An administrative claims measure of payments made for Medicare patients for a 30-day episode of care for acute myocardial infarction.

BACKGROUND: Understanding both cost and quality across institutions is a critical first step to illuminating the value of care purchased by Medicare. Under contract with the Centers for Medicare and Medicaid Services, we developed a method for profiling hospitals by 30-day episode-of-care costs (payments for Medicare beneficiaries) for acute myocardial infarction (AMI). METHODS: We developed a hierarchical generalized linear regression model to calculate hospital risk-standardized payment (RSP) for a 30-day episode for AMI. Using 2008 Medicare claims, we identified hospitalizations for patients 65 years of age or older with a discharge diagnosis of ICD-9 codes 410.xx. We defined an AMI episode as the date of admission plus 30 days. To reflect clinical care, we omitted or averaged payment adjustments for geographic factors and policy initiatives. We risk-adjusted for clinical variables identified in the 12 months preceding and including the AMI hospitalization. Using combined 2008-2009 data, we assessed measure reliability using an intraclass correlation coefficient and calculated the final RSP. RESULTS: The final model included 30 variables and resulted in predictive ratios (average predicted payment/average total payment) close to 1. The intraclass correlation coefficient score was 0.79. Across 2382 hospitals with ≥ 25 hospitalizations, the unadjusted mean payment was $20,324 ranging from $11,089 to $41,897. The mean RSP was $21,125 ranging from $13,909 to $28,979. CONCLUSIONS: This study introduces a claims-based measure of RSP for an AMI 30-day episode of care. The RSP varies among hospitals, with a 2-fold range in payments. When combined with quality measures, this payment measure will help profile high-value care.

Development of a clinical registry-based 30-day readmission measure for coronary artery bypass grafting surgery.

BACKGROUND: Reducing readmissions is a major healthcare reform goal, and reimbursement penalties are imposed for higher-than-expected readmission rates. Most readmission risk models and performance measures are based on administrative rather than clinical data. METHODS AND RESULTS: We examined rates and predictors of 30-day all-cause readmission following coronary artery bypass grafting surgery by using nationally representative clinical data (2008-2010) from the Society of Thoracic Surgeons National Database linked to Medicare claims records. Among 265 434 eligible Medicare records, 226 960 (86%) were successfully linked to Society of Thoracic Surgeons records; 162 572 (61%) isolated coronary artery bypass grafting admissions constituted the study cohort. Logistic regression was used to identify readmission risk factors; hierarchical regression models were then estimated. Risk-standardized readmission rates ranged from 12.6% to 23.6% (median, 16.8%) among 846 US hospitals with ≥30 eligible cases and ≥90% of eligible Centers for Medicare and Medicaid Services records linked to the Society of Thoracic Surgeons database. Readmission predictors (odds ratios [95% confidence interval]) included dialysis (2.02 [1.87-2.19]), severe chronic lung disease (1.58 [1.49-1.68]), creatinine (2.5 versus 1.0 or lower:1.49 [1.41-1.57]; 2.0 versus 1.0 or lower: 1.37 [1.32-1.43]), insulin-dependent diabetes mellitus (1.45 [1.39-1.51]), obesity in women (body surface area 2.2 versus 1.8: 1.44 [1.35-1.53]), female sex (1.38 [1.33-1.43]), immunosuppression (1.38 [1.28-1.49]), preoperative atrial fibrillation (1.36 [1.30-1.42]), age per 10-year increase (1.36 [1.33-1.39]), recent myocardial infarction (1.24 [1.08-1.42]), and low body surface area in men (1.22 [1.14-1.30]). C-statistic was 0.648. Fifty-two hospitals (6.1%) had readmission rates statistically better or worse than expected. CONCLUSIONS: A coronary artery bypass grafting surgery readmission measure suitable for public reporting was developed by using the national Society of Thoracic Surgeons clinical data linked to Medicare readmission claims.

Variation in hospital-level risk-standardized complication rates following elective primary total hip and knee arthroplasty.

