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Original Contribution |

Improving the Quality of Care for Medicare Patients With Acute Myocardial Infarction:  Results From the Cooperative Cardiovascular Project FREE

Thomas A. Marciniak, MD; Edward F. Ellerbeck, MD; Martha J. Radford, MD; Timothy F. Kresowik, MD; Jay A. Gold, MD; Harlan M. Krumholz, MD; Catarina I. Kiefe, MD, PhD; Richard M. Allman, MD; Robert A. Vogel, MD; Stephen F. Jencks, MD
[+] Author Affiliations

From the Health Care Financing Administration, Baltimore, Md (Drs Marciniak and Jencks); the Iowa Foundation for Medical Care, Des Moines (Drs Ellerbeck and Kresowik); the University of Kansas Medical Center, Kansas City (Dr Ellerbeck); the Connecticut Peer Review Organization, Middletown (Drs Radford and Krumholz); the Division of Cardiology, University of Connecticut, Farmington (Dr Radford); the Department of Surgery, University of Iowa, Iowa City (Dr Kresowik); MetaStar Inc, Madison, Wis, and the Department of Preventive Medicine and the Health Policy Institute, Medical College of Wisconsin, Milwaukee (Dr Gold); the Cardiovascular Section, Yale University School of Medicine, New Haven, Conn (Dr Krumholz); the Alabama Quality Assurance Foundation and the Department of Veterans Affairs, Birmingham (Drs Kiefe and Allman); the Division of Preventive Medicine (Dr Kiefe) and the Center for Aging (Dr Allman), University of Alabama at Birmingham; and the Department of Cardiology, University of Maryland, Baltimore (Dr Vogel).


JAMA. 1998;279(17):1351-1357. doi:10.1001/jama.279.17.1351.
Text Size: A A A
Published online

Context.— Medicare has a legislative mandate for quality assurance, but the effectiveness of its population-based quality improvement programs has been difficult to establish.

Objective.— To improve the quality of care for Medicare patients with acute myocardial infarction.

Design.— Quality improvement project with baseline measurement, feedback, remeasurement, and comparison samples.

Setting.— All acute care hospitals in the United States.

Patients.— Preintervention and postintervention samples included all Medicare patients in Alabama, Connecticut, Iowa, and Wisconsin discharged with principal diagnoses of acute myocardial infarctions during 2 periods, June 1992 through December 1992 and August 1995 through November 1995. Indicator comparisons were made with a random sample of Medicare patients in the rest of the nation discharged with acute myocardial infarctions from August 1995 through November 1995. Mortality comparisons involved all Medicare patients nationwide with inpatient claims for acute myocardial infarctions during 2 periods, June 1992 through May 1993 and August 1995 through July 1996.

Intervention.— Data feedback by peer review organizations.

Main Outcome Measures.— Quality indicators derived from clinical practice guidelines, length of stay, and mortality.

Results.— Performance on all quality indicators improved significantly in the 4 pilot states. Administration of aspirin during hospitalization in patients without contraindications improved from 84% to 90% (P<.001), and prescription of β-blockers at discharge improved from 47% to 68% (P<.001). Mortality at 30 days decreased from 18.9% to 17.1% (P=.005) and at 1 year from 32.3% to 29.6% (P<.001). These improvements in quality occurred during a period when median length of stay decreased from 8 days to 6 days. Performance on all quality indicators except reperfusion was better in the pilot states than in the rest of the nation in 1995, and the differences were statistically significant for aspirin use at discharge (P<.001), β-blocker use (P<.001), and smoking cessation counseling (P=.02). Postinfarction mortality was not significantly different between the pilot states and the rest of the nation during the baseline period, although it was slightly but significantly better in the pilot states during the follow-up period (absolute mortality difference at 1 year, 0.9%; P=.004).

Conclusions.— The quality of care for Medicare patients with acute myocardial infarction has improved in the Cooperative Cardiovascular Project pilot states. Performance on the defined quality indicators appeared to be better in the pilot states than in the rest of the nation in 1995 and was associated with reduced mortality.

