke MA, Fournier GM, Prasad K. Physician social networks and geographical variation in medical care. The Brookings Institution website. https://www.brookings.edu/wp-content/uploads/2016/06/07healthcare_burke.pdf Published July 15, 2003. Accessed August 27, 2016.

9. Ikeda N, Saito E, Kondo N, et al. What has made the population of Japan healthy? Lancet. 2011;378(9796):1094-1105. doi: 10.1016/S0140-6736(11)61055-6.

10. Vikatmaa P, Mitchell D, Jensen LP, et al. Variation in clinical practice in carotid surgery in nine countries 2005-2010. lessons from VASCUNET and recommendations for the future of national clinical audit. Eur J Vasc Endovasc Surg. 2012;44(1):11-17. doi: 10.1016/j.ejvs.2012.04.013

11. McPherson K, Wennberg JE, Hovind OB, Clifford P. Small-area variations in the use of common surgical procedures: an international comparison of New England, England, and Norway. N Engl J Med. 1982;307(21):1310-1314.

12. Matsuda S, Ishikawa KB, Kuwabara K, Fujimori K, Fushimi K, Hashimoto H. Development and use of the Japanese case-mix system. Eurohealth. 2008;14(3):25-30.

13. Iwamoto M, Higashi T, Miura H, et al. Accuracy of using Diagnosis Procedure Combination administrative claims data for estimating the amount of opioid consumption among cancer patients in Japan. Jpn J Clin Oncol. 2015;45(11):1036-1041. doi: 10.1093/jjco/hyv130.

14. Birkmeyer JD, Gust C, Dimick JB, Birkmeyer NJ, Skinner JS. Hospital quality and the cost of inpatient surgery in the United States. Ann Surg. 2012;255(1):1-5. doi: 10.1097/SLA.0b013e3182402c17.

15. Pancreatic resection mortality rate technical specifications: inpatient quality indicators #9 (IQI #9). Agency for Healthcare Research and Quality website. http://www.qualityindicators.ahrq.gov/Downloads/Modules/IQI/V50/TechSpecs/IQI_09_Pancreatic_Resection_Mortality%20Rate.pdf. Accessed August 27, 2016.

16. Abdominal aortic aneurysm (AAA) repair mortality rate: inpatient quality indicators #11 (IQI #11). Agency for Healthcare Research and Quality website. http://www.qualityindicators.ahrq.gov/Downloads/Modules/IQI/V45/TechSpecs/IQI%2011%20Abdominal%20Aortic%20Aneurysm%20(AAA)%20Repair%20Mortality%20Rate.pdf. Accessed August 27, 2016.

17. Coronary artery bypass graft (CABG) mortality rate: inpatient quality indicators #12 (IQI #12). Agency for Healthcare Research and Quality website. http://www.qualityindicators.ahrq.gov/Downloads/Modules/IQI/V45/TechSpecs/IQI%2012%20Coronary%20Artery%20Bypass%20Graft%20(CABG)%20Mortality%20Rate.pdf. Accessed August 27, 2016.

18. Ghaferi AA, Birkmeyer JD, Dimick JB. Complications, failure to rescue, and mortality with major inpatient surgery in Medicare patients. Ann Surg. 2009;250(6):1029-1034.

19. Sugihara T, Yasunaga H, Horiguchi H, et al. Performance comparisons in major uro-oncological surgeries between the USA and Japan. Int J Urol. 2014;21(11):1145-1150. doi: 10.1111/iju.12548.

20. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130-1139.

21. Iezzoni LI, Daley J, Heeren T, et al. Identifying complications of care using administrative data. Med Care. 1994;32(7):700-715.

22. Weingart SN, Iezzoni LI, Davis RB, et al. Use of administrative data to find substandard care: validation of the complications screening program. Med Care. 2000;38(8):796-806.

23. Hopkins WG, Hewson DJ. Variability of competitive performance of distance runners. Med Sci Sports Exerc. 2001;33(9):1588-1592.

24. Dimick JB, Staiger DO, Birkmeyer JD. Ranking hospitals on surgical mortality: the importance of reliability adjustment. Health Serv Res. 2010;45(6, pt 1):1614-1629. doi: 10.1111/j.1475-6773.2010.01158.x.

