Outcomes Trends for Acute Myocardial Infarction, Congestive Heart Failure, and Pneumonia, 2005-2009

The American Journal of Managed CareJanuary 2016
Volume 22
Issue 1

The 3 core measures of acute myocardial infarction, congestive heart failure, and pneumonia are the leading causes of hospital admissions and expenditures. Our study sets the benchmark foundation for outcome evaluations of CMS’s value-based purchasing program and the Affordable Care Act.


Objectives: The CMS core conditions—acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia—are a focus of hospital quality reporting and its value-based purchasing program. The study's purpose was to assess national trends of in-hospital mortality and resource utilization for these core measures.

Study Design: A time series study using outcomes from the 5 yearly cycles of the Nationwide Inpatient Sample (2005-2009).

Methods: Stratum-specific χ2 test for independence (binary or categorical parameters) or t test for a contrasted mean (continuous parameters) were used to identify parameters that changed significantly over time (in-hospital mortality, length of stay, cost, charges, severity of illness, diagnoses per case, procedures per case). Multiple logistic and linear regression models were used to identify factors associated with in-hospital deaths, hospital charges, and length of stay (LOS).

Results: In-hospital mortality decreased for AMI, CHF, and pneumonia. LOS was unchanged for CHF, but decreased for AMI and pneumonia. Average inflation-adjusted charges per case increased for all 3 conditions, while the average inflation-adjusted cost per case decreased for CHF and remained stable for AMI and pneumonia. The proportion of patients with extreme disability and extreme likelihood of dying, as defined by All-Patient-Refined Diagnosis Related Group, increased for all 3 diagnoses. The number of diagnoses and procedures were independently associated with LOS, cost, and charges for all 3 conditions.

Conclusions: Many measures of quality of inpatient care and resource utilization for CMS core conditions improved despite increases in patient complexity and risk of mortality. Further research is necessary to determine the exact causes of these improvements.

Am J Manag Care. 2016;22(1):e9-e17

Take-Away Points

It will be essential to review outcomes from the implementation of national programs to control healthcare costs. Our study provides the benchmark foundation from which to evaluate 3 core measures that have increased healthcare expenditures.

  • Acute myocardial infarction, congestive heart failure, and pneumonia, the 3 core measures discussed in this study, have seen an increase in patient complexity.
  • While charges have increased in the care of these patients, costs have remained stable.
  • Inpatient mortality and hospitalizations have decreased over time, suggesting that ambulatory care settings may be absorbing more of the care of these complex patients.

Cardiovascular disease (CVD) remains the number one disease affecting Americans and consumes the most healthcare dollars.1,2 The American Heart Association’s statistical update on heart disease and stroke reports that CVD outpaces all cancers in healthcare resource consumption and mortality.2 CVD consumed $316.4 billion in 2010 and causes 1 in 3 deaths per year. The major contributors to these figures are acute myocardial infarction (AMI) and congestive heart failure (CHF); 1 in 9 death certificates indicates heart failure as a cause of death.1-3

Overall, pneumonia ranks as the fifth-most frequent cause of admission, but the second-most frequent cause for those aged less than 17 years and more than 85 years.3 There were 1.1 million discharges with the primary diagnosis of pneumonia in 2010.3 Pneumonia also accounts for 16.1 deaths per 100,000 people, consumes $10.5 billion annually, and contributes 2.9% to annual costs for all hospitalizations.4,5

Given the impact of these 3 core diseases—AMI, CHF, and pneumonia—on our public health, and the financial costs to our healthcare system, major efforts have been focused on improving their outcomes. In 2002, hospitals accredited by the Joint Commission started collecting data on standardized care process measures to improve care. In 2004, the Joint Commission and CMS synergized their efforts and developed the National Hospital Quality Measures, standard measure sets common to both organizations. Currently, there are 12 broad classifications covering major areas of potential improvement including AMI, CHF, and pneumonia.6,7

