When I was four years old, Grandpa always cut off the crust of the bread before I ate my peanut butter and jelly sandwich.

When I was seven years old, Grandpa took me to the circus and bought me cotton candy. He didn't care when I got the sticky stuff all over my face and dress.

When I was nine years old, Grandpa took me out on my birthday for a chocolate ice cream cone with rainbow sprinkles on top.

I didn't know he had high blood pressure. And neither did he.

He made me laugh. He made me feel so good deep down inside.

At age eleven I returned home from school to find Grandpa had been taken to the hospital with a stroke.

I cut the crust off his bread, got him cotton candy and an ice cream cone so he would feel better.

I went with Mommy to see him. She was stopped at the nurses' station. They wanted to talk to her.

I broke away and ran down the hall to his room. His bed was empty. Grandpa had died. No one told me.

Grandpa never got to eat the peanut butter and jelly sandwich with the crust cut off.

Maybe if he had, things would have turned out differently.

When I was four years old, Grandpa always cut off the crust of the bread before I ate my peanut butter and jelly sandwich.

When I was seven years old, Grandpa took me to the circus and bought me cotton candy. He didn't care when I got the sticky stuff all over my face and dress.

When I was nine years old, Grandpa took me out on my birthday for a chocolate ice cream cone with rainbow sprinkles on top.

I didn't know he had high blood pressure. And neither did he.

He made me laugh. He made me feel so good deep down inside.

At age eleven I returned home from school to find Grandpa had been taken to the hospital with a stroke.

I cut the crust off his bread, got him cotton candy and an ice cream cone so he would feel better.

I went with Mommy to see him. She was stopped at the nurses' station. They wanted to talk to her.

I broke away and ran down the hall to his room. His bed was empty. Grandpa had died. No one told me.

Grandpa never got to eat the peanut butter and jelly sandwich with the crust cut off.

Maybe if he had, things would have turned out differently.

When I was four years old, Grandpa always cut off the crust of the bread before I ate my peanut butter and jelly sandwich.

When I was seven years old, Grandpa took me to the circus and bought me cotton candy. He didn't care when I got the sticky stuff all over my face and dress.

When I was nine years old, Grandpa took me out on my birthday for a chocolate ice cream cone with rainbow sprinkles on top.

I didn't know he had high blood pressure. And neither did he.

He made me laugh. He made me feel so good deep down inside.

At age eleven I returned home from school to find Grandpa had been taken to the hospital with a stroke.

I cut the crust off his bread, got him cotton candy and an ice cream cone so he would feel better.

I went with Mommy to see him. She was stopped at the nurses' station. They wanted to talk to her.

I broke away and ran down the hall to his room. His bed was empty. Grandpa had died. No one told me.

Grandpa never got to eat the peanut butter and jelly sandwich with the crust cut off.

Maybe if he had, things would have turned out differently.

The most seriously ill medical patients are often admitted to an academic institution and taken care of on a teaching service.14 Previously published reports have found that, despite substantial differences in case mix, being admitted to a teaching hospital is associated with reduced morbidity and risk‐adjusted mortality for various conditions compared with receiving care delivered at a nonacademic hospital.2, 513 For example, among 248 major teaching, minor teaching, and nonteaching hospitals in New York state, Polanczyk et al. found that major teaching hospital status was an important determinant of outcomes in patients hospitalized with myocardial infarction, heart failure, or stroke.1

Some studies have noted that the high cost of care at teaching hospitals may offset these potential benefits.1, 6, 12, 13 In a retrospective analysis of 2674 Medicare patients, Taylor et al. determined that adjusted mortality rates were usually lower and Medicare payments usually higher in major teaching hospitals than in for‐profit hospitals.13 However, in a study of 80,851 patients admitted to 39 hospitals in northeastern Ohio, Rosenthal et al. reported both lower hospital mortality and shorter length of hospital stay (LOS) of patients admitted to major teaching hospitals than of patients admitted to nonteaching hospitals.12

Understanding the differences in economic and clinical outcomes between teaching and nonteaching medical services is topical in today's health care environment. Comparisons across institutions are inherently cumbersome because of the number of variables, other than teaching status, that can potentially contribute to differences in outcomes. A study comparing teaching and nonteaching services within a single institution could provide results unencumbered by such confounding factors. Accordingly, we sought to compare the teaching service with the nonteaching service at our academic community hospital to see if there were notable differences between the 2 services in case mix, costs, and clinical outcomes.

PATIENTS AND METHODS

Our analysis was based on administrative data for 2189 patients who were admitted to a 450‐bed university‐affiliated community hospital from February through October 2002 and assigned to 1 of the 3 teaching services staffed by residents in internal medicine and a faculty attending (n = 1637) or to a nonteaching service staffed by hospitalists or clinic‐based internists (n = 552).

Care on the nonteaching service was provided by 4 hospitalists and 12 clinic‐based internists. The nonteaching service generally had no interns or residents but occasionally had a third‐ or fourth‐year medical student on rotation. Care on the teaching services was provided under the supervision of 5 hospitalists and 18 clinic‐based internists. The day‐to‐day clinical decisions on the teaching services were made by the upper‐level resident (PGY‐2 or ‐3) assigned to the particular service, with the attending physicians acting in a supervisory role. Four of the 5 hospitalists rotated between nonteaching and teaching services. Cross‐coverage for teaching services was provided by other residents (by a night float team that rotated monthly), whereas a night attending only provided coverage for the nonteaching service. Patient handoffs occurred more commonly on the nonteaching service, where attendings rotated every 1‐2 weeks compared with the teaching services, where interns and residents rotated monthly and attendings changed every 2‐4 weeks.

All admissions to the medical services were screened and approved by either the chief medical resident or a designated faculty member who carried the departmental admission pager. Patients were randomly allocated to the respective teams based on patient load, in accordance with ACGME‐ and residency programimposed limits, rather than according to patient diagnoses. Differences between groups in severity of illness were minimized by limiting levels of acuity and including only patients admitted to the medical ward and not to the intensive care, coronary care, or intermediate care units. Patients on both model services were admitted to geographically shared wards with the same nursing staff and other ancillary personnel. All residents and faculty had similar access to hospital resources such as academic meetings, clinical protocols, practice‐based guidelines, and quality improvement initiatives.

The main outcome measures were total hospital costs; LOS; hospital readmission within 30 days; in‐hospital mortality; number of tests and procedures ordered; and pharmacy, laboratory, radiology, and procedural costs and costs for physical, speech, occupational, and respiratory therapy consultations. Financial data for patient care excluding physician fees were based on actual direct and indirect costs and were estimated using an activity‐based system (Transition Systems, Inc., Eclypsis Corporation, Boca Raton, FL). Department‐specific costs represented actual variable costs and did not include indirect (overhead) costs. Hospital length of stay was defined as the number of days from the time a patient was admitted to the general medicine service to the day discharged from the hospital, even if the patient was transferred to another service before discharge. Hospital readmission for the same primary diagnosis within 30 days after discharge was used to compare the quality of care on the 2 types of services.

We assessed the case mix on the 2 services by comparing the distribution of the 10 most frequent diagnosis‐related groups (DRGs) in the data set, plus angina, arrhythmia, and hypertension combined into a single category (Table 1). The chi‐square test was used to test differences between the 2 services in the proportion of each DRG. To obtain a surrogate index for case severity, the list of coexisting or comorbid conditions present at the time of admission was used to calculate the mean number of comorbidities per patient. The morbidity experience of the 2 patient populations was compared using the Student t test for 2 independent samples.

We compared the main outcome measures for teaching and nonteaching services using 3 analytic methods. First, the crude difference in total costs, service‐ and diagnosis‐specific costs, and length of hospital stay and the unadjusted odds ratio for readmission, in‐hospital mortality, and services ordered were calculated. The Student t test for 2 independent samples was used to compare total cost, LOS, and DRG‐specific and service‐specific costs. The chi‐square test was used to compare readmission rate, in‐hospital mortality, and number of services ordered. Second, we used multiple linear regression and logistic regression analyses to estimate the difference in the main outcome measures of the 2 medical services, adjusted for age, sex, insurance classification, number of comorbidities, and primary DRGs. The Wald test was used to obtain P values for testing differences between teaching and nonteaching services.

In observational studies, multiple linear regression models are commonly used to remove the effects of confounding factors. However, regression methods do not ensure the balance in the distribution of covariates, and imbalance becomes more problematic as the number of covariates increases. To manage the imbalance of case mix and other potential confounders, we used a propensity score method to balance confounding variables between the 2 groups.17 Specifically, by performing logistic regression with the potential confounding variables as covariates, we estimated the propensity score or the probability of being assigned to the teaching services for each patient (Tables 2 and 3). The collection of multiple characteristics was collapsed into a single composite score called the propensity score, and this score was used as if it were the only confounding variable. Patients were stratified to quintiles based on their propensity score, and the balance of the distribution of each potential confounder in the 5 propensity strata was checked, and we estimated the overall difference between the 2 medical services with the weighted average of the strata‐specific difference, where the weights were proportional to the stratum size. The Z test was used to derive P values for comparing the total hospital costs, LOS, and service‐specific costs of the 2 medical services. The Mantel‐Haenszel test was used to determine whether the 2 medical services had the same risk of readmission, death, and frequency of diagnostic or consultation services ordered. In all analyses we report P values without adjusting for multiple comparisons. The significance level of hypothesis testing was set at .05.

RESULTS

The study consisted of 2189 patients (1036 men) whose mean age was 67.2 years (SD = 19.0 years). Patient demographics and frequencies of various DRGs on the 2 services are shown in Table 1. The distribution of insurance classifications (eg, third‐party payer, Medicare, Medicaid, private pay) wase comparable between teaching and nonteaching groups. No statistically significant differences between the 2 services in patient characteristics and distribution of the 10 most common DRGs in the data set were observed except for patients with metabolic disorders (P = .01) and other respiratory infections (P = .03). The mean number of comorbidities was also comparable between teaching and nonteaching services (6.7 vs. 6.7; P = .99).

Care on the teaching service was not associated with a significant increase in overall costs per patient ($5572 vs. $5576, P = .99). Crude comparison of other main outcome measures showed that the LOS (4.92 vs. 5.10 days; P = .43), odds of readmission within 30 days (202/1637 vs. 57/552; P = .21), and odds of in‐hospital mortality (61/1637 vs. 25/552; P = .40) were comparable for teaching and nonteaching services (Tables 2 and 3). Using multiple linear regression analysis, the estimated adjusted differences were only $61 (P = .84) in overall costs and 0.13 days (P = .54) in LOS between teaching and nonteaching services. Estimated adjusted risk of readmission within 30 days was 25% higher (P = .17), and in‐hospital mortality was 24% lower (P = .28) for patients treated on the medical teaching services. Using the propensity score method, the estimated difference between teaching and nonteaching services was $130 (P = .70) in overall costs and 0.08 days (P = .73) in length of stay. Risk of readmission within 30 days was 26% higher (P = .15), and in‐hospital mortality was 18% lower (P = .41) for the teaching service. Because the distributions of overall costs and length of stay were heavily skewed, we also performed statistical analyses using logarithm‐transformed data on these 2 outcomes. The results using all 4 analytic methods showed that care on the teaching services was not associated with statistically significant differences in total hospital costs, LOS, risk of readmission, and in‐hospital mortality.

Service‐specific cost analyses showed that mean laboratory costs per patient ($937 vs. $810; P = .02) and mean radiology costs per patient ($134 vs. $96; P = .01) were higher for teaching services, whereas costs for the pharmacy ($233 vs. $229; P = .85) and for speech therapy ($2.4 vs. $2.4; P = .95), physical therapy ($6.6 vs. $7.2; P = .52), occupational therapy ($3.9 vs. $3.4; P = .43), respiratory therapy ($46 vs. $41; P = .42), pulmonary function testing ($0.4 vs. $0.4; P = .99), and GI endoscopy procedures ($5.9 vs. $5.8; P = .94) were not significantly different. A comparison of the number of consults or tests ordered indicated physicians on the teaching service did not order more radiology (1411/1637 vs. 471/552; P = .61), speech therapy (128/1637 vs. 37/552; P = .39), physical therapy (611/1637 vs. 207/552; P = .94), occupational therapy (369/1637 vs. 109/552; P = .17), respiratory therapy (893/1637 vs. 283/552; P = .19), or pulmonary function testing (75/1637 vs. 27/552; P = .89) consultations or GI endoscopy procedures (188/1637 vs. 65/552; P = .18). Inferential results derived by multiple linear regression and logistic regression analyses, as well as the propensity score method, all agreed with the results derived using crude comparisons and concluded that, except for laboratory and radiology costs, patients treated on the teaching services did not have higher service‐specific costs or more therapies and consultations.

To remove the potential confounding effects of the 5 hospitalists who rotated between teaching and nonteaching services, we removed 875 patients (125 on the nonteaching and 750 on the teaching service) from the original data set who were cared for by these physicians, and repeated crude, multivariate, and propensity score analyses. In the data subset (Tables 4 and 5), laboratory costs remained higher on the teaching service, but the difference in radiology costs between teaching and nonteaching services seen in the total data set diminished and did not remain statistically significant when hospitalists were excluded from the analysis.

DISCUSSION

We found that care delivered on the resident‐based teaching services at our academic community hospital was not associated with increases in overall costs, pharmacy costs, or consultative services ordered, although laboratory‐related costs and radiology costs were slightly higher than for the nonteaching service. In addition, clinical outcomes were not significantly different between teaching and nonteaching services in terms of hospital length of stay, in‐hospital mortality, and 30‐day readmission rate.

