Harlan M Krumholz

Yale University, New Haven, Connecticut, United States

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Publications (811)8464.07 Total impact

  • Cynthia A Jackevicius, Harlan M Krumholz
    JAMA Internal Medicine 04/2014; 174(4):649. · 10.58 Impact Factor
  • Elizabeth H Bradley, Leslie A Curry, Harlan M Krumholz
    JAMA The Journal of the American Medical Association 03/2014; 311(11):1160. · 29.98 Impact Factor
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    ABSTRACT: -Although non-invasive positive pressure ventilation (NIPPV) for patients with acute decompensated heart failure (ADHF) was introduced almost 20 years ago, the variation in its use among hospitals remains unknown. We sought to define hospital practice patterns of NIPPV use for ADHF and their relationship with intubation and mortality. -We conducted a cross-sectional study using a database maintained by Premier, Inc., that includes a date-stamped log of all billed items for hospitalizations at over 400 hospitals. We examined hospitalizations for ADHF in this database from 2005-2010 and included hospitals with annual average volume of greater than 25 such hospitalizations. We identified 384 hospitals that encompassed 524,430 hospitalizations (median annual average volume: 206). We used hierarchical logistic regression models to calculate hospital-level outcomes: risk-standardized NIPPV rate (RS-NIPPV), risk-standardized intubation rate (RSIR), and in-hospital risk-standardized mortality rate (RSMR). We grouped hospitals into quartiles by RS-NIPPV and compared RSMRs and RSIRs across quartiles. Median RS-NIPPV was 6.2% (interquartile range, 2.8-9.3%; 5th percentile, 0.2%; 95th percentile, 14.8%). There was no clear pattern of RSMRs across quartiles. The bottom quartile of hospitals had higher RSIR (11.4%) than each of the other quartiles (9.0%, 9.7%, and 9.1%; P<0.02 for all comparisons). -Substantial variation exists among hospitals in the use of NIPPV for ADHF without evidence for differences in mortality. There may be a threshold effect in relation to intubation rates, with the lowest utilizers of NIPPV having higher intubation rates.
    Circulation Heart Failure 03/2014; · 6.68 Impact Factor
  • JAMA The Journal of the American Medical Association 03/2014; 311(10):1063-5. · 29.98 Impact Factor
  • Kumar Dharmarajan, Harlan M Krumholz
    JAMA Internal Medicine 03/2014; 174(3):481-2. · 10.58 Impact Factor
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    ABSTRACT: Older women experience higher complication rates and mortality after percutaneous coronary intervention (PCI) than men, but there is limited evidence about sex-based differences in outcomes among younger patients. We compared rates of complications and inhospital mortality by sex for younger and older PCI patients. A total of 1,079,751 hospital admissions for PCI were identified in the CathPCI Registry(®) from 2005 to 2008. Complication rates (general, bleeding, bleeding with transfusion, and vascular) and inhospital mortality after PCI were compared by sex and age (<55 and ≥55 years). Analyses were adjusted for demographic and clinical factors and stratified by PCI type (elective, urgent, or emergency). Overall, 6% of patients experienced complications, and 1% died inhospital. Unadjusted complication rates were higher for women compared with men in both age groups. In risk-adjusted analyses, younger women (odds ratio 1.24, 95% CI 1.16-1.33) and older women (1.27, 1.09-1.47) were more likely to experience any complication than similarly aged men. The increased risk persisted across complication categories and PCI type. Within age groups, risk-adjusted mortality was marginally higher for young women (1.19, 1.00-1.41), but not for older women (1.03, 0.97-1.10). In analyses stratified by PCI type, young women had twice the mortality risk after an elective procedure as young men (2.04, 1.15-3.61). Women, regardless of age, experience more complications after PCI than men; young women are at increased mortality risk after an elective PCI. Identifying strategies to reduce adverse outcomes, particularly for women younger than 55 years, is important.