BACKGROUND: Little is known about the variation in complication rates among U.S. hospitals that perform elective total hip arthroplasty (THA) and total knee arthroplasty (TKA) procedures. The purpose of this study was to use National Quality Forum (NQF)-endorsed hospital-level risk-standardized complication rates to describe variations in, and disparities related to, hospital quality for elective primary THA and TKA procedures performed in U.S. hospitals. METHODS: We conducted a cross-sectional analysis of national Medicare Fee-for-Service data. The study cohort included 878,098 Medicare fee-for-service beneficiaries, sixty-five years or older, who underwent elective THA or TKA from 2008 to 2010 at 3479 hospitals. Both medical and surgical complications were included in the composite measure. Hospital-specific complication rates were calculated from Medicare claims with use of hierarchical logistic regression to account for patient clustering and were risk-adjusted for age, sex, and patient comorbidities. We determined whether hospitals with higher proportions of Medicaid patients and black patients had higher risk-standardized complication rates. RESULTS: The crude rate of measured complications was 3.6%. The most common complications were pneumonia (0.86%), pulmonary embolism (0.75%), and periprosthetic joint infection or wound infection (0.67%). The median risk-standardized complication rate was 3.6% (range, 1.8% to 9.0%). Among hospitals with at least twenty-five THA and TKA patients in the study cohort, 103 (3.6%) were better and seventy-five (2.6%) were worse than expected. Hospitals with the highest proportion of Medicaid patients had slightly higher but similar risk-standardized complication rates (median, 3.6%; range, 2.0% to 7.1%) compared with hospitals in the lowest decile (3.4%; 1.7% to 6.2%). Findings were similar for the analysis involving the proportion of black patients. CONCLUSIONS: There was more than a fourfold difference in risk-standardized complication rates across U.S. hospitals in which elective THA and TKA are performed. Although hospitals with higher proportions of Medicaid and black patients had rates similar to those of hospitals with lower proportions, there is a continued need to monitor for disparities in outcomes. These findings suggest there are opportunities for quality improvement among hospitals in which elective THA and TKA procedures are performed.

Association between occurrence of a postoperative complication and readmission: implications for quality improvement and cost savings.

OBJECTIVE: To estimate the effect of preventing postoperative complications on readmission rates and costs. BACKGROUND: Policymakers are targeting readmission for quality improvement and cost savings. Little is known regarding mutable factors associated with postoperative readmissions. METHODS: Patient records (2005-2008) from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) were linked to Medicare inpatient claims. Risk factors, procedure, and 30-day postoperative complications were determined from ACS-NSQIP. The 30-day postoperative readmission and costs were determined from Medicare. Occurrence of a postoperative complication included surgical site infections and cardiac, pulmonary, neurologic, and renal complications. Multivariate regression models predicted the effect of reducing complication rates on risk-adjusted readmission rates and costs by procedure. RESULTS: The 30-day postoperative readmission rate was 12.8%. Complication rates for readmitted and nonreadmitted patients were 53% and 16% (P < 0.001). Patients with a postoperative complication had higher predicted probability of readmission and cost of readmission than patients without a complication. For the 20 procedures accounting for the greatest number of readmissions, reducing ACS-NSQIP complication rates by a relative 5% could result in prevention of 2092 readmissions per year and a savings to Medicare of $31.0 million per year. Preventing all ACS-NSQIP complications for these procedures could result in prevention of 41,846 readmissions per year and a savings of $620.3 million per year. CONCLUSIONS: This study provides substantial evidence that efforts to reduce postoperative readmissions should begin by focusing on postoperative complications that can be reliably and validly measured. Such an approach will not eliminate all postoperative readmissions but will likely have a major effect on readmission rates.

Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia.