Figures in this Article

HEALTH CARE quality is a topic of current concern. Quality has been the focus of recent articles in the medical literature1,2 and is being addressed by a presidential commission. These current concerns about quality are partially motivated by speculations about the effect of managed health care on quality and access, and may be related to a realization that quality cannot be assumed but rather must be understood and engineered.

Since 1992 the Health Care Financing Administration (HCFA) has been implementing a continuous quality improvement approach to ensuring the quality of care for its Medicare beneficiaries. HCFA's Health Care Quality Improvement Initiative is implemented by its contractors, the peer review organizations (PROs).3 The first project of this program is the Cooperative Cardiovascular Project (CCP), which focuses on treatment of patients with acute myocardial infarction (AMI).

The CCP began with the development of quality indicators for the treatment of AMI. A steering committee convened by HCFA and the American Medical Association (AMA) drafted quality indicators heavily based on clinical practice guidelines developed by the American College of Cardiology (ACC) and the American Heart Association (AHA).4 The PROs in 4 states, Alabama, Connecticut, Iowa, and Wisconsin, refined the quality indicators and developed data collection instruments and computer algorithms for them. These PROs abstracted data from medical records of Medicare patients in their states who were discharged with a principal diagnosis of AMI from June 1992 through February 1993 and evaluated the rates for each quality indicator. The results of this baseline data collection have been reported previously.5

The 4 PROs provided the results to the practitioners in their states during 1994 and encouraged the initiation of quality improvement activities for the treatment of AMI. To evaluate the effects of these activities, we collected a follow-up sample of AMI cases in the pilot states and a comparison sample of AMI cases from the rest of the nation in 1995.

Sample Selection

For all samples we identified cases using hospital bills (UB-92 claims form data) in the Medicare National Claims History File. The National Claims History File includes all claims submitted for Medicare patients treated under fee-for-service arrangements but does not include UB-92 submissions for all patients treated under Medicare managed care risk contracts. We sampled only claims with an ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification) principal diagnosis code of 410 (AMI), excluding codes with a fifth digit of 2, which designates a subsequent episode of care. For the baseline sample we identified all AMI claims submitted by hospitals in the 4 pilot states that had discharge dates between June 1, 1992, and December 31, 1992. We used cases from January 1993 and February 1993 for the quality improvement feedback, but we excluded these months from our analyses because the sampling for them was incomplete because of timing. We used cases with discharge dates between August 1, 1995, and November 30, 1995, for the follow-up sample. We selected this latter sampling time frame to allow 6 months of elapsed time from the completion of the feedback sessions and to accumulate a sample size sufficient for a second round of feedback sessions.

For the nonpilot indicator comparison sample we randomly selected cases from all states other than the pilot states from the follow-up period of August 1, 1995, through November 30, 1995. We randomly sampled 2400 cases to provide 90% power for detecting a 5% absolute difference in a typical quality indicator. This sample size provides low power (40%) for detecting even substantial (10%) improvement in mortality.

For mortality comparisons we sampled all AMI claims from June 1, 1992, through May 31, 1993, and from August 1, 1995, through July 31, 1996. We selected only the first AMI episode for patients with more than 1 AMI during these periods. We matched the patients to the Medicare Enrollment Database to determine survival.

Data Collection and Quality Control

Copies of medical records corresponding to discharge dates were requested from hospitals. For the baseline sample the copies were sent to and abstracted at the 4 state PROs. For the follow-up and nonpilot samples the record copies were abstracted at 2 clinical data abstraction centers. The 2 abstraction centers were established by HCFA through contracts with private organizations experienced in medical abstractions to maintain abstraction consistency and efficiency for its national quality improvement projects, including CCP.

Trained abstracters extracted predefined variables from the medical record copies. Data were entered directly into computers using interactive software with online data definitions, help, and edits. The clinical data abstracted included all variables needed to calculate the quality indicator rates as well as measures of risk, comorbidity, and complications. The CCP abstraction software, which includes the operational definitions of all variables, is available through the CCP Internet home page at http://www.hcfa.gov/.