In 2012, CMS continued efforts to improve quality care and to control costs by introducing the hospital value-based purchasing (VBP) program, funded through a 1% reduction in diagnosis-related group (DRG) payments for discharges. Of this 1% portion, 70% can be “earned back” based on adherence to the approved clinical processes of care for selected patient outcomes. The core measures of AMI, CHF, and pneumonia were selected for the fiscal year 2013 hospital VBP program.8 Hospitals responded with several initiatives (ie, broader use of new care delivery systems, such as telemedicine and hospital-based disease management programs) to reduce costs, hospital readmissions, length of stay (LOS), and inpatient mortality.9-13

The goal of this paper, then, was to establish a base of comparison for VBP by examining—prior to its implementation—national trends for inpatient mortality, LOS, cost, and charges related to these 3 core measures in a population of all-payers and all patients 18 years or older.


Patient Population and Data Source

For this study, we used the National Inpatient Sample (NIS) database, an all-payer database of hospital discharges maintained as part of the Healthcare Cost and Utilization Project by the Agency for Healthcare Research and Quality. The NIS approximates a 20% stratified sample of community hospitals in the United States. The sampling frame in each of the study’s surveys was a sample of hospitals that represented 90% to 95% of all hospital discharges for the particular year. Specifically, between 2005 and 2009, each NIS cycle included information on approximately 8 million discharges from approximately 1000 hospitals located in 37 (in 2005) to 44 (in 2009) states. Each NIS record represents a single hospital discharge and includes a unique identifier; basic demographics for the patient; admission and disposition type; up to 15 primary and secondary diagnoses; a list of potential comorbidities; up to 15 primary and secondary procedures; expected primary insurance payer; total hospital charges excluding physician and other professional services; LOS; type of hospital (teaching/nonteaching), bed size, and location; and mean cost-to-charge ratio.14

Furthermore, the severity of illness scores defined by the All Patient Refined Diagnosis-Related Groups (APR-DRGs)13 were evaluated and reported together with the respective risk of mortality scores. The APR-DRGs expand the basic DRG structure by adding 4 severity-of-illness and 4 risks-of-mortality subclasses to each DRG: for severity of illness, minor, moderate, major, or extreme loss of function; for mortality risk, minor, moderate, major, or extreme likelihood of dying. The APR-DRG severity scores of 3 and 4 (major/extreme loss of function), likelihood-of-dying scores of 3 and 4 (major/extreme likelihood of dying), and the number of diagnoses were used as markers for the severity and complexity of cases, respectively. The outcomes included in this study were in-hospital mortality and resource utilization, which included the length of inpatient stay, as well as the total costs and charges per discharge. These outcomes were assessed in each calendar year and in patient subgroups as described below.

Eligibility Criteria

Table 1

We included all adult (18 years or older) discharges from the NIS between 2005 and 2009 with the principal diagnoses of AMI, CHF, or pneumonia. The diagnoses and procedures were identified using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). summarizes the multiple ICD-9-CM codes for each principal diagnosis used for the study.

Statistical Analysis

We obtained national estimates for the total number of hospitalizations for each of the 3 diagnoses and total resource utilization parameters by calendar year, by totaling individual discharge sampling weights (which are the inverse of probability selection for the sample from the entire population). Since not all participating hospitals had reported their average cost-to-charge ratios, the national estimates of total costs were reweighted to account for all discharges where cost estimates were missing.14 All charges and costs were adjusted to the year 2009 using medical consumer price indices for the study years.

All available clinical and socioeconomic parameters were compared for change between years using a stratum-specific χ2 test for independence (for binary or categorical parameters) or a t test for a contrasted mean (for continuous parameters). Additionally, resource utilization was studied separately for 2 age groups: 18 to 64 years and 65 years or older. Taylor series linearization was used to account for the stratum units used in NIS to sample hospitals based on geographic region, control, location, teaching status, and bed size.