IMPORTANCE: The Centers for Medicare & Medicaid Services publicly reports hospital 30-day, all-cause, risk-standardized mortality rates (RSMRs) and 30-day, all-cause, risk-standardized readmission rates (RSRRs) for acute myocardial infarction, heart failure, and pneumonia. The evaluation of hospital performance as measured by RSMRs and RSRRs has not been well characterized. OBJECTIVE: To determine the relationship between hospital RSMRs and RSRRs overall and within subgroups defined by hospital characteristics. DESIGN, SETTING, AND PARTICIPANTS: We studied Medicare fee-for-service beneficiaries discharged with acute myocardial infarction, heart failure, or pneumonia between July 1, 2005, and June 30, 2008 (4506 hospitals for acute myocardial infarction, 4767 hospitals for heart failure, and 4811 hospitals for pneumonia). We quantified the correlation between hospital RSMRs and RSRRs using weighted linear correlation; evaluated correlations in groups defined by hospital characteristics; and determined the proportion of hospitals with better and worse performance on both measures. MAIN OUTCOME MEASURES: Hospital 30-day RSMRs and RSRRs. RESULTS: Mean RSMRs and RSRRs, respectively, were 16.60% and 19.94% for acute myocardial infarction, 11.17% and 24.56% for heart failure, and 11.64% and 18.22% for pneumonia. The correlations between RSMRs and RSRRs were 0.03 (95% CI, -0.002 to 0.06) for acute myocardial infarction, -0.17 (95% CI, -0.20 to -0.14) for heart failure, and 0.002 (95% CI, -0.03 to 0.03) for pneumonia. The results were similar for subgroups defined by hospital characteristics. Although there was a significant negative linear relationship between RSMRs and RSRRs for heart failure, the shared variance between them was only 2.9% (r2 = 0.029), with the correlation most prominent for hospitals with RSMR <11%. CONCLUSION AND RELEVANCE: Risk-standardized mortality rates and readmission rates were not associated for patients admitted with an acute myocardial infarction or pneumonia and were only weakly associated, within a certain range, for patients admitted with heart failure.

Linkage of a clinical surgical registry with Medicare inpatient claims data using indirect identifiers.

BACKGROUND: A variety of data sources are available for measuring the quality of health care. Linking records from different sources can create unique and powerful databases that can be used to evaluate clinically relevant questions and direct health care policy. The objective of this study was to develop and validate a deterministic linkage algorithm that uses indirect patient identifiers to reliably match records from a surgical clinical registry with Medicare inpatient claims data. METHODS: Patient records from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), years 2005-2008, were linked to claims data in the Medicare Provider Analysis and Review file (MedPAR) by the use of a deterministic linkage algorithm and the following indirect patient identifiers: hospital, age, sex, diagnosis, procedure and dates of admission, discharge, and procedure. We validated the linkage procedure by systematically reviewing subsets of matched and unmatched records and by determining agreement on patient-level coding of inpatient mortality. RESULTS: Of the 150,454 records in ACS-NSQIP eligible for matching, 80.5% were linked to a MedPAR record. This percentage is within the expected match range given the estimated percentage of ACS-NSQIP patients likely to be Medicare beneficiaries. Systematic checks revealed no evidence of bias in the linkage procedure and there was excellent agreement on patient-level coding of mortality (kappa 0.969). The final linked database contained 121,070 patient records from 217 hospitals. CONCLUSION: This study demonstrates the feasibility and validity of a method for linking 2 data sources without direct personal identifiers. As clinical registries and other data sources continue to proliferate, linkage algorithms such as described here will be critical for quality measurement purposes.

A comparison of clinical registry versus administrative claims data for reporting of 30-day surgical complications.

OBJECTIVES: To compare the recording of 30-day postoperative complications between a national clinical registry and Medicare inpatient claims data and to determine whether the addition of outpatient claims data improves concordance with the clinical registry. BACKGROUND: Policymakers are increasingly discussing use of postoperative complication rates for value-based purchasing. There is debate regarding the optimal data source for such measures. METHODS: Patient records (2005-2008) from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) were linked to Medicare inpatient and outpatient claims data sets. We assessed the ability of (1) Medicare inpatient claims and (2) Medicare inpatient and outpatient claims to detect a core set of ACS-NSQIP 30-day postoperative complications: superficial surgical site infection (SSI), deep/organ-space SSI, any SSI (superficial and/or deep/organ-space), urinary tract infection, pneumonia, sepsis, deep venous thrombosis (DVT), pulmonary embolism, venous thromboembolism (DVT and/or pulmonary embolism), and myocardial infarction. Agreement of patient-level complications by ACS-NSQIP versus Medicare was assessed by κ statistics. RESULTS: A total of 117,752 patients from more than 200 hospitals were studied. The sensitivity of inpatient claims data for detecting ACS-NSQIP complications ranged from 0.27 to 0.78; the percentage of false-positives ranged from 48% to 84%. Addition of outpatient claims data improved sensitivity slightly but also greatly increased the percentage of false-positives. Agreement was routinely poor between clinical and claims data for patient-level complications. CONCLUSIONS: This analysis demonstrates important differences between ACS-NSQIP and Medicare claims data sets for measuring surgical complications. Poor accuracy potentially makes claims data suboptimal for evaluating surgical complications. These findings have meaningful implications for performance measures currently being considered.

