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BACKGROUND: Concern persists that extended shifts in medical residency programs may adversely affect patient safety. METHODS: We conducted a cluster-randomized noninferiority trial in 63 internal-medicine residency programs during the 2015–2016 academic year. Programs underwent randomization to a group with standard duty hours, as adopted by the Accreditation Council for Graduate Medical Education (ACGME) in July 2011, or to a group with more flexible duty-hour rules that did not specify limits on shift length or mandatory time off between shifts. The primary outcome for each program was the change in unadjusted 30-day mortality from the pretrial year to the trial year, as ascertained from Medicare claims. We hypothesized that the change in 30-day mortality in the flexible programs would not be worse than the change in the standard programs (difference-in-difference analysis) by more than 1 percentage point (noninferiority margin). Secondary outcomes were changes in five other patient safety measures and risk-adjusted outcomes for all measures. RESULTS: The change in 30-day mortality (primary outcome) among the patients in the flexible programs (12.5% in the trial year vs. 12.6% in the pretrial year) was noninferior to that in the standard programs (12.2% in the trial year vs. 12.7% in the pretrial year). The test for noninferiority was significant (P = 0.03), with an estimate of the upper limit of the one-sided 95% confidence interval (0.93%) for a between-group difference in the change in mortality that was less than the prespecified noninferiority margin of 1 percentage point. Differences in changes between the flexible programs and the standard programs in the unadjusted rate of readmission at 7 days, patient safety indicators, and Medicare payments were also below 1 percentage point; the noninferiority criterion was not met for 30-day readmissions or prolonged length of hospital stay. Risk-adjusted measures generally showed similar findings. CONCLUSIONS: Allowing program directors flexibility in adjusting duty-hour schedules for trainees did not adversely affect 30-day mortality or several other measured outcomes of patient safety. (Funded by the National Heart, Lung, and Blood Institute and Accreditation Council for Graduate Medical Education; iCOMPARE ClinicalTrials.gov number, NCT02274818.)

BACKGROUND: A purpose of duty-hour regulations is to reduce sleep deprivation in medical trainees, but their effects on sleep, sleepiness, and alertness are largely unknown. METHODS: We randomly assigned 63 internal-medicine residency programs in the United States to follow either standard 2011 duty-hour policies or flexible policies that maintained an 80-hour workweek without limits on shift length or mandatory time off between shifts. Sleep duration and morning sleepiness and alertness were compared between the two groups by means of a noninferiority design, with outcome measures including sleep duration measured with actigraphy, the Karolinska Sleepiness Scale (with scores ranging from 1 [extremely alert] to 9 [extremely sleepy, fighting sleep]), and a brief computerized Psychomotor Vigilance Test (PVT-B), with long response times (lapses) indicating reduced alertness. RESULTS: Data were obtained over a period of 14 days for 205 interns at six flexible programs and 193 interns at six standard programs. The average sleep time per 24 hours was 6.85 hours (95% confidence interval [CI], 6.61 to 7.10) among those in flexible programs and 7.03 hours (95% CI, 6.78 to 7.27) among those in standard programs. Sleep duration in flexible programs was noninferior to that in standard programs (between-group difference, −0.17 hours per 24 hours; one-sided lower limit of the 95% confidence interval, −0.45 hours; noninferiority margin, −0.5 hours; P = 0.02 for noninferiority), as was the score on the Karolinska Sleepiness Scale (between-group difference, 0.12 points; one-sided upper limit of the 95% confidence interval, 0.31 points; non-inferiority margin, 1 point; P

Background Concern persists that inflexible duty-hour rules in medical residency programs may adversely affect the training of physicians. Methods We randomly assigned 63 internal medicine residency programs in the United States to be governed by standard duty-hour policies of the 2011 Accreditation Council for Graduate Medical Education (ACGME) or by more flexible policies that did not specify limits on shift length or mandatory time off between shifts. Measures of educational experience included observations of the activities of interns (first-year residents), surveys of trainees (both interns and residents) and faculty, and intern examination scores. Results There were no significant between-group differences in the mean percentages of time that interns spent in direct patient care and education nor in trainees’ perceptions of an appropriate balance between clinical demands and education (primary outcome for trainee satisfaction with education; response rate, 91%) or in the assessments by prog...

POLICY POINTS: Patients with low socioeconomic status (SES) experience poorer survival rates after diagnosis of breast cancer, even when enrolled in Medicare and Medicaid. Most of the difference in survival is due to more advanced cancer on presentation and the general poor health of lower SES patients, while only a very small fraction of the SES disparity is due to differences in cancer treatment. Even when comparing only low‐ versus not‐low‐SES whites (without confounding by race) the survival disparity between disparate white SES populations is very large and is associated with lower use of preventive care, despite having insurance. CONTEXT: Disparities in breast cancer survival by socioeconomic status (SES) exist despite the “safety net” programs Medicare and Medicaid. What is less clear is the extent to which SES disparities affect various racial and ethnic groups and whether causes differ across populations. METHODS: We conducted a tapered matching study comparing 1,890 low‐SES (LSES) non‐Hispanic white, 1,824 black, and 723 Hispanic white women to 60,307 not‐low‐SES (NLSES) non‐Hispanic white women, all in Medicare and diagnosed with invasive breast cancer between 1992 and 2010 in 17 US Surveillance, Epidemiology, and End Results (SEER) regions. LSES Medicare patients were Medicaid dual‐eligible and resided in neighborhoods with both high poverty and low education. NLSES Medicare patients had none of these factors. Measurements: 5‐year and median survival. FINDINGS: LSES non‐Hispanic white patients were diagnosed with more stage IV disease (6.6% vs 3.6%; p < 0.0001), larger tumors (24.6 mm vs 20.2 mm; p < 0.0001), and more chronic diseases such as diabetes (37.8% vs 19.0%; p < 0.0001) than NLSES non‐Hispanic white patients. Disparity in 5‐year survival (NLSES − LSES) was 13.7% (p < 0.0001) when matched for age, year, and SEER site (a 42‐month difference in median survival). Additionally, matching 55 presentation factors, including stage, reduced the disparity to 4.9% (p = 0.0012), but further matching on treatments yielded little further change in disparity: 4.6% (p = 0.0014). Survival disparities among LSES blacks and Hispanics, also versus NLSES whites, were significantly associated with presentation factors, though black patients also displayed disparities related to initial treatment. Before being diagnosed, all LSES populations used significantly less preventive care services than matched NLSES controls. CONCLUSIONS: In Medicare, SES disparities in breast cancer survival were large (even among non‐Hispanic whites) and predominantly related to differences of presentation characteristics at diagnosis rather than differences in treatment. Preventive care was less frequent in LSES patients, which may help explain disparities at presentation.

IntroductionMedical trainees’ duty hours have received attention globally; restrictions in Europe, New Zealand and some Canadian provinces are much lower than the 80 hours per week enforced in USA. In USA, resident duty hours have been implemented without evidence simultaneously reflecting competing concerns about patient safety and physician education. The objective is to prospectively evaluate the implications of alternative resident duty hour rules for patient safety, trainee education and intern sleep and alertness.Methods and analysis63 US internal medicine training programmes were randomly assigned 1:1 to the 2011 Accreditation Council for Graduate Medical Education resident duty hour rules or to rules more flexible in intern shift length and number of hours off between shifts for academic year 2015–2016. The primary outcome is calculated for each programme as the difference in 30-day mortality rate among Medicare beneficiaries with any of several prespecified principal diagnoses in the intervention year minus 30-day mortality in the preintervention year among Medicare beneficiaries with any of several prespecified principal diagnoses. Additional safety outcomes include readmission rates, prolonged length of stay and costs. Measures derived from trainees’ and faculty responses to surveys and from time-motion studies of interns compare the educational experiences of residents. Measures derived from wrist actigraphy, subjective ratings and psychomotor vigilance testing compare the sleep and alertness of interns. Differences between duty hour groups in outcomes will be assessed by intention-to-treat analyses.Ethics and disseminationThe University of Pennsylvania Institutional Review Board (IRB) approved the protocol and served as the IRB of record for 40 programmes that agreed to sign an Institutional Affiliation Agreement. Twenty-three programmes opted for a local review process.Trial registration numberNCT02274818; Pre-results.