Multiple logistic models were used to identify factors associated with in-hospital deaths while accounting for potential confounders. Similarly, factors that impacted hospital charges and LOS were assessed using multiple linear regressions after logarithmic transformation of the respective outcomes. Coefficients from these models were exponentiated to yield a percentage change in the outcomes associated with each predictor. All P values of .05 or less were considered to be statistically significant.

All analyses were run with SAS version 9.1 (SAS Institute, Cary, North Carolina) and SUDAAN version 10.0 (RTI International, Research Triangle Park, North Carolina). The study was reviewed and approved as meeting the exempt criteria by the Inova Health System Institutional Review Board.


Table 2

Overall, 33.3 million hospitalizations were included in the 5 years of NIS, ranging from 6.54 to 6.84 million yearly, representing 164.14 million hospitalizations nationwide. Of those 33.3 million, the overall numbers of pneumonia, AMI, and CHF cases decreased. The decrease for pneumonia was from 1.17 million in 2005 (3.64% of all hospitalizations) to 1.01 million in 2009 (3.04%); for AMI, 662,000 in 2005 (2.07%) to 633,000 in 2009 (1.91%); and for CHF, 1.09 million in 2005 (3.39%) to 1.02 million (3.09%) in 2009 ().


Table 3

Over time, approximately 53% of patients were female; 77% were white. Reported ethnicities for the remainder, were, on average, 10.5% black, 7.5% Hispanic, 2% Asian, and less than 1% Native American. Sixty percent of the patients were 65 years or older; of these, the majority were Medicare patients, although the proportion of Medicare patients in the entire pneumonia cohort decreased from 70.22% in 2005 to 63.68% in 2009 (P <.0001) ().

Table 4

Table 5

The in-hospital mortality for pneumonia decreased from 4.56% in 2005 to 3.98% in 2009 (P <.0001). The LOS decreased from 5.80 days in 2005 to 5.55 days in 2009 (P = .0002) (). The average charges per case increased from $25,543 in 2005 to $30,256 in 2009 (P <.001), while the average cost per case did not change (Table 4 and by the age groups).

eAppendix Table 1

Over the 5 years, the proportion of cases with a major loss of function increased from 36.08% to 39.74%; those with an extreme loss of function increased from 7.08% to 12.02%. Also, the proportion of patients with a major and extreme likelihood of dying increased: respectively, from 16.40% in 2005 to 24.93% in 2009, and from 4.38% in 2005 to 8.62% in 2009 (P <.0001). The number of procedures per case increased as well, from 0.68 in 2005 to 0.75 in 2009 (P = .0019) (Table 4, for the individual procedures [eAppendices available at www.ajmc.com]).

After multivariate analysis, major or extreme disability and the number of procedures were independent predictors of in-hospital mortality. Major or extreme disability, likelihood of death, number of diagnoses, and number of procedures were independently associated with LOS, charges, and cost (eAppendix Table 2)

Acute Myocardial Infarction

The majority of patients admitted with acute myocardial infarction (AMI) were male; in 2005 and 2009, 40.72% and 39.51% were female, respectively (Table 3). The majority, 77.94%, were white. Blacks, Hispanics, Asians, and Native Americans represented an average of 8.77%, 7.16%, 2.07%, and 0.62% of patients, respectively. Fifty-eight percent of patients were aged 65 years or more, which closely paralleled the 57% of the sample who were insured by Medicare, the largest payer group. The figure, however, decreased over time, from 59.17% in 2005 to 55.94% in 2009 (P <.0001).

In-hospital mortality also decreased, from 6.81% in 2005 to 5.56% in 2009 (P <.0001). Average LOS decreased, too, from 5.23 days in 2005 with to 4.95 days in 2009 (P = .001) (Table 4). The average charges per case increased from $54,546 in 2005 to $62,917 in 2009 (P = .0004), while the average cost per case did not change.