Regional associations between Medicare Advantage penetration and administrative claims-based measures of hospital outcomes.

BACKGROUND: Risk-standardized measures of hospital outcomes reported by the Centers for Medicare and Medicaid Services include Medicare fee-for-service (FFS) patients and exclude Medicare Advantage (MA) patients due to data availability. MA penetration varies greatly nationwide and seems to be associated with increased FFS population risk. Whether variation in MA penetration affects the performance on the Centers for Medicare and Medicaid Service measures is unknown. OBJECTIVE: To determine whether the MA penetration rate is associated with outcomes measures based on FFS patients. RESEARCH DESIGN: In this retrospective study, 2008 MA penetration was estimated at the Hospital Referral Region (HRR) level. Risk-standardized mortality rates and risk-standardized readmission rates for heart failure, acute myocardial infarction, and pneumonia from 2006 to 2008 were estimated among HRRs, along with several markers of FFS population risk. Weighted linear regression was used to test the association between each of these variables and MA penetration among HRRs. RESULTS: Among 304 HRRs, MA penetration varied greatly (median, 17.0%; range, 2.1%-56.6%). Although MA penetration was significantly (P<0.05) associated with 5 of the 6 markers of FFS population risk, MA penetration was insignificantly (P≥0.05) associated with 5 of 6 hospital outcome measures. CONCLUSION: Risk-standardized mortality rates and risk-standardized readmission rates for heart failure, acute myocardial infarction, and pneumonia do not seem to differ systematically with MA penetration, lending support to the widespread use of these measures even in areas of high MA penetration.

Comparison of hospital risk-standardized mortality rates calculated by using in-hospital and 30-day models: an observational study with implications for hospital profiling.

BACKGROUND: In-hospital mortality measures, which are widely used to assess hospital quality, are not based on a standardized follow-up period and may systematically favor hospitals with shorter lengths of stay (LOSs). OBJECTIVE: To assess the agreement between performance measures of U.S. hospitals by using risk-standardized in-hospital and 30-day mortality rates. DESIGN: Observational study. SETTING: Nonfederal acute care hospitals in the United States with at least 30 admissions for acute myocardial infarction (AMI), heart failure (HF), and pneumonia from 2004 to 2006. PATIENTS: Medicare fee-for-service patients admitted for AMI, HF, or pneumonia from 2004 to 2006. MEASUREMENTS: The primary outcomes were in-hospital and 30-day risk-standardized mortality rates (RSMRs). RESULTS: Included patients comprised 718,508 admissions to 3135 hospitals for AMI, 1,315,845 admissions to 4209 hospitals for HF, and 1,415,237 admissions to 4498 hospitals for pneumonia. The hospital-level mean patient LOS varied across hospitals for each condition, ranging from 2.3 to 13.7 days for AMI, 3.5 to 11.9 days for HF, and 3.8 to 14.8 days for pneumonia. The mean RSMR differences (30-day RSMR minus in-hospital RSMR) were 5.3% (SD, 1.3) for AMI, 6.0% (SD, 1.3) for HF, and 5.7% (SD, 1.4) for pneumonia; distributions varied widely across hospitals. Performance classifications differed between the in-hospital and 30-day models for 257 hospitals (8.2%) for AMI, 456 (10.8%) for HF, and 662 (14.7%) for pneumonia. Hospital mean LOS was positively correlated with in-hospital RSMRs for all 3 conditions. LIMITATION: Medicare claims data were used for risk adjustment. CONCLUSION: In-hospital mortality measures provide a different assessment of hospital performance than 30-day mortality and are biased in favor of hospitals with shorter LOSs. PRIMARY FUNDING SOURCE: The Centers for Medicare & Medicaid Services and National Heart, Lung, and Blood Institute.

Skilled nursing facility referral and hospital readmission rates after heart failure or myocardial infarction.