BACKGROUND Racial disparities in general surgical outcomes are known to exist but not well understood. OBJECTIVES To determine if black-white disparities in general surgery mortality for Medicare patients are attributable to poorer health status among blacks on admission or differences in the quality of care provided by the admitting hospitals. RESEARCH DESIGN Matched cohort study using Tapered Multivariate Matching. SUBJECTS All black elderly Medicare general surgical patients (N=18,861) and white-matched controls within the same 6 states or within the same 838 hospitals. MEASURES Thirty-day mortality (primary); others include in-hospital mortality, failure-to-rescue, complications, length of stay, and readmissions. RESULTS Matching on age, sex, year, state, and the exact same procedure, blacks had higher 30-day mortality (4.0% vs. 3.5%, P

Kidney transplant recipients often receive antibody induction. Previous studies of induction therapy were often limited by short follow-up and/or absence of information about complications. After linking Organ Procurement and Transplantation Network data with Medicare claims, we compared outcomes between three induction therapies for kidney recipients. Using novel matching techniques developed on the basis of 15 clinical and demographic characteristics, we generated 1:1 pairs of alemtuzumab-rabbit antithymocyte globulin (rATG) (5330 pairs) and basiliximab-rATG (9378 pairs) recipients. We used paired Cox regression to analyze the primary outcomes of death and death or allograft failure. Secondary outcomes included death or sepsis, death or lymphoma, death or melanoma, and healthcare resource utilization within 1 year. Compared with rATG recipients, alemtuzumab recipients had higher risk of death (hazard ratio [HR], 1.14; 95% confidence interval [95% CI], 1.03 to 1.26; P

By far, the most common method to compare hospital outcomes is indirect standardization, often accomplished by dividing the observed number of patient events at a hospital by the expected number of patient events derived from the population (Fleiss, Levin, and Paik 2003; Iezzoni 2012) to get the traditional “O/E” metric. In some cases the observed number of events (O) is replaced by a predicted number of events (P), where P is a function of O, as is done for Medicare’s Hospital Compare random effects model, where the quantity “P/E” is reported (Krumholz et al. 2006a,b; Silber et al. 2010). At times, investigators prefer to report (O-E)/N rather than O/E, which has the advantage that the metric is clearly defined and stable even when E is near zero (Chassin et al. 1989; Silber, Rosenbaum, and Ross 1995). All approaches have one aspect in common, that being the reliance on each individual hospital’s own patient case mix to be used when comparing across hospitals. The fact that the indirect standardization approach allows two hospitals with very different distributions of patients to be compared represents both a strength and weakness of the technique. In a sense, indirect standardization tries to describe what has happened when the hospital sees the mix of patients it usually sees, while direct standardization tries to describe what would happen if the hospital saw a mix of patients based on another population of interest. Direct standardization will utilize this external reference population to weight the results of the index hospital (Fleiss, Levin, and Paik 2003). Speaking informally, a hospital administrator trying to improve the care of the current patient population is interested in indirect standardization (i.e., would other hospitals do better with my patients?), while a patient looking at a ranking of hospitals to select one would be interested in direct standardization—(i.e., where would it be best for me to go if I resembled the typical patients used for direct standardization, or how well has the hospital done with other patients who have the condition for which I am being admitted). On the one hand, judging a hospital based on its own patient population makes intuitive sense—the hospital is being judged on cases relevant to what it sees. While it may be convenient to compare hospitals even if they treat very different types of patients, a concern of indirect standardization is that two hospitals that see very different types of patients may be unfairly compared, as the lack of overlap in patient populations places a great deal of burden on the indirectly standardized model to appropriately account for these nonoverlapping patient factors. If the model fails to incorporate the important differences between each hospital’s patient populations, then the inferences concerning hospital comparison with O/E methods may be misleading. An alternative approach that we have introduced is to use a form of direct standardization (Silber et al. 2014, this issue), that uses a template of patients and compares a multivariate matched set of patients across hospitals, thereby achieving closely overlapping patient distributions for observable variables—far more close on measured characteristics than if patients had been randomly assigned to hospitals. In this previous work we described how multivariate template matching, as a method to compare hospitals using direct standardization, produced excellent matches across 217 hospitals performing general and orthopedic surgery across three states. It had the strength that each hospital was evaluated with the same template; therefore, there was less concern about nonoverlapping patients, when hospitals did have overlapping patients to compare. When a hospital’s Chief Medical Officer desires to know precisely how well his or her hospital performs on its own distribution of patients, and not on an external template that may not be representative of the type of patients seen at his or her specific hospital, then direct standardization may not be the method of choice. In other words, the Chief Medical Officer may want to know, “How well do we do with the patients we see?” In this article, we develop a new method to perform indirect standardization with multivariate template matching, and we introduce what we define as “hospital-specific template matching” a form of direct standardization with a hospital’s own patients (thereby resembling aspects of indirect standardization). This approach will allow a close look at how well a hospital performs on the patients it sees by constructing a template of patients representative of the hospital’s own patient distributions, finding other hospitals that also see similar patients to compare outcomes between the hospital of interest and other hospitals that can be matched to the index hospital’s template. In so doing, we hope to provide a new method to better implement indirect standardization analyses for improving a hospital’s quality of care specifically tailored to the index hospital’s most relevant patients—the patients they see.

OBJECTIVE To investigate the association between obesity and perioperative acute kidney injury (AKI), controlling for preoperative kidney dysfunction. BACKGROUND More than 30% of patients older than 60 years are obese and, therefore, at risk for kidney disease. Postoperative AKI is a significant problem. METHODS We performed a matched case-control study of patients enrolled in the Obesity and Surgical Outcomes Study, using data of Medicare claims enriched with detailed chart review. Each AKI patient was matched with a non-AKI control similar in procedure type, age, sex, race, emergency status, transfer status, baseline estimated glomerular filtration rate, admission APACHE score, and the risk of death score with fine balance on hospitals. RESULTS We identified 514 AKI cases and 694 control patients. Of the cases, 180 (35%) followed orthopedic procedures and 334 (65%) followed colon or thoracic surgery. After matching, obese patients undergoing a surgical procedure demonstrated a 65% increase in odds of AKI within 30 days from admission (odds ratio = 1.65, P < 0.005) when compared with the nonobese patients. After adjustment for potential confounders, the odds of postoperative AKI remained elevated in the elderly obese (odds ratio = 1.68, P = 0.01.) CONCLUSIONS : Obesity is an independent risk factor for postoperative AKI in patients older than 65 years. Efforts to optimize kidney function preoperatively should be employed in this at-risk population along with keen monitoring and maintenance of intraoperative hemodynamics. When subtle reductions in urine output or a rising creatinine are observed postoperatively, timely clinical investigation is warranted to maximize renal recovery.

Background: Using Pennsylvania Medicare claims from 1995 to 1996, the authors previously reported that anesthesia procedure length appears longer in blacks than whites. In a new study using a different and larger data set, the authors now examine whether body mass index (BMI), not available in Medicare claims, explains this difference. The authors also examine the relative contributions of surgical and anesthesia times. Methods: The Obesity and Surgical Outcomes Study of 47 hospitals throughout Illinois, New York, and Texas abstracted chart information including BMI on elder Medicare patients (779 blacks and 14,596 whites) undergoing hip and knee replacement and repair, colectomy, and thoracotomy between 2002 and 2006. The authors matched all black Medicare patients to comparable whites and compared procedure lengths. Results: Mean BMI in the black and white populations was 30.24 and 28.96 kg/m2, respectively (P < 0.0001). After matching on age, sex, procedure, comorbidities, hospital, and BMI, mean white BMI in the comparison group was 30.1 kg/m2 (P = 0.94). The typical matched pair difference (black–white) in anesthesia (induction to recovery room) procedure time was 7.0 min (P = 0.0019), of which 6 min reflected the surgical (cut-to-close) time difference (P = 0.0032). Within matched pairs, where the difference in procedure times was greater than 30 min between patients, blacks more commonly had longer procedure times (Odds = 1.39; P = 0.0008). Conclusions: Controlling for patient characteristics, BMI, and hospital, elder black Medicare patients experienced slightly but significantly longer procedure length than their closely matched white controls. Procedure length difference was almost completely due to surgery, not anesthesia.

OBJECTIVES With differential payment between Medicaid and Non-Medicaid services, we asked whether style-of-practice differs between similar Medicaid and Non-Medicaid children with complex chronic conditions (CCCs) undergoing surgery. SUMMARY OF BACKGROUND DATA Surgery in children with CCCs accounts for a disproportionately large percentage of resource utilization at major children's hospitals. METHODS A matched cohort design, studying 23,582 pairs of children with CCCs undergoing surgery (Medicaid matched to Non-Medicaid within the same hospital) from 2009 to 2013 in 41 Children's Hospitals. Patients were matched on age, sex, principal procedure, CCCs, and other characteristics. RESULTS Median cost in Medicaid patients was $21,547 versus $20,527 in Non-Medicaid patients (5.0% higher, P < 0.001). Median paired difference in cost (Medicaid minus Non-Medicaid) was $320 [95% confidence interval (CI): $208, $445], (1.6% higher, P < 0.001). 90th percentile costs were $133,640 versus $127,523, (4.8% higher, P < 0.001). Mean paired difference in length of stay (LOS) was 0.50 days (95% CI: 0.36, 0.65), (P < 0.001). ICU utilization was 2.8% higher (36.7% vs 35.7%, P < 0.001). Finally, in-hospital mortality pooled across all pairs was higher in Medicaid patients (0.38% vs 0.22%, P = 0.002). After adjusting for multiple testing, no individual hospital displayed significant differences in cost between groups, only 1 hospital displayed significant differences in LOS and 1 in ICU utilization. CONCLUSIONS Treatment style differences between Medicaid and Non-Medicaid children were small, suggesting little disparity with in-hospital surgical care for patients with CCCs operated on within Children's Hospitals. However, in-hospital mortality, although rare, was slightly higher in Medicaid patients and merits further investigation.