Several previous interinstitutional studies have documented greater utilization of resources at academic medical centers as a tradeoff for improved clinical outcomes.2, 4, 12, 13 One frequently offered explanation for higher costs at teaching hospitals is the purported tendency of resident physicians to order more tests and consults and to more heavily rely on modern diagnostic and therapeutic modalities. Apart from the number of tests and procedures ordered, differences in administrative, personnel, and other nonshared costs may account for higher overall costs at teaching hospitals reported in earlier studies. These variables, however, did not differ in our comparison of teaching and nonteaching services within the same institution because they were equally shared.

Studies that have looked at the hospitalist experience at academic centers and community hospitals have demonstrated improved efficiency associated with the use of hospitalist physicians.1517 At the University of Chicago, hospitalist care was associated with lower costs and short‐term mortality in the second year of hospitalist experience.15, 16 The authors suggested that disease‐specific physician experience in the hospitalist model may lead to reduced resource consumption and improved patient outcomes. The focus of our study was not a comparison of hospitalist with nonhospitalist models. However, when we excluded patients cared for by hospitalist physicians from our costs, services, and outcomes analyses, laboratory costs remained the only significant difference between teaching and nonteaching services.

Other than teaching hospital status and use of hospitalist physicians, institutional characteristics that can potentially influence clinical outcomes include hospital size, location, ownership, case mix, access to on‐site specialized diagnostic and therapeutic equipment, and availability of specialty services.15, 16 However, all these variables were identical in our study of teaching versus nonteaching services within the same community hospital, thereby allowing an uncontaminated estimation of the effect of teaching status on resource utilization and clinical outcomes. Although both teaching and nonteaching services were sometimes headed by attendings who participated in both models, teaching services differed notably in being run by resident team leaders with attendings performing a largely supervisory role.

We recognize several limitations of our study. Patients were quasirandomly triaged to teaching and nonteaching services according to patient loads without any consideration for diagnoses, comorbidities, or severity of illness. Therefore, it is quite possible there were unascertainable differences in disease severity and case mix between the teaching and nonteaching services. Notably, there was some discordance in the number of patients with nonpneumonia respiratory infection and the number with metabolic disorders assigned between the 2 services. However, 8 of the 10 most common primary diagnoses in the data set were similarly distributed between the 2 services, and the mean number of secondary diagnoses per patient was also not statistically different. More importantl we employed multiple regression analysis and a propensity score method to account for any imbalance in case mix and other potential confounders such as sex, age, and insurance classifications. These advanced statistical methods produced results similar to the unadjusted method and, hence, strengthen our conclusion that care delivered on the resident‐based teaching services at our academic community hospital was not significantly associated with increases in overall patient care costs, LOS, readmission rate, or in‐hospital mortality. Having hospitalist physicians on both teaching and nonteaching services may have had some effect on the practice patterns of other physicians, creating greater similarities than might have been expected otherwise. Data used in this study were obtained from only 1 academic institution, and caution should be exercised in extrapolating our findings to other settings unless substantiated by other studies.

The most seriously ill medical patients are often admitted to an academic institution and taken care of on a teaching service.14 Previously published reports have found that, despite substantial differences in case mix, being admitted to a teaching hospital is associated with reduced morbidity and risk‐adjusted mortality for various conditions compared with receiving care delivered at a nonacademic hospital.2, 513 For example, among 248 major teaching, minor teaching, and nonteaching hospitals in New York state, Polanczyk et al. found that major teaching hospital status was an important determinant of outcomes in patients hospitalized with myocardial infarction, heart failure, or stroke.1

Some studies have noted that the high cost of care at teaching hospitals may offset these potential benefits.1, 6, 12, 13 In a retrospective analysis of 2674 Medicare patients, Taylor et al. determined that adjusted mortality rates were usually lower and Medicare payments usually higher in major teaching hospitals than in for‐profit hospitals.13 However, in a study of 80,851 patients admitted to 39 hospitals in northeastern Ohio, Rosenthal et al. reported both lower hospital mortality and shorter length of hospital stay (LOS) of patients admitted to major teaching hospitals than of patients admitted to nonteaching hospitals.12

Understanding the differences in economic and clinical outcomes between teaching and nonteaching medical services is topical in today's health care environment. Comparisons across institutions are inherently cumbersome because of the number of variables, other than teaching status, that can potentially contribute to differences in outcomes. A study comparing teaching and nonteaching services within a single institution could provide results unencumbered by such confounding factors. Accordingly, we sought to compare the teaching service with the nonteaching service at our academic community hospital to see if there were notable differences between the 2 services in case mix, costs, and clinical outcomes.

PATIENTS AND METHODS

Our analysis was based on administrative data for 2189 patients who were admitted to a 450‐bed university‐affiliated community hospital from February through October 2002 and assigned to 1 of the 3 teaching services staffed by residents in internal medicine and a faculty attending (n = 1637) or to a nonteaching service staffed by hospitalists or clinic‐based internists (n = 552).

Care on the nonteaching service was provided by 4 hospitalists and 12 clinic‐based internists. The nonteaching service generally had no interns or residents but occasionally had a third‐ or fourth‐year medical student on rotation. Care on the teaching services was provided under the supervision of 5 hospitalists and 18 clinic‐based internists. The day‐to‐day clinical decisions on the teaching services were made by the upper‐level resident (PGY‐2 or ‐3) assigned to the particular service, with the attending physicians acting in a supervisory role. Four of the 5 hospitalists rotated between nonteaching and teaching services. Cross‐coverage for teaching services was provided by other residents (by a night float team that rotated monthly), whereas a night attending only provided coverage for the nonteaching service. Patient handoffs occurred more commonly on the nonteaching service, where attendings rotated every 1‐2 weeks compared with the teaching services, where interns and residents rotated monthly and attendings changed every 2‐4 weeks.

All admissions to the medical services were screened and approved by either the chief medical resident or a designated faculty member who carried the departmental admission pager. Patients were randomly allocated to the respective teams based on patient load, in accordance with ACGME‐ and residency programimposed limits, rather than according to patient diagnoses. Differences between groups in severity of illness were minimized by limiting levels of acuity and including only patients admitted to the medical ward and not to the intensive care, coronary care, or intermediate care units. Patients on both model services were admitted to geographically shared wards with the same nursing staff and other ancillary personnel. All residents and faculty had similar access to hospital resources such as academic meetings, clinical protocols, practice‐based guidelines, and quality improvement initiatives.

The main outcome measures were total hospital costs; LOS; hospital readmission within 30 days; in‐hospital mortality; number of tests and procedures ordered; and pharmacy, laboratory, radiology, and procedural costs and costs for physical, speech, occupational, and respiratory therapy consultations. Financial data for patient care excluding physician fees were based on actual direct and indirect costs and were estimated using an activity‐based system (Transition Systems, Inc., Eclypsis Corporation, Boca Raton, FL). Department‐specific costs represented actual variable costs and did not include indirect (overhead) costs. Hospital length of stay was defined as the number of days from the time a patient was admitted to the general medicine service to the day discharged from the hospital, even if the patient was transferred to another service before discharge. Hospital readmission for the same primary diagnosis within 30 days after discharge was used to compare the quality of care on the 2 types of services.

We assessed the case mix on the 2 services by comparing the distribution of the 10 most frequent diagnosis‐related groups (DRGs) in the data set, plus angina, arrhythmia, and hypertension combined into a single category (Table 1). The chi‐square test was used to test differences between the 2 services in the proportion of each DRG. To obtain a surrogate index for case severity, the list of coexisting or comorbid conditions present at the time of admission was used to calculate the mean number of comorbidities per patient. The morbidity experience of the 2 patient populations was compared using the Student t test for 2 independent samples.

We compared the main outcome measures for teaching and nonteaching services using 3 analytic methods. First, the crude difference in total costs, service‐ and diagnosis‐specific costs, and length of hospital stay and the unadjusted odds ratio for readmission, in‐hospital mortality, and services ordered were calculated. The Student t test for 2 independent samples was used to compare total cost, LOS, and DRG‐specific and service‐specific costs. The chi‐square test was used to compare readmission rate, in‐hospital mortality, and number of services ordered. Second, we used multiple linear regression and logistic regression analyses to estimate the difference in the main outcome measures of the 2 medical services, adjusted for age, sex, insurance classification, number of comorbidities, and primary DRGs. The Wald test was used to obtain P values for testing differences between teaching and nonteaching services.

In observational studies, multiple linear regression models are commonly used to remove the effects of confounding factors. However, regression methods do not ensure the balance in the distribution of covariates, and imbalance becomes more problematic as the number of covariates increases. To manage the imbalance of case mix and other potential confounders, we used a propensity score method to balance confounding variables between the 2 groups.17 Specifically, by performing logistic regression with the potential confounding variables as covariates, we estimated the propensity score or the probability of being assigned to the teaching services for each patient (Tables 2 and 3). The collection of multiple characteristics was collapsed into a single composite score called the propensity score, and this score was used as if it were the only confounding variable. Patients were stratified to quintiles based on their propensity score, and the balance of the distribution of each potential confounder in the 5 propensity strata was checked, and we estimated the overall difference between the 2 medical services with the weighted average of the strata‐specific difference, where the weights were proportional to the stratum size. The Z test was used to derive P values for comparing the total hospital costs, LOS, and service‐specific costs of the 2 medical services. The Mantel‐Haenszel test was used to determine whether the 2 medical services had the same risk of readmission, death, and frequency of diagnostic or consultation services ordered. In all analyses we report P values without adjusting for multiple comparisons. The significance level of hypothesis testing was set at .05.

RESULTS

The study consisted of 2189 patients (1036 men) whose mean age was 67.2 years (SD = 19.0 years). Patient demographics and frequencies of various DRGs on the 2 services are shown in Table 1. The distribution of insurance classifications (eg, third‐party payer, Medicare, Medicaid, private pay) wase comparable between teaching and nonteaching groups. No statistically significant differences between the 2 services in patient characteristics and distribution of the 10 most common DRGs in the data set were observed except for patients with metabolic disorders (P = .01) and other respiratory infections (P = .03). The mean number of comorbidities was also comparable between teaching and nonteaching services (6.7 vs. 6.7; P = .99).

Care on the teaching service was not associated with a significant increase in overall costs per patient ($5572 vs. $5576, P = .99). Crude comparison of other main outcome measures showed that the LOS (4.92 vs. 5.10 days; P = .43), odds of readmission within 30 days (202/1637 vs. 57/552; P = .21), and odds of in‐hospital mortality (61/1637 vs. 25/552; P = .40) were comparable for teaching and nonteaching services (Tables 2 and 3). Using multiple linear regression analysis, the estimated adjusted differences were only $61 (P = .84) in overall costs and 0.13 days (P = .54) in LOS between teaching and nonteaching services. Estimated adjusted risk of readmission within 30 days was 25% higher (P = .17), and in‐hospital mortality was 24% lower (P = .28) for patients treated on the medical teaching services. Using the propensity score method, the estimated difference between teaching and nonteaching services was $130 (P = .70) in overall costs and 0.08 days (P = .73) in length of stay. Risk of readmission within 30 days was 26% higher (P = .15), and in‐hospital mortality was 18% lower (P = .41) for the teaching service. Because the distributions of overall costs and length of stay were heavily skewed, we also performed statistical analyses using logarithm‐transformed data on these 2 outcomes. The results using all 4 analytic methods showed that care on the teaching services was not associated with statistically significant differences in total hospital costs, LOS, risk of readmission, and in‐hospital mortality.

Service‐specific cost analyses showed that mean laboratory costs per patient ($937 vs. $810; P = .02) and mean radiology costs per patient ($134 vs. $96; P = .01) were higher for teaching services, whereas costs for the pharmacy ($233 vs. $229; P = .85) and for speech therapy ($2.4 vs. $2.4; P = .95), physical therapy ($6.6 vs. $7.2; P = .52), occupational therapy ($3.9 vs. $3.4; P = .43), respiratory therapy ($46 vs. $41; P = .42), pulmonary function testing ($0.4 vs. $0.4; P = .99), and GI endoscopy procedures ($5.9 vs. $5.8; P = .94) were not significantly different. A comparison of the number of consults or tests ordered indicated physicians on the teaching service did not order more radiology (1411/1637 vs. 471/552; P = .61), speech therapy (128/1637 vs. 37/552; P = .39), physical therapy (611/1637 vs. 207/552; P = .94), occupational therapy (369/1637 vs. 109/552; P = .17), respiratory therapy (893/1637 vs. 283/552; P = .19), or pulmonary function testing (75/1637 vs. 27/552; P = .89) consultations or GI endoscopy procedures (188/1637 vs. 65/552; P = .18). Inferential results derived by multiple linear regression and logistic regression analyses, as well as the propensity score method, all agreed with the results derived using crude comparisons and concluded that, except for laboratory and radiology costs, patients treated on the teaching services did not have higher service‐specific costs or more therapies and consultations.

To remove the potential confounding effects of the 5 hospitalists who rotated between teaching and nonteaching services, we removed 875 patients (125 on the nonteaching and 750 on the teaching service) from the original data set who were cared for by these physicians, and repeated crude, multivariate, and propensity score analyses. In the data subset (Tables 4 and 5), laboratory costs remained higher on the teaching service, but the difference in radiology costs between teaching and nonteaching services seen in the total data set diminished and did not remain statistically significant when hospitalists were excluded from the analysis.