The proportion of patients with major or extreme loss of function increased over the 5-year period, from 24.39% in 2005 to 27.93% in 2009, and from 13.52% in 2005 to 16.99% in 2009, respectively (P <.0001). The proportion of patients with a major or extreme likelihood of dying increased also, from 27.18% in 2005 to 27.68% in 2009, and 11.93% in 2005 to 15.95% in 2009, respectively (P <.0001). The total number of diagnoses per case also increased from 8.19 in 2005 to 10.51 in 2009 (P <.0001). The number of procedures per case increased from 3.93 in 2005 to 4.89 in 2009 (P <.0001) (Table 4, Table 5 by the age groups; eAppendix Table 1 for the individual procedures).

After multivariate analysis, major or extreme disability were the only independent predictors of in-hospital mortality. The number of diagnoses per case and number of procedures per case were inversely proportional to in-hospital mortality. Major or extreme disability, risk of mortality, number of diagnoses, and number of procedures were independently associated with LOS, charges, and cost (eAppendix Table 2).

Congestive Heart Failure

Female patients represented 51% of all cases of congestive heart failure (CHF) (Table 3). The majority (70.18%) of patients were white, while blacks, Hispanics, Asians, and Native Americans represented 18.71%, 8.01%, 1.68%, and 0.57% of patients, respectively. The overwhelming majority of patients (73%, a figure that remained stable throughout the 5-year period) were 65 years or older. Medicare was the largest payer group, but with a decreasing trend (dropping from 76.63% in 2005 to 74.09% in 2009; P = .0005).

In-hospital mortality decreased from 3.78% in 2005 to 3.21% in 2009 (P <.0001). The overall LOS remained stable, 5.37 days on average over the 5-year time period. Average charges increased from $31,028 in 2005 to $35,551 in 2009 (P = .0045) in inflation-adjusted dollars. In contrast, the average cost per case decreased ($11,006 in 2005 to $10,377 in 2009; P = .06).

The proportion of patients with major or extreme loss of function increased from 34.07% in 2005 to 44.89% in 2009, and from 5.85% in 2005 to 9.68% in 2009, respectively. A similar increase was found for the proportion of admissions with major or extreme risk of dying: from 23.86% in 2005 to 33.28% in 2009, and from 6.90% in 2005 to 11.52% in 2009, respectively. The complexity of illness, reflected by the total number of diagnoses per case, increased from 8.98 diagnoses per case in 2005 to 12.22 diagnoses per case in 2009 (P <.0001). However, the number of procedures per case did not change (Table 4, Table 5 by the age groups, eAppendix Table 1 for the individual procedures).

After adjustment for major socioeconomic confounders, the independent predictors of in-hospital mortality were found to be major or extreme disability, mortality risk, and the number of procedures per case. The number of diagnoses per case was inversely proportional to in-hospital mortality. Major or extreme disability, mortality risk, number of diagnoses, and number of procedures were independently associated with LOS, charges, and cost (eAppendix Table 2). For illustrative purposes, using eAppendix Table 2, for individuals admitted to the hospitals included in the NIS database with the primary diagnosis of CHF, each additional diagnosis on the record was associated with a 2.43% longer length of stay, 1.61% higher charges, 1.41% higher cost of treatment, and 0.975 lower odds of in-hospital death.


Our large analysis demonstrates that in the United States between 2005 and 2009, many measures of inpatient quality of care and resource utilization for pneumonia, AMI, and CHF improved—despite a universal increase in the proportion of patients with major or extreme severity of illness and likelihood of dying. In addition, charges increased but the cost of care actually decreased. These findings indicate that the push to improve quality through mandatory reporting may be contributing to improving patient outcomes and reducing cost of care.