    American heart journal 03/2014; 167(3):376-83. · 4.65 Impact Factor
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    ABSTRACT: The quality of the relationship between a patient and their usual source of care may impact outcomes, especially after an acute clinical event requiring regular follow-up. To examine the association between the presence and strength of a usual source of care with mortality and readmission after hospitalization for acute myocardial infarction (AMI). Prospective Registry Evaluating Myocardial Infarction: Event and Recovery (PREMIER), an observational, 19-center study. AMI patients discharged between January 2003 and June 2004. The strength of the usual source of care was categorized as none, weak, or strong based upon the duration and familiarity of the relationship. Main outcome measures were readmissions and mortality at 6 months and 12 months post-AMI, examined in multivariable analysis adjusting for socio-demographic characteristics, access and barriers to care, financial status, baseline risk factors, and AMI severity. Among 2,454 AMI patients, 441 (18.0 %) reported no usual source of care, whereas 247 (10.0 %) and 1,766 (72.0 %) reported weak and strong usual sources of care, respectively. When compared with a strong usual source of care, adults with no usual source of care had higher 6-month mortality rates [adjusted hazard ratio (aHR) = 3.15, 95 % CI, 1.79-5.52; p < 0.001] and 12-month mortality rates (aHR = 1.92, 95 % CI, 1.19-3.12; p = 0.01); adults with a weak usual source of care trended toward higher mortality at 6 months (aHR = 1.95, 95 % CI, 0.98-3.88; p = 0.06), but not 12 months (p = 0.23). We found no association between the usual source of care and readmissions. Adults with no or weak usual sources of care have an increased risk for mortality following AMI, but not for readmission.
    Journal of General Internal Medicine 02/2014; · 3.28 Impact Factor
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    ABSTRACT: IMPORTANCE Current guidelines allow substantial discretion in use of noninvasive cardiac imaging for patients without acute myocardial infarction (AMI) who are being evaluated for ischemia. Imaging use may affect downstream testing and outcomes. OBJECTIVE To characterize hospital variation in use of noninvasive cardiac imaging and the association of imaging use with downstream testing, interventions, and outcomes. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional study of hospitals using 2010 administrative data from Premier, Inc, including patients with suspected ischemia on initial evaluation who were seen in the emergency department, observation unit, or inpatient ward; received at least 1 cardiac biomarker test on day 0 or 1; and had a principal discharge diagnosis for a common cause of chest discomfort, a sign or symptom of cardiac ischemia, and/or a comorbidity associated with coronary disease. We excluded patients with AMI. MAIN OUTCOMES AND MEASURES At each hospital, the proportion of patients who received noninvasive imaging to identify cardiac ischemia and the subsequent rates of admission, coronary angiography, and revascularization procedures. RESULTS We identified 549 078 patients at 224 hospitals. The median (interquartile range) hospital noninvasive imaging rate was 19.8% (10.9%-27.7%); range, 0.2% to 55.7%. Median hospital imaging rates by quartile were Q1, 6.0%; Q2, 15.9%; Q3, 23.5%; Q4, 34.8%. Compared with Q1, Q4 hospitals had higher rates of admission (Q1, 32.1% vs Q4, 40.0%), downstream coronary angiogram (Q1, 1.2% vs Q4, 4.9%), and revascularization procedures (Q1, 0.5% vs Q4, 1.9%). Hospitals in Q4 had a lower yield of revascularization for noninvasive imaging (Q1, 7.6% vs Q4, 5.4%) and for angiograms (Q1, 41.2% vs Q4, 38.8%). P <.001 for all comparisons. Readmission rates to the same hospital for AMI within 2 months were not different by quartiles (P = .51). Approximately 23% of variation in imaging use was attributable to the behavior of individual hospitals. CONCLUSIONS AND RELEVANCE Hospitals vary in their use of noninvasive cardiac imaging in patients with suspected ischemia who do not have AMI. Hospitals with higher imaging rates did not have substantially different rates of therapeutic interventions or lower readmission rates for AMI but were more likely to admit patients and perform angiography.