DISCUSSION

We found that care delivered on the resident‐based teaching services at our academic community hospital was not associated with increases in overall costs, pharmacy costs, or consultative services ordered, although laboratory‐related costs and radiology costs were slightly higher than for the nonteaching service. In addition, clinical outcomes were not significantly different between teaching and nonteaching services in terms of hospital length of stay, in‐hospital mortality, and 30‐day readmission rate.

Several previous interinstitutional studies have documented greater utilization of resources at academic medical centers as a tradeoff for improved clinical outcomes.2, 4, 12, 13 One frequently offered explanation for higher costs at teaching hospitals is the purported tendency of resident physicians to order more tests and consults and to more heavily rely on modern diagnostic and therapeutic modalities. Apart from the number of tests and procedures ordered, differences in administrative, personnel, and other nonshared costs may account for higher overall costs at teaching hospitals reported in earlier studies. These variables, however, did not differ in our comparison of teaching and nonteaching services within the same institution because they were equally shared.

Studies that have looked at the hospitalist experience at academic centers and community hospitals have demonstrated improved efficiency associated with the use of hospitalist physicians.1517 At the University of Chicago, hospitalist care was associated with lower costs and short‐term mortality in the second year of hospitalist experience.15, 16 The authors suggested that disease‐specific physician experience in the hospitalist model may lead to reduced resource consumption and improved patient outcomes. The focus of our study was not a comparison of hospitalist with nonhospitalist models. However, when we excluded patients cared for by hospitalist physicians from our costs, services, and outcomes analyses, laboratory costs remained the only significant difference between teaching and nonteaching services.

Other than teaching hospital status and use of hospitalist physicians, institutional characteristics that can potentially influence clinical outcomes include hospital size, location, ownership, case mix, access to on‐site specialized diagnostic and therapeutic equipment, and availability of specialty services.15, 16 However, all these variables were identical in our study of teaching versus nonteaching services within the same community hospital, thereby allowing an uncontaminated estimation of the effect of teaching status on resource utilization and clinical outcomes. Although both teaching and nonteaching services were sometimes headed by attendings who participated in both models, teaching services differed notably in being run by resident team leaders with attendings performing a largely supervisory role.

We recognize several limitations of our study. Patients were quasirandomly triaged to teaching and nonteaching services according to patient loads without any consideration for diagnoses, comorbidities, or severity of illness. Therefore, it is quite possible there were unascertainable differences in disease severity and case mix between the teaching and nonteaching services. Notably, there was some discordance in the number of patients with nonpneumonia respiratory infection and the number with metabolic disorders assigned between the 2 services. However, 8 of the 10 most common primary diagnoses in the data set were similarly distributed between the 2 services, and the mean number of secondary diagnoses per patient was also not statistically different. More importantl we employed multiple regression analysis and a propensity score method to account for any imbalance in case mix and other potential confounders such as sex, age, and insurance classifications. These advanced statistical methods produced results similar to the unadjusted method and, hence, strengthen our conclusion that care delivered on the resident‐based teaching services at our academic community hospital was not significantly associated with increases in overall patient care costs, LOS, readmission rate, or in‐hospital mortality. Having hospitalist physicians on both teaching and nonteaching services may have had some effect on the practice patterns of other physicians, creating greater similarities than might have been expected otherwise. Data used in this study were obtained from only 1 academic institution, and caution should be exercised in extrapolating our findings to other settings unless substantiated by other studies.

The most seriously ill medical patients are often admitted to an academic institution and taken care of on a teaching service.14 Previously published reports have found that, despite substantial differences in case mix, being admitted to a teaching hospital is associated with reduced morbidity and risk‐adjusted mortality for various conditions compared with receiving care delivered at a nonacademic hospital.2, 513 For example, among 248 major teaching, minor teaching, and nonteaching hospitals in New York state, Polanczyk et al. found that major teaching hospital status was an important determinant of outcomes in patients hospitalized with myocardial infarction, heart failure, or stroke.1

Some studies have noted that the high cost of care at teaching hospitals may offset these potential benefits.1, 6, 12, 13 In a retrospective analysis of 2674 Medicare patients, Taylor et al. determined that adjusted mortality rates were usually lower and Medicare payments usually higher in major teaching hospitals than in for‐profit hospitals.13 However, in a study of 80,851 patients admitted to 39 hospitals in northeastern Ohio, Rosenthal et al. reported both lower hospital mortality and shorter length of hospital stay (LOS) of patients admitted to major teaching hospitals than of patients admitted to nonteaching hospitals.12

Understanding the differences in economic and clinical outcomes between teaching and nonteaching medical services is topical in today's health care environment. Comparisons across institutions are inherently cumbersome because of the number of variables, other than teaching status, that can potentially contribute to differences in outcomes. A study comparing teaching and nonteaching services within a single institution could provide results unencumbered by such confounding factors. Accordingly, we sought to compare the teaching service with the nonteaching service at our academic community hospital to see if there were notable differences between the 2 services in case mix, costs, and clinical outcomes.

PATIENTS AND METHODS

Our analysis was based on administrative data for 2189 patients who were admitted to a 450‐bed university‐affiliated community hospital from February through October 2002 and assigned to 1 of the 3 teaching services staffed by residents in internal medicine and a faculty attending (n = 1637) or to a nonteaching service staffed by hospitalists or clinic‐based internists (n = 552).

Care on the nonteaching service was provided by 4 hospitalists and 12 clinic‐based internists. The nonteaching service generally had no interns or residents but occasionally had a third‐ or fourth‐year medical student on rotation. Care on the teaching services was provided under the supervision of 5 hospitalists and 18 clinic‐based internists. The day‐to‐day clinical decisions on the teaching services were made by the upper‐level resident (PGY‐2 or ‐3) assigned to the particular service, with the attending physicians acting in a supervisory role. Four of the 5 hospitalists rotated between nonteaching and teaching services. Cross‐coverage for teaching services was provided by other residents (by a night float team that rotated monthly), whereas a night attending only provided coverage for the nonteaching service. Patient handoffs occurred more commonly on the nonteaching service, where attendings rotated every 1‐2 weeks compared with the teaching services, where interns and residents rotated monthly and attendings changed every 2‐4 weeks.

All admissions to the medical services were screened and approved by either the chief medical resident or a designated faculty member who carried the departmental admission pager. Patients were randomly allocated to the respective teams based on patient load, in accordance with ACGME‐ and residency programimposed limits, rather than according to patient diagnoses. Differences between groups in severity of illness were minimized by limiting levels of acuity and including only patients admitted to the medical ward and not to the intensive care, coronary care, or intermediate care units. Patients on both model services were admitted to geographically shared wards with the same nursing staff and other ancillary personnel. All residents and faculty had similar access to hospital resources such as academic meetings, clinical protocols, practice‐based guidelines, and quality improvement initiatives.

The main outcome measures were total hospital costs; LOS; hospital readmission within 30 days; in‐hospital mortality; number of tests and procedures ordered; and pharmacy, laboratory, radiology, and procedural costs and costs for physical, speech, occupational, and respiratory therapy consultations. Financial data for patient care excluding physician fees were based on actual direct and indirect costs and were estimated using an activity‐based system (Transition Systems, Inc., Eclypsis Corporation, Boca Raton, FL). Department‐specific costs represented actual variable costs and did not include indirect (overhead) costs. Hospital length of stay was defined as the number of days from the time a patient was admitted to the general medicine service to the day discharged from the hospital, even if the patient was transferred to another service before discharge. Hospital readmission for the same primary diagnosis within 30 days after discharge was used to compare the quality of care on the 2 types of services.

We assessed the case mix on the 2 services by comparing the distribution of the 10 most frequent diagnosis‐related groups (DRGs) in the data set, plus angina, arrhythmia, and hypertension combined into a single category (Table 1). The chi‐square test was used to test differences between the 2 services in the proportion of each DRG. To obtain a surrogate index for case severity, the list of coexisting or comorbid conditions present at the time of admission was used to calculate the mean number of comorbidities per patient. The morbidity experience of the 2 patient populations was compared using the Student t test for 2 independent samples.

We compared the main outcome measures for teaching and nonteaching services using 3 analytic methods. First, the crude difference in total costs, service‐ and diagnosis‐specific costs, and length of hospital stay and the unadjusted odds ratio for readmission, in‐hospital mortality, and services ordered were calculated. The Student t test for 2 independent samples was used to compare total cost, LOS, and DRG‐specific and service‐specific costs. The chi‐square test was used to compare readmission rate, in‐hospital mortality, and number of services ordered. Second, we used multiple linear regression and logistic regression analyses to estimate the difference in the main outcome measures of the 2 medical services, adjusted for age, sex, insurance classification, number of comorbidities, and primary DRGs. The Wald test was used to obtain P values for testing differences between teaching and nonteaching services.

In observational studies, multiple linear regression models are commonly used to remove the effects of confounding factors. However, regression methods do not ensure the balance in the distribution of covariates, and imbalance becomes more problematic as the number of covariates increases. To manage the imbalance of case mix and other potential confounders, we used a propensity score method to balance confounding variables between the 2 groups.17 Specifically, by performing logistic regression with the potential confounding variables as covariates, we estimated the propensity score or the probability of being assigned to the teaching services for each patient (Tables 2 and 3). The collection of multiple characteristics was collapsed into a single composite score called the propensity score, and this score was used as if it were the only confounding variable. Patients were stratified to quintiles based on their propensity score, and the balance of the distribution of each potential confounder in the 5 propensity strata was checked, and we estimated the overall difference between the 2 medical services with the weighted average of the strata‐specific difference, where the weights were proportional to the stratum size. The Z test was used to derive P values for comparing the total hospital costs, LOS, and service‐specific costs of the 2 medical services. The Mantel‐Haenszel test was used to determine whether the 2 medical services had the same risk of readmission, death, and frequency of diagnostic or consultation services ordered. In all analyses we report P values without adjusting for multiple comparisons. The significance level of hypothesis testing was set at .05.

RESULTS

The study consisted of 2189 patients (1036 men) whose mean age was 67.2 years (SD = 19.0 years). Patient demographics and frequencies of various DRGs on the 2 services are shown in Table 1. The distribution of insurance classifications (eg, third‐party payer, Medicare, Medicaid, private pay) wase comparable between teaching and nonteaching groups. No statistically significant differences between the 2 services in patient characteristics and distribution of the 10 most common DRGs in the data set were observed except for patients with metabolic disorders (P = .01) and other respiratory infections (P = .03). The mean number of comorbidities was also comparable between teaching and nonteaching services (6.7 vs. 6.7; P = .99).

Care on the teaching service was not associated with a significant increase in overall costs per patient ($5572 vs. $5576, P = .99). Crude comparison of other main outcome measures showed that the LOS (4.92 vs. 5.10 days; P = .43), odds of readmission within 30 days (202/1637 vs. 57/552; P = .21), and odds of in‐hospital mortality (61/1637 vs. 25/552; P = .40) were comparable for teaching and nonteaching services (Tables 2 and 3). Using multiple linear regression analysis, the estimated adjusted differences were only $61 (P = .84) in overall costs and 0.13 days (P = .54) in LOS between teaching and nonteaching services. Estimated adjusted risk of readmission within 30 days was 25% higher (P = .17), and in‐hospital mortality was 24% lower (P = .28) for patients treated on the medical teaching services. Using the propensity score method, the estimated difference between teaching and nonteaching services was $130 (P = .70) in overall costs and 0.08 days (P = .73) in length of stay. Risk of readmission within 30 days was 26% higher (P = .15), and in‐hospital mortality was 18% lower (P = .41) for the teaching service. Because the distributions of overall costs and length of stay were heavily skewed, we also performed statistical analyses using logarithm‐transformed data on these 2 outcomes. The results using all 4 analytic methods showed that care on the teaching services was not associated with statistically significant differences in total hospital costs, LOS, risk of readmission, and in‐hospital mortality.

Service‐specific cost analyses showed that mean laboratory costs per patient ($937 vs. $810; P = .02) and mean radiology costs per patient ($134 vs. $96; P = .01) were higher for teaching services, whereas costs for the pharmacy ($233 vs. $229; P = .85) and for speech therapy ($2.4 vs. $2.4; P = .95), physical therapy ($6.6 vs. $7.2; P = .52), occupational therapy ($3.9 vs. $3.4; P = .43), respiratory therapy ($46 vs. $41; P = .42), pulmonary function testing ($0.4 vs. $0.4; P = .99), and GI endoscopy procedures ($5.9 vs. $5.8; P = .94) were not significantly different. A comparison of the number of consults or tests ordered indicated physicians on the teaching service did not order more radiology (1411/1637 vs. 471/552; P = .61), speech therapy (128/1637 vs. 37/552; P = .39), physical therapy (611/1637 vs. 207/552; P = .94), occupational therapy (369/1637 vs. 109/552; P = .17), respiratory therapy (893/1637 vs. 283/552; P = .19), or pulmonary function testing (75/1637 vs. 27/552; P = .89) consultations or GI endoscopy procedures (188/1637 vs. 65/552; P = .18). Inferential results derived by multiple linear regression and logistic regression analyses, as well as the propensity score method, all agreed with the results derived using crude comparisons and concluded that, except for laboratory and radiology costs, patients treated on the teaching services did not have higher service‐specific costs or more therapies and consultations.