In-hospital mortality improved for these 3 high-impact diagnoses, even after adjustment for sociodemographic changes and higher patient acuity. During the same time period, hospital LOS decreased for patients with pneumonia and AMI, and remained stable for those with CHF. The exact causes of improvement in in-hospital mortality cannot be determined from our analysis. However, LOS may have been partially impacted by the significant increase in the number of patients discharged to another hospital or care facility, or to home with healthcare assistance. Indeed, for pneumonia, discharge disposition to another hospital increased from 1.90% to 2.28% (P = .0017); and for CHF discharge disposition to home with assistance increased from 16.31% to 18.94% (P <.0001). These changes in discharge disposition have become common as cost-saving measures instituted under the case management model.8 Therefore, simply reporting of length of stay may be somewhat misleading and create a false sense of a decreased LOS.

Other events, occurring simultaneously with the CMS initiatives, which may have had a direct impact on the improvement in the care measures, include the increased use of outpatient services, telemedicine, and evidence-based care guidelines. The impact of diagnostic coding must also be taken into account.11-29 Another factor may have been patients’ increased use of ambulatory care settings to access preventive care, rather than relying on the acute care provided by hospitals’ emergency departments (EDs). Also, over the past several years, ambulatory care walk-in clinics have become more widespread, allowing patients to obtain care at lower costs. In addition, the earlier discharge patterns of hospitals have pushed ambulatory care, home health, and rehabilitation centers to absorb the care of patients at earlier stages of recovery.17-19

Another likely reason for the reported improvements of the care measures has been the rigorous implementation of evidenced-based guidelines to deliver appropriate and timely care. The American Heart Association developed the “Get with the Guideline Campaign” to improve care for patients with heart disease, specifically CHF and AMI. Recent studies on management strategies for CHF and AMI have found that use of these guidelines resulted in lower risk-adjusted mortality.22-29

In 2007, the Infectious Diseases Society of America/American Thoracic Society developed a consensus guideline on the management of community-acquired pneumonia in adults,28-30 which was updated in 2009 by the American College of Emergency Physicians. The guideline addressed 2 issues: drawing blood cultures only if clinically indicated, and administering the first dose of empiric antibiotics to the patient before they leave the ED. These changes have resulted in more timely and efficient care.30,31 The use of electronic medical records and an electronic decision tool to standardize care in the ED have also been useful in reducing inpatient mortality among patients with pneumonia.32

The increase in the severity of disease may be a result of the medical coding process. An investigation into the association of diagnostic coding with hospitalization and mortality trends for patients with pneumonia found a significant decline in hospitalizations for a primary diagnosis of pneumonia but a tripling for patients admitted with a primary diagnosis of sepsis. When the groups were combined, there was little change in hospitalizations or mortality, suggesting it was the change in coding that made the difference, not a change in the quality of care.33

Our study also addressed inpatient charges for all types of payers, as opposed to the traditional focus on Medicare patients. After adjusting for inflation, charges for all diagnoses increased over the time period. However, costs remained the same for AMI and pneumonia while decreasing for CHF, suggesting that these results may be due to differences in the cost-to-charge ratios set by hospital systems. Nonetheless, the stability of costs in the setting of increasing patient complexity and severity may signify that the cost-cutting measures hospitals have implemented in the face of the new Medicare reimbursement rules and the Affordable Care Act are having the desired effect.8,32-44

Others argue that costs and outcomes are not associated.43,44 A recent study from Oregon, where Medicaid program monies are rationed, found that patients who were awarded health insurance through Medicaid did seek and receive appropriate care. However, there were no significant differences in patient outcomes between those who received care and those who did not.45 In another study, funded by the Institute of Medicine to study payment reforms, it was found that an area of the correct size of a healthcare facility must be targeted, and the authors suggested the use of accountable care organizations—small enough to assign accountability but large enough to pool risk and even out individual variability.46


The limitations of our study include the lack of data about specific process measures and postdischarge outcomes, and the unknown impact of documentation practices. Lack of process measures, such as medication usage, hinders the identification of exactly what improved outcomes over this time period. Postdischarge follow-up (readmission rates and 30-day mortality rates) was unavailable; however, many argue that most readmissions are not preventable, are an imperfect measure of hospital quality, and are inversely proportional to mortality. Furthermore, the argument continues, inpatient mortality correlates well with 30-day mortality, although this measure favors hospitals with lower LOS.36-40 Finally, documentation practices, either because of electronic health records or coding, may account for observed increases in severity of illness.44