    JAMA Internal Medicine 02/2014; · 10.58 Impact Factor
  • Jeffrey B Low, Joseph S Ross, Harlan M Krumholz
    Spine 02/2014; · 2.16 Impact Factor
  • Source
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    ABSTRACT: -Prior claims analyses suggest that the use of intravenous inotropic therapy for patients hospitalized with heart failure varies substantially by hospital. Whether differences in the clinical characteristics of the patients explain observed differences in the use of inotropic therapy is not known. -We sought to characterize institutional variation in inotrope use among patients hospitalized with heart failure before and after accounting for patients' clinical factors. Hierarchical generalized linear regression models estimated risk-standardized hospital-level rates of inotrope use within 209 hospitals participating in Get With The Guidelines-Heart Failure (GWTG-HF) registry between 2005-2011. The association between risk-standardized rates of inotrope use and clinical outcomes were determined. Overall, an inotropic agent was administered in 7,691 of 126,564 (6.1%) HF hospitalizations: dobutamine 43%, dopamine 24%, milrinone 17%, or a combination 16%. Patterns of inotrope use were stable over the 7-year study period. Use of inotropes varied significantly between hospitals even after accounting for patient and hospital characteristics (median risk-standardized hospital rate 5.9%, IQR 3.7-8.6%, range 1.3-32.9%). After adjusting for case mix and hospital structural differences, model intra-class correlation indicated that 21% of the observed variation in inotrope use was potentially attributable to random hospital effects (i.e. institutional preferences). Hospitals with higher risk-standardized inotrope use had modestly longer risk-standardized length of stay (p=0.005) but had no difference in risk-standardized inpatient mortality (p=0.12) CONCLUSIONS: -Use of intravenous inotropic agents during hospitalization for heart failure varies significantly among U.S. hospitals, even after accounting for patient and hospital factors.
    Circulation Heart Failure 01/2014; · 6.68 Impact Factor
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    ABSTRACT: Changes in adverse-event rates among Medicare patients with common medical conditions and conditions requiring surgery remain largely unknown. We used Medicare Patient Safety Monitoring System data abstracted from medical records on 21 adverse events in patients hospitalized in the United States between 2005 and 2011 for acute myocardial infarction, congestive heart failure, pneumonia, or conditions requiring surgery. We estimated trends in the rate of occurrence of adverse events for which patients were at risk, the proportion of patients with one or more adverse events, and the number of adverse events per 1000 hospitalizations. The study included 61,523 patients hospitalized for acute myocardial infarction (19%), congestive heart failure (25%), pneumonia (30%), and conditions requiring surgery (27%). From 2005 through 2011, among patients with acute myocardial infarction, the rate of occurrence of adverse events declined from 5.0% to 3.7% (difference, 1.3 percentage points; 95% confidence interval [CI], 0.7 to 1.9), the proportion of patients with one or more adverse events declined from 26.0% to 19.4% (difference, 6.6 percentage points; 95% CI, 3.3 to 10.2), and the number of adverse events per 1000 hospitalizations declined from 401.9 to 262.2 (difference, 139.7; 95% CI, 90.6 to 189.0). Among patients with congestive heart failure, the rate of occurrence of adverse events declined from 3.7% to 2.7% (difference, 1.0 percentage points; 95% CI, 0.5 to 1.4), the proportion of patients with one or more adverse events declined from 17.5% to 14.2% (difference, 3.3 percentage points; 95% CI, 1.0 to 5.5), and the number of adverse events per 1000 hospitalizations declined from 235.2 to 166.9 (difference, 68.3; 95% CI, 39.9 to 96.7). Patients with pneumonia and those with conditions requiring surgery had no significant declines in adverse-event rates. From 2005 through 2011, adverse-event rates declined substantially among patients hospitalized for acute myocardial infarction or congestive heart failure but not among those hospitalized for pneumonia or conditions requiring surgery. (Funded by the Agency for Healthcare Research and Quality and others.).
