for over a decade , improving hospital performance on patient safety and patient outcomes have been national priorities in the united states.1 , 2 , 3 , 4 , 5 , 6 , 7 hospitals with high inhospital adverse event , mortality , or unplanned readmission rates are considered to provide poorer quality of care.7 , 8 , 9 studies estimate that the excess annual cost attributed to measurable medical errors is around $ 17  billion10 and that unplanned readmissions result in an additional $ 15  billion in annual medicare expenditures.4 although extensive nationwide efforts have focused on improving patient safety and outcomes for acute myocardial infarction ( ami ) in particular,11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 with some recent data showing that patient safety and outcomes for acute cardiovascular disease have improved,19 , 20 , 21 inhospital adverse events , shortterm mortality , and unplanned readmission rates for ami patients remain high , with considerable variation across hospitals.22 
  efforts to improve patient safety , reduce hospital mortality , and reduce unplanned readmission rates are largely pursued independently . their impact on one another is unclear or thought to be small.23 , 24 , 25 , 26 previous studies have shown that patients with 1 or more adverse events are more likely to have higher mortality or to be readmitted , but these studies were limited to a few measures and local data sources.27 , 28 , 29 , 30 , 31 , 32 they also focused on patientlevel analyses , rather than connecting hospitallevel performance on patient safety with other important hospitallevel outcomes . an association between adverse events and outcomes of individual patients indicates that adverse events may lead to worse outcomes , but it may not reflect hospital performance . a patient experiencing more adverse events may be sicker , and these events may not reflect the performance of the hospital that treated that patient . depending on case mix , a hospital with a high raw adverse event rate may also have a high raw mortality or readmission rate , but a high raw adverse event rate does not indicate that the hospital has worse performance in patient safety . how hospital performance on patient safety associates with hospital performance on outcomes is unclear from a patientlevel analysis . without evidence of a link between patient safety and mortality and readmission at the hospital level , hospitals may not view their investment in improving safety as benefiting readmission rates or even mortality and hence may not recognize harm reduction as a potential strategy for lessening these key patient outcomes . accordingly , we sought to investigate the association at the hospitallevel between a wide range of inhospital adverse event rates and both mortality and readmission rates for medicare feeforservice patients with ami , an acute condition that may be more sensitive to inhospital adverse events . to accomplish this analysis on a national scale , we used data from the agency for healthcare research and quality ( ahrq ) 's medicare patient safety monitoring system ( mpsms ) , the nation 's largest randomly selected hospital medical recordabstracted patient safety database,33 and data from the centers for medicare & medicaid services ( cms ) , which includes hospital performance on mortality and readmissions for over 4000 medicarecertified hospitals , to assess the relationship between hospital performance on patient safety and hospital performance on 30day allcause mortality and unplanned readmissions for medicare feeforservice patients discharged with ami . the mpsms data , at the individual patient level , includes 21 inhospital adverse event measures19 ( table  1 ) jointly developed by federal agencies and private health care organizations.33 starting from 2009 , mpsms medical records were drawn from the cms  validation sample  for processofcare measures required for the hospital inpatient quality reporting program , which includes a multistage random sample of allpayer patients hospitalized for ami , heart failure , pneumonia , and major surgical conditions . all hospitals in the mpsms data were randomly selected . hospital inpatient quality reporting program validation sample also includes 200 hospitals targeted based on past data quality or performance ( selected after the random sample was drawn ) ; these were not included in mpsms data . approximately 17  500 records were randomly selected from 4000 hospitals for 2009 and 34  000 records from 1400 hospitals for 2010 and 2011 . the 2012 data included 27  200 records from 1110 hospitals and 2013 data included 17  900 records from 730 hospitals . each hospital in 20102013 contributed approximately equal numbers of randomly selected records to the mpsms . medical record abstraction was conducted at the cms clinical data abstraction center . based on 80 monthly reabstractions , the agreement between abstraction and reabstraction ranged from 94% to 99% for data elements used to identify adverse events.19 , 27 , 33 
  list of the 21 adverse event measures the cms mortality and readmission data for ami are available from the hospital compare website , at the individual hospital level . the data includes hospitalspecific total discharges , 30day allcause riskstandardized mortality , and readmission rates for all acute care hospitals treating medicare feeforservice patients aged 65  years or older . cms combined data from a 3year period to report mortality and readmission rates for each hospital . the 2 recent reporting periods were july 1 , 2009 through june 30 , 2012 and july 1 , 2010 through june 30 , 2013 . not all hospitals were repeated in each reporting period . to include the maximum number of hospitals from both the mpsms and cms data , we combined the two 3yearperiod data sets into a single 4yearperiod data set from july 1 , 2009 to june 30 , 2013 . if a hospital had data in both periods , we averaged its mortality and readmission rates weighted by its total discharges in each period for each outcome . to align with the cms data , we restricted mpsms data to only medicare feeforservice patients aged 65  years or older discharged for ami from july 1 , 2009 to june 30 , 2013 . patients discharged to a skilled nursing facility or other