Data quality was monitored and maintained by random reabstractions, calculation of reliability statistics such as interrater agreement rates and κ values,6 determinations of reasons for discrepancies, and improvement actions such as focused abstracter training. For the baseline sample, 912 cases were randomly selected and reabstracted by a second abstracter. For the CCP pilot follow-up sample and for other CCP samples abstracted at the clinical data abstraction centers, cases were randomly reabstracted on a continuous basis to generate reasonable confidence intervals (CIs) for reliability statistics on a quarterly basis (eg, 95% CI, 85-94 for an observed agreement rate of 90%). For all CCP samples, more than 3000 cases were reabstracted for quality control purposes at the clinical data abstraction centers.

Interrater reliability of the abstractions was good. The reliability of the baseline data has been reported previously,5 with agreement rates exceeding 94% and κ values ranging from 0.72 to 0.88 for determining treatments. For the clinical data abstraction center abstractions, overall variable agreement averaged about 95% for the time periods during which the follow-up and nonpilot samples were abstracted. Individual variable abstraction reliability varied depending on the nature of the variable. Simple laboratory or drug variables that were well documented in medical records were highly reliable, whereas softer clinical history variables that had inconsistent documentation patterns in medical records were less reliable (eg, agreement rates of about 80% for the time of symptom onset prior to hospital arrival).

We supplemented the clinical abstractions with other data, such as diagnoses and procedures, extracted from Medicare billing records. We also extracted dates of death from the Medicare Enrollment Database. Dates of death in the Medicare Enrollment Database are derived from both the discharge dates of billing records indicating a discharge disposition of death and from the Master Beneficiary Record obtained from the Social Security Administration. The Medicare Enrollment Database has accurate records of the vital status of Medicare beneficiaries,7 but entries from Social Security records include unverified dates of death recorded as the last day of the month when the exact date is unavailable from a death certificate. We eliminated cases with unverified dates of death from the mortality analyses if the case could not be classified with certainty at the evaluation time. For instance, for 30-day mortality statistics, we eliminated cases with unverified dates of death if survival was between 30 and 59 days after admission as described in an earlier report.8 We found unverified dates of death for 168 patients with confirmed AMI (ie, about 1% of such patients or 2.2% of deaths).

Quality Indicators

The CCP focuses primarily on quality indicators, measurable aspects of care that are presumed on the basis of evidence or consensus to be related to the quality of the care delivered and to favorable outcomes. The CCP indicators were developed by a panel of experts convened by HCFA and the AMA, as reported previously.5 Validation studies of the indicators also were reported previously.9 The definitions of the quality indicators are listed in Table 1.

Most quality indicators are defined as rates, eg, the ratio of the number of eligible patients who received a treatment to the total number of patients eligible for that treatment. We calculated indicator rates for 2 different levels of eligibility. For an eligible candidate indicator, we included all patients who met minimum eligibility requirements (eg, for aspirin use at discharge, we included all patients with confirmed AMI who were discharged alive). For an ideal candidate indicator, we considered the same set of eligible candidates but we excluded all patients with possible contraindications (eg, for aspirin use at discharge, we excluded patients with contraindications such as recent bleeding or warfarin use). Minimum eligibility requirements are shown in Table 1 under the heading Eligible and contraindications are listed under the heading Exclusions. Ideal indicators are frequently used in PRO feedback sessions because clinicians understand them to represent cases that should have been treated. Because the ideal indicators exclude many cases and may be more susceptible to measurement error and because case mix adjustment is less critical for population estimates, we analyzed both levels of indicator eligibility and compared the results for consistency. The only exception is for the smoking cessation counseling indicator. We used 1 major eligibility condition (current smoker status) and did not analyze this indicator by different levels of eligibility.

The definitions in Table 1 were converted to computer algorithms programmed in the Stata programming language.10 The indicator eligibility requirements involve evaluating many different variables. Some of these variables, such as the occurrence of significant bleeding, are soft clinical variables subject to variability in interpretation. Because the abstractions were done by 2 different sets of organizations at 2 different times, we evaluated the frequency distributions of the exclusion variables and avoided using those that appeared to have different interpretations between the baseline and follow-up abstractions or among the 4 pilot state baseline samples. We analyzed all data sets summarized in this article with the same Stata programs. Because the indicator algorithms are complex, we have made them available through the CCP Internet home page.