    New England Journal of Medicine 01/2014; 370(4):341-51. · 51.66 Impact Factor
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    ABSTRACT: The methods and results of health research are documented in study protocols, full study reports (detailing all analyses), journal reports, and participant-level datasets. However, protocols, full study reports, and participant-level datasets are rarely available, and journal reports are available for only half of all studies and are plagued by selective reporting of methods and results. Furthermore, information provided in study protocols and reports varies in quality and is often incomplete. When full information about studies is inaccessible, billions of dollars in investment are wasted, bias is introduced, and research and care of patients are detrimentally affected. To help to improve this situation at a systemic level, three main actions are warranted. First, academic institutions and funders should reward investigators who fully disseminate their research protocols, reports, and participant-level datasets. Second, standards for the content of protocols and full study reports and for data sharing practices should be rigorously developed and adopted for all types of health research. Finally, journals, funders, sponsors, research ethics committees, regulators, and legislators should endorse and enforce policies supporting study registration and wide availability of journal reports, full study reports, and participant-level datasets.
    The Lancet 01/2014; · 39.06 Impact Factor
  • Journal of the American College of Cardiology 01/2014; 63(1):92-100. · 14.09 Impact Factor
  • Aakriti Gupta, Harlan Krumholz
    BMJ (Clinical research ed.). 01/2014; 348:g1552.
  • Journal of the American College of Cardiology 01/2014; 63(12):A21. · 14.09 Impact Factor
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    ABSTRACT: Background Variation in hospital admission rates of patients presenting to the Emergency Department (ED) may represent an opportunity to improve practice. We seek to describe national variation in hospital admission rates from the ED, and determine the degree to which variation is not explained by patient characteristics or hospital factors. Methods We conducted a cross-sectional analysis of a nationally-representative sample of ED visits among adults within the 2010 National Hospital Ambulatory Care Survey ED data of hospitals with admission rates from the ED between 5-50%. We calculated risk-standardized hospital admission rates (RSAR) from the ED using contemporary hospital profiling methodology, accounting for patients’ socio-demographic and clinical characteristics. Results Among 19,831 adult ED visits in 252 hospitals, there were 4,148 hospital admissions from the ED. After accounting for patients’ socio-demographic and clinical factors, the median RSAR from the ED was 16.9% (IQR: 15.0%-20.4%), and 8.1% of the variation in RSARs was attributable to an institution-specific effect. Even after accounting for hospital teaching status, ownership, urban/rural location, and geographical location, 7.0% of the variation in RSARs from the ED was still attributable to an institution-specific effect. Conclusions and Relevance There was variation in hospital admission rates from the ED in the U.S., even after adjusting for patients’ socio-demographic and clinical characteristics and accounting for hospital factors. Our findings suggest that suggesting that the likelihood of being admitted from the ED is not only dependent on clinical factors, but also at which hospital the patient seeks care.
    The American Journal of Emergency Medicine. 01/2014;
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    ABSTRACT: Background We previously found the use of ezetimibe increased rapidly with different patterns between the United States (US) and Canada prior to the landmark ENHANCE trial, which was reported in January 2008, and failed to show that the drug slowed the progression of atherosclerosis. What is not known is how practice in the two countries changed after the ENHANCE trial. We examined ezetimibe use trends in the US and Canada before and after the reporting of the ENHANCE trial. Methods We conducted a population-based, retrospective, time-series analysis using the data collected by IMS Health in the US and Compuscript in Canada from January 1, 2002 to December 31, 2009. The main outcome measure was monthly number of prescriptions for ezetimibe-containing products. Results The monthly number of ezetimibe prescriptions/100,000 population rose from 6 to 1082 in the US from November 2002 to January 2008, then significantly declined to 572/100,000 population by December 2009 after the release of the ENHANCE trial, a decrease of 47.1% (P < 0.001). In contrast, in Canada, use continuously rose from 2 to 495/100,000 population from June 2003 to December 2009 (P = 0.2). US expenditures totaled $2.24 billion in 2009. Conclusions Ezetimibe remains commonly used in both the US and Canada. Ezetimibe use has decreased in the US post-ENHANCE, whereas use has gradually but steadily increased in Canada. The diverging patterns of ezetimibe use in the US and Canada requires further investigation as it reveals that a common evidence base is eliciting very different utilization patterns in neighboring countries.