levelofcare facilities , except transferred to an acute care hospital , were included in the mpsms as well as in the cms data . patient characteristics for the cms data have been reported elsewhere.24 , 25 , 26 patient characteristics for mpsms data were obtained from medical records , including demographics ( age , sex , and race ) , common clinical comorbidities ( heart failure , obesity , coronary artery disease , renal disease , cerebrovascular disease , chronic obstructive pulmonary disease , cancer , and diabetes ) , and smoking status . hospital characteristics were obtained from the american hospital association 's 2013 annual survey database , including teaching status ( teaching vs nonteaching ) ; joint commission certification status ( yes / no ) ; geographical location ( urban vs rural ) ; ownership ( private notforprofit vs others ) ; bed size ; nursetopatient ratio ; perform cardiac catheterization and/or percutaneous coronary intervention procedures ( yes / no ) ; and perform coronary artery bypass graft surgery ( yes / no ) . our primary outcomes were the hospitalspecific riskstandardized 30day allcause mortality and allcause unplanned readmission rates . mortality was measured from the date of admission ; readmission was measured from the date of discharge for patients who were discharged alive and were not transferred to another acutecare hospital . we measured inhospital adverse events as the rate of occurrence of inhospital adverse events for which patients were at risk . the numerator for this rate is the number of adverse events that occurred , and the denominator is the number of adverse events for which patients were at risk.19 all patients were at risk for at least 2 adverse events , inhospital falls , and hospitalacquired pressure ulcers , but only a subset of patients were at risk for other events ( eg , only patients who received warfarin were at risk for a warfarinassociated adverse event ) . we reported the rate of patients with 1 or more adverse events during a hospitalization . the numerator for this rate is the number of patients who experienced 1 or more adverse events , and the denominator is the number of patients in the mpsms data ( table  2).19 among the 21 inhospital adverse event measures , no patient had a knee joint replacement and 3 had a hip joint replacement during the index ami hospitalization . we included these 3 patients because they had a principal discharge diagnosis code of ami . illustration of calculating of adverse event rates the numerator is the number of adverse events that occurred and the denominator is the number of adverse events for which patients were at risk . the numerator is the number of patients who experienced 1 or more adverse events and the denominator is the number of hospitalizations . using the cms riskstandardized method for profiling hospitals,34 , 35 , 36 , 37 we fitted a hierarchical generalized linear model with a poisson link function to model the occurrence of adverse events as a function of patients age , sex , and clinical comorbidities . the number of adverse events for which patients were at risk was the offset in the model . using this model we linked the hospitalspecific riskstandardized occurrence rate of adverse events data with the hospitalspecific riskstandardized mortality and readmission data at the hospital level . to ensure that each hospital had an adequate sample size , we included only hospitals with at least 30 patients in the cms data and at least 40 adverse events for which patients were at risk over the study period ( table  3 ) . the threshold of 40 was selected because the median number of adverse events for which patients were at risk per hospital was 36 for ami . with this larger threshold , we reduced the likelihood that a hospital 's low adverse event rate is attributed to an insufficient number of adverse events for which patients were at risk . of hospitals by outcome we identified 2530 unique hospitals that each had 30 or more patients from the cms mortality and readmission data and 921 unique hospitals that each had 40 or more adverse events for which patients were at risk from the mpsms adverse events data . the threshold of 40 was selected based on the fact that the median numbers of adverse events for which patients were at risk per hospital was 36 for ami . with this larger threshold , we reduced the likelihood that a hospital 's low adverse event rate is attributed to an insufficient number of adverse events for which patients were at risk . after linking the mortality and readmission data to adverse events data ami indicates acute myocardial infarction ; cms , centers for medicare & medicaid services ; mpsms , medicare patient safety monitoring system . the median numbers of adverse events for which patients were at risk was 36 for ami . to describe patient and hospital characteristics and the rates of individual adverse event measures , we classified each hospital into tertiles based on its riskstandardized occurrence rate of adverse events . we also classified each hospital into tertiles based on its number of adverse events for which patients were at risk and compared the 2 distributions . to quantify the difference in the occurrence of adverse events across hospitals , we computed betweenhospital variation in the occurrence rate of adverse events after accounting for patient characteristics using an odds ratio representing the odds of a patient experiencing an adverse event when treated in a hospital 1 sd above the overall average relative to being treated at a hospital 1 sd below the overall average.38 