With increasing Medicaid coverage, it has become especially important to determine whether racial differences exist within the Medicaid system. We asked whether disparities exist in hospital practice and patient outcomes between matched black and white Medicaid children with chronic conditions undergoing surgery.We conducted a matched cohort study, matching 6,398 pairs within states on detailed patient characteristics using data from 25 states contributing adequate Medicaid Analytic eXtract claims for admissions of children with chronic conditions undergoing the same surgical procedures between January 1, 2009 and November 30, 2010 for ages 1 to 18 years.The black patient 30-day revisit rate was 19.3% vs 19.8% in matched white patients (p = 0.61), 30-day readmission rates were 7.0% vs 6.9% (p = 0.43), and 30-day mortality rates were 0.38% vs 0.19% (p = 0.06), respectively. A higher percentage of black patients exceeded their own state's individual median length of stay (44.0% vs 39.6%; p0.001) and median ICU length of stay (25.9% vs 23.8%; p0.001). Intensive care unit use was higher in black patients (25.9% vs 23.8%; p0.001). After adjusting for multiple testing, only 2 states were found to differ significantly by race (New York for length of stay and New Jersey for ICU use).We did not observe disparities in 30-day revisits and readmissions for chronically ill children in Medicaid undergoing surgery, and only slight differences in length of stay, ICU length of stay, and use of the ICU, where blacks displayed somewhat elevated rates compared with white controls.

BACKGROUND AND OBJECTIVES: Black children with asthma comprise one-third of all asthma patients in Medicaid. With increasing Medicaid coverage, it has become especially important to monitor Medicaid for differences in hospital practice and patient outcomes by race. METHODS: A multivariate matched cohort design, studying 11 079 matched pairs of children in Medicaid (black versus white matched pairs from inside the same state) admitted for asthma between January 1, 2009 and November 30, 2010 in 33 states contributing adequate Medicaid Analytic eXtract claims. RESULTS: Ten-day revisit rates were 3.8% in black patients versus 4.2% in white patients (P = .12); 30-day revisit and readmission rates were also not significantly different by race (10.5% in black patients versus 10.8% in white patients; P = .49). Length of stay (LOS) was also similar; both groups had a median stay of 2.0 days, with a slightly lower percentage of black patients exceeding their own state’s median LOS (30.2% in black patients versus 31.8% in white patients; P = .01). The mean paired difference in LOS was 0.00 days (95% confidence interval, –0.08 to 0.08). However, ICU use was higher in black patients than white patients (22.2% versus 17.5%; P < .001). After adjusting for multiple testing, only 4 states were found to differ significantly, but only in ICU use, where blacks had higher rates of use. CONCLUSIONS: For closely matched black and white patients, racial disparities concerning asthma admission outcomes and style of practice are small and generally nonsignificant, except for ICU use, where we observed higher rates in black patients.

BACKGROUND AND OBJECTIVES: With American children experiencing increased Medicaid coverage, it has become especially important to determine if practice patterns differ between Medicaid and non-Medicaid patients. Auditing such potential differences must carefully compare like patients to avoid falsely identifying suspicious practice patterns. We asked if we could observe differences in practice patterns between Medicaid and non-Medicaid patients admitted for asthma inside major children’s hospitals. METHODS: A matched cohort design, studying 17 739 matched pairs of children (Medicaid to non-Medicaid) admitted for asthma in the same hospital between April 1, 2011 and March 31, 2014 in 40 Children’s Hospital Association hospitals contributing data to the Pediatric Hospital Information System database. Patients were matched on age, sex, asthma severity, and other patient characteristics. RESULTS: Medicaid patient median cost was $4263 versus $4160 for non-Medicaid patients (P < .001). Additionally, the median cost difference (Medicaid minus non-Medicaid) between individual pairs was only $84 (95% confidence interval: 44 to 124), and the mean cost difference was only $49 (95% confidence interval: –72 to 170). The 90th percentile costs were also similar between groups ($10 710 vs $10 948; P < .07). Length of stay (LOS) was also very similar; both groups had a median stay of 1 day, with a similar percentage of patients exceeding the 90th percentile of individual hospital LOS (7.1% vs 6.7%; P = .14). ICU use was also similar (10.1% vs 10.6%; P = .12). CONCLUSIONS: For closely matched patients within the same hospital, Medicaid status did not importantly influence costs, LOS, or ICU use.

Objective To improve the predictions provided by Medicare's Hospital Compare (HC) to facilitate better informed decisions regarding hospital choice by the public. Data Sources/Setting Medicare claims on all patients admitted for Acute Myocardial Infarction between 2009 through 2011. Study Design Cohort analysis using a Bayesian approach, comparing the present assumptions of HC (using a constant mean and constant variance for all hospital random effects), versus an expanded model that allows for the inclusion of hospital characteristics to permit the data to determine whether they vary with attributes of hospitals, such as volume, capabilities, and staffing. Hospital predictions are then created using directly standardized estimates to facilitate comparisons between hospitals. Data Collection/Extraction Methods Medicare fee-for-service claims. Principal Findings Our model that included hospital characteristics produces very different predictions from the current HC model, with higher predicted mortality rates at hospitals with lower volume and worse characteristics. Using Chicago as an example, the expanded model would advise patients against seeking treatment at the smallest hospitals with worse technology and staffing. Conclusion To aid patients when selecting between hospitals, the Centers for Medicare and Medicaid Services (CMS) should improve the HC model by permitting its predictions to vary systematically with hospital attributes such as volume, capabilities, and staffing.

Objective To develop a method to allow a hospital to compare its performance using its entire patient population to the outcomes of very similar patients treated elsewhere. Data Sources/Setting Medicare claims in orthopedics and common general, gynecologic, and urologic surgery from Illinois, New York, and Texas from 2004 to 2006. Study Design Using two example “focal” hospitals, each hospital's patients were matched to 10 very similar patients selected from 619 other hospitals. Data Collection/Extraction Methods All patients were used at each focal hospital, and we found the 10 closest matched patients from control hospitals with exactly the same principal procedure as each focal patient. Principal Findings We achieved exact matches on all procedures and very close matches for other patient characteristics for both hospitals. There were few to no differences between each hospital's patients and their matched control patients on most patient characteristics, yet large and significant differences were observed for mortality, failure-to-rescue, and cost. Conclusion Indirect standardization matching can produce fair audits of quality and cost, allowing for a comprehensive, transparent, and relevant assessment of all patients at a focal hospital. With this approach, hospitals will be better able to benchmark their performance and determine where quality improvement is most needed.

Within the past 2 years, two major policy reforms that affect teaching hospitals were implemented: an unfunded mandate to further restrict resident duty hours (Accreditation Council for Graduate Medical Education 2010) and the Patient Protection and Affordable Care Act (ACA). The Accreditation Council for Graduate Medical Education (ACGME) approved a new set of resident duty hour restrictions to be implemented by July 1, 2011. Revised work-hour rules will decrease maximum shift length from 30 to 16 hours for PGY-1 residents and 28 hours, including 24 hours of clinical duty plus a 4-hour extension when needed, for PGY-2 residents and above (Accreditation Council for Graduate Medical Education 2010). Hospitals will incur additional, uncompensated personnel costs if excess resident work is transferred to substitute providers. It is estimated that compliance with these new regulations will cost $1.17–1.42 billion, assuming a mixture of substitute labor (Nuckols and Escarce 2012). Concurrently, the ACA will likely put greater financial pressure on hospitals through reduced annual market basket updates for inpatient hospitals, a 75 percent decrease in Medicare Disproportionate Share Hospital (DSH) payments, and penalties for hospitals with readmission rates above threshold levels. Several of these changes will become effective by October 1, 2012 (Accreditation Council for Graduate Medical Education 2010; The Henry J. Kaiser Family Foundation 2010). How will these financial pressures affect teaching hospitals' ability to implement the revised work-hour rules while preserving or improving quality? Analyses of past duty hour reform demonstrated either no change (Fletcher et al. 2004; Volpp et al. 2007a, 2009; Jagsi et al. 2008; Rosen et al. 2009; Silber 2009b) or small improvements in outcomes (Horwitz et al. 2007; Shetty and Bhattacharya 2007; Volpp et al. 2007b) associated with the reform. However, these studies did not examine whether the financial status of teaching hospitals influenced their ability to implement the 2003 resident work-hour rules without worsening patient outcomes, given implementation costs of up to $1.1 billion per year (Nuckols and Escarce 2005. To the extent that financially stressed teaching hospitals substituted highly skilled inputs such as hospitalists in place of residents, the net impact on outcomes in these hospitals might even have been favorable (Shetty and Bhattacharya 2007; Roy et al. 2008). These findings would have important implications for the likely impact of current ACGME efforts to further restrict duty hours on quality. In this manuscript, we present results from an analysis of the impact of the underlying financial health of hospitals on the association between implementation of ACGME duty hour rules and a comprehensive set of patient outcome measures among Medicare fee-for-service patients admitted to short-term, acute-care U.S. nonfederal teaching hospitals. We compared trends in risk-adjusted mortality, patient safety event rates (Rosen et al. 2009), failure-to-rescue (FTR) (Silber et al. 1999), and prolonged length of stay (PLOS) (Volpp et al. 2009) among less versus more financially healthy teaching hospitals to examine whether hospitals that were financially distressed at baseline had more difficulty implementing ACGME duty hour rules in a manner that protected or improved patient outcomes.