Feedback

The PROs in the 4 pilot states provided the results from the baseline sample to practitioners in their states from 1994 through January 1995. The PROs used consistent definitions of the indicators and similar approaches to data presentation based on continuous quality improvement theory. Results were presented in a positive manner emphasizing approaches for improvement. The PROs collaborated with appropriate professional organizations such as local chapters of the ACC, and requested quality improvement plans from hospitals if the indicator results suggested opportunities for improvement. The details of the feedback materials and sessions were left to the discretion of the individual PROs. Samples of speaker notes for typical hospital presentations are contained in the CCP data analysis package available through the CCP Internet home page.

The major setting for feedback sessions was the acute care hospital treating AMI patients. Of 390 hospitals represented in the baseline sample, 379 received CCP feedback. Fifty-four percent of the hospitals received feedback by on-site presentations by the PROs' physicians, 18% received feedback at regional seminars, 13% received feedback from telephone conferences, and 16% received feedback from report mailings. Seventy-three percent of the hospitals submitted formal responses to the PROs. Responses included creating or revising standing orders and critical pathways, additional educational efforts and data dissemination, and follow-up data monitoring. Aspirin use, smoking cessation counseling, and thrombolytic administration were the indicators most frequently addressed.

Statistical Analysis

Because our sampling used billing record diagnoses of AMI that may not be accurate, we restricted most analyses to cases with confirmation of AMI based on clinical data. We considered cases to be clinically confirmed if the creatine kinase–MB fraction was greater than 0.05, if the lactate dehydrogenase (LDH) level exceeded 1.5 times the upper limit of normal with LDH1 greater than LDH2, or if 2 of the following 3 conditions were documented: chest pain, a 2-fold elevation of creatine kinase level, or a report of a new AMI on an electrocardiogram.

Different units of analysis were used for different end points. For indicators we used hospitalizations, for mortality we used patients, and for invasive procedures we used episodes. For episodes we followed the ICD-9-CM convention and defined an episode as all admissions occurring within 8 weeks of the initial admission. One patient, if multiple hospitalizations or episodes were sampled, could contribute more than 1 value to an indicator or invasive procedure rate calculation. Each patient was counted only once for mortality calculations, with survival calculated from the admission date of the first hospitalization in a sample period.

We designed our sampling and data collection approach for quality improvement purposes, so our samples include cases that should be excluded from analyses for other purposes. For indicator rates we excluded cases based on transfer status at either admission or discharge if the reliability of data collection could be adversely affected. For example, for aspirin use at discharge, we excluded transfer patients because hospital records frequently do not include information about planned postdischarge medication use for patients transferred to another facility. All such exclusions are noted in the indicator definitions in Table 1. For mortality statistics we excluded cases transferred from another hospital because we did not have an estimate of the date of occurrence for the AMI, and we also excluded cases with unverified dates of death.

Analyses of the abstracted data were performed using the Stata statistical package,10 and analyses of claims data for mortality comparisons were performed using SAS.11 For comparisons among all 3 samples, we used the χ2 test for significance of changes in categorical variables such as indicator results, and the Kruskal-Wallis test for significance of differences in the distributions of continuous variables. For 2 sample comparisons we used the χ2 test for categorical variables and the Wilcoxon rank sum test for continuous variables. Confidence intervals were calculated for the rate differences based on normal approximations. We also plotted Kaplan-Meier survival curves and calculated log-rank and Wilcoxon tests of significance of differences in the survival curves. All CIs are 95% and all P values are 2-tailed and unadjusted for multiple comparisons.

Sample Statistics

Overall, 23535 (96%) of 24509 records for patient discharges sampled were obtained and abstracted successfully. The abstraction completion rate was slightly higher for the follow-up period. Table 2 shows summary statistics for the samples, the sizes of the analytic subsets (units of analysis) we used, and the sizes of some intermediate subsets used to derive them.