  to evaluate the association between hospital performance on patient safety and hospital performance on mortality , we fitted 2 mixedeffects models to estimate the : ( 1 ) hospitalspecific riskstandardized mortality rate as a function of the hospitalspecific observed occurrence rate of adverse events and ( 2 ) hospitalspecific riskstandardized mortality rate as a function of the hospitalspecific riskstandardized occurrence rate of adverse events , with and without adjusting for the hospital characteristics described previously . we repeated these models for the readmission measure as well . because different states have different patient safety regulations and quality improvement initiatives that may impact hospital performance within a state , all models were fitted with statespecific random intercepts to account for withinstate and betweenstate variation and weighted by the hospitalspecific number of adverse events for which patients were at risk . we conducted a second analysis that included all hospitals regardless of their volume of possible adverse events for which patients were at risk . the institutional review board ( irb ) at solutions irb ( http://www.solutionsirb.com ) determined that the requirement for informed consent could be waived based on the nature of the study . the mpsms data , at the individual patient level , includes 21 inhospital adverse event measures19 ( table  1 ) jointly developed by federal agencies and private health care organizations.33 starting from 2009 , mpsms medical records were drawn from the cms  validation sample  for processofcare measures required for the hospital inpatient quality reporting program , which includes a multistage random sample of allpayer patients hospitalized for ami , heart failure , pneumonia , and major surgical conditions . all hospitals in the mpsms data were randomly selected . hospital inpatient quality reporting program validation sample also includes 200 hospitals targeted based on past data quality or performance ( selected after the random sample was drawn ) ; these were not included in mpsms data . approximately 17  500 records were randomly selected from 4000 hospitals for 2009 and 34  000 records from 1400 hospitals for 2010 and 2011 . the 2012 data included 27  200 records from 1110 hospitals and 2013 data included 17  900 records from 730 hospitals . each hospital in 20102013 contributed approximately equal numbers of randomly selected records to the mpsms . medical record abstraction was conducted at the cms clinical data abstraction center . based on 80 monthly reabstractions , the agreement between abstraction and reabstraction ranged from 94% to 99% for data elements used to identify adverse events.19 , 27 , 33 
  list of the 21 adverse event measures the cms mortality and readmission data for ami are available from the hospital compare website , at the individual hospital level . the data includes hospitalspecific total discharges , 30day allcause riskstandardized mortality , and readmission rates for all acute care hospitals treating medicare feeforservice patients aged 65  years or older . cms combined data from a 3year period to report mortality and readmission rates for each hospital . the 2 recent reporting periods were july 1 , 2009 through june 30 , 2012 and july 1 , 2010 through june 30 , 2013 . not all hospitals were repeated in each reporting period . to include the maximum number of hospitals from both the mpsms and cms data , we combined the two 3yearperiod data sets into a single 4yearperiod data set from july 1 , 2009 to june 30 , 2013 . if a hospital had data in both periods , we averaged its mortality and readmission rates weighted by its total discharges in each period for each outcome . to align with the cms data , we restricted mpsms data to only medicare feeforservice patients aged 65  years or older discharged for ami from july 1 , 2009 to june 30 , 2013 . patients discharged to a skilled nursing facility or other levelofcare facilities , except transferred to an acute care hospital , were included in the mpsms as well as in the cms data . patient characteristics for the cms data have been reported elsewhere.24 , 25 , 26 patient characteristics for mpsms data were obtained from medical records , including demographics ( age , sex , and race ) , common clinical comorbidities ( heart failure , obesity , coronary artery disease , renal disease , cerebrovascular disease , chronic obstructive pulmonary disease , cancer , and diabetes ) , and smoking status . hospital characteristics were obtained from the american hospital association 's 2013 annual survey database , including teaching status ( teaching vs nonteaching ) ; joint commission certification status ( yes / no ) ; geographical location ( urban vs rural ) ; ownership ( private notforprofit vs others ) ; bed size ; nursetopatient ratio ; perform cardiac catheterization and/or percutaneous coronary intervention procedures ( yes / no ) ; and perform coronary artery bypass graft surgery ( yes / no ) . our primary outcomes were the hospitalspecific riskstandardized 30day allcause mortality and allcause unplanned readmission rates . mortality was measured from the date of admission ; readmission was measured from the date of discharge for patients who were discharged alive and were not transferred to another acutecare hospital . we measured inhospital adverse events as the rate of occurrence of inhospital adverse events for which patients were at risk . the numerator for this rate is the number of adverse events that occurred , and the denominator is the number of adverse events for which patients were at risk.19 all patients were at risk for at least 2 adverse events , inhospital falls , and hospitalacquired pressure ulcers , but only a subset of patients were at risk for other events ( eg , only patients who received warfarin were at risk for a warfarinassociated adverse event ) . we reported the rate of patients with 1 or more adverse events during a hospitalization . the numerator for this rate is the number of patients who experienced 1 or more adverse events , and the denominator is the number of patients in the mpsms data ( table  2).19 among the 21 inhospital adverse event measures , no patient had a knee joint replacement and 3 had a hip joint replacement during the index ami hospitalization . we included these 3 patients because they had a principal discharge diagnosis code of ami . illustration of calculating of adverse event rates the numerator is the number of adverse events that occurred and the denominator is the number of adverse events for which patients were at risk . the numerator is the number of patients who experienced 1 or more adverse events and the denominator is the number of hospitalizations . using the cms riskstandardized method for profiling hospitals,34 , 35 , 36 , 37 we fitted a hierarchical generalized linear model with a poisson link function to model the occurrence of adverse events as a