Introduction Procedure length is a fundamental variable associated with quality of care, though seldom studied on a large scale. The authors sought to estimate procedure length through information obtained in the anesthesia claim submitted to Medicare to validate this method for future studies. Methods The Obesity and Surgical Outcomes Study enlisted 47 hospitals located across New York, Texas, and Illinois to study patients undergoing hip, knee, colon, and thoracotomy procedures. A total of 15,914 charts were abstracted to determine body mass index and initial patient physiology. Included in this abstraction were induction, cut, close, and recovery room times. This chart information was merged to Medicare claims that included anesthesia Part B billing information. Correlations between chart times and claim times were analyzed, models developed, and median absolute differences in minutes calculated. Results Of the 15,914 eligible patients, there were 14,369 for whom both chart and claim times were available for analysis. For these 14,369, the Spearman correlation between chart and claim time was 0.94 (95% CI 0.94, 0.95), and the median absolute difference between chart and claim time was only 5 min (95% CI: 5.0, 5.5). The anesthesia claim can also be used to estimate surgical procedure length, with only a modest increase in error. Conclusion The anesthesia bill found in Medicare claims provides an excellent source of information for studying surgery time on a vast scale throughout the United States. However, errors in both chart abstraction and anesthesia claims can occur. Care must be taken in the handling of outliers in these data.

The seminal works of John Wennberg (Wennberg and Gittelsohn 1973; Wennberg, Freeman, and Culp 1987;) documenting substantial geographic variation in use of health care services, more recently updated by Fisher et al. (2003a,b); concerning aggressive treatment style, have played an important role in understanding our health care system. The Dartmouth Atlas (Wennberg et al. 1998) now calculates several measures of the resources expended in the last 2 years of life for Medicare patients diagnosed with one of nine medical conditions to capture a hospital's style of aggressiveness. Some of the most widely referred to measures are average total spending, ICU days, and hospital inpatient days for these patients. Patients and policy makers are now able to evaluate a hospital on its aggressive style, that is, how many resources it uses to treat patients at the end of life. As aggressive treatment style is one of the most widely cited causes of waste in our health care system (Fisher et al. 2003a,b; Orszag and Ellis 2007; Fisher, Bynum, and Skinner 2009; Gawande 2009; Orszag 2009; The White House Office of the Press Secretary 2009;), we sought to better understand how this style of practice may influence surgical outcomes in the Medicare population. We ask whether the level of intensity of treatment, or what we will refer to as “aggressive treatment style” as defined by the Dartmouth Atlas, has benefits or risks associated with mortality outcomes. We examine whether hospital-level measures of intensity or aggressiveness are predictors of outcomes among surgical patients. While it has been asserted that aggressiveness increases complications and worsens mortality rates (Fisher et al. 2003b; Skinner, Staiger, and Fisher 2006; Consumer Reports Health.org 2008; Gawande 2009;), we assess whether there is an increased rate of complications in surgical patients at more aggressive hospitals, and whether levels of aggressiveness are associated with differences in mortality. An advantage of studying surgical patients is that one can more easily define complications than in medical patients (Lawthers et al. 2000), and in surgical patients we can measure failure-to-rescue (the probability of surviving complications), which is an outcome less susceptible to confounding from unmeasured patient severity than complications and mortality (Silber, Rosenbaum, and Ross 1995a; Silber et al. 1992, 1995b, 2007). The analysis focuses on the association between aggressive treatment style and patient outcomes after surgery. We do not investigate whether “aggressiveness” is associated with the incidence of surgery, although we conduct some analyses that assess the issue of potential patient selection by studying low-variation procedures (Wennberg and Gittelsohn 1973; Wennberg, Freeman, and Culp 1987;) (or less discretionary procedures) and ask whether we observe the same findings as in the overall study.

Objective. We ask whether Medicare's Hospital Compare random effects model correctly assesses acute myocardial infarction (AMI) hospital mortality rates when there is a volume–outcome relationship. Data Sources/Study Setting. Medicare claims on 208,157 AMI patients admitted in 3,629 acute care hospitals throughout the United States. Study Design. We compared average-adjusted mortality using logistic regression with average adjusted mortality based on the Hospital Compare random effects model. We then fit random effects models with the same patient variables as in Medicare's Hospital Compare mortality model but also included terms for hospital Medicare AMI volume and another model that additionally included other hospital characteristics. Principal Findings. Hospital Compare's average adjusted mortality significantly underestimates average observed death rates in small volume hospitals. Placing hospital volume in the Hospital Compare model significantly improved predictions. Conclusions. The Hospital Compare random effects model underestimates the typically poorer performance of low-volume hospitals. Placing hospital volume in the Hospital Compare model, and possibly other important hospital characteristics, appears indicated when using a random effects model to predict outcomes. Care must be taken to insure the proper method of reporting such models, especially if hospital characteristics are included in the random effects model.

Earlier work demonstrated that ACGME duty hour reform did not adversely affect mortality, with slight improvement noted among specific subgroups.To determine whether resident duty hour reform differentially affected the mortality risk of high severity patients or patients who experienced post-operative complications (failure-to-rescue).Observational study using interrupted time series analysis with data from July 1, 2000 - June 30, 2005. Fixed effects logistic regression was used to examine the change in the odds of mortality or failure-to-rescue (FTR) in more versus less teaching-intensive hospitals before and after duty hour reform.All unique Medicare patients (n = 8,529,595) admitted to short-term acute care non-federal hospitals and all unique VA patients (n = 318,636 patients) with principal diagnoses of acute myocardial infarction, congestive heart failure, gastrointestinal bleeding, stroke or a DRG classification of general, orthopedic or vascular surgery.We measured mortality within 30 days of hospital admission and FTR, measured by death among patients who experienced a surgical complication. The odds of mortality and FTR generally changed at similar rates for higher and lower risk patients in more vs. less teaching intensive hospitals. For example, comparing the mortality risk for the 10% of Medicare patients with highest risk to the other 90% of patients in post-reform year 1 for combined medical an OR of 1.01 [95% CI 0.90, 1.13], for combined surgical an OR of 0.91 [95% CI 0.80, 1.04], and for FTR an OR of 0.94 [95% CI 0.80, 1.09]. Findings were similar in year 2 for both Medicare and VA. The two exceptions were a relative increase in mortality for the highest risk medical (OR 1.63 [95% CI 1.08, 2.46]) and a relative decrease in the high risk surgical patients within VA in post-reform year 1 (OR 0.52 [95% CI 0.29, 0.96]).ACGME duty hour reform was not associated with any consistent improvements or worsening in mortality or failure-to-rescue rates for high risk medical or surgical patients.

Methods to assess the quality of care delivered by hospitals have grown in importance with monitoring by state agencies (Hannan et al. 1997) and the growth of pay-for-performance plans (Glickman et al. 2007; Profit et al. 2007). Most of these programs rely upon risk-adjusted outcome measures to assess the care provided by hospitals or physician practices. The risk-adjustment method must account for differences in case mix between groups of hospitals that may care for different patients. However, prior work from our group (Lorch et al. 2004, 2008) and others (Srivastava and Homer 2003; Merenstein, Egleston, and Diener-West 2005; Odetola, Gebremariam, and Freed 2007) has found that patients hospitalized in children's hospitals, located primarily in urban areas, have longer risk-adjusted lengths of stay (LOS) and higher mortality rates. These results are counterintuitive. Children's hospitals have teaching programs and care for the largest volume of pediatric patients in any given geographic region—characteristics typically associated with improved quality. Based on these measurements, then, either children's hospitals counterintuitively provide substandard care or there are systematic, unmeasured differences in illness severity in these prior analyses. Measuring severity of illness is more difficult in children. Most children hospitalized for common pediatric conditions are relatively healthy, without comorbid conditions that are frequently used in standard risk-adjustment techniques (Charlson et al. 1987; Elixhauser et al. 1998). These comorbid conditions, along with age, race, gender, insurance status, and admission source, are typically used to construct a risk-adjustment model. We could augment these models by including other validated measures, such as All Patient's Related Diagnostic-Related Groups (Muldoon 1999). However, many of these measures include procedures and diagnoses that occurred during the hospitalization. These adjusted risk-adjustment tools may control for inappropriate care performed by a hospital (Romano and Chan 2000). Other validated methods have augmented the traditional risk-adjustment model for patients requiring intensive care (Pollack, Patel, and Ruttimann 1996; Richardson et al. 2001). One potential improvement is adding the travel time or distance between a patient's home and the hospital. This improvement is based on prior work in adult patients that found an association between greater travel time and illness severity (Hogan 1988; Adams and Wright 1991; Adams et al. 1991; Welch, Larson, and Welch 1993; Basu 2005; Basu and Mobley 2007). Geocoded measures are relatively easy to obtain with Geographic Information Software using the exact location of the hospital and the child's residence, whereas nongeocoded measures can rapidly measure the distance between zip codes of the hospital and child's residence. If prior work is true, then either variable could augment the traditional risk-adjustment model that uses comorbid conditions, age, source of admission, and other demographic data. The goal of this project was to determine whether travel variables could explain previously reported differences in LOS, readmission, or death at children's hospitals versus other hospital types. By explaining most of these systematic differences, we could then use these augmented risk-adjustment models to identify high- and low-performing hospitals. First, we will validate the geocoded travel time measure for individual children: Do sicker children travel further for care? Second, we will determine whether any severity adjustment tool beyond the typical comorbidity model could explain differences in LOS, readmission, or death at children's hospitals. Severity adjustment tools used in this analysis to augment the typical risk-adjustment model include geocoded travel time; nongeocoded travel distance; excess travel time; individual hospital market share; and a physiologic-based scoring system developed by MediQual (Iezzoni and Moskowitz 1988).