BACKGROUND: Substantial hospital-level variation in the risk of readmission after hospitalization for heart failure (HF) or acute myocardial infarction (AMI) has been reported. Prior studies have documented considerable state-level variation in rates of discharge to skilled nursing facilities (SNFs), but evaluation of hospital-level variation in SNF rates and its relationship to hospital-level readmission rates is limited. METHODS: Hospital-level 30-day all-cause risk-standardized readmission rates (RSRRs) were calculated using claims data for fee-for-service Medicare patients hospitalized with a principal diagnosis of HF or AMI from 2006-2008. Medicare claims were used to calculate rates of discharge to SNF following HF-specific or AMI-specific admissions in hospitals with ≥25 HF or AMI patients, respectively. Weighted regression was used to quantify the relationship between RSRRs and SNF rates for each condition. RESULTS: Mean RSRR following HF admission among 4101 hospitals was 24.7%, and mean RSRR after AMI admission among 2453 hospitals was 19.9%. Hospital-level SNF rates ranged from 0% to 83.8% for HF and from 0% to 77.8% for AMI. No significant relationship between RSRR after HF and SNF rate was found in adjusted regression models (P=.15). RSRR after AMI increased by 0.03 percentage point for each 1 absolute percentage point increase in SNF rate in adjusted regression models (P=.001). Overall, HF and AMI SNF rates explained <1% and 4% of the variation for their respective RSRRs. CONCLUSION: SNF rates after HF or AMI hospitalization vary considerably across hospitals, but explain little of the variation in 30-day all-cause readmission rates for these conditions.

National performance on door-in to door-out time among patients transferred for primary percutaneous coronary intervention.

BACKGROUND: Delays in treatment time are commonplace for patients with ST-segment elevation acute myocardial infarction who must be transferred to another hospital for percutaneous coronary intervention. Experts have recommended that door-in to door-out (DIDO) time (ie, time from arrival at the first hospital to transfer from that hospital to the percutaneous coronary intervention hospital) should not exceed 30 minutes. We sought to describe national performance in DIDO time using a new measure developed by the Centers for Medicare & Medicaid Services. METHODS: We report national median DIDO time and examine associations with patient characteristics (age, sex, race, contraindication to fibrinolytic therapy, and arrival time) and hospital characteristics (number of beds, geographic region, location [rural or urban], and number of cases reported) using a mixed effects multivariable model. RESULTS: Among 13,776 included patients from 1034 hospitals, only 1343 (9.7%) had a DIDO time within 30 minutes, and DIDO exceeded 90 minutes for 4267 patients (31.0%). Mean estimated times (95% CI) to transfer based on multivariable analysis were 8.9 (5.6-12.2) minutes longer for women, 9.1 (2.7-16.0) minutes longer for African Americans, 6.9 (1.6-11.9) minutes longer for patients with contraindication to fibrinolytic therapy, shorter for all age categories (except >75 years) relative to the category of 18 to 35 years, 15.3 (7.3-23.5) minutes longer for rural hospitals, and 14.4 (6.6-21.3) minutes longer for hospitals with 9 or fewer transfers vs 15 or more in 2009 (all P < .001). CONCLUSION: Among patients presenting to emergency departments and requiring transfer to another facility for percutaneous coronary intervention, the DIDO time rarely met the recommended 30 minutes.

Improvements in door-to-balloon time in the United States, 2005 to 2010.

BACKGROUND: Registry studies have suggested improvements in door-to-balloon times, but a national assessment of the trends in door-to-balloon times is lacking. Moreover, we do not know whether improvements in door-to-balloon times were shared equally among patient and hospital groups. METHODS AND RESULTS: This analysis includes all patients reported by hospitals to the Centers for Medicare & Medicaid Services for inclusion in the time to percutaneous coronary intervention (acute myocardial infarction-8) inpatient measure from January 1, 2005, through September 30, 2010. For each calendar year, we summarized the characteristics of patients reported for the measure, including the number and percentage in each group, the median time to primary percutaneous coronary intervention, and the percentage with time to primary percutaneous coronary intervention within 75 minutes and within 90 minutes. Door-to-balloon time declined from a median of 96 minutes in the year ending December 31, 2005, to a median of 64 minutes in the 3 quarters ending September 30, 2010. There were corresponding increases in the percentage of patients who had times <90 minutes (44.2% to 91.4%) and <75 minutes (27.3% to 70.4%). The declines in median times were greatest among groups that had the highest median times during the first period: patients >75 years of age (median decline, 38 minutes), women (35 minutes), and blacks (42 minutes). CONCLUSION: National progress has been achieved in the timeliness of treatment of patients with ST-segment-elevation myocardial infarction who undergo primary percutaneous coronary intervention.

An administrative claims model for profiling hospital 30-day mortality rates for pneumonia patients.