function of patients age , sex , and clinical comorbidities . the number of adverse events for which patients were at risk was the offset in the model . using this model we linked the hospitalspecific riskstandardized occurrence rate of adverse events data with the hospitalspecific riskstandardized mortality and readmission data at the hospital level . to ensure that each hospital had an adequate sample size , we included only hospitals with at least 30 patients in the cms data and at least 40 adverse events for which patients were at risk over the study period ( table  3 ) . the threshold of 40 was selected because the median number of adverse events for which patients were at risk per hospital was 36 for ami . with this larger threshold , we reduced the likelihood that a hospital 's low adverse event rate is attributed to an insufficient number of adverse events for which patients were at risk . of hospitals by outcome we identified 2530 unique hospitals that each had 30 or more patients from the cms mortality and readmission data and 921 unique hospitals that each had 40 or more adverse events for which patients were at risk from the mpsms adverse events data . the threshold of 40 was selected based on the fact that the median numbers of adverse events for which patients were at risk per hospital was 36 for ami . with this larger threshold , we reduced the likelihood that a hospital 's low adverse event rate is attributed to an insufficient number of adverse events for which patients were at risk . after linking the mortality and readmission data to adverse events data ami indicates acute myocardial infarction ; cms , centers for medicare & medicaid services ; mpsms , medicare patient safety monitoring system . the median numbers of adverse events for which patients were at risk was 36 for ami . to describe patient and hospital characteristics and the rates of individual adverse event measures , we classified each hospital into tertiles based on its riskstandardized occurrence rate of adverse events . we also classified each hospital into tertiles based on its number of adverse events for which patients were at risk and compared the 2 distributions . to quantify the difference in the occurrence of adverse events across hospitals , we computed betweenhospital variation in the occurrence rate of adverse events after accounting for patient characteristics using an odds ratio representing the odds of a patient experiencing an adverse event when treated in a hospital 1 sd above the overall average relative to being treated at a hospital 1 sd below the overall average.38 
  to evaluate the association between hospital performance on patient safety and hospital performance on mortality , we fitted 2 mixedeffects models to estimate the : ( 1 ) hospitalspecific riskstandardized mortality rate as a function of the hospitalspecific observed occurrence rate of adverse events and ( 2 ) hospitalspecific riskstandardized mortality rate as a function of the hospitalspecific riskstandardized occurrence rate of adverse events , with and without adjusting for the hospital characteristics described previously . we repeated these models for the readmission measure as well . because different states have different patient safety regulations and quality improvement initiatives that may impact hospital performance within a state , all models were fitted with statespecific random intercepts to account for withinstate and betweenstate variation and weighted by the hospitalspecific number of adverse events for which patients were at risk . we conducted a second analysis that included all hospitals regardless of their volume of possible adverse events for which patients were at risk . the institutional review board ( irb ) at solutions irb ( http://www.solutionsirb.com ) determined that the requirement for informed consent could be waived based on the nature of the study . the final sample included 793 hospitals ( table  3 ) from the linked mpsms and cms data . the hospital median ( interquartile range [ iqr ] ) number of adverse events for which patients were at risk was 59 ( 29 ) ( figure  1 , right panel ) . the cms data included 167  734 and 174  457 ami patients for the mortality and readmission outcomes , respectively . the median ( iqr ) numbers of patients per hospital for the mortality and readmission measures were 149 ( 226 ) and 158 ( 262 ) ( figure  1 , left and middle panels ) . table  4 , which is at the hospital level , shows hospital characteristics by the tertiles of hospital 's riskstandardized occurrence rate of adverse events for which patients were at risk . teaching hospitals and hospitals with a high proportion of patients with diabetes mellitus , obesity , and cerebrovascular disease had higher occurrence rates of adverse events ( table  4 ) . box  and whisker plots of the distributions of hospitalspecific patient volumes and the number of adverse events for which patients were at risk . the length of the box represents the interquartile range ( iqr ) , the horizontal line in the box interior represents the median , the whiskers represent the 1.5 iqr of the 25th quartile or 1.5 iqr of the 75th quartile , and the dots represent outliers . the median ( iqr ) numbers of patients per hospital for the mortality and unplanned readmission measures were 149 ( 226 ) and 158 ( 262 ) ; the median ( iqr ) number of adverse events for which patients were at risk was 59 ( 29 ) . hospital characteristics ami indicates acute myocardial infarction ; iqr , interquartile range ; mpsms , medicare patient safety monitoring system . hospitals ( n=793 ) had at least 30 patients for mortality and readmission measures and had at least 40 adverse events for which patients were at risk . based on mpsms abstracted data . for the occurrence of adverse events , mortality , and readmission , respectively , the ranges ( minimum to maximum ) of riskstandardized rates were 10.3% to 19.9% , 14.3% to 21.8% , and 1.4% to 19.3% . hospitalspecific riskstandardized occurrence rate of adverse events was widely dispersed across hospitals ( figure  2 , right panel ) . the mean hospitalspecific mortality and readmission rates were 14.7% and 18.0% , respectively ( figure  2 , left and middle panels ) . box  and whisker plots of the distributions