The expression “you can't rush mother nature” is certainly true when considering the discharge of infants from the neonatal intensive care unit (NICU). An infant born 2 or 3 months premature may often spend an equivalent time or more in the NICU maturing until additional physiologic functioning makes discharge feasible. The costs associated with prematurity are staggering. The Institute of Medicine (IOM) estimates that annual medical costs associated with preterm birth in the United States was $16.9 billion or $33,200 per infant born premature (IOM 2007). The average inpatient costs in the first year of life for premature infants

Chemotherapy-associated adverse events (AEs) including neutropenia, anemia, thrombocytopenia, mucositis and dehydration are common and often occur together in clusters. As a consequence, it makes little clinical sense to discuss the costs associated with an isolated AE, ignoring real-life clustering. This study estimates the costs of AEs based on a new approach—the clinically relevant AE cluster. The study population included all cancer patients receiving chemotherapy (N = 2,067) over 1 year at The University of Pennsylvania Health System. AE clusters were determined using algorithms based on the start and end dates of AEs and dates of chemotherapy administration. Models were developed to estimate the total and specific daily costs associated with the presence of AE clusters using loglinear variance estimation. The study found that the cost of a hospitalized day was $1,444 (95% CI 1,340–1,548). Neutropenia in isolation added another $130 (–278–538) to a hospitalized day while neutropenia plus thrombocytopenia increased daily costs by $461 (244–677) and the cluster of anemia, neutropenia and thrombocytopenia added $840 per day (629–1,051). Anemia alone added $568 (346–789) to the hospital day and mucositis, in conjunction with other AEs, increased the daily hospital costs by $466 (201–732). Estimating the costs of chemotherapy-associated AEs can be achieved using clinically meaningful chemotherapy-associated AE clusters. Models incorporating clusters can be utilized to estimate the expected costs of chemotherapy associated AEs.

Background Procedure times are important variables that often are included in studies of quality and efficiency. However, due to the need for costly chart review, most studies are limited to single-institution analyses. In this article, the authors describe how well the anesthesia claim from Medicare can estimate chart times. Methods The authors abstracted information on time of induction and entrance to the recovery room ("anesthesia chart time") from the charts of 1,931 patients who underwent general and orthopedic surgical procedures in Pennsylvania. The authors then merged the associated bills from claims data supplied from Medicare (Part B data) that included a variable denoting the time in minutes for the anesthesia service. The authors also investigated the time from incision to closure ("surgical chart time") on a subset of 1,888 patients. Results Anesthesia claim time from Medicare was highly predictive of anesthesia chart time (Kendall's rank correlation tau = 0.85, P < 0.0001, median absolute error = 5.1 min) but somewhat less predictive of surgical chart time (Kendall's tau = 0.73, P < 0.0001, median absolute error = 13.8 min). When predicting chart time from Medicare bills, variables reflecting procedure type, comorbidities, and hospital type did not significantly improve the prediction, suggesting that errors in predicting the chart time from the anesthesia bill time are not related to these factors; however, the individual hospital did have some influence on these estimates. Conclusions Anesthesia chart time can be well estimated using Medicare claims, thereby facilitating studies with vastly larger sample sizes and much lower costs of data collection.

Asthma is the most common serious chronic disease of the pediatric population (Newacheck and Taylor 1992; Goodman, Stukel, and Chang 1998; Mannino et al. 1998). Annually, there are approximately 200,000 childhood asthmatic admissions in the United States, representing more than three billion dollars in expenditures per year (Weiss, Gergen, and Hodgson 1992). Since asthma admissions consume such a large proportion of pediatric health care resources, differences in the style of treatment of asthmatic patients may contribute to substantial differences in health care expenditures. While much has been written concerning differences that may exist in length of stay (LOS), expenditure, and style of practice between children's and general hospitals that care for children (Meurer et al. 1998; Silber, Gleeson, and Zhao 1999a), and between states with regard to hospitalization rates (Goodman, Stukel, and Chang 1998), LOS, and resource utilization (Samuels et al. 1998), a clearer understanding concerning the differences between the treated populations and the care given across such hospital groupings is needed to better guide policy. What is apparent from the literature is that (1) New York hospitals have especially long LOS as compared to other states (Homer et al. 1996; Goodman, Stukel, and Chang 1998) or as compared to pediatric LOS guidelines (Sills et al. 2000; Harman and Kelleher 2001; Rutledge 1998; Bauchner, Vinci, and Chessare 2000); (2) children's hospitals generally have greater charges associated with asthma admissions and often longer patient stays than general hospitals (Meurer et al. 1998; Silber, Gleeson, and Zhao 1999; Samuels et al. 1998), although adjustment often makes these length-of-stay figures more similar (Meurer et al. 1998); and, (3) health maintenance organization (HMO) concentration is relatively lower in New York (38 percent) than in Pennsylvania (45 percent) (ISH Health Group 1996). However, it remains unclear why LOS differences exist between hospitals located in different states and between hospitals with different characteristics. To gain a better understanding as to why differences in LOS exist between hospitals, this study examines two new measures of hospital length of stay. By studying not just the length of stay, but the odds that a stay will become prolonged, and the length of stay after a stay is prolonged (defined as the “conditional length of stay” [CLOS]) (Silber et al. 1999b), this report aims to provide insight into state, city, and hospital differences in the patterns of stay for asthma patients. Through this approach, we aim to aid policymakers to better understand some potential etiologies for these differences, so that future policy initiatives can better focus on likely avenues for successful interventions.

Over the past two decades, live kidney donation by older individuals (≥55 years) has become more common. Given strong associations of older age with cardiovascular disease, nephrectomy could make older donors vulnerable to death and cardiovascular events. We performed a cohort study among older live kidney donors who were matched to healthy older individuals in the Health and Retirement Study. The primary outcome was mortality ascertained through national death registries. Secondary outcomes ascertained among pairs with Medicare coverage included death or cardiovascular disease ascertained through Medicare claims data. During the period from 1996 – 2006, there were 5717 older donors in the United States. We matched 3368 donors 1:1 to older healthy non-donors. Among donors and matched pairs, the mean age was 59 years; 41% were male and 7% were black race. In median follow-up of 7.8 years, mortality was not different between donors and matched pairs (p=0.21). Among donors with Medicare, the combined outcome of death/CVD (p=0.70) was also not different between donors and non-donors. In summary, carefully selected older kidney donors do not face a higher risk of death or CVD. These findings should be provided to older individuals considering live kidney donation.

Supplemental Digital Content is available in the text., Purpose To determine whether the 2003 Accreditation Council for Graduate Medical Education (ACGME) duty hours reform affected medical knowledge as reflected by written board scores for internal medicine (IM) residents. Method The authors conducted a retrospective cohort analysis of postgraduate year 1 (PGY-1) Internal Medicine residents who started training before and after the 2003 duty hour reform using a merged data set of American Board of Internal Medicine (ABIM) Board examination and the National Board of Medical Examiners (NMBE) United States Medical Licensing Examination (USMLE) Step 2 Clinical Knowledge test scores. Specifically, using four regression models, the authors compared IM residents beginning PGY-1 training in 2000 and completing training unexposed to the 2003 duty hours reform (PGY-1 2000 cohort, n = 5,475) to PGY-1 cohorts starting in 2001 through 2005 (n = 28,008), all with some exposure to the reform. Results The mean ABIM board score for the unexposed PGY-1 2000 cohort (n = 5,475) was 491, SD = 85. Adjusting for demographics, program, and USMLE Step 2 exam score, the mean differences (95% CI) in ABIM board scores between the PGY-1 2001, 2002, 2003, 2004 and 2005 cohorts minus the PGY-1 2000 cohort were −5.43 (−7.63, −3.23), −3.44 (−5.65, −1.24), 2.58 (0.36, 4.79), 11.10 (8.88, 13.33) and 11.28 (8.98, 13.58) points respectively. None of these differences exceeded one-fifth of an SD in ABIM board scores. Conclusions The duty hours reforms of 2003 did not meaningfully affect medical knowledge as measured by scores on the ABIM board examinations.

There are reasons to audit hospitals with respect to cost and quality. An insurer, such as Medicare, Medicaid, or a private insurance company, wants to ensure efficient and safe practices. A hospital’s Chief Medical Officer wants to benchmark efficiency and quality in comparison to performance at other hospitals. Since patients are not randomly allocated to hospitals, hospitals understandably fear unfair comparisons in which differing outcomes are attributed to poor quality when in fact they reflect a sicker patient population (Berwick and Wald 1990). Hospitals are commonly compared by indirect standardization (Kitagawa 1955; Silber, Rosenbaum, and Ross 2003; Fleiss, Levin, and Paik 2003; Iezzoni 2012). Indirect standardization describes how a hospital’s observed patient outcomes compare with how those same patients may have fared at the typical or average hospital. Such an analysis allows the substrate of information used for comparisons (observable patient case mix and severity) to vary across hospitals—that is, the patients at each hospital may be quite different. However, through model adjustments, analysts can attempt to make important comparisons regarding outcomes and quality. Unlike indirect methods, we will develop quality audits using multivariate template matching—a form of direct standardization (Fleiss, Levin, and Paik 2003). Direct standardization considers a fixed patient population and asks how different hospitals would perform with the same or similar patients. Using this method, we select similar patients at different hospitals so that comparison of hospital outcomes and processes are fair and easily interpretable. Furthermore, because we are constructing actual matched samples of patients at each hospital, template audits produce actionable interventions to better understand why specific hospitals fared better or worse because individual patients, or groups of patients, in the template can be closely examined. Our goal is to make hospital audits more credible, and useful, to the Chief Medical Officer.