To remove the potential confounding effects of the 5 hospitalists who rotated between teaching and nonteaching services, we removed 875 patients (125 on the nonteaching and 750 on the teaching service) from the original data set who were cared for by these physicians, and repeated crude, multivariate, and propensity score analyses. In the data subset (Tables 4 and 5), laboratory costs remained higher on the teaching service, but the difference in radiology costs between teaching and nonteaching services seen in the total data set diminished and did not remain statistically significant when hospitalists were excluded from the analysis.

DISCUSSION

We found that care delivered on the resident‐based teaching services at our academic community hospital was not associated with increases in overall costs, pharmacy costs, or consultative services ordered, although laboratory‐related costs and radiology costs were slightly higher than for the nonteaching service. In addition, clinical outcomes were not significantly different between teaching and nonteaching services in terms of hospital length of stay, in‐hospital mortality, and 30‐day readmission rate.

Several previous interinstitutional studies have documented greater utilization of resources at academic medical centers as a tradeoff for improved clinical outcomes.2, 4, 12, 13 One frequently offered explanation for higher costs at teaching hospitals is the purported tendency of resident physicians to order more tests and consults and to more heavily rely on modern diagnostic and therapeutic modalities. Apart from the number of tests and procedures ordered, differences in administrative, personnel, and other nonshared costs may account for higher overall costs at teaching hospitals reported in earlier studies. These variables, however, did not differ in our comparison of teaching and nonteaching services within the same institution because they were equally shared.

Studies that have looked at the hospitalist experience at academic centers and community hospitals have demonstrated improved efficiency associated with the use of hospitalist physicians.1517 At the University of Chicago, hospitalist care was associated with lower costs and short‐term mortality in the second year of hospitalist experience.15, 16 The authors suggested that disease‐specific physician experience in the hospitalist model may lead to reduced resource consumption and improved patient outcomes. The focus of our study was not a comparison of hospitalist with nonhospitalist models. However, when we excluded patients cared for by hospitalist physicians from our costs, services, and outcomes analyses, laboratory costs remained the only significant difference between teaching and nonteaching services.

Other than teaching hospital status and use of hospitalist physicians, institutional characteristics that can potentially influence clinical outcomes include hospital size, location, ownership, case mix, access to on‐site specialized diagnostic and therapeutic equipment, and availability of specialty services.15, 16 However, all these variables were identical in our study of teaching versus nonteaching services within the same community hospital, thereby allowing an uncontaminated estimation of the effect of teaching status on resource utilization and clinical outcomes. Although both teaching and nonteaching services were sometimes headed by attendings who participated in both models, teaching services differed notably in being run by resident team leaders with attendings performing a largely supervisory role.

We recognize several limitations of our study. Patients were quasirandomly triaged to teaching and nonteaching services according to patient loads without any consideration for diagnoses, comorbidities, or severity of illness. Therefore, it is quite possible there were unascertainable differences in disease severity and case mix between the teaching and nonteaching services. Notably, there was some discordance in the number of patients with nonpneumonia respiratory infection and the number with metabolic disorders assigned between the 2 services. However, 8 of the 10 most common primary diagnoses in the data set were similarly distributed between the 2 services, and the mean number of secondary diagnoses per patient was also not statistically different. More importantl we employed multiple regression analysis and a propensity score method to account for any imbalance in case mix and other potential confounders such as sex, age, and insurance classifications. These advanced statistical methods produced results similar to the unadjusted method and, hence, strengthen our conclusion that care delivered on the resident‐based teaching services at our academic community hospital was not significantly associated with increases in overall patient care costs, LOS, readmission rate, or in‐hospital mortality. Having hospitalist physicians on both teaching and nonteaching services may have had some effect on the practice patterns of other physicians, creating greater similarities than might have been expected otherwise. Data used in this study were obtained from only 1 academic institution, and caution should be exercised in extrapolating our findings to other settings unless substantiated by other studies.

Founded in 1997 by 2 community‐based hospitalists, Win Whitcomb and John Nelson, the National Association of Inpatient Physicians was renamed the Society of Hospital Medicine in 2003 and celebrates its 10th anniversary this year. Evolving from the enthusiastic engagement by the attendees at the first hospital medicine CME meeting in the spring of 1997,1 this new organization has grown into a robust voice for improving the care of hospitalized patients. The Society has actively attempted to represent a big tent welcoming participation from everyone involved in hospital care. The name change to the Society of Hospital Medicine (SHM) reflected the recognition that a team is needed to achieve the goal of optimizing care of the hospitalized patient. Merriam‐Webster defines society as companionship or association with one's fellows and a voluntary association of individuals for common ends; especially an organized group working together or periodically meeting because of common interests, beliefs, or profession.2 The hospital medicine team working together includes nurses, pharmacists, case managers, social workers, physicians, and administrators in addition to dieticians, respiratory therapists, and physical and occupational therapists. With a focus on patient‐centered care and quality improvement, SHM eagerly anticipates future changes in health care, seeking to help its membership adapt to and manage the expected change.

As an integral component of the hospital care delivery team, physicians represent the bulk of membership in SHM. Thus, development of hospital medicine as a medical specialty has concerned many of its members. Fortunately, progress is being made, and Bob Wachter is chairing a task force on this for the American Board of Internal Medicine.3 Certainly, content in the field is growing exponentially, with textbooks (including possibly 3 separate general references for adult and pediatric hospital medicine), multiple printed periodicals, and this successful peer‐reviewed journal listed in MEDLINE and PubMed. In addition, most academic medical centers now have thriving groups of hospitalists, and many are establishing or plan separate divisions within their respective departments of medicine (eg, Northwestern, UCSan Francisco, UCSan Diego, Duke, Mayo Clinic). These events confirm how hospital medicine has progressed to become a true specialty of medicine and justify the publication of its own set of core competencies.4 We believe some form of certification is inevitable. This will be supported by development of residency tracks and fellowships in hospital medicine.5

Most remarkable about the Society of Hospital Medicine has been its ability to collaborate with multiple medical societies, governmental agencies, foundations, and organizations seeking to improve care for hospitalized patients (see Table 1). These relationships represent the teamwork approach that hospitalists take into their hospitals on a daily basis. We hope to build on these collaborations and work toward more interactive efforts to identify optimal delivery of health care in the hospital setting, while also reaching out to ambulatory‐based providers to ensure smooth transitions of care. Such efforts will require innovative approaches to educating SHM members and altering the standard approach to continuing medical education (CME). Investment in the concept of hospitalists by the John A. Hartford Foundation with a $1.4 million grant to improve the discharge process (Improving Hospital Care Transitions for Older Adults) exemplifies SHM's commitment to collaboration, with more than 10 organizations participating on the advisory board.

As SHM and its growing membership, which now exceeds 6500, stride into the future, we embrace advances in educational approaches to enhancing health care delivery and expect to play a leadership role in applying them. Increasingly, use of pay‐for‐performance (P4P) will attempt to align payment incentives to promote better quality care by rewarding providers that perform well.6 SHM aims to train hospitalists through use of knowledge translation which combines the right educational tools with involvement of the entire health care team, yielding truly effective CME.7 A reinvention of CME that links it to care delivery and improving performance, it is supported by governmental health care leaders.8 This approach moves CME to where hospitalists deliver care, targets all participants (patients, nurses, pharmacists, and doctors), and has content based around initiatives to improve health care.

Such a quality improvement model would take advantage of SHM's Quality Improvement Resource Rooms (hospitalmedicine.org), marking an important shift toward translating evidence into practice. SHM will also continue with its efforts to lead in nonclinical training, as exemplified by its popular biannual leadership training courses. We expect this will expand to provide much‐needed QI training in the future.

In its first 10 years SHM has accomplished much already, but the best days for hospital medicine lie ahead of us. There will be more than 30,000 hospitalists practicing at virtually every hospital in the United States, with high expectations for teams of health professionals providing patient‐centered care with documented quality standards. SHM is poised to work with all our partner organizations to do our part to create the hospital of the future. Our patients are counting on all of us.

Founded in 1997 by 2 community‐based hospitalists, Win Whitcomb and John Nelson, the National Association of Inpatient Physicians was renamed the Society of Hospital Medicine in 2003 and celebrates its 10th anniversary this year. Evolving from the enthusiastic engagement by the attendees at the first hospital medicine CME meeting in the spring of 1997,1 this new organization has grown into a robust voice for improving the care of hospitalized patients. The Society has actively attempted to represent a big tent welcoming participation from everyone involved in hospital care. The name change to the Society of Hospital Medicine (SHM) reflected the recognition that a team is needed to achieve the goal of optimizing care of the hospitalized patient. Merriam‐Webster defines society as companionship or association with one's fellows and a voluntary association of individuals for common ends; especially an organized group working together or periodically meeting because of common interests, beliefs, or profession.2 The hospital medicine team working together includes nurses, pharmacists, case managers, social workers, physicians, and administrators in addition to dieticians, respiratory therapists, and physical and occupational therapists. With a focus on patient‐centered care and quality improvement, SHM eagerly anticipates future changes in health care, seeking to help its membership adapt to and manage the expected change.

As an integral component of the hospital care delivery team, physicians represent the bulk of membership in SHM. Thus, development of hospital medicine as a medical specialty has concerned many of its members. Fortunately, progress is being made, and Bob Wachter is chairing a task force on this for the American Board of Internal Medicine.3 Certainly, content in the field is growing exponentially, with textbooks (including possibly 3 separate general references for adult and pediatric hospital medicine), multiple printed periodicals, and this successful peer‐reviewed journal listed in MEDLINE and PubMed. In addition, most academic medical centers now have thriving groups of hospitalists, and many are establishing or plan separate divisions within their respective departments of medicine (eg, Northwestern, UCSan Francisco, UCSan Diego, Duke, Mayo Clinic). These events confirm how hospital medicine has progressed to become a true specialty of medicine and justify the publication of its own set of core competencies.4 We believe some form of certification is inevitable. This will be supported by development of residency tracks and fellowships in hospital medicine.5

Most remarkable about the Society of Hospital Medicine has been its ability to collaborate with multiple medical societies, governmental agencies, foundations, and organizations seeking to improve care for hospitalized patients (see Table 1). These relationships represent the teamwork approach that hospitalists take into their hospitals on a daily basis. We hope to build on these collaborations and work toward more interactive efforts to identify optimal delivery of health care in the hospital setting, while also reaching out to ambulatory‐based providers to ensure smooth transitions of care. Such efforts will require innovative approaches to educating SHM members and altering the standard approach to continuing medical education (CME). Investment in the concept of hospitalists by the John A. Hartford Foundation with a $1.4 million grant to improve the discharge process (Improving Hospital Care Transitions for Older Adults) exemplifies SHM's commitment to collaboration, with more than 10 organizations participating on the advisory board.

As SHM and its growing membership, which now exceeds 6500, stride into the future, we embrace advances in educational approaches to enhancing health care delivery and expect to play a leadership role in applying them. Increasingly, use of pay‐for‐performance (P4P) will attempt to align payment incentives to promote better quality care by rewarding providers that perform well.6 SHM aims to train hospitalists through use of knowledge translation which combines the right educational tools with involvement of the entire health care team, yielding truly effective CME.7 A reinvention of CME that links it to care delivery and improving performance, it is supported by governmental health care leaders.8 This approach moves CME to where hospitalists deliver care, targets all participants (patients, nurses, pharmacists, and doctors), and has content based around initiatives to improve health care.

Such a quality improvement model would take advantage of SHM's Quality Improvement Resource Rooms (hospitalmedicine.org), marking an important shift toward translating evidence into practice. SHM will also continue with its efforts to lead in nonclinical training, as exemplified by its popular biannual leadership training courses. We expect this will expand to provide much‐needed QI training in the future.

In its first 10 years SHM has accomplished much already, but the best days for hospital medicine lie ahead of us. There will be more than 30,000 hospitalists practicing at virtually every hospital in the United States, with high expectations for teams of health professionals providing patient‐centered care with documented quality standards. SHM is poised to work with all our partner organizations to do our part to create the hospital of the future. Our patients are counting on all of us.

Founded in 1997 by 2 community‐based hospitalists, Win Whitcomb and John Nelson, the National Association of Inpatient Physicians was renamed the Society of Hospital Medicine in 2003 and celebrates its 10th anniversary this year. Evolving from the enthusiastic engagement by the attendees at the first hospital medicine CME meeting in the spring of 1997,1 this new organization has grown into a robust voice for improving the care of hospitalized patients. The Society has actively attempted to represent a big tent welcoming participation from everyone involved in hospital care. The name change to the Society of Hospital Medicine (SHM) reflected the recognition that a team is needed to achieve the goal of optimizing care of the hospitalized patient. Merriam‐Webster defines society as companionship or association with one's fellows and a voluntary association of individuals for common ends; especially an organized group working together or periodically meeting because of common interests, beliefs, or profession.2 The hospital medicine team working together includes nurses, pharmacists, case managers, social workers, physicians, and administrators in addition to dieticians, respiratory therapists, and physical and occupational therapists. With a focus on patient‐centered care and quality improvement, SHM eagerly anticipates future changes in health care, seeking to help its membership adapt to and manage the expected change.