of hospitalspecific 30day allcause riskstandardized mortality and unplanned readmission rates and hospitalspecific riskstandardized occurrence rates of adverse events for which patients were at risk . the length of the box represents the interquartile range ( iqr ) , the horizontal line in the box interior represents the median , the whiskers represent the 1.5 iqr of the 25th quartile or 1.5 iqr of the 75th quartile , and the dots represent outliers . for mortality , unplanned readmission , and adverse events , respectively , the ranges ( minimum to maximum ) of riskstandardized rates were 10.3% to 19.9% , 14.3% to 21.8% , and 1.4% to 19.3% . table  5 , which is at the patient level , shows the number of patients and adverse event rates for each of the 21 adverse measures as well as the 2 composite adverse event rates measures by the tertiles of hospital 's riskstandardized occurrence rate of adverse events for which patients were at risk . the number of adverse events for which patients were at risk per hospitalization varied by patient characteristics and ranged from 2 to 18 . on average , patients were at risk for 7.4 events per hospitalization . hospitals with a high riskstandardized occurrence rate of adverse events usually had more patients at risk . for the first , second , and third tertiles , respectively , patients were at risk for 6.7 , 7.3 , and 8.3 events per hospitalization ( table  5 ) . nevertheless , 32.0% of hospitals in the highest tertile of adverse events for which patients were at risk were in the lowest tertile of riskstandardized occurrence rate of adverse events . conversely , 25.8% of hospitals in the lowest tertile of patients at risk had a higher rate of adverse events ( figure  3 ) . hospital 's riskstandardized occurrence rate of adverse events for which patients were at risk by tertiles , july 1 , 2009 to june 30 , 2013 ( aged 65  years or older , medicare feeforservice patients discharged with acute myocardial infarction ) n / a indicates not applicable . the numerator is the number of adverse events that occurred and the denominator is the number of adverse events for which patients were at risk . the numerator is the number of patients who experienced one or more adverse events and the denominator is the number of hospitalizations . hospitalspecific riskstandardized occurrence rates of adverse events by tertile versus hospitalspecific total number of adverse events for which patients were at risk by tertile . of the 793 hospitals , the overall occurrence rate of adverse events , in which the numerator is the number of adverse events that occurred and the denominator is the number of adverse events for which patients were at risk , was 3.8% . the overall rate of patients with 1 or more adverse events per hospitalization , in which the numerator is the number of patients who experienced 1 or more adverse events and the denominator is the number of patients in the mpsms data , was 20.1% ( tables  2 and 5 ) . a change of 1% point in the occurrence rate of adverse events for which patients were at risk was associated with an average change of 4.8% points ( 95% ci , 4.74.9 ) in the rate of patients with 1 or more adverse events per hospitalization ( figure  4 ) . for teaching and nonteaching hospital groups , respectively , the  means ( sd ) of riskstandardized hospitalspecific rate of  occurrence of adverse events were 5.3 ( 18.7 ) and 4.1 ( 19.2 ) . relationship between the observed rate of patients had 1 or more adverse events and occurrence rate of adverse events for which patients were at risk . the odds of an adverse event occurring when treated at a hospital 1  sd above the overall average relative to the odds of an adverse event occurring when treated at a hospital 1  sd below the overall average were 3.86 ( 95% ci , 3.624.12 ) . the hospital characteristicadjusted associations between the observed occurrence rate of adverse events and riskstandardized mortality and readmission rates were statistically significant ( figure  5 , top panel ) . the hospital characteristicadjusted associations between the riskstandardized occurrence rate of adverse events and riskstandardized mortality and readmission rates were also statistically significant ( figure  5 , bottom panel ) . a change of 1% point in the riskstandardized occurrence rate of adverse events was associated with an average change in the riskstandardized mortality rate of 4.86% points ( 95% ci , 0.798.94 ) and an average change in the riskstandardized readmission rate of 3.44% points ( 95% ci , 0.196.68 ) . because the overall occurrence rate of adverse events was 3.8% , a 1% point change in this rate translates to a relative change of 26.3% . the second analysis , which included all hospitals regardless of their volume of adverse events for which patients were at risk , did not change the results substantially ( figure  6 ) . a change of 1% point in the riskstandardized occurrence rate of adverse events was associated with average changes in the riskstandardized mortality rate of 5.13% points ( 95% ci , 2.238.03 ) and riskstandardized readmission rate of 2.51% points ( 95% ci , 0.214.82 ) . point estimates and 95% cis of the associations between the hospitalspecific riskstandardized 30day mortality rate and hospitalspecific riskstandardized occurrence rate of adverse events and hospitalspecific riskstandardized 30day unplanned readmission rate and hospitalspecific riskstandardized occurrence rate of adverse events . hospital characteristics included in the adjusted models were teaching status ( teaching vs nonteaching ) ; joint commission certification status ( yes / no ) ; geographical location ( urban vs rural ) ; ownership ( private notforprofit vs others ) ; bed size ; nursetopatient ratio ; perform cardiac catheterization and/or percutaneous coronary intervention procedures ( yes / no ) ; and perform coronary artery bypass graft surgery ( yes / no ) . modela models the riskstandardized mortality or unplanned readmission rate as a function of hospitalspecific observed occurrence rate of adverse events ; modelb models the riskstandardized mortality or unplanned readmission rate as a function of hospitalspecific riskstandardized occurrence rate of adverse events . point estimates and 95% cis of the