    American Heart Journal. 01/2014;
  • Kasia J Lipska, Harlan M Krumholz
    JAMA Internal Medicine 12/2013; · 10.58 Impact Factor
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    ABSTRACT: We sought to evaluate the impact of coronary artery calcium (CAC) in individuals at the extremes of risk factor (RF) burden. 6698 individuals from the Multi-Ethnic Study of Atherosclerosis (MESA) were followed for coronary heart disease (CHD) events over mean 7.1 ± 1 years. Annualized CHD event rates were compared among each RF category (0, 1, 2, or ≥3) after stratification by CAC score (0, 1-100, 101-300, and >300). The following traditional modifiable RFs were considered: cigarette smoking, LDL cholesterol ≥3.4 mmol/L, low HDL cholesterol, hypertension, and diabetes. There were 1067 subjects (16%) with 0 RFs, whereas 1205 (18%) had ≥3 RFs. Among individuals with 0 RFs, 68% had CAC 0, whereas 12 and 5% had CAC >100 and >300, respectively. Among individuals with ≥3 RFs, 35% had CAC 0, whereas 34 and 19% had CAC >100 and >300, respectively. Overall, 339 (5.1%) CHD events occurred. Individuals with 0 RFs and CAC >300 had an event rate 3.5 times higher than individuals with ≥3 RFs and CAC 0 (10.9/1000 vs. 3.1/1000 person-years). Similar results were seen across categories of Framingham risk score. Among individuals at the extremes of RF burden, the distribution of CAC is heterogeneous. The presence of a high CAC burden, even among individuals without RFs, is associated with an elevated event rate, whereas the absence of CAC, even among those with many RF, is associated with a low event rate. Coronary artery calcium has the potential to further risk stratify asymptomatic individuals at the extremes of RF burden.
    European Heart Journal 12/2013; · 14.10 Impact Factor
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    ABSTRACT: IMPORTANCE It is unknown whether hospital transfer rates for patients with acute myocardial infarction admitted to nonprocedure hospitals (facilities that do not provide catheterization) vary and whether these rates further influence revascularization rates, length of stay, and mortality. OBJECTIVES To examine hospital differences in transfer rates for elderly patients with acute myocardial infarction across nonprocedure hospitals and to determine whether these rates are associated with revascularization rates, length of stay, and mortality. DESIGN, SETTING, AND PARTICIPANTS We used Medicare claims data from January 1, 2006, to December 31, 2008, to assess transfer rates in nonprocedure hospitals, stratified according to transfer rates as low (≤20%), mid-low (>20%-30%), mid-high (>30%-40%), or high (>40%). Data were analyzed for 55 962 Medicare fee-for-service patients admitted to 901 nonprocedure US hospitals with more than 25 admissions per year for acute myocardial infarction. MAIN OUTCOMES AND MEASURES We compared rates of catheterization, percutaneous coronary intervention, and coronary artery bypass graft surgery during hospitalization and within 60 days, as well as hospital total length of stay, across groups. We measured risk-standardized mortality rates at 30 days and 1 year. RESULTS The median transfer rate was 29.4% (interquartile range [25th-75th percentile], 21.8%-37.8%). Higher transfer rates were associated with higher rates of catheterization (P < .001), percutaneous coronary intervention (P < .001), and coronary artery bypass graft surgery (P < .001). Median length of stay was not meaningfully different across the groups. There was no meaningful evidence of associations between transfer rates and risk-standardized mortality at 30 days (mean [SD], 22.3% [2.6%], 22.1% [2.3%], 22.3% [2.4%], and 21.7% [2.1%], respectively; P = .054) or 1 year (43.9% [2.3%], 43.6% [2.2%], 43.5% [2.4%], and 42.8% [2.2%], respectively; P < .001) for low, mid-low, mid-high, and high transfer groups. CONCLUSIONS AND RELEVANCE Nonprocedure hospitals vary substantially in their use of the transfer process for elderly patients admitted with acute myocardial infarction. High-transfer hospitals had greater use of invasive cardiac procedures after admission compared with low-transfer hospitals. However, higher transfer rates were not associated with a significantly lower risk-standardized mortality rate at 30 days. Moreover, at 1 year there was only a 1.1% difference (42.8% vs 43.9%) between hospitals with higher and lower transfer rates. These findings suggest that, as a single intervention, promoting the transfer of patients admitted with acute myocardial infarction may not improve hospital outcomes.