Patient Characteristics

Patient characteristics are shown in Table 3. Demographics of the patients in the 2 pilot samples are similar, whereas the nonpilot sample has a slightly higher proportion of blacks. For Medicare AMI discharges, women are represented almost as frequently as men. The percentage of women is slightly higher if only those aged 65 and older are included (eg, 49.8% female in the follow-up sample). The small increase in the female representation from baseline to follow-up shown in Table 3 is not statistically significant. However, we intentionally restricted data in Table 3 to patients who were not transferred from another hospital so these data report characteristics for the same subset of patients for whom we tabulated mortality. For all patients with confirmed AMI, the increase in the percentage of women in the follow-up sample is nominally statistically significant (P=.04).

We sampled all Medicare AMI discharges, including those occurring in patients younger than 65 years who were eligible for Medicare because of disability or end-stage renal disease. The percentage of Medicare AMI patients younger than 65 years is approximately 6% to 7% in these samples. We included these cases in most analyses, but excluding them does not significantly change any of the differences noted herein.

Quality Indicators

Table 4 shows comparisons of the quality indicator rates in the 3 samples. The results for the pilot baseline to follow-up comparison are consistent. All indicators show significant improvements from baseline to follow-up in both the eligible and the ideal candidate versions, except for reperfusion in ideal candidates. All improvements other than reperfusion in ideal candidates are statistically significant and magnitudes of improvements in the 2 versions of the same indicator are similar.

Table Graphic Jump LocationTable 4.—Comparison of Indicator Rates*

The reperfusion in ideal candidates indicator may be susceptible to variations in abstraction, because its denominator incorporates a number of soft variables such as the time from symptom onset. In addition to improvement in the reperfusion indicator in eligible candidates, the speed with which thrombolysis was accomplished improved significantly from baseline to follow-up. The median time from arrival to administration of thrombolytics decreased from 56 minutes to 41 minutes in ideal candidates (P<.001). The percentage of ideal candidates who received thrombolytics and who were administered the drugs within 1 hour after arrival improved from 57.1% to 70.8% (P<.001), and the percentage who received thrombolytics within 30 minutes (the National Heart Attack Alert Program goal) increased from 17.6% to 30.1% (P<.001).

Performance on the quality indicators was better in the pilot states in 1995 than in the rest of the nation, with all but 2 of the 13 indicator comparisons suggesting higher rates in the pilot states. The differences for 2 of the indicators, aspirin use at discharge and β-blocker use at discharge, are statistically significant (P<.001), and the difference for a third indicator, smoking cessation counseling, is also statistically significant (P=.02). Time to thrombolysis did not differ significantly between the 2 samples, eg, median time to thrombolysis in ideal candidates was 41 minutes for the pilot states and 43.5 minutes for the nonpilot states (P=.99).

Mortality

Table 5 compares mortality in the 3 abstraction samples. Both short-term (30-day) and longer-term (1-year) mortality improved significantly between the baseline and follow-up periods, with about a 10% relative reduction in mortality at both evaluation times. Survival improvement did not appear to diminish with increasing length of follow-up, as shown by the Kaplan-Meier survival curves in Figure 1. Differences in survival for all ages and for those 65 years and older are statistically significant (P<.001) by both the log-rank and Wilcoxon tests.

Table Graphic Jump LocationTable 5.—Comparison of Mortality Rates in Abstracted Samples*
Graphic Jump Location
Survival curves showing improvement in survival in pilot states from baseline to follow-up period.

Table 6 compares mortality from the claims data. There were no significant differences in either 30-day or 1-year mortality between the pilot state patients and those in the rest of the nation during the baseline period. Mortality was significantly better in the pilot states during the follow-up period, and the difference in 1-year mortality between the pilot states and the rest of the nation was statistically significant (P=.004). The differences in survival during the follow-up period were also statistically significant by both the log-rank test (P=.003) and the Wilcoxon test (P=.004).