As an integral component of the hospital care delivery team, physicians represent the bulk of membership in SHM. Thus, development of hospital medicine as a medical specialty has concerned many of its members. Fortunately, progress is being made, and Bob Wachter is chairing a task force on this for the American Board of Internal Medicine.3 Certainly, content in the field is growing exponentially, with textbooks (including possibly 3 separate general references for adult and pediatric hospital medicine), multiple printed periodicals, and this successful peer‐reviewed journal listed in MEDLINE and PubMed. In addition, most academic medical centers now have thriving groups of hospitalists, and many are establishing or plan separate divisions within their respective departments of medicine (eg, Northwestern, UCSan Francisco, UCSan Diego, Duke, Mayo Clinic). These events confirm how hospital medicine has progressed to become a true specialty of medicine and justify the publication of its own set of core competencies.4 We believe some form of certification is inevitable. This will be supported by development of residency tracks and fellowships in hospital medicine.5

Most remarkable about the Society of Hospital Medicine has been its ability to collaborate with multiple medical societies, governmental agencies, foundations, and organizations seeking to improve care for hospitalized patients (see Table 1). These relationships represent the teamwork approach that hospitalists take into their hospitals on a daily basis. We hope to build on these collaborations and work toward more interactive efforts to identify optimal delivery of health care in the hospital setting, while also reaching out to ambulatory‐based providers to ensure smooth transitions of care. Such efforts will require innovative approaches to educating SHM members and altering the standard approach to continuing medical education (CME). Investment in the concept of hospitalists by the John A. Hartford Foundation with a $1.4 million grant to improve the discharge process (Improving Hospital Care Transitions for Older Adults) exemplifies SHM's commitment to collaboration, with more than 10 organizations participating on the advisory board.

As SHM and its growing membership, which now exceeds 6500, stride into the future, we embrace advances in educational approaches to enhancing health care delivery and expect to play a leadership role in applying them. Increasingly, use of pay‐for‐performance (P4P) will attempt to align payment incentives to promote better quality care by rewarding providers that perform well.6 SHM aims to train hospitalists through use of knowledge translation which combines the right educational tools with involvement of the entire health care team, yielding truly effective CME.7 A reinvention of CME that links it to care delivery and improving performance, it is supported by governmental health care leaders.8 This approach moves CME to where hospitalists deliver care, targets all participants (patients, nurses, pharmacists, and doctors), and has content based around initiatives to improve health care.

Such a quality improvement model would take advantage of SHM's Quality Improvement Resource Rooms (hospitalmedicine.org), marking an important shift toward translating evidence into practice. SHM will also continue with its efforts to lead in nonclinical training, as exemplified by its popular biannual leadership training courses. We expect this will expand to provide much‐needed QI training in the future.

In its first 10 years SHM has accomplished much already, but the best days for hospital medicine lie ahead of us. There will be more than 30,000 hospitalists practicing at virtually every hospital in the United States, with high expectations for teams of health professionals providing patient‐centered care with documented quality standards. SHM is poised to work with all our partner organizations to do our part to create the hospital of the future. Our patients are counting on all of us.

Why measure hospital quality? One popular premise is that measurement and transparency will inform consumer decision making and drive volume to high‐quality programs, providing incentives for improvement and raising the bar nationally. In this issue of the Journal of Hospital Medicine, Halasyamani and Davis report that there is relatively poor correlation between the Hospital Compare scores of the Centers for Medicare and Medicaid Services (CMS) and U.S. News and World Report's Best Hospitals rankings.1 The authors note that this is not necessarily surprising, as the methodologies of these rating systems are quite different, although their purposes are functionally similar.

Clearly, these 2 popular quality evaluation systems reflect different underlying constructs (which may or may not actually describe quality). And therein lies a central dilemma for health care professionals and academics: we haven't agreed among ourselves on reliable and meaningful quality metrics; so how can we, or even should we, expect the public to use available data to make health care decisions?

The 2 constructs in this particular comparison are certainly divergent in design. For the Hospital Compare ratings, the CMS used detailed process‐of‐care measures, expensively abstracted from the medical record, for just 3 medical conditions: acute myocardial infarction, congestive heart failure, and community‐acquired pneumonia. The U.S. News Best Hospitals rankings used reputation (based on a survey of physicians), severity‐adjusted mortality rate, staffing ratio, and key technologies offered by hospitals. Halasyamani and Davis conclude that consumers may be left to wonder how to reconcile these discordant rating systems. At the same time, they acknowledge that it is not yet clear whether public reporting will affect consumers' health care choices. Available evidence suggests that when making choices about health care, patients are much more likely to consult family and friends than an Internet site that posts quality information.2 There is as yet no conclusive evidence that quality data drive consumer decision making. Furthermore, acute myocardial infarction patients rarely have the opportunity to choose a hospital, even if they had access to the data.

The assessment of hospital quality is not only a challenge for patients, it's still perplexing for those of us immersed in health care. The scope of measures of quality is both broad and incomplete. At the microsystem and individual clinical syndrome level, we have a plethora of process measures that are evidence based (such as the CMS Hospital Compare measures) but appear to move meaningful outcomes only slightly, if at all. The evidence linking the pneumonia measures, for instance, to significant outcomes such as lower mortality or (rarely studied) better functional outcomes is extremely limited or nonexistent.3, 4

At the other end of the continuum are sweeping metrics such as risk‐adjusted in‐hospital mortality, which may be important and yet has 2 significant limitations. First, mortality rates in acute care are generally so low that this is not a useful outcome of interest for most clinical conditions. Its utility is really limited to well‐studied procedures such as cardiac surgery. Second, mortality rate reduction is extraordinarily difficult to link meaningfully to specific process interventions with available information and tools. For high‐volume complex medical conditions, such as pneumonia, nonsurgically‐managed cardiac disease, and oncology, we cannot as yet reliably use in‐hospital mortality rate as a descriptor for quality of care because the populations are so diverse and the statistical tools so crude. The public reporting of these data is even more complex because it often lags behind current data by years and may be significantly affected by sample size.

Even when we settle on a few, well‐defined process metrics, we have problems with complete and accurate reporting of data. In Halasyamani and Davis's study, only 2.9% of hospitals reported all 14 Hospital Compare core performance measures used in their analysis.1 Evidence suggests that poor performance is a strong disincentive to voluntarily report quality measures to the public.5 And because there is no evidence that this type of transparency initiative will drive volume to higher‐quality programs, publicly reporting quality measures may not provide a strong enough incentive for hospitals to allocate resources to the improvement of the quality of care they deliver in these specific areas.

The CMS has introduced financial incentives to encourage hospitals to report performance measures (regardless of the actual level of performance which is reported), providing financial rewards to top‐performing hospitals and/or to hospitals that actually demonstrate that strong performance may have a greater impact. The results of early studies suggested that that pay‐for‐performance did improve the quality of health care.6 Lindenauer et al. recently published the results of a large study evaluating adherence to quality measures in hospitals that voluntarily reported measures compared with those participating in a pay‐for‐performance demonstration project funded by the CMS. Hospitals engaged in both public reporting and pay‐for‐performance achieved modestly greater improvements in quality compared with those that only did public reporting.7 It is notable that this demonstration project generally produced modest financial rewards to those hospitals that improved performance.8 The optimal model to reward performance remains to be determined.7, 9, 10

There are a number of potentially harmful unintended consequences of poorly designed quality measures and associated transparency and incentive programs. The most obvious is opportunity cost. As the incentives become more tangible and meaningful, hospital quality leaders will be expected to step up efforts to improve performance in the specific process of care measures for which they are rewarded. Without caution, however, hospital quality leaders may develop a narrow focus in deciding where to apply their limited resources and may become distracted from other areas in dire need of improvement. Their boards of directors might appropriately argue that it is their fiduciary responsibility to focus on improving those aspects of quality that the payer community has highlighted as most important. If the metrics are excellent and the underlying constructs are in fact the right ones to advance quality in American acute care, this is a direction to be applauded. If the metrics are flawed and limited, which is the case today, then the risk is that resources will be wasted and diverted from more important priorities.

Even worse, an overly narrow focus may have unintended adverse clinical consequences. Recently, Wachter discussed several real‐world examples of unintended consequences of quality improvement efforts, including giving patients multiple doses of pneumococcal vaccines and inappropriately treating patients with symptoms that might indicate community‐acquired pneumonia with antibiotics.11 As hospitals attempt to improve their report cards, a significant risk exists that patients will receive excessive or unnecessary care in an attempt to meet specified timeliness goals.

The most important issue that has still not been completely addressed is whether improvements in process‐of‐care measures will actually improve patient outcomes. In a recent issue of this journal, Seymann concluded that there is strong evidence for influenza vaccination and the use of appropriate antibiotics for community‐acquired pneumonia12 but that other pneumonia quality measures were of less obvious clinical benefit. Controversy continues over whether the optimal timing of the initial treatment of community‐acquired pneumonia with antibiotics is 4 hours, as it currently stands, or 8 hours. Patients hospitalized with pneumonia may be motivated to quit smoking, but CMS requirements for smoking cessation advice/counseling can be satisfied with a simple pamphlet or a video, rather than interventions that involve counseling by specifically trained professionals and the use of pharmacotherapy, which are more likely to succeed. Although smoking cessation is an admirable goal, whether this is performed will not affect the quality of care that a patient with pneumonia receives during the index admission. In fact, it would be more important to counsel all patients about the hazards of smoking in an attempt to prevent pneumonia and acute myocardial infarction as well as a host of other smoking‐related illnesses.

In another example, Fonarow and colleagues examined the association between heart failure clinical outcomes and performance measures in a large observational cohort.13 The study found that current heart failure performance measures, aside from prescribing angiotensin‐converting inhibitor or angiotensin receptor blocker at discharge, had little relationship to mortality in the first 60‐90 days following discharge. On the other hand, the team found that being discharged on a beta blocker was associated with a significant reduction in mortality; however, beta blocker use is not part of the current CMS core measures. In addition, many patients hospitalized for heart failure may benefit from implantable cardioverter‐defibrillator therapy and/or cardiac resynchronization therapy,14 yet referral to a cardiologist to evaluate patients who may be suitable for these therapies is not a CMS core measure.

A similar, more comprehensive study recently evaluated whether performance on CMS quality measures for acute myocardial infarction, heart failure, and pneumonia correlated with condition‐specific inpatient, 30‐day, and 1‐year risk‐adjusted mortality rates.15 The study found that the best hospitals, those performing at the 75th percentile on quality measures, did have lower mortality rates than did hospitals performing at the 25th percentile, but the absolute risk reduction was small. Specifically, the absolute risk reduction for 30‐day mortality was 0.6%, 0.1%, and 0.1% for acute myocardial infarction, heart failure, and pneumonia, respectively. In attempting to explain their findings, the authors noted that current quality measures include only a subset of activities involved in the care of hospitalized patients. In addition, mortality rates are likely influenced by factors not included in current quality measures, such as the use of electronic health records, staffing levels, and other activities of quality oversight committees.

The era of measurement and accountability for providing high‐quality health care is upon us. Public reporting may lead to improvement in quality measures, but it is incumbent on the academic and provider communities as well as the payer community to ensure that the metrics are meaningful, reliable, and reproducible and, equally important, that they make a difference in essential clinical outcomes such as mortality, return to function, and avoidance of adverse events.10 Emerging evidence suggests the measures may need to be linked to meaningful financial incentives to the provider in order to accelerate change. Incentives directed at patients appear to be ineffective, clumsy, and slow to produce results.16

The time is right to revisit the quality measures currently used for transparency and incentives. We need a tighter, more reliable set of metrics that actually correlate with meaningful outcomes. Some evidence‐based measures appear to be missing from the current leading lists and some remain inadequately defined with regard to compliance. As a system, the measurement program contains poorly understood risks of unintended consequences. Above all else, local and national quality leaders need to be mindful that improving patient outcomes must be the central goal in our efforts to improve performance on process‐of‐care measures.

Why measure hospital quality? One popular premise is that measurement and transparency will inform consumer decision making and drive volume to high‐quality programs, providing incentives for improvement and raising the bar nationally. In this issue of the Journal of Hospital Medicine, Halasyamani and Davis report that there is relatively poor correlation between the Hospital Compare scores of the Centers for Medicare and Medicaid Services (CMS) and U.S. News and World Report's Best Hospitals rankings.1 The authors note that this is not necessarily surprising, as the methodologies of these rating systems are quite different, although their purposes are functionally similar.

Clearly, these 2 popular quality evaluation systems reflect different underlying constructs (which may or may not actually describe quality). And therein lies a central dilemma for health care professionals and academics: we haven't agreed among ourselves on reliable and meaningful quality metrics; so how can we, or even should we, expect the public to use available data to make health care decisions?

The 2 constructs in this particular comparison are certainly divergent in design. For the Hospital Compare ratings, the CMS used detailed process‐of‐care measures, expensively abstracted from the medical record, for just 3 medical conditions: acute myocardial infarction, congestive heart failure, and community‐acquired pneumonia. The U.S. News Best Hospitals rankings used reputation (based on a survey of physicians), severity‐adjusted mortality rate, staffing ratio, and key technologies offered by hospitals. Halasyamani and Davis conclude that consumers may be left to wonder how to reconcile these discordant rating systems. At the same time, they acknowledge that it is not yet clear whether public reporting will affect consumers' health care choices. Available evidence suggests that when making choices about health care, patients are much more likely to consult family and friends than an Internet site that posts quality information.2 There is as yet no conclusive evidence that quality data drive consumer decision making. Furthermore, acute myocardial infarction patients rarely have the opportunity to choose a hospital, even if they had access to the data.