Background Outcomes studies often need a level of detail that is not present in administrative data, therefore requiring abstraction of medical charts. Case-control methods may be used to improve statistical power and reduce abstraction costs, but limitations of exact matching often preclude the use of many covariates. Unlike exact matching, multivariate matching may allow cases to be matched simultaneously on hundreds of covariates. Objectives To develop multivariate matched case-control pairs in a study of death after surgery in the Medicare population. Research design Using 830 randomly selected index cases of patients who died within 60 days from admission, controls were found who did not die within that time period, matching on risk for death and other patient characteristics with up to 173 variables used simultaneously in the matching algorithms. Subjects General and orthopedic Medicare surgical cases in Pennsylvania from 1995 to 1996. Controls were either selected from across the entire state (108,765 possible subjects), or from within the same hospital as the case. Measures Percent bias reduction and the average difference between cases and controls in units of standard deviations. Results Matched controls were far more similar to cases (deaths) upon admission to the hospital than typical patients, both in statewide and within hospital matches. Bias reduction was usually greater than 50% and often approached 100%. The difference between cases and matched controls for most variables was usually below 0.2 SD. Conclusions Multivariate matching methods may aid in conducting studies with Medicare claims records by improving the quality of matches, thereby achieving a better understanding of the etiology of outcomes.

The New Jersey Comprehensive Immunization Project (NJ-CIP) is a computerized immunization registry being established in the state of New Jersey. The Leonard Davis Institute of Health Economics at the University of Pennsylvania and the University of Medicine and Dentistry of New Jersey are collaborating on a cost-effectiveness analysis of the project. This article presents a Net Present Value methodology for aggregating and analyzing all economically quantifiable aspects of the NJ-CIP. Baseline data have been collected for the analysis of the project. However, full project analysis will not be possible until the expected collection of postintervention data. The full cost-effectiveness evaluation is expected to be completed by February 1, 1997. Cost-effectiveness will be assessed by comparing changes in immunization rates with the total economic impact of the NJ-CIP. Changes in immunization coverage will be assessed using four different measures of immunization rates to provide insights into immunization coverage. The four immunization coverage measures are Lifetime Optimally Immunized (LOI), Adequately Immunized (AI), Percent of Life Undervaccinated (PLUV), and CASA Age Appropriate Immunization (CASA AAI). The article outlines potential benefits and costs associated with the project, such as increased immunization rates, reduced loss in parental income, and registry costs. The integration of this information into the cost-effectiveness analysis is also described.

Using Pennsylvania Medicare claims from 1995 to 1996, the authors previously reported that anesthesia procedure length appears longer in blacks than whites. In a new study using a different and larger data set, the authors now examine whether body mass index (BMI), not available in Medicare claims, explains this difference. The authors also examine the relative contributions of surgical and anesthesia times. The Obesity and Surgical Outcomes Study of 47 hospitals throughout Illinois, New York, and Texas abstracted chart information including BMI on elder Medicare patients (779 blacks and 14,596 whites) undergoing hip and knee replacement and repair, colectomy, and thoracotomy between 2002 and 2006. The authors matched all black Medicare patients to comparable whites and compared procedure lengths. Mean BMI in the black and white populations was 30.24 and 28.96 kg/m, respectively (P

Asthma is the most prevalent chronic illness among children, remaining a leading cause of pediatric hospitalizations and representing a major financial burden to many health care systems.To implement a new auditing process examining whether differences in hospital practice style may be associated with potential resource savings or inefficiencies in treating pediatric asthma admissions.A retrospective matched-cohort design study, matched for asthma severity, compared practice patterns for patients admitted to Children's Hospital Association hospitals contributing data to the Pediatric Hospital Information System (PHIS) database. With 3 years of PHIS data on 48 887 children, an asthma template was constructed consisting of representative children hospitalized for asthma between April 1, 2011, and March 31, 2014. The template was matched with either a 1:1, 2:1, or 3:1 ratio at each of 37 tertiary care children's hospitals, depending on available sample size.Treatment at each PHIS hospital.Cost, length of stay, and intensive care unit (ICU) utilization.After matching patients (n = 9100; mean [SD] age, 7.1 [3.6] years; 3418 [37.6%] females) to the template (n = 100, mean [SD] age, 7.2 [3.7] years; 37 [37.0%] females), there was no significant difference in observable patient characteristics at the 37 hospitals meeting the matching criteria. Despite similar characteristics of the patients, we observed large and significant variation in use of the ICUs as well as in length of stay and cost. For the same template-matched populations, comparing utilization between the 12.5th percentile (lower eighth) and 87.5th percentile (upper eighth) of hospitals, median cost varied by 87% ($3157 vs $5912 per patient; P .001); total hospital length of stay varied by 47% (1.5 vs 2.2 days; P .001); and ICU utilization was 254% higher (6.5% vs 23.0%; P .001). Furthermore, the patterns of resource utilization by patient risk differed significantly across hospitals. For example, as patient risk increased one hospital displayed significantly increasing costs compared with their matched controls (comparative cost difference: lowest risk, -34.21%; highest risk, 53.27%; P .001). In contrast, another hospital displayed significantly decreasing costs relative to their matched controls as patient risk increased (comparative cost difference: lowest risk, -10.12%; highest risk, -16.85%; P = .01).For children with asthma who had similar characteristics, we observed different hospital resource utilization; some values differed greatly, with important differences by initial patient risk. Through the template matching audit, hospitals and stakeholders can better understand where this excess variation occurs and can help to pinpoint practice styles that should be emulated or avoided.

Background Early rehospitalization (1 pre-KT hospitalizations (aOR 1.32, 95% CI 1.17–1.49) were more likely to be rehospitalized. All three health status metrics and early rehospitalization were independently associated with post-KT mortality in a multivariable Cox model (adjusted hazard ratio for rehospitalization: 1.41, 95% CI 1.28–1.56) Conclusions Pre-transplant metrics of health status, measured by dialysis providers or administrative data, are independently associated with early rehospitalization and mortality risk after KT. Transplant providers may consider utilizing metrics of pre-KT global health status as early signals of vulnerability when transitioning care after KT.

The literature suggests that hospitals with better nursing work environments provide better quality of care. Less is known about value (cost vs quality).To test whether hospitals with better nursing work environments displayed better value than those with worse nursing environments and to determine patient risk groups associated with the greatest value.A retrospective matched-cohort design, comparing the outcomes and cost of patients at focal hospitals recognized nationally as having good nurse working environments and nurse-to-bed ratios of 1 or greater with patients at control group hospitals without such recognition and with nurse-to-bed ratios less than 1. This study included 25 752 elderly Medicare general surgery patients treated at focal hospitals and 62 882 patients treated at control hospitals during 2004-2006 in Illinois, New York, and Texas. The study was conducted between January 1, 2004, and November 30, 2006; this analysis was conducted from April to August 2015.Focal vs control hospitals (better vs worse nursing environment).Thirty-day mortality and costs reflecting resource utilization.This study was conducted at 35 focal hospitals (mean nurse-to-bed ratio, 1.51) and 293 control hospitals (mean nurse-to-bed ratio, 0.69). Focal hospitals were larger and more teaching and technology intensive than control hospitals. Thirty-day mortality in focal hospitals was 4.8% vs 5.8% in control hospitals (P .001), while the cost per patient was similar: the focal-control was -$163 (95% CI = -$542 to $215; P = .40), suggesting better value in the focal group. For the focal vs control hospitals, the greatest mortality benefit (17.3% vs 19.9%; P .001) occurred in patients in the highest risk quintile, with a nonsignificant cost difference of $941 per patient ($53 701 vs $52 760; P = .25). The greatest difference in value between focal and control hospitals appeared in patients in the second-highest risk quintile, with mortality of 4.2% vs 5.8% (P .001), with a nonsignificant cost difference of -$862 ($33 513 vs $34 375; P = .12).Hospitals with better nursing environments and above-average staffing levels were associated with better value (lower mortality with similar costs) compared with hospitals without nursing environment recognition and with below-average staffing, especially for higher-risk patients. These results do not suggest that improving any specific hospital's nursing environment will necessarily improve its value, but they do show that patients undergoing general surgery at hospitals with better nursing environments generally receive care of higher value.

Background Reducing readmissions has become a focus in efforts by Medicare to improve health care quality and reduce costs. This study aimed to determine whether causes for readmission differed between obese and nonobese patients, possibly allowing for targeted interventions. Methods A matched case control study of Medicare patients admitted between 2002 and 2006 who were readmitted after hip or knee surgery, colectomy, or thoracotomy was performed. Patients were matched exactly for procedure, while also balancing on hospital, age, and sex. Conditional logistic regression was used to study the odds of readmission for very obese cases (body mass index >35 kg/m2) versus normal weight patients (body mass index of 20–30 kg/m2) after also controlling for race, transfer-in and emergency status, and comorbidities. Results Among 15,914 patient admissions, we identified 1,380 readmitted patients and 2,760 controls. The risk of readmission was increased for obese compared to nonobese patients both before and after controlling for comorbidities (before: odds ratio, 1.35; P = .003; after: odds ratio, 1.25; P = .04). Reasons for readmission varied by procedure but were not different by body mass index category. Conclusion Obese patients have an increased risk of readmission, yet the reasons for readmission in obese patients appear to be similar to those for nonobese patients, suggesting that improved postdischarge management for the obese cannot focus on a few specific causes of readmission but must instead provide a broad range of interventions.