    JAMA Internal Medicine 12/2013; · 10.58 Impact Factor

Publication Stats

30k Citations
8,464.07 Total Impact Points

Institutions

  • 1994–2014
    • Yale University
      • • Section of Cardiovascular Medicine
      • • Department of Internal Medicine
      • • Section of General Internal Medicine
      • • School of Medicine
      New Haven, Connecticut, United States
  • 2013
    • State University of New York Downstate Medical Center
      • Department of Internal Medicine
      Brooklyn, NY, United States
    • Universidade Federal de Sergipe
      São Christovão, Sergipe, Brazil
    • Wright State University
      • Department of Surgery
      Dayton, OH, United States
    • Instituto Federal Sergipe
      Aracaju, Sergipe, Brazil
    • Cedars-Sinai Medical Center
      • Department of Medicine
      Los Angeles, CA, United States
  • 2010–2013
    • Baystate Medical Center
      • Center for Quality of Care Research
      Springfield, MA, United States
    • Northwestern University
      • Department of Medicine
      Evanston, IL, United States
    • University of Chicago
      • Department of Obstetrics & Gynecology
      Chicago, Illinois, United States
    • Hospital General Universitario Gregorio Marañón
      Madrid, Madrid, Spain
  • 2007–2013
    • University of Michigan
      • • Department of Internal Medicine
      • • Division of Pediatric Cardiology
      Ann Arbor, Michigan, United States
    • Cleveland Clinic
      • Department of Internal Medicine
      Cleveland, OH, United States
    • Concordia University–Ann Arbor
      Ann Arbor, Michigan, United States
    • Howard Hughes Medical Institute
      Maryland, United States
    • Rice University
      Houston, Texas, United States
  • 2005–2013
    • Kaiser Permanente
      Oakland, California, United States
    • Robert Wood Johnson Foundation
      Princeton, New Jersey, United States
    • Eastern Virginia Medical School
      Norfolk, Virginia, United States
    • St. Luke's Hospital
      Cedar Rapids, Iowa, United States
    • The Toronto Centre for Phenogenomics
      Toronto, Ontario, Canada
  • 2004–2013
    • University of New Haven
      New Haven, Connecticut, United States
    • University of Pennsylvania
      Philadelphia, Pennsylvania, United States
  • 1993–2013
    • Yale-New Haven Hospital
      New Haven, Connecticut, United States
  • 2012
    • CUNY Graduate Center
      New York City, New York, United States
    • Beijing Fuwai Hospital
      Peping, Beijing, China
    • Children's Hospital Los Angeles
      Los Angeles, California, United States
    • Amsterdamse Hogeschool voor de Kunsten
      Amsterdamo, North Holland, Netherlands
    • The University of Chicago Medical Center
      Chicago, Illinois, United States
    • Brigham and Women's Hospital
      • Department of Emergency Medicine
      Boston, MA, United States
    • Massachusetts General Hospital
      Boston, Massachusetts, United States
  • 2005–2012
    • Saint Luke's Health System (KS, USA)
      Kansas City, Kansas, United States
  • 2011
    • Ecole des hautes études en santé publique
      Roazhon, Brittany, France
    • University College London
      Londinium, England, United Kingdom
    • Mayo Clinic - Rochester
      Rochester, Minnesota, United States
    • Weill Cornell Medical College
      New York City, New York, United States
    • Keck School of Medicine USC
      Los Angeles, California, United States