Many clinically meaningful outcomes related to the inpatient care of AMI, CHF, and pneumonia improved—in the context, notably, of more complex and severe disease. Hospital charges increased but costs remained stable, suggesting improvement in the quality of care. However, cost-effectiveness research is needed to determine how costs are actually related to these outcomes. Further research is also needed to discern which factors most influence the improved patient outcomes, and to account for any results that may accrue from coding changes. However, the trends noted in this review have laid the foundation for outcome comparison as a result of VBP.

Author Affiliations: Department of Medicine, Inova Fairfax Hospital (CV, AMi, AMo, ZMY), Falls Church, VA; Betty and Guy Beatty Center for Integrated Research, Inova Health System (MS, LH, ZMY), Falls Church, VA.

Source of Funding: The study was supported by Beatty Research Fund, Inova Health System.

Author Disclosures: Dr Younossi has been a consultant for Gilead, BMS, Intercept, and Abbvie; and has also previously received grants from Gilead, BMS, and Abbvie. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (ZY, CV, MS); acquisition of data (MS); analysis and interpretation of data (MS, CV, AMi, ZY); drafting of the manuscript (LH, AMo); critical revision of the manuscript for important intellectual content (MS, CV, AMi, ZY); statistical analysis (MS); provision of patients or study materials (MS); obtaining funding (ZY); administrative, technical, or logistic support (LH, AMo, ZY); and supervision (CV, ZY).

Address correspondence to: Chapy Venkatesan, MD, FACP, 3300 Gallows Rd, Department of Medicine, Inova Fairfax Hospital, Falls Church, VA 22042. E-mail: chapy.venkatesan@inova.org.


1. Facts about ORYX® for hospitals (national hospital quality measures). The Joint Commission website. http://www.jointcommission.org/facts_about_oryx_for_hospitals/. Published September 18, 2015. Accessed May 20, 2015.

2. Roger VL, Go AS, Lloyd-Jones DM, et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2012 update: a report from the American Heart Association. Circulation. 2012;125(1):e2-e220.

3. McCullough PA, Philbin EF, Spertus JA, Kaatz S, Sandberg KR, Weaver WD; Resource Utilization Among Congestive Heart Failure (REACH) study. Confirmation of a heart failure epidemic: findings from the resource utilization among congestive heart failure (REACH) study. J Am Coll Cardiol. 2002;39(1):60-69.

4. Health, United States, 2010: with special feature on death and dying. CDC website. http://www.cdc.gov/nchs/data/hus/hus10.pdf#listfigures. Published 2010. Accessed May 20, 2015.

5. Ruhnke GW, Coca-Perraillon M, Kitch BT, Cutler DM. Trends in mortality and medical spending in patients hospitalized for community-acquired pneumonia: 1993-2005. Med Care. 2010;48(12):1111-1116.

6. Whellan DJ, Greiner MA, Schulman KA, Curtis LH. Costs of inpatient care among Medicare beneficiaries with heart failure, 2001 to 2004. Circ Cardiovasc Qual Outcomes. 2010;3(1):33-40.

7. Health Care Spending and the Medicare Program [2015]. Medicare Payment Advisory Commission website. http://www.medpac.gov/-documents-/data-book. Accessed December 21, 2015.

8. 42 CFR parts 422 and 480: Medicare program; hospital inpatient value-based purchasing program [final rule]. Federal Register website. http://www.gpo.gov/fdsys/pkg/FR-2011-05-06/pdf/2011-10568.pdf. Accessed May 20, 2015.

9. Curtis LH, Greiner MA, Hammill BG, et al. Early and long-term outcomes of heart failure in elderly persons, 2001-2005. Arch Intern Med. 2008;168(22):2481-2488.