Table Graphic Jump LocationTable 6.—Comparison of Mortality Rates From Claims Data*
Other Measures

Rates for invasive cardiology procedures by episode increased significantly from baseline to follow-up. Angioplasty rates increased from 15.1% to 21.9% (P<.001) and catheterization rates increased slightly from 44.5% to 47.3% (P=.001). Coronary artery bypass surgery rates remained stable from 11.2% to 12% (P=.14). These rates do not include procedures performed during nonsampled hospitalization or on an outpatient basis. Invasive procedure rates by episode cannot be estimated for the nonpilot sample because multiple discharges per episode were infrequently sampled. Procedure rates for the sampled nonpilot discharges were not significantly different from the pilot follow-up rates.

Length of stay decreased from the baseline period to the follow-up period. For patients with confirmed MI who were not transferred in or out and who did not die in the hospital, the mean length of stay decreased from 9.8 days to 7.5 days and the median decreased from 8 days to 6 days (P<.001). Length of stay for the nonpilot cases did not differ significantly from the pilot follow-up cases.

The quality of care for Medicare patients with AMI has improved in the CCP pilot states between the baseline and follow-up periods. Improvements noted in the CCP quality indicators (process measures) are consistent and appear to be associated with an improvement in at least 1 important outcome measure, mortality. The magnitudes of the changes are sufficiently reassuring that meaningful improvements were accomplished, rather than small changes made significant by large sample sizes. Although a recent article has cautioned against attributing improvements in quality to large-scale programs like CCP,12 the differences between the pilot and nonpilot indicator and mortality rates at follow-up, combined with no differences in mortality rates at baseline and severity indexes at follow-up, suggest that CCP contributed to the improvement.

The improvement in 1 indicator is particularly striking. The use of β-blockers at discharge in minimally eligible patients increased from 32% to 50%, an 18% absolute increase or more than a 50% relative increase. In contrast, while the speed with which reperfusion was delivered improved dramatically, only modest improvements were noted in reperfusion rates. Only about 20% of patients 65 years and older with AMI received early reperfusion therapy.

The data show that Medicare patients with AMI have significant comorbidity, eg, diabetes in about 30% and a history of hypertension in about 60%. High comorbidity in patients 65 years and older may be one factor contributing to the low reperfusion rates. The data in Table 3 indicate slightly more comorbidity in the follow-up sample cases than in the baseline. Although some of the differences in comorbid factors are highly statistically significant, the magnitudes of the differences are usually small and could represent variations in abstracter interpretation rather than actual differences in patient characteristics. The closeness of the composite risk indexes (Medicare Mortality Predictor System [MMPS] and Acute Physiology and Chronic Health Evaluation [APACHE] III) for the pilot follow-up and nonpilot samples suggests that these samples are similar with regard to severity of illness. The data in Table 3 also suggest that the follow-up sample does not represent a healthier population and that comorbidity does not explain the lower mortality for the follow-up patients.

The sex distribution for elderly Medicare AMI patients does not show the male predominance typical of clinical trials. The distribution between men and women is close to equal for confirmed AMI in patients 65 years and older and, as the population ages, the sex distribution will shift to a female predominance in the next decade. Ischemic heart disease is very much a women's health issue for the Medicare population.

The results of the eligible and ideal indicators are consistent. Although we do not provide any evidence in this study regarding the superiority of either indicator for quality improvement feedback or for hospital rankings, our results suggest that the simpler eligible indicators are adequate for evaluating changes in large populations. The eligible indicators have an advantage of requiring much less data collection. For example, for aspirin use during hospitalization, we evaluated 22 more variables (details regarding possible contraindications) for the ideal indicator than for the eligible indicator. Eligible indicators may be more efficient for follow-up studies in populations over the course of several years or for other situations in which case mix is stable.

While we believe that these data suggest that CCP has contributed to a measurable improvement in the quality of AMI care in the pilot states, we acknowledge that our study has some limitations. The 4 pilot states are not a random subsample of the nation. The data from the follow-up period suggest that the patient characteristics are not significantly different in the pilot states than in the rest of the nation, but we lack a comparison group to confirm that the processes of care were similar in the pilot states and the rest of the nation at baseline. The similarity in mortality between the pilot states and the rest of the nation at baseline suggests that differences in care processes at baseline were minimal or noneffective.