    • Western University of Health Sciences
      • College of Pharmacy
      Pomona, CA, United States
  • 2004–2011
    • Vanderbilt University
      • Department of Pediatrics
      Nashville, MI, United States
  • 2009–2010
    • Tilburg University
      • CoRPS-Center of Research on Psychology in Somatic diseases
      Tilburg, North Brabant, Netherlands
  • 2007–2010
    • Mount Sinai School of Medicine
      Manhattan, New York, United States
  • 2004–2010
    • University of Toronto
      • Division of Cardiology
      Toronto, Ontario, Canada
    • Boston Medical Center
      Boston, Massachusetts, United States
  • 2006–2009
    • St. Luke's Hospital (MO, USA)
      Saint Louis, Michigan, United States
    • Harvard Medical School
      Boston, Massachusetts, United States
    • University of Massachusetts Medical School
      • Department of Medicine
      Worcester, MA, United States
    • Qualidigm
      Cromwell, Connecticut, United States
    • Agency for Healthcare Research and Quality
      Maryland, United States
  • 2005–2009
    • University of Missouri - Kansas City
      • "Saint Luke's" Mid America Heart Institute
      Kansas City, MO, United States
  • 2001–2009
    • Emory University
      • • Division of Cardiology
      • • School of Medicine
      Atlanta, GA, United States
    • Alpert Medical School - Brown University
      Providence, Rhode Island, United States
  • 2008
    • McLean Hospital
      Cambridge, Massachusetts, United States
    • University of Iowa
      Iowa City, Iowa, United States
    • U.S. Department of Veterans Affairs
      Washington, Washington, D.C., United States
    • Mayo Foundation for Medical Education and Research
      • Division of Cardiovascular Diseases
      Scottsdale, AZ, United States
    • University Health Network
      Toronto, Ontario, Canada
    • Sunnybrook Health Sciences Centre
      • Division of Cardiology
      Toronto, Ontario, Canada
    • University of Connecticut
      • Center for Public Health and Health Policy
      Storrs, CT, United States
    • VA Eastern Colorado Health Care System
      Denver, Colorado, United States
  • 2005–2008
    • University of Colorado
      • • Division of Cardiology
      • • Department of Medicine
      Denver, CO, United States
  • 2004–2007
    • Washington University in St. Louis
      San Luis, Missouri, United States
  • 1992–2006
    • Beth Israel Deaconess Medical Center
      • Department of Medicine
      Boston, Massachusetts, United States
  • 2001–2005
    • Mental Health Center of Denver
      Denver, Colorado, United States
  • 2001–2004
    • University of California, San Francisco
      • • Division of General Internal Medicine
      • • Department of Epidemiology and Biostatistics
      San Francisco, CA, United States
  • 2003
    • Overton Brooks VA Medical Center
      Shreveport, Louisiana, United States
    • National Institutes of Health
      • National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
      Bethesda, MD, United States
  • 2002
    • University of Maryland, Baltimore
      • Division of Cardiology
      Baltimore, MD, United States
    • Griffin Hospital
      Derby, Connecticut, United States
  • 1999–2002
    • Medical University of South Carolina
      • Department of Health Administration
      Charleston, SC, United States
  • 1998
    • Rush University Medical Center
      Chicago, Illinois, United States