The assessment of hospital quality is not only a challenge for patients, it's still perplexing for those of us immersed in health care. The scope of measures of quality is both broad and incomplete. At the microsystem and individual clinical syndrome level, we have a plethora of process measures that are evidence based (such as the CMS Hospital Compare measures) but appear to move meaningful outcomes only slightly, if at all. The evidence linking the pneumonia measures, for instance, to significant outcomes such as lower mortality or (rarely studied) better functional outcomes is extremely limited or nonexistent.3, 4

At the other end of the continuum are sweeping metrics such as risk‐adjusted in‐hospital mortality, which may be important and yet has 2 significant limitations. First, mortality rates in acute care are generally so low that this is not a useful outcome of interest for most clinical conditions. Its utility is really limited to well‐studied procedures such as cardiac surgery. Second, mortality rate reduction is extraordinarily difficult to link meaningfully to specific process interventions with available information and tools. For high‐volume complex medical conditions, such as pneumonia, nonsurgically‐managed cardiac disease, and oncology, we cannot as yet reliably use in‐hospital mortality rate as a descriptor for quality of care because the populations are so diverse and the statistical tools so crude. The public reporting of these data is even more complex because it often lags behind current data by years and may be significantly affected by sample size.

Even when we settle on a few, well‐defined process metrics, we have problems with complete and accurate reporting of data. In Halasyamani and Davis's study, only 2.9% of hospitals reported all 14 Hospital Compare core performance measures used in their analysis.1 Evidence suggests that poor performance is a strong disincentive to voluntarily report quality measures to the public.5 And because there is no evidence that this type of transparency initiative will drive volume to higher‐quality programs, publicly reporting quality measures may not provide a strong enough incentive for hospitals to allocate resources to the improvement of the quality of care they deliver in these specific areas.

The CMS has introduced financial incentives to encourage hospitals to report performance measures (regardless of the actual level of performance which is reported), providing financial rewards to top‐performing hospitals and/or to hospitals that actually demonstrate that strong performance may have a greater impact. The results of early studies suggested that that pay‐for‐performance did improve the quality of health care.6 Lindenauer et al. recently published the results of a large study evaluating adherence to quality measures in hospitals that voluntarily reported measures compared with those participating in a pay‐for‐performance demonstration project funded by the CMS. Hospitals engaged in both public reporting and pay‐for‐performance achieved modestly greater improvements in quality compared with those that only did public reporting.7 It is notable that this demonstration project generally produced modest financial rewards to those hospitals that improved performance.8 The optimal model to reward performance remains to be determined.7, 9, 10

There are a number of potentially harmful unintended consequences of poorly designed quality measures and associated transparency and incentive programs. The most obvious is opportunity cost. As the incentives become more tangible and meaningful, hospital quality leaders will be expected to step up efforts to improve performance in the specific process of care measures for which they are rewarded. Without caution, however, hospital quality leaders may develop a narrow focus in deciding where to apply their limited resources and may become distracted from other areas in dire need of improvement. Their boards of directors might appropriately argue that it is their fiduciary responsibility to focus on improving those aspects of quality that the payer community has highlighted as most important. If the metrics are excellent and the underlying constructs are in fact the right ones to advance quality in American acute care, this is a direction to be applauded. If the metrics are flawed and limited, which is the case today, then the risk is that resources will be wasted and diverted from more important priorities.

Even worse, an overly narrow focus may have unintended adverse clinical consequences. Recently, Wachter discussed several real‐world examples of unintended consequences of quality improvement efforts, including giving patients multiple doses of pneumococcal vaccines and inappropriately treating patients with symptoms that might indicate community‐acquired pneumonia with antibiotics.11 As hospitals attempt to improve their report cards, a significant risk exists that patients will receive excessive or unnecessary care in an attempt to meet specified timeliness goals.

The most important issue that has still not been completely addressed is whether improvements in process‐of‐care measures will actually improve patient outcomes. In a recent issue of this journal, Seymann concluded that there is strong evidence for influenza vaccination and the use of appropriate antibiotics for community‐acquired pneumonia12 but that other pneumonia quality measures were of less obvious clinical benefit. Controversy continues over whether the optimal timing of the initial treatment of community‐acquired pneumonia with antibiotics is 4 hours, as it currently stands, or 8 hours. Patients hospitalized with pneumonia may be motivated to quit smoking, but CMS requirements for smoking cessation advice/counseling can be satisfied with a simple pamphlet or a video, rather than interventions that involve counseling by specifically trained professionals and the use of pharmacotherapy, which are more likely to succeed. Although smoking cessation is an admirable goal, whether this is performed will not affect the quality of care that a patient with pneumonia receives during the index admission. In fact, it would be more important to counsel all patients about the hazards of smoking in an attempt to prevent pneumonia and acute myocardial infarction as well as a host of other smoking‐related illnesses.

In another example, Fonarow and colleagues examined the association between heart failure clinical outcomes and performance measures in a large observational cohort.13 The study found that current heart failure performance measures, aside from prescribing angiotensin‐converting inhibitor or angiotensin receptor blocker at discharge, had little relationship to mortality in the first 60‐90 days following discharge. On the other hand, the team found that being discharged on a beta blocker was associated with a significant reduction in mortality; however, beta blocker use is not part of the current CMS core measures. In addition, many patients hospitalized for heart failure may benefit from implantable cardioverter‐defibrillator therapy and/or cardiac resynchronization therapy,14 yet referral to a cardiologist to evaluate patients who may be suitable for these therapies is not a CMS core measure.

A similar, more comprehensive study recently evaluated whether performance on CMS quality measures for acute myocardial infarction, heart failure, and pneumonia correlated with condition‐specific inpatient, 30‐day, and 1‐year risk‐adjusted mortality rates.15 The study found that the best hospitals, those performing at the 75th percentile on quality measures, did have lower mortality rates than did hospitals performing at the 25th percentile, but the absolute risk reduction was small. Specifically, the absolute risk reduction for 30‐day mortality was 0.6%, 0.1%, and 0.1% for acute myocardial infarction, heart failure, and pneumonia, respectively. In attempting to explain their findings, the authors noted that current quality measures include only a subset of activities involved in the care of hospitalized patients. In addition, mortality rates are likely influenced by factors not included in current quality measures, such as the use of electronic health records, staffing levels, and other activities of quality oversight committees.

The era of measurement and accountability for providing high‐quality health care is upon us. Public reporting may lead to improvement in quality measures, but it is incumbent on the academic and provider communities as well as the payer community to ensure that the metrics are meaningful, reliable, and reproducible and, equally important, that they make a difference in essential clinical outcomes such as mortality, return to function, and avoidance of adverse events.10 Emerging evidence suggests the measures may need to be linked to meaningful financial incentives to the provider in order to accelerate change. Incentives directed at patients appear to be ineffective, clumsy, and slow to produce results.16

The time is right to revisit the quality measures currently used for transparency and incentives. We need a tighter, more reliable set of metrics that actually correlate with meaningful outcomes. Some evidence‐based measures appear to be missing from the current leading lists and some remain inadequately defined with regard to compliance. As a system, the measurement program contains poorly understood risks of unintended consequences. Above all else, local and national quality leaders need to be mindful that improving patient outcomes must be the central goal in our efforts to improve performance on process‐of‐care measures.

Why measure hospital quality? One popular premise is that measurement and transparency will inform consumer decision making and drive volume to high‐quality programs, providing incentives for improvement and raising the bar nationally. In this issue of the Journal of Hospital Medicine, Halasyamani and Davis report that there is relatively poor correlation between the Hospital Compare scores of the Centers for Medicare and Medicaid Services (CMS) and U.S. News and World Report's Best Hospitals rankings.1 The authors note that this is not necessarily surprising, as the methodologies of these rating systems are quite different, although their purposes are functionally similar.

Clearly, these 2 popular quality evaluation systems reflect different underlying constructs (which may or may not actually describe quality). And therein lies a central dilemma for health care professionals and academics: we haven't agreed among ourselves on reliable and meaningful quality metrics; so how can we, or even should we, expect the public to use available data to make health care decisions?

The 2 constructs in this particular comparison are certainly divergent in design. For the Hospital Compare ratings, the CMS used detailed process‐of‐care measures, expensively abstracted from the medical record, for just 3 medical conditions: acute myocardial infarction, congestive heart failure, and community‐acquired pneumonia. The U.S. News Best Hospitals rankings used reputation (based on a survey of physicians), severity‐adjusted mortality rate, staffing ratio, and key technologies offered by hospitals. Halasyamani and Davis conclude that consumers may be left to wonder how to reconcile these discordant rating systems. At the same time, they acknowledge that it is not yet clear whether public reporting will affect consumers' health care choices. Available evidence suggests that when making choices about health care, patients are much more likely to consult family and friends than an Internet site that posts quality information.2 There is as yet no conclusive evidence that quality data drive consumer decision making. Furthermore, acute myocardial infarction patients rarely have the opportunity to choose a hospital, even if they had access to the data.

The assessment of hospital quality is not only a challenge for patients, it's still perplexing for those of us immersed in health care. The scope of measures of quality is both broad and incomplete. At the microsystem and individual clinical syndrome level, we have a plethora of process measures that are evidence based (such as the CMS Hospital Compare measures) but appear to move meaningful outcomes only slightly, if at all. The evidence linking the pneumonia measures, for instance, to significant outcomes such as lower mortality or (rarely studied) better functional outcomes is extremely limited or nonexistent.3, 4

At the other end of the continuum are sweeping metrics such as risk‐adjusted in‐hospital mortality, which may be important and yet has 2 significant limitations. First, mortality rates in acute care are generally so low that this is not a useful outcome of interest for most clinical conditions. Its utility is really limited to well‐studied procedures such as cardiac surgery. Second, mortality rate reduction is extraordinarily difficult to link meaningfully to specific process interventions with available information and tools. For high‐volume complex medical conditions, such as pneumonia, nonsurgically‐managed cardiac disease, and oncology, we cannot as yet reliably use in‐hospital mortality rate as a descriptor for quality of care because the populations are so diverse and the statistical tools so crude. The public reporting of these data is even more complex because it often lags behind current data by years and may be significantly affected by sample size.

Even when we settle on a few, well‐defined process metrics, we have problems with complete and accurate reporting of data. In Halasyamani and Davis's study, only 2.9% of hospitals reported all 14 Hospital Compare core performance measures used in their analysis.1 Evidence suggests that poor performance is a strong disincentive to voluntarily report quality measures to the public.5 And because there is no evidence that this type of transparency initiative will drive volume to higher‐quality programs, publicly reporting quality measures may not provide a strong enough incentive for hospitals to allocate resources to the improvement of the quality of care they deliver in these specific areas.

The CMS has introduced financial incentives to encourage hospitals to report performance measures (regardless of the actual level of performance which is reported), providing financial rewards to top‐performing hospitals and/or to hospitals that actually demonstrate that strong performance may have a greater impact. The results of early studies suggested that that pay‐for‐performance did improve the quality of health care.6 Lindenauer et al. recently published the results of a large study evaluating adherence to quality measures in hospitals that voluntarily reported measures compared with those participating in a pay‐for‐performance demonstration project funded by the CMS. Hospitals engaged in both public reporting and pay‐for‐performance achieved modestly greater improvements in quality compared with those that only did public reporting.7 It is notable that this demonstration project generally produced modest financial rewards to those hospitals that improved performance.8 The optimal model to reward performance remains to be determined.7, 9, 10

There are a number of potentially harmful unintended consequences of poorly designed quality measures and associated transparency and incentive programs. The most obvious is opportunity cost. As the incentives become more tangible and meaningful, hospital quality leaders will be expected to step up efforts to improve performance in the specific process of care measures for which they are rewarded. Without caution, however, hospital quality leaders may develop a narrow focus in deciding where to apply their limited resources and may become distracted from other areas in dire need of improvement. Their boards of directors might appropriately argue that it is their fiduciary responsibility to focus on improving those aspects of quality that the payer community has highlighted as most important. If the metrics are excellent and the underlying constructs are in fact the right ones to advance quality in American acute care, this is a direction to be applauded. If the metrics are flawed and limited, which is the case today, then the risk is that resources will be wasted and diverted from more important priorities.

Even worse, an overly narrow focus may have unintended adverse clinical consequences. Recently, Wachter discussed several real‐world examples of unintended consequences of quality improvement efforts, including giving patients multiple doses of pneumococcal vaccines and inappropriately treating patients with symptoms that might indicate community‐acquired pneumonia with antibiotics.11 As hospitals attempt to improve their report cards, a significant risk exists that patients will receive excessive or unnecessary care in an attempt to meet specified timeliness goals.

The most important issue that has still not been completely addressed is whether improvements in process‐of‐care measures will actually improve patient outcomes. In a recent issue of this journal, Seymann concluded that there is strong evidence for influenza vaccination and the use of appropriate antibiotics for community‐acquired pneumonia12 but that other pneumonia quality measures were of less obvious clinical benefit. Controversy continues over whether the optimal timing of the initial treatment of community‐acquired pneumonia with antibiotics is 4 hours, as it currently stands, or 8 hours. Patients hospitalized with pneumonia may be motivated to quit smoking, but CMS requirements for smoking cessation advice/counseling can be satisfied with a simple pamphlet or a video, rather than interventions that involve counseling by specifically trained professionals and the use of pharmacotherapy, which are more likely to succeed. Although smoking cessation is an admirable goal, whether this is performed will not affect the quality of care that a patient with pneumonia receives during the index admission. In fact, it would be more important to counsel all patients about the hazards of smoking in an attempt to prevent pneumonia and acute myocardial infarction as well as a host of other smoking‐related illnesses.