associations between hospital performance on mortality and unplanned readmission rates and hospital performance on the occurrence rate of adverse events , regardless of hospitals volume of adverse events for which patients were at risk . the final sample included 793 hospitals ( table  3 ) from the linked mpsms and cms data . the hospital median ( interquartile range [ iqr ] ) number of adverse events for which patients were at risk was 59 ( 29 ) ( figure  1 , right panel ) . the cms data included 167  734 and 174  457 ami patients for the mortality and readmission outcomes , respectively . the median ( iqr ) numbers of patients per hospital for the mortality and readmission measures were 149 ( 226 ) and 158 ( 262 ) ( figure  1 , left and middle panels ) . table  4 , which is at the hospital level , shows hospital characteristics by the tertiles of hospital 's riskstandardized occurrence rate of adverse events for which patients were at risk . teaching hospitals and hospitals with a high proportion of patients with diabetes mellitus , obesity , and cerebrovascular disease had higher occurrence rates of adverse events ( table  4 ) . box  and whisker plots of the distributions of hospitalspecific patient volumes and the number of adverse events for which patients were at risk . the length of the box represents the interquartile range ( iqr ) , the horizontal line in the box interior represents the median , the whiskers represent the 1.5 iqr of the 25th quartile or 1.5 iqr of the 75th quartile , and the dots represent outliers . the median ( iqr ) numbers of patients per hospital for the mortality and unplanned readmission measures were 149 ( 226 ) and 158 ( 262 ) ; the median ( iqr ) number of adverse events for which patients were at risk was 59 ( 29 ) . hospital characteristics ami indicates acute myocardial infarction ; iqr , interquartile range ; mpsms , medicare patient safety monitoring system . hospitals ( n=793 ) had at least 30 patients for mortality and readmission measures and had at least 40 adverse events for which patients were at risk . for the occurrence of adverse events , mortality , and readmission , respectively , the ranges ( minimum to maximum ) of riskstandardized rates were 10.3% to 19.9% , 14.3% to 21.8% , and 1.4% to 19.3% . hospitalspecific riskstandardized occurrence rate of adverse events was widely dispersed across hospitals ( figure  2 , right panel ) . the mean hospitalspecific mortality and readmission rates were 14.7% and 18.0% , respectively ( figure  2 , left and middle panels ) . box  and whisker plots of the distributions of hospitalspecific 30day allcause riskstandardized mortality and unplanned readmission rates and hospitalspecific riskstandardized occurrence rates of adverse events for which patients were at risk . the length of the box represents the interquartile range ( iqr ) , the horizontal line in the box interior represents the median , the whiskers represent the 1.5 iqr of the 25th quartile or 1.5 iqr of the 75th quartile , and the dots represent outliers . for mortality , unplanned readmission , and adverse events , respectively , the ranges ( minimum to maximum ) of riskstandardized rates were 10.3% to 19.9% , 14.3% to 21.8% , and 1.4% to 19.3% . table  5 , which is at the patient level , shows the number of patients and adverse event rates for each of the 21 adverse measures as well as the 2 composite adverse event rates measures by the tertiles of hospital 's riskstandardized occurrence rate of adverse events for which patients were at risk . the number of adverse events for which patients were at risk per hospitalization varied by patient characteristics and ranged from 2 to 18 . on average , patients were at risk for 7.4 events per hospitalization . hospitals with a high riskstandardized occurrence rate of adverse events usually had more patients at risk . for the first , second , and third tertiles , respectively , patients were at risk for 6.7 , 7.3 , and 8.3 events per hospitalization ( table  5 ) . nevertheless , 32.0% of hospitals in the highest tertile of adverse events for which patients were at risk were in the lowest tertile of riskstandardized occurrence rate of adverse events . conversely , 25.8% of hospitals in the lowest tertile of patients at risk had a higher rate of adverse events ( figure  3 ) . hospital 's riskstandardized occurrence rate of adverse events for which patients were at risk by tertiles , july 1 , 2009 to june 30 , 2013 ( aged 65  years or older , medicare feeforservice patients discharged with acute myocardial infarction ) n / a indicates not applicable . the numerator is the number of adverse events that occurred and the denominator is the number of adverse events for which patients were at risk . the numerator is the number of patients who experienced one or more adverse events and the denominator is the number of hospitalizations . riskstandardized occurrence rates of adverse events by tertile versus hospitalspecific total number of adverse events for which patients were at risk by tertile . of the 793 hospitals , the overall occurrence rate of adverse events , in which the numerator is the number of adverse events that occurred and the denominator is the number of adverse events for which patients were at risk , was 3.8% . the overall rate of patients with 1 or more adverse events per hospitalization , in which the numerator is the number of patients who experienced 1 or more adverse events and the denominator is the number of patients in the mpsms data , was 20.1% ( tables  2 and 5 ) . a change of 1% point in the occurrence rate of adverse events for which patients were at risk was associated with an average change of 4.8% points ( 95% ci , 4.74.9 ) in the rate of patients with 1 or more adverse events per hospitalization ( figure  4 ) . for teaching and nonteaching hospital groups , respectively , the  means ( sd ) of riskstandardized hospitalspecific rate of  occurrence of adverse events were 5.3 ( 18.7 ) and 4.1 ( 19.2 ) . relationship between the observed rate of patients had 1 or more adverse events and occurrence rate of adverse events for which patients were at risk . the odds of an adverse event occurring when treated at a hospital 1  sd above the overall average relative to the odds of an adverse event