Nonpilot follow-up results are better than pilot baseline results, suggesting that there have been improvements in care in the rest of the nation. Many factors, such as dissemination of clinical trial results, professional society educational activities, commercially sponsored programs such as the National Registry of Myocardial Infarction,13 other national initiatives such as the National Heart Attack Alert Program,14 and state projects such as the Connecticut Medicare Hospital Information Project15 probably have contributed to the changes in both pilot and nonpilot states.

We have not yet analyzed any effects on costs. While we do not have formal cost impact studies, we are reassured by the observations that the 2 treatments with the widest applicability that improved significantly, administration of aspirin and administration of β-blockers, are inexpensive therapies, and that the process and mortality improvements were accompanied by a decreased average length of stay. We suspect that the decreased length of stay is predominantly caused by non-CCP forces, such as evolving practice patterns, the current emphasis on controlling costs in the medical community, and the influence of managed health care. The CCP may have contributed to declining lengths of stay because quality improvement mechanisms such as critical pathways promoted by the PROs during CCP feedback frequently have goals for both resource conservation and quality improvement. We believe that CCP provides some evidence that quality improvement is achievable in today's environment of cost control.

We should not be too complacent about the positive results. As compared with current clinical trials for AMI that report short-term mortality results of about 5% in selected patients, AMI in the older population remains a deadly disease. Mortality rates are 18% at 30 days and 30% at 1 year, and more patients die before they reach a hospital.16 We suspect that there may still be room for improvement even in the pilot states and that we have more lessons to learn from CCP and other sources about the optimal care of older patients diagnosed as having AMI.

Because CCP pilot baseline results suggested that opportunities for improving Medicare AMI care existed, HCFA proceeded with CCP activities in the rest of the nation in 1995 and 1996. The CCP national effort has proceeded from data collection (an 8-month sample of Medicare AMI discharges from all hospitals in nonpilot states) through feedback from the PROs. We plan to resample all states for 1997, although with a sample size that will produce reasonably precise national statistics but not hospital-specific statistics. We have learned much from the lessons of the CCP pilot, and are hopeful that the national effort will produce results comparable to those reported herein.