10. Capelastegui A, España PP, Quintana JM, et al. Declining length of hospital stay for pneumonia and post discharge outcomes. Am J Med. 2008;121(10):845-852.

11. Martín-Lesende I, Orruño E, Bilbao A, et al. Impact of telemonitoring home care patients with heart failure or chronic lung disease from primary care on healthcare resource use (the TELBIL study randomised controlled trial). BMC Health Serv Res. 2013;13:118.

12. Desai AS, Stevenson LW. Connecting the circle from home to heart-failure disease management. N Engl J Med. 2010;363(24):2364-2367.

13. Chen S, Cheng A, Mehta K. A review of telemedicine business models. Telemed J E Health. 2013;19(4):287-297.

14. Cost-to-charge ratio files: 2009 nationwide inpatient sample (NIS) user guide. HCUP, Agency for Healthcare Research and Quality website. http://www.hcup-us.ahrq.gov/db/state/CCR2009NISUserGuide.pdf. Published June 10, 2011. Accessed May 20, 2015.

15. Moore A. Telehealth. remote control of care. monitoring patients with chronic conditions in. Health Serv J. 2014;124(6401):24-25.

16. Pietrzak E, Cotea C, Pullman S. Primary and secondary prevention of cardiovascular disease: is there a place for Internet-based interventions? J Cardiopulm Rehabil Prev. 2014;34(5):303-317.

17. Chmiel C, Huber CA, Rosemann T, et al. Walk-ins seeking treatment at an emergency department or general practitioner out-of-hours service: a cross-sectional comparison. BMC Health Serv Res. 2011;11:94.

18. Simonet D. Cost reduction strategies for emergency services: insurance role, practice changes and patients accountability. Health Care Anal. 2009;17(1):1-19.

19. Gupta S, Sukhal S, Agarwal R, Das K. Quick diagnosis units--an effective alternative to hospitalization for diagnostic workup: a systematic review. J Hosp Med. 2014;9(1):54-59.

20. All Patient Refined Diagnosis Related Groups (APR-DRGs) version 20.0: methodology overview. HCUP, Agency for Healthcare Research and Quality website. http://www.hcup-us.ahrq.gov/db/nation/nis/APR-DRGsV20MethodologyOverviewandBibliography.pdf. Published 2003. Accessed May 20, 2015.

21. Kulkarni VT, Ross JS, Wang Y, et al. Regional density of cardiologists and rates of mortality for acute myocardial infarction and heart failure. Circ Cardiovasc Qual Outcomes. 2013;6(3):352-359.

22. Wang TY, Dai D, Hernandez AF, et al. The importance of consistent, high-quality acute myocardial infarction and heart failure care results from the American Heart Association’s Get with the Guidelines Program. J Am Coll Cardiol. 2011;58(6):637-644.

23. Gardetto NJ, Greaney K, Arai L, et al. Critical pathway for the management of acute heart failure at the Veterans Affairs San Diego Healthcare System: transforming performance measures into cardiac care. Crit Pathw Cardiol. 2008;7(3):153-172.

24. Reynolds D, Albert NM, Curtis AB, et al. Race and improvements in the use of guideline-recommended therapies for patients with heart failure: findings from IMPROVE HF. J Natl Med Assoc. 2012;104(5-6):287-298.

25. Tam LM, Fonarow GC, Bhatt DL, et al; GWTG Steering Committee and Investigators. Achievement of guideline-concordant care and in-hospital outcomes in patients with coronary artery disease in teaching and nonteaching hospitals: results from the Get With The Guidelines--Coronary Artery Disease Program. Circ Cardiovasc Qual Outcomes. 2013:6(1):58-65.

26. Somma KA, Bhatt DL, Fonarow GC, et al. Guideline adherence after ST-segment elevation versus non-ST segment elevation myocardial infarction. Circ Cardiovasc Qual Outcomes. 2012; 5(5):654-661.