25. Corallo AN, Croxford R, Goodman DC, Bryan EL, Srivastava D, Stukel TA. A systematic review of medical practice variation in OECD countries. Health Policy. 2014;114(1):5-14. doi: 10.1016/j.healthpol.2013.08.002.

26. Baily MN, Garber AM. Health care productivity. Brookings website. https://www.brookings.edu/wp-content/uploads/1997/01/1997_bpeamicro_baily.pdf. Published 1997. Accessed August 27, 2016.

27. Geographic variation in health care spending. Congressional Budget Office website. https://www.cbo.gov/sites/default/files/cbofiles/ftpdocs/89xx/doc8972/02-15-geoghealth.pdf. Published Feburary 2008. Accessed August 27, 2016.

28. Newhouse JP, Garber AM, Graham RP, McCoy MA, Mancher M, Kibria A; Committee on Geographic Variation in Health Care Spending and Promotion of High-Value Care; Board on Health Care Services; Institute of Medicine. Variation in Health Care Spending: Target Decision Making, Not Geography. Washington, DC: National Academies Press; 2013.

29. Yasunaga H, Hashimoto H, Horiguchi H, Miyata H, Matsuda S. Variation in cancer surgical outcomes associated with physician and nurse staffing: a retrospective observational study using the Japanese Diagnosis Procedure Combination Database. BMC Health Serv Res. 2012;12(1):129. doi: 10.1186/1472-6963-12-129.

30. Miyata H, Motomura N, Kondo MJ, Fushimi K, Ishikawa KB, Takamoto S. Toward quality improvement of cardiovascular surgery in Japan: an estimation of regionalization effects from a nationwide survey. Health Policy. 2009;91(3):246-251. doi: 10.1016/j.healthpol.2008.11.003.

31. Birkmeyer JD, Stukel TA, Siewers AE, Goodney PP, Wennberg DE, Lucas FL. Surgeon volume and operative mortality in the United States. N Engl J Med. 2003;349(22):2117-2127.

32. Yamashita K, Ikai H, Nishimura M, Fushimi K, Imanaka Y. Effect of certified training facilities for intensive care specialists on mortality in Japan. Crit Care Resusc. 2013;15(1):28-32.

33. Murata A, Matsuda S, Kuwabara K, et al. An observational study using a national administrative database to determine the impact of hospital volume on compliance with clinical practice guidelines. Med Care. 2011;49(3):313-320. doi: 10.1097/MLR.0b013e3182028954.

34. Hirose M, Imanaka Y, Ishizaki T, Evans E. How can we improve the quality of health care in Japan? learning from JCQHC hospital accreditation. Health Policy. 2003;66(1):29-49.

35. Birkmeyer JD, Reames BN, McCulloch P, Carr AJ, Campbell WB, Wennberg JE. Understanding of regional variation in the use of surgery. Lancet. 2013;382(9898):1121-1129. doi: 10.1016/S0140-6736(13)61215-5.

36. O’Neill L, Kuder J. Explaining variation in physician practice patterns and their propensities to recommend services. Med Care Res Rev. 2005;62(3):339-357.

37. Kurashima Y, Watanabe Y, Ebihara Y, Murakami S, Shichinohe T, Hirano S. Where do we start? the first survey of surgical residency education in Japan. Am J Surg. 2016;211(2):405-410. doi: 10.1016/j.amjsurg.2015.09.004.

38. Viswanathan HN, Salmon JW. Accrediting organizations and quality improvement. Am J Manag Care. 2000;6(10):1117-1130.

39. Public reporting as a quality improvement strategy—closing the quality gap: revisiting the state of the science [AHRQ Publication No. 12-E011-EF]. Agency for Healthcare Research and Quality website. https://www.effectivehealthcare.ahrq.gov/ehc/products/343/1199/EvidReport208_CQGPublicReporting_FinalReport_20120724.pdf. Published July 2012.Accessed August 27, 2016.

40. Osborne NH, Nicholas LH, Ryan AM, Thumma JR, Dimick JB. Association of hospital participation in a quality reporting program with surgical outcomes and expenditures for Medicare beneficiaries. JAMA. 2015;313(5):496-504. doi: 10.1001/jama.2015.25.