In another example, Fonarow and colleagues examined the association between heart failure clinical outcomes and performance measures in a large observational cohort.13 The study found that current heart failure performance measures, aside from prescribing angiotensin‐converting inhibitor or angiotensin receptor blocker at discharge, had little relationship to mortality in the first 60‐90 days following discharge. On the other hand, the team found that being discharged on a beta blocker was associated with a significant reduction in mortality; however, beta blocker use is not part of the current CMS core measures. In addition, many patients hospitalized for heart failure may benefit from implantable cardioverter‐defibrillator therapy and/or cardiac resynchronization therapy,14 yet referral to a cardiologist to evaluate patients who may be suitable for these therapies is not a CMS core measure.

A similar, more comprehensive study recently evaluated whether performance on CMS quality measures for acute myocardial infarction, heart failure, and pneumonia correlated with condition‐specific inpatient, 30‐day, and 1‐year risk‐adjusted mortality rates.15 The study found that the best hospitals, those performing at the 75th percentile on quality measures, did have lower mortality rates than did hospitals performing at the 25th percentile, but the absolute risk reduction was small. Specifically, the absolute risk reduction for 30‐day mortality was 0.6%, 0.1%, and 0.1% for acute myocardial infarction, heart failure, and pneumonia, respectively. In attempting to explain their findings, the authors noted that current quality measures include only a subset of activities involved in the care of hospitalized patients. In addition, mortality rates are likely influenced by factors not included in current quality measures, such as the use of electronic health records, staffing levels, and other activities of quality oversight committees.

The era of measurement and accountability for providing high‐quality health care is upon us. Public reporting may lead to improvement in quality measures, but it is incumbent on the academic and provider communities as well as the payer community to ensure that the metrics are meaningful, reliable, and reproducible and, equally important, that they make a difference in essential clinical outcomes such as mortality, return to function, and avoidance of adverse events.10 Emerging evidence suggests the measures may need to be linked to meaningful financial incentives to the provider in order to accelerate change. Incentives directed at patients appear to be ineffective, clumsy, and slow to produce results.16

The time is right to revisit the quality measures currently used for transparency and incentives. We need a tighter, more reliable set of metrics that actually correlate with meaningful outcomes. Some evidence‐based measures appear to be missing from the current leading lists and some remain inadequately defined with regard to compliance. As a system, the measurement program contains poorly understood risks of unintended consequences. Above all else, local and national quality leaders need to be mindful that improving patient outcomes must be the central goal in our efforts to improve performance on process‐of‐care measures.

More than 33 million patients undergo surgery annually in the United States. Approximately 8 million of these patients either have known coronary artery disease or risk factors for it, and an estimated 50,000 patients sustain a perioperative myocardial infarction, with an additional 1 million developing another medical complication. An integrated comprehensive approach is necessary to risk‐stratify these patients in an attempt to reduce these complications.

The basic role of risk stratification is to identify those patients at increased risk for complications; however, we are looking for a small number of patients at high risk in a population of relatively low‐risk patients. Most surgical patients do well, and further diagnostic testing has a low yield in predicting those likely to have a complication (poor positive predictive value [PPV]). Our goal should be to determine the underlying potential triggers of cardiac complications and institute measures to prevent them. After briefly reviewing pathophysiology, risk indices, and guidelines for preoperative cardiac risk assessment and diagnostic testing, we will focus on risk reduction strategies including prophylactic revascularization (CABG/PCI) and medical therapy.

PATHOPHYSIOLOGY OF PERIOPERATIVE MYOCARDIAL INFARCTION

Perioperative myocardial infarctions result from myocardial ischemia or plaque rupture and coronary thrombosis.1 Myocardial ischemia may be caused by increased oxygen demand or decreased oxygen delivery. Surgical trauma, anesthesia, pain, hypothermia, and bleeding trigger a stress state. This in turn increases catecholamine release, leading to tachycardia, hypertension, and increased oxygen demand. Anesthesia, hypotension, bleeding, and anemia may produce hypoxia, with subsequently decreased delivery of oxygen. Surgical trauma initiates an inflammatory response, leading to plaque fissuring, and a hypercoagulable state, which can result in acute coronary thrombosis. Perioperative prophylaxis should target these potential triggers.

CARDIAC RISK INDICES AND GUIDELINES

Over the past 3 decades, a number of cardiac risk indices have been published. The older group of indices was most notable for Goldman's original cardiac risk index2 and Detsky's modification.3 The newer group consists of the American College of Physicians (ACP) guidelines4 (now considered outdated), the American College of Cardiology/American Heart Association (ACC/AHA) guidelines (to be updated again in early 2007),5 and the Lee revised cardiac risk index (RCRI).6

The 2002 ACC guidelines5 outline how to determine the need for additional cardiac (usually noninvasive) testing (NIT): after ascertaining the urgency of surgery, history of revascularization procedures, and previous stress test results (if any), a combination of clinical risk predictors, surgery‐specific risk, and patient self‐reported exercise capacity should be entered into an algorithm. The guidelines state a shortcut can be used: noninvasive testing should be considered if a patient has any 2 of the following: (1) intermediate clinical risk (stable angina or old MI, compensated heart failure, diabetes mellitus, renal insufficiency), (2) high‐risk surgery (aortic or major vascular procedures, prolonged surgery with significant expected blood loss or fluid shifts), or (3) poor exercise capacity (<4 METs). Patients with major clinical predictors (unstable coronary syndromes, decompensated heart failure, severe valvular heart disease, or hemodynamically significant arrhythmias) should not undergo elective surgery without further workup or treatment. The ACP guidelines use the Detsky3 modified CRI and low‐risk variables to suggest any need for further testing depending on type of surgery (vascular or nonvascular). At times these 2 guidelines offer conflicting recommendations, with the ACC more likely than the ACP to recommend NIT. The RCRI, which was developed prospectively and has been validated, uses 6 predictors of major cardiac complicationshigh‐risk surgery, coronary artery disease, stroke, congestive heart failure, diabetes mellitus requiring insulin, and serum creatinine > 2 mg/dL. Patients with 0 or 1 risk factors are considered at low risk, those with 2 risk factors at moderate risk, and those with 3 or more risk factors at high risk (10% complication rate). Although the RCRI does not make recommendations about whether to test, it has been incorporated into a number of algorithms combining risk stratification with recommendations about noninvasive testing as well as use of perioperative beta‐blockers.710 0

DIAGNOSTIC CARDIAC TESTS

Tests should not be done if the results will not alter patient management. If further assessment is indicated based on the ACC/AHA algorithm, other risk indices,10 or criteria independent of the need for surgery, the physician must decide whether to do a noninvasive (eg, echocardiogram or stress test) or an invasive test (coronary angiography). Unless a patient has independent criteria for angiography or, occasionally, a very high prior probability of significant CAD based on multiple risk factors, noninvasive testing is usually the preferred first step. A resting echocardiogram is potentially useful for providing information about suspected valvular heart disease but is not a consistent predictor of ischemic events.

For ambulatory patients, exercise stress testing is usually preferred over pharmacologic testing; in the perioperative setting, the usefulness of exercise testing is limited by the indications for obtaining stress testing (namely, poor functional status) as well as its main limitation, patient inability to reach 85% of the target heart rate. As a result, pharmacologic stress testing should be the primary modality for patients requiring preoperative risk stratification. Pharmacologic stress testing can be done with nuclear imaging (dipyridamole or adenosine thallium) or echocardiography (dobutamine echocardiography). For the most part, the results are comparable,11, 12 with both having excellent negative predictive values (NPV > 95%) but poor positive predictive values (PPV < 20%); however, dobutamine echocardiography tends to have fewer false positives. Dipyridamole or adenosine testing is relatively contraindicated with bronchospasm and COPD but is preferred over exercise or dobutamine for patients with a left bundle‐branch block. Suspected critical aortic stenosis is a contraindication to stress testing. Positive noninvasive findings should result in prophylactic measures, either medical therapy or an invasive procedure.

CORONARY REVASCULARIZATION

PHARMACOLOGIC STRATEGIES

HOW SHOULD I INCORPORATE EVIDENCE INTO PRACTICE?

More than 33 million patients undergo surgery annually in the United States. Approximately 8 million of these patients either have known coronary artery disease or risk factors for it, and an estimated 50,000 patients sustain a perioperative myocardial infarction, with an additional 1 million developing another medical complication. An integrated comprehensive approach is necessary to risk‐stratify these patients in an attempt to reduce these complications.

The basic role of risk stratification is to identify those patients at increased risk for complications; however, we are looking for a small number of patients at high risk in a population of relatively low‐risk patients. Most surgical patients do well, and further diagnostic testing has a low yield in predicting those likely to have a complication (poor positive predictive value [PPV]). Our goal should be to determine the underlying potential triggers of cardiac complications and institute measures to prevent them. After briefly reviewing pathophysiology, risk indices, and guidelines for preoperative cardiac risk assessment and diagnostic testing, we will focus on risk reduction strategies including prophylactic revascularization (CABG/PCI) and medical therapy.

PATHOPHYSIOLOGY OF PERIOPERATIVE MYOCARDIAL INFARCTION

Perioperative myocardial infarctions result from myocardial ischemia or plaque rupture and coronary thrombosis.1 Myocardial ischemia may be caused by increased oxygen demand or decreased oxygen delivery. Surgical trauma, anesthesia, pain, hypothermia, and bleeding trigger a stress state. This in turn increases catecholamine release, leading to tachycardia, hypertension, and increased oxygen demand. Anesthesia, hypotension, bleeding, and anemia may produce hypoxia, with subsequently decreased delivery of oxygen. Surgical trauma initiates an inflammatory response, leading to plaque fissuring, and a hypercoagulable state, which can result in acute coronary thrombosis. Perioperative prophylaxis should target these potential triggers.

CARDIAC RISK INDICES AND GUIDELINES

Over the past 3 decades, a number of cardiac risk indices have been published. The older group of indices was most notable for Goldman's original cardiac risk index2 and Detsky's modification.3 The newer group consists of the American College of Physicians (ACP) guidelines4 (now considered outdated), the American College of Cardiology/American Heart Association (ACC/AHA) guidelines (to be updated again in early 2007),5 and the Lee revised cardiac risk index (RCRI).6

The 2002 ACC guidelines5 outline how to determine the need for additional cardiac (usually noninvasive) testing (NIT): after ascertaining the urgency of surgery, history of revascularization procedures, and previous stress test results (if any), a combination of clinical risk predictors, surgery‐specific risk, and patient self‐reported exercise capacity should be entered into an algorithm. The guidelines state a shortcut can be used: noninvasive testing should be considered if a patient has any 2 of the following: (1) intermediate clinical risk (stable angina or old MI, compensated heart failure, diabetes mellitus, renal insufficiency), (2) high‐risk surgery (aortic or major vascular procedures, prolonged surgery with significant expected blood loss or fluid shifts), or (3) poor exercise capacity (<4 METs). Patients with major clinical predictors (unstable coronary syndromes, decompensated heart failure, severe valvular heart disease, or hemodynamically significant arrhythmias) should not undergo elective surgery without further workup or treatment. The ACP guidelines use the Detsky3 modified CRI and low‐risk variables to suggest any need for further testing depending on type of surgery (vascular or nonvascular). At times these 2 guidelines offer conflicting recommendations, with the ACC more likely than the ACP to recommend NIT. The RCRI, which was developed prospectively and has been validated, uses 6 predictors of major cardiac complicationshigh‐risk surgery, coronary artery disease, stroke, congestive heart failure, diabetes mellitus requiring insulin, and serum creatinine > 2 mg/dL. Patients with 0 or 1 risk factors are considered at low risk, those with 2 risk factors at moderate risk, and those with 3 or more risk factors at high risk (10% complication rate). Although the RCRI does not make recommendations about whether to test, it has been incorporated into a number of algorithms combining risk stratification with recommendations about noninvasive testing as well as use of perioperative beta‐blockers.710 0

DIAGNOSTIC CARDIAC TESTS

Tests should not be done if the results will not alter patient management. If further assessment is indicated based on the ACC/AHA algorithm, other risk indices,10 or criteria independent of the need for surgery, the physician must decide whether to do a noninvasive (eg, echocardiogram or stress test) or an invasive test (coronary angiography). Unless a patient has independent criteria for angiography or, occasionally, a very high prior probability of significant CAD based on multiple risk factors, noninvasive testing is usually the preferred first step. A resting echocardiogram is potentially useful for providing information about suspected valvular heart disease but is not a consistent predictor of ischemic events.

For ambulatory patients, exercise stress testing is usually preferred over pharmacologic testing; in the perioperative setting, the usefulness of exercise testing is limited by the indications for obtaining stress testing (namely, poor functional status) as well as its main limitation, patient inability to reach 85% of the target heart rate. As a result, pharmacologic stress testing should be the primary modality for patients requiring preoperative risk stratification. Pharmacologic stress testing can be done with nuclear imaging (dipyridamole or adenosine thallium) or echocardiography (dobutamine echocardiography). For the most part, the results are comparable,11, 12 with both having excellent negative predictive values (NPV > 95%) but poor positive predictive values (PPV < 20%); however, dobutamine echocardiography tends to have fewer false positives. Dipyridamole or adenosine testing is relatively contraindicated with bronchospasm and COPD but is preferred over exercise or dobutamine for patients with a left bundle‐branch block. Suspected critical aortic stenosis is a contraindication to stress testing. Positive noninvasive findings should result in prophylactic measures, either medical therapy or an invasive procedure.