27. Kociol RD, Peterson ED, Hammill BG, et al. National survey of hospital strategies to reduce heart failure readmissions: findings from the Get With the Guidelines-Heart Failure Registry. Circ Heart Fail. 2012;5(6):680-687.

28. Pneumonia measures. The Joint Commission website. http://www.jointcommission.org/pneumonia/. Published January 9, 2015. Accessed May 20, 2015.

29. Mandell LA, Wunderink RG, Anzueto A, et al; Infectious Diseases Society of America; American Thoracic Society. Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the management of community-acquired pneumonia in adults. Clin Infect Dis. 2007;44(suppl 2):S27-S72.

30. Reissig A, Mempel C, Schumacher U, Copetti R, Gross F, Aliberti S. Microbiological diagnosis and antibiotic therapy in patients with community-acquired pneumonia and acute COPD exacerbation in daily clinical practice: comparison to current guidelines. Lung. 2013;191(3):239-246.

31. Hurst JM, Bosso JA. Antimicrobial stewardship in the management of community-acquired pneumonia. Curr Opin Infect Dis. 2013;26(2):184-188.

32. Jones B, Jones J, Stoddard G, et al. Impact of an electronic decision support tool on outcomes for emergency department patients with pneumonia. Eur Respir J. 2013;42(suppl 57):5044.

33. Lagu T, Rothberg MB, Shieh MS, Pekow PS, Steingrub JS, Lindenauer PK. Hospitalizations, costs, and outcomes of severe sepsis in the United States 2003 to 2007. Crit Care Med. 2012;40(3):754-761.

34. Joynt KE, Jha AK. The relationship between cost and quality: no free lunch. JAMA. 2012; 307(10):1082-1083.

35. Affordable Care Act. HHS website. http://www.hhs.gov/opa/affordable-care-act/index.html. Accessed September 19, 2014.

36. Jha AK, Joynt KE, Orav EJ, Epstein AM. The long-term effect of premier pay for performance on patient outcomes. N Engl J Med. 2012;366(17):1606-1615.

37. Patterson ME, Hernandez AF, Hammill BG, et al. Process of care performance measures and long-term outcomes in patients hospitalized with heart failure. Med Care. 2010;48(3):210-216.

38. Romley JA, Jena AB, Goldman DP. Hospital spending and inpatient mortality: evidence from California: an observational study. Ann Intern Med. 2011;154(3):160-167.

39. Schreyögg J, Stargardt T. The trade-off between costs and outcomes: the case of acute myocardial infarction. Health Serv Res. 2010;45(6, pt 1):1585-1601.

40. Stukel TA, Fisher ES, Alter DA, et al. Association of hospital spending intensity with mortality and readmission rates in Ontario hospitals. JAMA. 2012;307(10):1037-1045.

41. Colla CH, Wennberg DE, Meara E, et al. Spending differences associated with the Medicare Physician Group Practice Demonstration. JAMA. 2012;308(10):1015-1023.

42. Bradley EH, Herrin J, Elbel B, et al. Hospital quality for acute myocardial infarction: correlation among process measures and relationship with short-term mortality. JAMA. 2006;296(1):72-78.

43. Borzecki AM, Christiansen CL, Chew P, Loveland S, Rosen AK. Comparison of in-hospital versus 30-day mortality assessments for selected medical conditions. Med Care. 2010;48(12):1117-1121.

44. Himmelstein DU, Wright A, Woolhandler S. Hospital computing and the costs and quality of care: a national study. Am J Med. 2010;123(1):40-46.

45. Allen H, Baicker K, Taubman S, Wright B, Finkelstein A. The Oregon health insurance experiment: when limited policy resources provide research opportunities. J Health Polit Policy Law. 2013;38(6):1183-1192.

46. Zhang Y, Baik SH, Fendrick AM, Baicker K. Comparing local and regional variation in health care spending. N Engl J Med. 2012;367(18):1724-1731.

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