To study the medical and financial outcomes associated with surgery in elderly obese patients and to ask if obesity itself influences outcomes above and beyond the effects from comorbidities that are known to be associated with obesity.Obesity is a surgical risk factor not present in Medicare's risk adjustment or payment algorithms, as BMI is not collected in administrative claims.A total of 2045 severely or morbidly obese patients (BMI ≥ 35 kg/m, aged between 65 and 80 years) selected from 15,914 elderly patients in 47 hospitals undergoing hip and knee surgery, colectomy, and thoracotomy were matched to 2 sets of 2045 nonobese patients (BMI = 20-30 kg/m). A "limited match" controlled for age, sex, race, procedure, and hospital. A "complete match" also controlled for 30 additional factors such as diabetes and admission clinical data from chart abstraction.Mean BMI in the obese patients was 40 kg/m compared with 26 kg/m in the nonobese. In the complete match, obese patients displayed increased odds of wound infection: OR (odds ratio) = 1.64 (95% CI: 1.21, 2.21); renal dysfunction: OR = 2.05 (1.39, 3.05); urinary tract infection: OR = 1.55 (1.24, 1.94); hypotension: OR = 1.38 (1.07, 1.80); respiratory events: OR = 1.44 (1.19, 1.75); 30-day readmission: OR = 1.38 (1.08, 1.77); and a 12% longer length of stay (8%, 17%). Provider costs were 10% (7%, 12%) greater in obese than in nonobese patients, whereas Medicare payments increased only 3% (2%, 5%). Findings were similar in the limited match.Obesity increases the risks and costs of surgery. Better approaches are needed to reduce these risks. Furthermore, to avoid incentives to underserve this population, Medicare should consider incorporating incremental costs of caring for obese patients into payment policy and include obesity in severity adjustment models.

Procedure length is a fundamental variable associated with quality of care, though seldom studied on a large scale. The authors sought to estimate procedure length through information obtained in the anesthesia claim submitted to Medicare to validate this method for future studies.The Obesity and Surgical Outcomes Study enlisted 47 hospitals located across New York, Texas, and Illinois to study patients undergoing hip, knee, colon, and thoracotomy procedures. A total of 15,914 charts were abstracted to determine body mass index and initial patient physiology. Included in this abstraction were induction, cut, close, and recovery room times. This chart information was merged to Medicare claims that included anesthesia Part B billing information. Correlations between chart times and claim times were analyzed, models developed, and median absolute differences in minutes calculated.Of the 15,914 eligible patients, there were 14,369 for whom both chart and claim times were available for analysis. For these 14,369, the Spearman correlation between chart and claim time was 0.94 (95% CI 0.94, 0.95), and the median absolute difference between chart and claim time was only 5 min (95% CI: 5.0, 5.5). The anesthesia claim can also be used to estimate surgical procedure length, with only a modest increase in error.The anesthesia bill found in Medicare claims provides an excellent source of information for studying surgery time on a vast scale throughout the United States. However, errors in both chart abstraction and anesthesia claims can occur. Care must be taken in the handling of outliers in these data.

To determine the impact of state certificate of need programs (CON) on the number of hospitals with neonatal intensive care units (NICU) and the number of NICU beds. The presence of a CON program was verified from each state's department of health. Multivariable regression models determined the association between the absence of a CON program and each outcome after controlling for socioeconomic and demographic differences between states. A total of 30 states had CON programs that oversaw NICUs in 2008. Absence of such programs was associated with more hospitals with a NICU (Rate Ratio (RR) 2.06, 95% CI 1.74 to 2.45) and NICU beds (RR 1.96, 95% CI 1.89 to 2.03) compared with states with CON legislation, and increased all-infant mortality rates in states with a large metropolitan area. There has been an erosion of CON programs that oversee NICUs. CON programs are associated with more efficient delivery of neonatal care.

Aggressive treatment style, as defined by the Dartmouth Atlas of Health Care, has been implicated as an important factor contributing to excessively high medical expenditures. We aimed to determine the association between aggressive treatment style and surgical outcomes.Medicare admissions to 3,065 hospitals for general, orthopedic, and vascular surgery between 2000 and 2005 (N = 4,558,215 unique patients).A retrospective cohort analysis.For elderly surgical patients, aggressive treatment style was not associated with significantly increased complications, but it was associated with significantly reduced odds of mortality and failure-to-rescue. The odds ratio for complications in hospitals at the 75th percentile of aggressive treatment style compared with those at the 25th percentile (a U.S.$10,000 difference) was 1.01 (1.00-1.02), p.066; whereas the odds of mortality was 0.94 (0.93-0.95), p.0001; and for failure-to-rescue it was 0.93 (0.92-0.94), p.0001. Analyses that used alternative measures of aggressiveness--hospital days and ICU days--yielded similar results, as did analyses using only low-variation procedures.Attempting to reduce aggressive care that is not cost effective is a laudable goal, but policy makers should be aware that there may be improved outcomes associated with patients undergoing surgery in hospitals with a more aggressive treatment style.

A key goal of resident duty hour reform by the Accreditation Council for Graduate Medical Education (ACGME) in 2003 was to improve patient outcomes.To assess whether the reform led to a change in readmission rates.Observational study using multiple time series analysis with hospital discharge data from July 1, 2000 to June 30, 2005. Fixed effects logistic regression was used to examine the change in the odds of readmission in more versus less teaching-intensive hospitals before and after duty hour reform.All unique Medicare patients (n = 8,282,802) admitted to acute-care nonfederal hospitals with principal diagnoses of acute myocardial infarction, congestive heart failure, gastrointestinal bleeding, or stroke (combined medical group), or a DRG classification of general, orthopedic, or vascular surgery (combined surgical group).Primary outcome was 30-day all-cause readmission. Secondary outcomes were (1) readmission or death within 30 days of discharge, and (2) readmission, death during the index admission, or death within 30 days of discharge.For the combined medical group, there was no evidence of a change in readmission rates in more versus less teaching-intensive hospitals [OR = 0.99 (95% CI 0.94, 1.03) in post-reform year 1 and OR = 0.99 (95% CI 0.95, 1.04) in post-reform year 2]. There was also no evidence of relative changes in readmission rates for the combined surgical group: OR = 1.03 (95% CI 0.98, 1.08) for post-reform year 1 and OR = 1.02 (95% CI 0.98, 1.07) for post-reform year 2. Findings for the secondary outcomes combining readmission and death were similar.Among Medicare beneficiaries, there were no changes in hospital readmission rates associated with resident duty hour reform.

Hip fracture occurs in 340,000 older adults each year. Operative repair is the standard of care, maximizing the chances of functional recovery. Not receiving operative care may condemn patients to a lifetime of pain and potential immobility.To measure the incidence of nonoperative treatment for first-time hip fracture in a population-based cohort and to measure the odds of nonoperative treatment of hip fracture among patients of differing race and income.Retrospective cohort study of 165,861 Medicare beneficiaries admitted for hip fracture between March 31, 2002 and December 31, 2006 to hospitals in New York, Illinois, and Texas.Odds of nonoperative management of hip fracture, adjusted for fracture characteristics, comorbidities, source of admission, age, sex, race, income, and individual hospital effects.Nonoperative management occurred in 6.2% of patients (N = 10,283). After adjustment, black patients had a 79% increase in the odds of nonoperative management as compared with whites (OR 1.79, 95% CI 1.64-1.95). Low income itself was not associated with a change in the odds of nonoperative care. Among patients not receiving operative repair, blacks demonstrated lower mortality than whites at 7 days (7.96% vs. 20.17%, P0.0001) and 30 days (24.14% vs. 38.22%, P0.0001).Black race predicts an increased odds of nonoperative care for hip fracture. Among patients receiving nonoperative care, black patients demonstrated increased survival compared with whites. These results are consistent with differential selection of operative candidates by patient race.