occurring when treated at a hospital 1  sd below the overall average were 3.86 ( 95% ci , 3.624.12 ) . the hospital characteristicadjusted associations between the observed occurrence rate of adverse events and riskstandardized mortality and readmission rates were statistically significant ( figure  5 , top panel ) . the hospital characteristicadjusted associations between the riskstandardized occurrence rate of adverse events and riskstandardized mortality and readmission rates were also statistically significant ( figure  5 , bottom panel ) . a change of 1% point in the riskstandardized occurrence rate of adverse events was associated with an average change in the riskstandardized mortality rate of 4.86% points ( 95% ci , 0.798.94 ) and an average change in the riskstandardized readmission rate of 3.44% points ( 95% ci , 0.196.68 ) . because the overall occurrence rate of adverse events was 3.8% , a 1% point change in this rate translates to a relative change of 26.3% . the second analysis , which included all hospitals regardless of their volume of adverse events for which patients were at risk , did not change the results substantially ( figure  6 ) . a change of 1% point in the riskstandardized occurrence rate of adverse events was associated with average changes in the riskstandardized mortality rate of 5.13% points ( 95% ci , 2.238.03 ) and riskstandardized readmission rate of 2.51% points ( 95% ci , 0.214.82 ) . point estimates and 95% cis of the associations between the hospitalspecific riskstandardized 30day mortality rate and hospitalspecific riskstandardized occurrence rate of adverse events and hospitalspecific riskstandardized 30day unplanned readmission rate and hospitalspecific riskstandardized occurrence rate of adverse events . hospital characteristics included in the adjusted models were teaching status ( teaching vs nonteaching ) ; joint commission certification status ( yes / no ) ; geographical location ( urban vs rural ) ; ownership ( private notforprofit vs others ) ; bed size ; nursetopatient ratio ; perform cardiac catheterization and/or percutaneous coronary intervention procedures ( yes / no ) ; and perform coronary artery bypass graft surgery ( yes / no ) . modela models the riskstandardized mortality or unplanned readmission rate as a function of hospitalspecific observed occurrence rate of adverse events ; modelb models the riskstandardized mortality or unplanned readmission rate as a function of hospitalspecific riskstandardized occurrence rate of adverse events . point estimates and 95% cis of the associations between hospital performance on mortality and unplanned readmission rates and hospital performance on the occurrence rate of adverse events , regardless of hospitals volume of adverse events for which patients were at risk . in this study , we found that hospital performance on patient safety , measured by the hospitalspecific riskstandardized occurrence rate of adverse events for which patients were at risk , was associated with hospitalspecific riskstandardized 30day allcause mortality and unplanned readmission rates for patients with ami . adverse event rates may be a marker of overall hospital quality , including patient safety culture , which is increasingly being recognized as associated with patient outcomes.39 , 40 , 41 , 42 , 43 it is possible that some of the interventions to reduce the risk of adverse events could also improve other patient outcomes . it is also possible that changes made at the hospital level that reduce mortality and unplanned readmissions may also improve patient safety performance , as indicated by fewer adverse events.29 , 44 , 45 furthermore , patients who experience 1 or more adverse events may be at higher risk for mortality and readmission , specifically attributed to harm caused by the adverse event.19 , 27 , 28 , 29 , 30 , 31 , 32 
  our study , based on medical recordabstracted patient safety information , represents a unique , large , and recent investigation of the associations between hospital performance on patient safety and mortality and readmissions for medicare patients with ami in the united states . previous studies focused on the association between adverse events and mortality or readmission rates at the patient level . this study , however , extends that association to the hospital level by linking hospital performance on patient safety to hospital performance on mortality and readmission rates . in a patientlevel analysis , for example , lyder et  al.27 found that patients who developed pressure ulcers during their hospitalizations had higher 30day mortality and readmission rates . friedman et  al.28 studied 1.5  million surgical care patients across 1088 hospitals and found that the 30day readmission rate was 5% higher for patients with 1 or more inhospital adverse events when compared with those with no adverse events . vorhies et  al.30 reported that adverse events were associated with increased readmissions for medicare feeforservice patients with hip replacements . we chose to study the hospital level because it is the most actionable , given that improvement efforts require system changes beyond the reach of the individual practitioner and patient . although the literature has emphasized patient socioeconomic influences on readmissions , our findings suggest that hospital specific factors also play a role . getting hospitals to invest in those improvement efforts is a major undertaking . by showing an association between patient safety and mortality and readmissions for ami patients at the hospital level although our findings do not establish a causal relationship , they suggest that patient safety improvement may be afforded a place among other common strategies to reduce mortality and unplanned readmissions . further research is warranted to determine whether such improvement measurably reduces mortality and unplanned readmissions for other conditions . although we found that hospitals with a higher volume of patients at risk were more likely to have a higher adverse event rate , not all hospitals with a higher volume of patients at risk had a higher rate of adverse events . in our study , 32% of hospitals with a higher volume of adverse events for which patients were at risk had a lower riskstandardized occurrence rate of adverse events , and 26% of hospitals with a lower volume of patients at risk had a higher rate of adverse events . thus , regardless of the hospital 's volume of patients at risk , hospitals with a higher adverse event rate have room to improve their care . for example , the overall patient atrisk rate for the catheterassociated urinary tract infection indicator was 35% , but we can reduce both the number of adverse events and the number of patients at risk for this indicator by not placing a urinary catheter unless absolutely necessary .  