BACKGROUND: Outcome measures for patients hospitalized with pneumonia may complement process measures in characterizing quality of care. We sought to develop and validate a hierarchical regression model using Medicare claims data that produces hospital-level, risk-standardized 30-day mortality rates useful for public reporting for patients hospitalized with pneumonia. METHODOLOGY/PRINCIPAL FINDINGS: Retrospective study of fee-for-service Medicare beneficiaries age 66 years and older with a principal discharge diagnosis of pneumonia. Candidate risk-adjustment variables included patient demographics, administrative diagnosis codes from the index hospitalization, and all inpatient and outpatient encounters from the year before admission. The model derivation cohort included 224,608 pneumonia cases admitted to 4,664 hospitals in 2000, and validation cohorts included cases from each of years 1998-2003. We compared model-derived state-level standardized mortality estimates with medical record-derived state-level standardized mortality estimates using data from the Medicare National Pneumonia Project on 50,858 patients hospitalized from 1998-2001. The final model included 31 variables and had an area under the Receiver Operating Characteristic curve of 0.72. In each administrative claims validation cohort, model fit was similar to the derivation cohort. The distribution of standardized mortality rates among hospitals ranged from 13.0% to 23.7%, with 25(th), 50(th), and 75(th) percentiles of 16.5%, 17.4%, and 18.3%, respectively. Comparing model-derived risk-standardized state mortality rates with medical record-derived estimates, the correlation coefficient was 0.86 (Standard Error = 0.032). CONCLUSIONS/SIGNIFICANCE: An administrative claims-based model for profiling hospitals for pneumonia mortality performs consistently over several years and produces hospital estimates close to those using a medical record model.

An administrative claims measure suitable for profiling hospital performance based on 30-day all-cause readmission rates among patients with acute myocardial infarction.

BACKGROUND: National attention has increasingly focused on readmission as a target for quality improvement. We present the development and validation of a model approved by the National Quality Forum and used by the Centers for Medicare & Medicaid Services for hospital-level public reporting of risk-standardized readmission rates for patients discharged from the hospital after an acute myocardial infarction. METHODS AND RESULTS: We developed a hierarchical logistic regression model to calculate hospital risk-standardized 30-day all-cause readmission rates for patients hospitalized with acute myocardial infarction. The model was derived using Medicare claims data for a 2006 cohort and validated using claims and medical record data. The unadjusted readmission rate was 18.9%. The final model included 31 variables and had discrimination ranging from 8% observed 30-day readmission rate in the lowest predictive decile to 32% in the highest decile and a C statistic of 0.63. The 25th and 75th percentiles of the risk-standardized readmission rates across 3890 hospitals were 18.6% and 19.1%, with fifth and 95th percentiles of 18.0% and 19.9%, respectively. The odds of all-cause readmission for a hospital 1 SD above average were 1.35 times that of a hospital 1 SD below average. Hospital-level adjusted readmission rates developed using the claims model were similar to rates produced for the same cohort using a medical record model (correlation, 0.98; median difference, 0.02 percentage points). CONCLUSIONS: This claims-based model of hospital risk-standardized readmission rates for patients with acute myocardial infarction produces estimates that are excellent surrogates for those produced from a medical record model.

National patterns of risk-standardized mortality and readmission for acute myocardial infarction and heart failure. Update on publicly reported outcomes measures based on the 2010 release.

BACKGROUND: Patient outcomes provide a critical perspective on quality of care. The Centers for Medicare and Medicaid Services (CMS) is publicly reporting hospital 30-day risk-standardized mortality rates (RSMRs) and risk-standardized readmission rates (RSRRs) for patients hospitalized with acute myocardial infarction (AMI) and heart failure (HF). We provide a national perspective on hospital performance for the 2010 release of these measures. METHODS AND RESULTS: The hospital RSMRs and RSRRs are calculated from Medicare claims data for fee-for-service Medicare beneficiaries, 65 years or older, hospitalized with AMI or HF between July 1, 2006, and June 30, 2009. The rates are calculated using hierarchical logistic modeling to account for patient clustering, and are risk-adjusted for age, sex, and patient comorbidities. The median RSMR for AMI was 16.0% and for HF was 10.8%. Both measures had a wide range of hospital performance with an absolute 5.2% difference between hospitals in the 5th versus 95th percentile for AMI and 5.0% for HF. The median RSRR for AMI was 19.9% and for HF was 24.5% (3.9% range for 5th to 95th percentile for AMI, 6.7% for HF). Distinct regional patterns were evident for both measures and both conditions. CONCLUSIONS: High RSRRs persist for AMI and HF and clinically meaningful variation exists for RSMRs and RSRRs for both conditions. Our results suggest continued opportunities for improvement in patient outcomes for HF and AMI.