Kerr EA, Mittman BS, Hays RD, Leake B, Brook RH. Quality assurance in capitated physician groups: where is the emphasis?  JAMA.1996;276:1236-1239.
Angell M, Kassirer JP. Quality and the medical marketplace: following elephants.  N Engl J Med.1996;335:883-885.
Jencks SF, Wilensky GR. The Health Care Quality Improvement Initiative: a new approach to quality assurance in Medicare.  JAMA.1992;268:900-903.
Gunnar RM, Bourdillon PDV, Dixon DW.  et al.  Guidelines for the early management of patients with acute myocardial infarction: a report of the American College of Cardiology/American Heart Association Task Force on Assessment of Diagnostic and Therapeutic Cardiovascular Procedures (Subcommittee to Develop Guidelines for the Early Management of Patients With Acute Myocardial Infarction).  J Am Coll Cardiol.1990;16:249-292.
Ellerbeck EF, Jencks SF, Radford MJ.  et al.  Quality of care for Medicare patients with acute myocardial infarction: a four-state pilot study from the Cooperative Cardiovascular Project.  JAMA.1995;273:1509-1514.
Fleiss JL. The measurement of interrater agreement. In: Statistical Methods for Rates and Proportions. New York, NY: John Wiley & Sons Inc; 1981:212-236.
Fleming C, Fisher ES, Chang CH, Bubolz TA, Malenka DJ. Study outcomes and hospital utilization in the elderly: the advantages of a merged database for Medicare and Veterans Affairs hospitals.  Med Care.1992;30:377-391.
Normand ST, Glickman ME, Sharma GVRK, McNeil BJ. Using admission characteristics to predict short-term mortality from myocardial infarction in elderly patients: results from the Cooperative Cardiovascular Project.  JAMA.1996;275:1322-1328.
Lambert-Huber DA, Ellerbeck EF, Wallace RG, Radford MJ, Kresowik TF, Allison JA. Quality of care indicators for patients with acute myocardial infarction: pilot validation of the indicators.  Clin Performance Qual Health Care.1994;2:219-222.
 Stata Statistical Software.  Release 4.0. College Station, Tex: Stata Corp; 1995.
 SAS [computer software]. Release 6.11. Cary, NC: SAS Institute Inc; 1996.
Ghali WA, Ash AS, Hall RE, Moskowitz MA. Statewide quality improvement initiatives and mortality after cardiac surgery.  JAMA.1997;277:379-382.
Rogers WJ, Bowlby LJ, Chandra NC.  et al.  Treatment of myocardial infarction in the United States (1990 to 1993): observations from the National Registry of Myocardial Infarction.  Circulation.1994;90:2103-2114.
National Heart Attack Alert Program Coordinating Committee, 60 Minutes to Treatment Working Group.  Emergency Department: Rapid Identification and Treatment of Patients With Acute Myocardial Infarction.  Bethesda, Md: US Dept of Health and Human Services, National Institutes of Health, and National Heart, Lung, and Blood Institute; 1993. National Institutes of Health publication 93-3278.
Meehan TP, Radford MJ, Vaccarino LV.  et al.  A collaborative project in Connecticut to improve the care of patients with acute myocardial infarction.  Jt Comm J Qual Improv.1996;22:751-761.
Ryan TJ, Anderson JL, Antman EM.  et al.  ACC/AHA guidelines for the management of patients with acute myocardial infarction: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Management of Acute Myocardial Infarction).  J Am Coll Cardiol.1996;28:1328-1428.

Figures

Graphic Jump Location
Survival curves showing improvement in survival in pilot states from baseline to follow-up period.

Tables

Table Graphic Jump LocationTable 4.—Comparison of Indicator Rates*
Table Graphic Jump LocationTable 5.—Comparison of Mortality Rates in Abstracted Samples*
Table Graphic Jump LocationTable 6.—Comparison of Mortality Rates From Claims Data*

References

Kerr EA, Mittman BS, Hays RD, Leake B, Brook RH. Quality assurance in capitated physician groups: where is the emphasis?  JAMA.1996;276:1236-1239.
Angell M, Kassirer JP. Quality and the medical marketplace: following elephants.  N Engl J Med.1996;335:883-885.
Jencks SF, Wilensky GR. The Health Care Quality Improvement Initiative: a new approach to quality assurance in Medicare.  JAMA.1992;268:900-903.
Gunnar RM, Bourdillon PDV, Dixon DW.  et al.  Guidelines for the early management of patients with acute myocardial infarction: a report of the American College of Cardiology/American Heart Association Task Force on Assessment of Diagnostic and Therapeutic Cardiovascular Procedures (Subcommittee to Develop Guidelines for the Early Management of Patients With Acute Myocardial Infarction).  J Am Coll Cardiol.1990;16:249-292.
Ellerbeck EF, Jencks SF, Radford MJ.  et al.  Quality of care for Medicare patients with acute myocardial infarction: a four-state pilot study from the Cooperative Cardiovascular Project.  JAMA.1995;273:1509-1514.
Fleiss JL. The measurement of interrater agreement. In: Statistical Methods for Rates and Proportions. New York, NY: John Wiley & Sons Inc; 1981:212-236.
Fleming C, Fisher ES, Chang CH, Bubolz TA, Malenka DJ. Study outcomes and hospital utilization in the elderly: the advantages of a merged database for Medicare and Veterans Affairs hospitals.  Med Care.1992;30:377-391.
Normand ST, Glickman ME, Sharma GVRK, McNeil BJ. Using admission characteristics to predict short-term mortality from myocardial infarction in elderly patients: results from the Cooperative Cardiovascular Project.  JAMA.1996;275:1322-1328.
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The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
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