a higher volume of patients at risk , may actually indicate overuse of certain health care interventions , and not necessarily reflect casemix differences . having more opportunities to harm is not an adequate rationale to harm more , but should be the impetus to harm less . care.1 our study illustrates that variation exists in the risk of adverse events across acute care hospitals in the united states . reducing such variation by focusing on patient safety interventions may favorably impact downstream outcomes , such as mortality and unplanned readmissions . because the occurrence rate of adverse events was 4% and the rate of patients with 1 or more adverse events was 20% for medicare patients discharged with ami , an absolute 1% point decrease in the occurrence rate of adverse events is equal to a relative decline of 25% in that rate itself , or an absolute 5% point reduction in the rate of patients with 1 or more adverse events . ample opportunities for reducing inhospital adverse events exist , including promoting transparency and facilitating open discussion to prevent recurring errors,46 implementing and meaningfully using electronic health records,47 adopting and implementing evidencebased patient safety strategies,48 and , most important , enhancing patient safety culture.49 
  our study has several limitations . we focused on adverse events that were both detected and documented during the index hospitalization and were unable to identify events that occurred but were not documented . restricted by the sample size of the mpsms data , we were not able to determine whether some of the adverse events have stronger relationships with mortality or unplanned readmission than others . restricted by the cms mortality and readmission data , only medicare feeforservice beneficiaries were included in this study , who were older and more female than the general population of patients with ami . limited by the scope of the mpsms data , our model to calculate the riskstandardized occurrence rate of adverse events might not fully account for patient characteristics . though it is possible that some proportion of the adverse events detected in mpsms data were not preventable , each of these 21 adverse event measures is characterized as being frequently preventable with the delivery of highquality care . still , this study distinguishes itself by the breadth and standardization of events measured and its national scope . for medicare feeforservice patients discharged with ami , hospitals with poorer patient safety performance tended to have poorer performance on 30day allcause mortality and unplanned readmission rates . this strengthens the evidence that mortality and unplanned readmissions reflect the quality of hospital care . this work was supported by contract hhsa290201200003c from the agency for healthcare research and quality , us department of health and human services ( rockville , md ) . dr krumholz is partially funded by grant 1u01hl10527002 ( krumholz , center for cardiovascular outcomes research at yale university ) from the national heart , lung , and blood institute . dr normand is partially supported by a grant ( r01 gm111339 , normand ) from the national institutes of health , and dr wang is partially supported by the us environmental protection agency ( rd83490001 , dominici ) , national institutes of health ( r21 es02258501 , dominici ; r21 es024012 , zanobetti ; r01 gm111339 , normand ; r01 es024332 , zanobetti ) , and the agency for healthcare research and quality ( k18 hs021991 , dominici ) . dr krumholz is the recipient of research grants from medtronic and from johnson & johnson , through yale university , to develop methods of clinical trial data sharing , and chairs a cardiac scientific advisory board for unitedhealth . drs krumholz and normand work under contract to the centers for medicare & medicaid services to develop and maintain performance measures .
backgroundlittle is known regarding the relationship between hospital performance on adverse event rates and hospital performance on 30day mortality and unplanned readmission rates for medicare feeforservice patients hospitalized for acute myocardial infarction ( ami).methods and resultsusing 20092013 medical recordabstracted patient safety data from the agency for healthcare research and quality 's medicare patient safety monitoring system and hospital mortality and readmission data from the centers for medicare & medicaid services , we fitted a mixedeffects model , adjusting for hospital characteristics , to evaluate whether hospital performance on patient safety , as measured by the hospitalspecific riskstandardized occurrence rate of 21 common adverse event measures for which patients were at risk , is associated with hospitalspecific 30day allcause riskstandardized mortality and unplanned readmission rates for medicare patients with ami . 
 the unit of analysis was at the hospital level . 
 the final sample included 793 acute care hospitals that treated 30 or more medicare patients hospitalized for ami and had 40 or more adverse events for which patients were at risk . 
 the occurrence rate of adverse events for which patients were at risk was 3.8% . 
 a 1% point change in the riskstandardized occurrence rate of adverse events was associated with average changes in the same direction of 4.86% points ( 95% ci , 0.798.94 ) and 3.44% points ( 95% ci , 0.196.68 ) for the riskstandardized mortality and unplanned readmission rates , respectively.conclusionsfor medicare feeforservice patients discharged with ami , hospitals with poorer patient safety performance were also more likely to have poorer performance on 30day allcause mortality and on unplanned readmissions .