The performance of US hospitals as reflected in risk-standardized 30-day mortality and readmission rates for medicare beneficiaries with pneumonia.

BACKGROUND: Pneumonia is a leading cause of hospitalization and death in the elderly, and remains the subject of both local and national quality improvement efforts. OBJECTIVE: To describe patterns of hospital and regional performance in the outcomes of elderly patients with pneumonia. DESIGN: Cross-sectional study using hospital and outpatient Medicare claims between 2006 and 2009. SETTING: A total of 4,813 nonfederal acute care hospitals in the United States and its organized territories. PATIENTS: Hospitalized fee-for-service Medicare beneficiaries age 65 years and older who received a principal diagnosis of pneumonia. INTERVENTION: None. MEASUREMENTS: Hospital and regional level risk-standardized 30-day mortality and readmission rates. RESULTS: Of the 1,118,583 patients included in the mortality analysis 129,444 (11.6%) died within 30 days of hospital admission. The median (Q1, Q3) hospital 30-day risk-standardized mortality rate for patients with pneumonia was 11.1% (10.0%, 12.3%), and despite controlling for differences in case mix, ranged from 6.7% to 20.9%. Among the 1,161,817 patients included in the readmission analysis 212,638 (18.3%) were readmitted within 30 days of hospital discharge. The median (Q1, Q3) 30-day risk-standardized readmission rate was 18.2% (17.2%, 19.2%) and ranged from 13.6% to 26.7%. Risk-standardized mortality rates varied across hospital referral regions from a high of 14.9% to a low of 8.7%. Risk-standardized readmission rates varied across hospital referral regions from a high of 22.2% to a low of 15%. CONCLUSIONS: Risk-standardized 30-day mortality and, to a lesser extent, readmission rates for patients with pneumonia vary substantially across hospitals and regions and may present opportunities for quality improvement, especially at low performing institutions and areas.

Reduction in acute myocardial infarction mortality in the United States: risk-standardized mortality rates from 1995-2006.

CONTEXT: During the last 2 decades, health care professional, consumer, and payer organizations have sought to improve outcomes for patients hospitalized with acute myocardial infarction (AMI). However, little has been reported about improvements in hospital short-term mortality rates or reductions in between-hospital variation in short-term mortality rates. OBJECTIVE: To estimate hospital-level 30-day risk-standardized mortality rates (RSMRs) for patients discharged with AMI. DESIGN, SETTING, AND PATIENTS: Observational study using administrative data and a validated risk model to evaluate 3,195,672 discharges in 2,755,370 patients discharged from nonfederal acute care hospitals in the United States between January 1, 1995, and December 31, 2006. Patients were 65 years or older (mean, 78 years) and had at least a 12-month history of fee-for-service enrollment prior to the index hospitalization. Patients discharged alive within 1 day of an admission not against medical advice were excluded, because it is unlikely that these patients had sustained an AMI. MAIN OUTCOME MEASURE: Hospital-specific 30-day all-cause RSMR. RESULTS: At the patient level, the odds of dying within 30 days of admission if treated at a hospital 1 SD above the national average relative to that if treated at a hospital 1 SD below the national average were 1.63 (95% CI, 1.60-1.65) in 1995 and 1.56 (95% CI, 1.53-1.60) in 2006. In terms of hospital-specific RSMRs, a decrease from 18.8% in 1995 to 15.8% in 2006 was observed (odds ratio, 0.76; 95% CI, 0.75-0.77). A reduction in between-hospital heterogeneity in the RSMRs was also observed: the coefficient of variation decreased from 11.2% in 1995 to 10.8%, the interquartile range from 2.8% to 2.1%, and the between-hospital variance from 4.4% to 2.9%. CONCLUSION: Between 1995 and 2006, the risk-standardized hospital mortality rate for Medicare patients discharged with AMI showed a significant decrease, as did between-hospital variation.

An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with heart failure.