The assessment of health care quality has become more important with the increase in information available to the general public, which includes Medicare's “Hospital Compare” website (U.S. Department of Health & Human Services 2008) and the publication of hospital infection rates in Pennsylvania (Pennsylvania Health Care Cost Containment Council 2008). One proposed measure is risk-adjusted readmission rates. Early research (Ashton et al. 1997) suggested that lower-quality providers incompletely evaluate or manage a patient in 13 studies, leading to higher readmission rates. Similar results were noted more recently for coronary artery bypass graft surgery (Hannan et al. 2003). However, recently published work in congestive heart failure (Kossovsky et al. 2000; Luthi et al. 2004; Fonarow et al. 2007;) did not find such an association, leading to questions about the value of readmission rates as a quality measure (Clarke 2004). There are several potential reasons for these conflicting results. Hospitals may have different admission criteria for patients with a given disease, both for the initial hospitalization and for the readmission (Goodman et al. 1994). Available risk-adjustment models may not adequately control for these differences. The prevalence of readmissions may be too low to detect a difference between hospitals. Finally, most studies did not account for the outpatient facility where the patient receives care after discharge. Outpatient care may influence a patient's risk of readmission, as demonstrated by research on medication discrepancies (Coleman et al. 2005) or other interventions (Rich et al. 1995; Benbassat and Taragin 2000;). However, no study has examined the combined effect of inpatient and outpatient facilities on readmission rates. To properly evaluate risk-adjusted readmission rates as an inpatient quality of care measure, we must examine a population of patients with high readmission rates, uniform admission criteria, and valid measures of inpatient care as a “test case.” Prematurely born infants are such a group. Besides their high overall readmission rates, (Cavalier et al. 1996; Furman et al. 1996; Joffe et al. 1999; Smith et al. 2004; Morris, Gard, and Kennedy 2005; Underwood, Danielsen, and Gilbert 2007;) all infants born under 34 weeks gestational age (GA) are admitted to a neonatal intensive care unit (NICU), which eliminates differences in admission criteria. Prior work has also suggested that higher quality NICUs have higher volumes and lower complication rates than their peers (Phibbs et al. 1996; Lorch et al. 2007; Phibbs et al. 2007;). Premature infants, who are interesting in their own right, also can serve as an analog for patients with other chronic illnesses, such as congestive heart failure. The goal of this study, then, was to assess risk-adjusted readmission rates as a measure of inpatient quality of care by studying their role in NICUs. The specific aims were to (1) determine the statistical significance and explained variation in risk-adjusted readmission rates attributable to the NICU when outpatient facilities are omitted—the most commonly used analysis; (2) determine the change in explained variation when outpatient facilities were added to the analysis; and (3) measure the association between readmission rates and characteristics of a high-quality NICU or outpatient facility (Phibbs et al. 1996; Lorch et al. 2007; Phibbs et al. 2007;). To answer these three aims, we will present the results of three separate analyses using data from a retrospective, population cohort of premature infants with complete follow-up data through 1 year after discharge. The first, “naive” analysis determines the variation in readmission rates attributable to the NICU while ignoring outpatient facilities (aim 1). We then add outpatient facilities to the naive model and determine the variation explained by NICUs and outpatient facilities (aim 2). The third aim of this study will clarify which previously validated facility-quality metrics are associated with lower readmission rates.

Resident duty hour reforms of 2003 had the potential to create a major impact on the delivery of inpatient care.We examine whether the reforms influenced the probability of a patient experiencing a prolonged hospital length of stay (PLOS), a measure reflecting either inefficiency of care or the development of complications that may slow the rate of discharge.Conditional logistic models to compare PLOS in more versus less teaching-intensive hospitals before and after the reform, adjusting for patient comorbidities, common time trends, and hospital site.Medicare (N = 6,059,015) and Veterans Affairs (VA) (N = 210,276) patients admitted for medical conditions (acute myocardial infarction, heart failure, stroke, or gastrointestinal bleeding) or surgical procedures (general, orthopedic, and vascular) from July 2000 to June 2005.Prolonged length of stay.Modeling all medical conditions together, the odds of prolonged stay in the first year post reform at more versus less teaching intensive hospitals was 1.01 (95% CI: 0.97-1.05) for Medicare and 1.07 (0.94-1.20) for the VA. Results were similarly negative in the second year post reform. For "combined surgery" the post year 1 odds ratios were 1.04 (0.98-1.09) and 0.94 (0.78-1.14) for Medicare and the VA respectively, and similarly unchanged in post year 2. Isolated increases in the probability of prolonged stay did occur for some vascular surgery procedures.Hospitals generally found ways to cope with duty hour reform without increasing the prevalence of prolonged hospital stays, a marker of either inefficient care or complications.

Objective: Improving patient safety was a strong motivation behind duty hour regulations implemented by Accreditation Council for Graduate Medical Education on July 1, 2003. We investigated whether rates of patient safety indicators (PSIs) changed after these reforms. Research Design: Observational study of patients admitted to Veterans Health Administration (VA) (N = 826,047) and Medicare (N = 13,367,273) acute-care hospitals from July 1, 2000 to June 30, 2005. We examined changes in patient safety events in more versus less teaching-intensive hospitals before (2000–2003) and after (2003–2005) duty hour reform, using conditional logistic regression, adjusting for patient age, gender, comorbidities, secular trends, baseline severity, and hospital site. Measures: Ten PSIs were aggregated into 3 composite measures based on factor analyses: “Continuity of Care,” “Technical Care,” and “Other” composites. Results: Continuity of Care composite rates showed no significant changes postreform in hospitals of different teaching intensity in either VA or Medicare. In the VA, there were no significant changes postreform for the technical care composite. In Medicare, the odds of a Technical Care PSI event in more versus less teaching-intensive hospitals in postreform year 1 were 1.12 (95% CI; 1.01–1.25); there were no significant relative changes in postreform year 2. Other composite rates increased in VA in postreform year 2 in more versus less teaching-intensive hospitals (odds ratio, 1.63; 95% CI; 1.10–2.41), but not in Medicare in either postreform year. Conclusions: Duty hour reform had no systematic impact on PSI rates. In the few cases where there were statistically significant increases in the relative odds of developing a PSI, the magnitude of the absolute increases were too small to be clinically meaningful.

OBJECTIVE. Increased crowding may affect the care that is delivered to hospitalized patients, particularly around the time of admission. There is little information about the impact of admission-day crowding on the outcome of children who are hospitalized with common pediatric conditions. METHODS. A population cohort was constructed of children who were aged 1 to 17 years and were hospitalized in Pennsylvania and New York between April 1, 1996, and June 30, 1998, with 1 of 19 common pediatric conditions (N = 116235). Condition-specific Cox regression and logit models were developed to estimate the effect of admission-day occupancy on 4 outcome measures after controlling for illness severity and site of care: length of stay; 21-day readmission; prolonged stay or a stay longer than the typical, uncomplicated stay for that condition as a measure of care delivered to patients with uncomplicated courses; and conditional length of stay as a measure of care delivered to patients whose stays are prolonged. RESULTS. For children who were admitted with respiratory disease, increasing admission-day occupancy from 60% to 100% was associated with a 0.25-day increase in the average length of stay. Increased admission-day occupancy above 60% was also associated with higher odds of a prolonged stay but not with a change in 21-day readmission rates or conditional length of stay. For children who were admitted with nonrespiratory conditions, increased admission-day occupancy was not associated with changes to any length-of-stay outcome. CONCLUSIONS. Increased admission-day occupancy was associated with longer lengths of stay for less complicated respiratory admissions but not for children who were admitted with the most serious conditions. These results suggest that medical professionals, during times of increased workload, first focus their attention on more acutely ill children with a complicated course and thus delay treatment of children who have less complicated courses but require time-consuming management and treatment.

Use of failure-to-rescue (FTR) as an indicator of hospital quality has increased over the past decade, but recent authors have used different sets of complications and deaths to define this measure. This study examines the reliability and validity of different FTR measures currently in use.We studied 3 definitions: (1) "original" FTR (using all deaths); (2) FTR-N, a "nursing sensitive" definition that uses only specific complications and deaths; and (3) FTR-A [another restricted definition of FTR used by Agency for Healthcare Research and Quality (AHRQ) for analyzing Healthcare Cost and Utilization Project (HCUP) data]. Each FTR measure was applied to 403,679 general surgical patients across 1567 hospitals reported in 1999-2000 Medicare MEDPAR data.Although FTR used all deaths, FTR-N and FTR-A definitions omitted 49% and 42% of deaths, respectively. Reliability was better for FTR than FTR-A or FTR-N (rho = 0.32 vs. 0.18 vs. 0.18, respectively).Hospitals ranked by adjusted mortality were highly correlated with FTR (Kendall's tau = 0.83) and less correlated with FTR-A (tau = 0.43) and FTR-N (tau = 0.41). Adjusting for patient characteristics, all FTR measures showed strong associations with bed-to-nurse ratio, nursing mix, teaching status, and hospital size; however, hospital "high technology" was not as well associated with FTR-N.For general surgery, more limited definitions used by FTR-N and FTR-A omit over 40% of deaths, display less reliability, and may have more questionable validity than the original FTR measure. We encourage analysts to use the original FTR definition that uses all deaths when analyzing hospital quality of care.

Purpose Chemotherapy for ovarian cancer is usually administered by medical oncologists (MOs) or gynecologic oncologists (GOs). GOs perform a broad spectrum of surgical and medical activities while managing a limited number of diseases; MOs specialize in the administration of chemotherapy but manage a broad array of diseases. We asked whether survival, treatment, and toxicity differed according to the type of specialist providing the chemotherapy after surgery. Patients and Methods Using Surveillance, Epidemiology, and End Results (SEER) -Medicare data for patients ≥ 65 years old from 1991 through 2001 from eight SEER sites, we identified 344 patients with ovarian cancer who were treated with chemotherapy by a GO after surgery. Using optimal matching and propensity scores based on 36 characteristics, we matched these patients to 344 similar patients who were operated on and staged by the same type of surgeon but who received chemotherapy from an MO. Results MOs administered chemotherapy over more weeks than did the GOs (16.5 v 12.1 weeks, respectively; P < .0023), and MO patients had substantially more weeks that included chemotherapy-associated adverse events than GO patients (16.2 v 8.9 weeks, respectively; P < .0001). However, there was no difference in 5-year survival rate between the GO and MO groups (35% v 34%, respectively; P = .45). Conclusion GO- and MO-treated patients who were closely matched on prognostic characteristics experienced very different rates of chemotherapy-associated adverse events and very different chemotherapy treatment styles by specialty type; however, their survival was virtually identical.