BACKGROUND: A model using administrative claims data that is suitable for profiling hospital performance for heart failure would be useful in quality assessment and improvement efforts. METHODS AND RESULTS: We developed a hierarchical regression model using Medicare claims data from 1998 that produces hospital risk-standardized 30-day mortality rates. We validated the model by comparing state-level standardized estimates with state-level standardized estimates calculated from a medical record model. To determine the stability of the model over time, we used annual Medicare cohorts discharged in 1999-2001. The final model included 24 variables and had an area under the receiver operating characteristic curve of 0.70. In the derivation set from 1998, the 25th and 75th percentiles of the risk-standardized mortality rates across hospitals were 11.6% and 12.8%, respectively. The 95th percentile was 14.2%, and the 5th percentile was 10.5%. In the validation samples, the 5th and 95th percentiles of risk-standardized mortality rates across states were 9.9% and 13.9%, respectively. Correlation between risk-standardized state mortality rates from claims data and rates derived from medical record data was 0.95 (SE=0.015). The slope of the weighted regression line from the 2 data sources was 0.76 (SE=0.04) with intercept of 0.03 (SE=0.004). The median difference between the claims-based state risk-standardized estimates and the chart-based rates was <0.001 (25th percentile=-0.003; 75th percentile=0.002). The performance of the model was stable over time. CONCLUSIONS: This administrative claims-based model produces estimates of risk-standardized state mortality that are very good surrogates for estimates derived from a medical record model.

An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with an acute myocardial infarction.

BACKGROUND: A model using administrative claims data that is suitable for profiling hospital performance for acute myocardial infarction would be useful in quality assessment and improvement efforts. We sought to develop a hierarchical regression model using Medicare claims data that produces hospital risk-standardized 30-day mortality rates and to validate the hospital estimates against those derived from a medical record model. METHODS AND RESULTS: For hospital estimates derived from claims data, we developed a derivation model using 140,120 cases discharged from 4664 hospitals in 1998. For the comparison of models from claims data and medical record data, we used the Cooperative Cardiovascular Project database. To determine the stability of the model over time, we used annual Medicare cohorts discharged in 1995, 1997, and 1999-2001. The final model included 27 variables and had an area under the receiver operating characteristic curve of 0.71. In a comparison of the risk-standardized hospital mortality rates from the claims model with those of the medical record model, the correlation coefficient was 0.90 (SE=0.003). The slope of the weighted regression line was 0.95 (SE=0.007), and the intercept was 0.008 (SE=0.001), both indicating strong agreement of the hospital estimates between the 2 data sources. The median difference between the claims-based hospital risk-standardized mortality rates and the chart-based rates was <0.001 (25th and 75th percentiles, -0.003 and 0.003). The performance of the model was stable over time. CONCLUSIONS: This administrative claims-based model for profiling hospitals performs consistently over several years and produces estimates of risk-standardized mortality that are good surrogates for estimates from a medical record model.

Trends in care practices and outcomes among Medicare beneficiaries with diabetes.

BACKGROUND: While diabetes is a major issue for the aging U.S. population, few studies have described the recent trends in both preventive care practices and complications among the Medicare population with diabetes. Using the Medicare Quality Monitoring System (MQMS), this 2004 study describes these trends from 1992 to 2001 and how these rates vary across demographic subgroups. METHODS: Outcomes include age- and gender-adjusted rates of 15 indicators associated with diabetes care from 1992 to 2001, the absolute change in rates from 1992 to 2001, and 2001 rates by demographic subgroups. The data were cross-sectional samples of Medicare beneficiaries with diabetes from 1992 to 2001 from the Medicare 5% Standard Analytic Files. RESULTS: Use of preventive care practices rose from 1992 to 2001: 45 percentage points for HbA1c tests, 51 for lipid tests, 8 for eye exams, and 38 for self-monitoring of glucose levels (all p<0.05). Rates for short-term and some long-term complications of diabetes (e.g., lower-extremity amputations and cardiovascular conditions) fell from 1992 to 2001 (p<0.05). However, rates of other long-term complications such as nephropathy, blindness, and retinopathy rose during the period (p<0.05). Nonwhites and beneficiaries aged <65 and >85 exhibited consistently higher complication rates and lower use of preventive services. CONCLUSIONS: The Medicare program has seen some significant improvement in preventive care practices and significant declines in lower-limb amputations and cardiovascular conditions. However, rates for other long-term complications have increased, with evidence of subgroup disparities. The MQMS results provide an early warning for policymakers to focus on the diabetes care provided to some vulnerable subgroups.