CORONARY REVASCULARIZATION

PHARMACOLOGIC STRATEGIES

HOW SHOULD I INCORPORATE EVIDENCE INTO PRACTICE?

More than 33 million patients undergo surgery annually in the United States. Approximately 8 million of these patients either have known coronary artery disease or risk factors for it, and an estimated 50,000 patients sustain a perioperative myocardial infarction, with an additional 1 million developing another medical complication. An integrated comprehensive approach is necessary to risk‐stratify these patients in an attempt to reduce these complications.

The basic role of risk stratification is to identify those patients at increased risk for complications; however, we are looking for a small number of patients at high risk in a population of relatively low‐risk patients. Most surgical patients do well, and further diagnostic testing has a low yield in predicting those likely to have a complication (poor positive predictive value [PPV]). Our goal should be to determine the underlying potential triggers of cardiac complications and institute measures to prevent them. After briefly reviewing pathophysiology, risk indices, and guidelines for preoperative cardiac risk assessment and diagnostic testing, we will focus on risk reduction strategies including prophylactic revascularization (CABG/PCI) and medical therapy.

PATHOPHYSIOLOGY OF PERIOPERATIVE MYOCARDIAL INFARCTION

Perioperative myocardial infarctions result from myocardial ischemia or plaque rupture and coronary thrombosis.1 Myocardial ischemia may be caused by increased oxygen demand or decreased oxygen delivery. Surgical trauma, anesthesia, pain, hypothermia, and bleeding trigger a stress state. This in turn increases catecholamine release, leading to tachycardia, hypertension, and increased oxygen demand. Anesthesia, hypotension, bleeding, and anemia may produce hypoxia, with subsequently decreased delivery of oxygen. Surgical trauma initiates an inflammatory response, leading to plaque fissuring, and a hypercoagulable state, which can result in acute coronary thrombosis. Perioperative prophylaxis should target these potential triggers.

CARDIAC RISK INDICES AND GUIDELINES

Over the past 3 decades, a number of cardiac risk indices have been published. The older group of indices was most notable for Goldman's original cardiac risk index2 and Detsky's modification.3 The newer group consists of the American College of Physicians (ACP) guidelines4 (now considered outdated), the American College of Cardiology/American Heart Association (ACC/AHA) guidelines (to be updated again in early 2007),5 and the Lee revised cardiac risk index (RCRI).6

The 2002 ACC guidelines5 outline how to determine the need for additional cardiac (usually noninvasive) testing (NIT): after ascertaining the urgency of surgery, history of revascularization procedures, and previous stress test results (if any), a combination of clinical risk predictors, surgery‐specific risk, and patient self‐reported exercise capacity should be entered into an algorithm. The guidelines state a shortcut can be used: noninvasive testing should be considered if a patient has any 2 of the following: (1) intermediate clinical risk (stable angina or old MI, compensated heart failure, diabetes mellitus, renal insufficiency), (2) high‐risk surgery (aortic or major vascular procedures, prolonged surgery with significant expected blood loss or fluid shifts), or (3) poor exercise capacity (<4 METs). Patients with major clinical predictors (unstable coronary syndromes, decompensated heart failure, severe valvular heart disease, or hemodynamically significant arrhythmias) should not undergo elective surgery without further workup or treatment. The ACP guidelines use the Detsky3 modified CRI and low‐risk variables to suggest any need for further testing depending on type of surgery (vascular or nonvascular). At times these 2 guidelines offer conflicting recommendations, with the ACC more likely than the ACP to recommend NIT. The RCRI, which was developed prospectively and has been validated, uses 6 predictors of major cardiac complicationshigh‐risk surgery, coronary artery disease, stroke, congestive heart failure, diabetes mellitus requiring insulin, and serum creatinine > 2 mg/dL. Patients with 0 or 1 risk factors are considered at low risk, those with 2 risk factors at moderate risk, and those with 3 or more risk factors at high risk (10% complication rate). Although the RCRI does not make recommendations about whether to test, it has been incorporated into a number of algorithms combining risk stratification with recommendations about noninvasive testing as well as use of perioperative beta‐blockers.710 0

DIAGNOSTIC CARDIAC TESTS

Tests should not be done if the results will not alter patient management. If further assessment is indicated based on the ACC/AHA algorithm, other risk indices,10 or criteria independent of the need for surgery, the physician must decide whether to do a noninvasive (eg, echocardiogram or stress test) or an invasive test (coronary angiography). Unless a patient has independent criteria for angiography or, occasionally, a very high prior probability of significant CAD based on multiple risk factors, noninvasive testing is usually the preferred first step. A resting echocardiogram is potentially useful for providing information about suspected valvular heart disease but is not a consistent predictor of ischemic events.

For ambulatory patients, exercise stress testing is usually preferred over pharmacologic testing; in the perioperative setting, the usefulness of exercise testing is limited by the indications for obtaining stress testing (namely, poor functional status) as well as its main limitation, patient inability to reach 85% of the target heart rate. As a result, pharmacologic stress testing should be the primary modality for patients requiring preoperative risk stratification. Pharmacologic stress testing can be done with nuclear imaging (dipyridamole or adenosine thallium) or echocardiography (dobutamine echocardiography). For the most part, the results are comparable,11, 12 with both having excellent negative predictive values (NPV > 95%) but poor positive predictive values (PPV < 20%); however, dobutamine echocardiography tends to have fewer false positives. Dipyridamole or adenosine testing is relatively contraindicated with bronchospasm and COPD but is preferred over exercise or dobutamine for patients with a left bundle‐branch block. Suspected critical aortic stenosis is a contraindication to stress testing. Positive noninvasive findings should result in prophylactic measures, either medical therapy or an invasive procedure.

CORONARY REVASCULARIZATION

PHARMACOLOGIC STRATEGIES

HOW SHOULD I INCORPORATE EVIDENCE INTO PRACTICE?

See one, do one, teach one is a refrain familiar to all physicians. Historically, most procedural training has occurred at the bedside. In this model, senior residents, subspecialty fellows, or faculty members would demonstrate procedural skills to junior trainees, who would subsequently practice the procedures on patients, often with uneven, risky results. Acquisition of procedural skills by residents and fellows on inpatient wards is suboptimal for at least 2 reasons beyond the risks to patient safety: (1) clinical priorities are more important than educational priorities in this setting, and (2) the patient, not the medical learner, is the most important person in the room.

Recently, several new factors have challenged the traditional medical education model. For a variety of reasons, general internists currently perform far fewer invasive procedures than they used to.1 A heightened focus on patient safety and quality raises questions about the qualifications needed to perform invasive procedures. Assessment requirements have also become more stringent. The Accreditation Council for Graduate Medical Education (ACGME) now requires the use of measures that yield reliable and valid data to document the competence of trainees performing invasive procedures.2 In 2006 these factors, and the challenge to educate, assess, and certify residents, prompted the American Board of Internal Medicine to revise its certification requirements and remove the need for technical proficiency in several procedures including paracentesis, central venous catheter placement, and thoracentesis.3, 4

Two studies reported in this issue of the Journal of Hospital Medicine highlight important issues about preparing residents to perform invasive procedures. These include the educational limits of routine clinical care and the challenge to design rigorous educational interventions that improve residents' skills. Miranda and colleagues5 designed a clinical trial to evaluate an educational intervention in which residents practiced insertion of subclavian and internal jugular venous catheters under the supervision of a hospitalist faculty member. The goal was to reduce the frequency of femoral venous catheters placed at their institution. Although residents demonstrated increased knowledge and confidence after the educational intervention, the actual number of subclavian and internal jugular venous catheter insertions was lower in the intervention group, and was rare overall. The intervention did not achieve the stated goal of reducing the number of femoral venous catheters placed by residents. This research highlights that residents cannot be trained to perform invasive procedures through clinical experience alone. In addition, it demonstrates that brief educational interventions are also insufficient. Whether a longer and more robust educational intervention might have shown different results is uncertain, but many experts believe that opportunities for deliberate practice6 using standardized and sustained treatments7 can be a powerful tool to boost the procedural skills of physicians.

At the same institution, Lucas and colleagues studied the impact of a procedural service on the number of invasive procedures performed on a general medicine inpatient service.8 They found a 48% increase in procedure attempts when the procedure service staffed by an experienced faulty member was available. However, no improvement in success rate or reduction in complications was demonstrated. Thus, opportunities for trainees to perform procedures increased, but the presence of a faculty member to provide direct supervision did not improve the quality of the procedures accomplished.

Together these reports highlight challenges and opportunities in training residents to perform invasive procedures. Both studies involved the procedural skills of residents. One used an educational intervention, the other featured faculty supervision. Both studies produced outcomes that suggest improved procedural training, but neither improved the actual quality of delivered care. A brief educational intervention increased resident confidence and knowledge but did not increase the quality or number of procedures performed by residents. Opportunities to perform invasive procedures increased dramatically when an experienced attending physician was available to supervise residents. However, more education was not provided, and the quality of procedures performed did not improve.

Given these limitations, how should physicians learn to perform invasive procedures? We endorse a systematic approach to achieve high levels of procedural skills in resident physicians. First, procedures should be carefully selected. Only those essential to future practice should be required. If possible, opportunities should be available for selected trainees to develop skills in performing additional procedures relevant to their future careers. An example would be the opportunity for residents in a hospitalist track to develop proficiency in central venous catheter insertion through clinical experience, didactic education, and rigorous skill assessment. Second, dedicated programs are needed to train and assess residents in procedural skills. Reliance on clinical experience alone is inadequate because of the low frequency at which most procedures are performed and the inability to standardize assessments in routine clinical practice.

Simulation technology is a powerful adjunct to traditional clinical training and has been demonstrated to be highly effective in developing procedural skills in disciplines such as endoscopy9 and laparoscopic surgery.10 At our institution, a simulation‐based training program has been used to help residents achieve11 and maintain12 a high level of skill in performing advanced cardiac life support procedures. We use simulation to provide opportunities for deliberate practice in a controlled environment in which immediate feedback is emphasized and mastery levels are reached. The rigorous curriculum is standardized, but learner progress is individualized depending on the practice time needed to achieve competency standards.

Most important, when training physicians to perform invasive procedures, it is critical to use interventions and training programs that can be linked to improvements in actual clinical care. The studies by Miranda et al. and Lucas et al. highlight the utility of focused educational programs to complement clinical training as well as the positive impact of direct faculty supervision. These results are important starting points for programs to consider as they train and certify residents in required procedural skills. However, much work remains to be done. These studies have revealed that improvements in patient care outcomes are not likely to occur unless robust, learner‐centered educational programs are combined with adequate opportunities for residents to perform procedures under appropriate supervision.

See one, do one, teach one is a refrain familiar to all physicians. Historically, most procedural training has occurred at the bedside. In this model, senior residents, subspecialty fellows, or faculty members would demonstrate procedural skills to junior trainees, who would subsequently practice the procedures on patients, often with uneven, risky results. Acquisition of procedural skills by residents and fellows on inpatient wards is suboptimal for at least 2 reasons beyond the risks to patient safety: (1) clinical priorities are more important than educational priorities in this setting, and (2) the patient, not the medical learner, is the most important person in the room.

Recently, several new factors have challenged the traditional medical education model. For a variety of reasons, general internists currently perform far fewer invasive procedures than they used to.1 A heightened focus on patient safety and quality raises questions about the qualifications needed to perform invasive procedures. Assessment requirements have also become more stringent. The Accreditation Council for Graduate Medical Education (ACGME) now requires the use of measures that yield reliable and valid data to document the competence of trainees performing invasive procedures.2 In 2006 these factors, and the challenge to educate, assess, and certify residents, prompted the American Board of Internal Medicine to revise its certification requirements and remove the need for technical proficiency in several procedures including paracentesis, central venous catheter placement, and thoracentesis.3, 4

Two studies reported in this issue of the Journal of Hospital Medicine highlight important issues about preparing residents to perform invasive procedures. These include the educational limits of routine clinical care and the challenge to design rigorous educational interventions that improve residents' skills. Miranda and colleagues5 designed a clinical trial to evaluate an educational intervention in which residents practiced insertion of subclavian and internal jugular venous catheters under the supervision of a hospitalist faculty member. The goal was to reduce the frequency of femoral venous catheters placed at their institution. Although residents demonstrated increased knowledge and confidence after the educational intervention, the actual number of subclavian and internal jugular venous catheter insertions was lower in the intervention group, and was rare overall. The intervention did not achieve the stated goal of reducing the number of femoral venous catheters placed by residents. This research highlights that residents cannot be trained to perform invasive procedures through clinical experience alone. In addition, it demonstrates that brief educational interventions are also insufficient. Whether a longer and more robust educational intervention might have shown different results is uncertain, but many experts believe that opportunities for deliberate practice6 using standardized and sustained treatments7 can be a powerful tool to boost the procedural skills of physicians.

At the same institution, Lucas and colleagues studied the impact of a procedural service on the number of invasive procedures performed on a general medicine inpatient service.8 They found a 48% increase in procedure attempts when the procedure service staffed by an experienced faulty member was available. However, no improvement in success rate or reduction in complications was demonstrated. Thus, opportunities for trainees to perform procedures increased, but the presence of a faculty member to provide direct supervision did not improve the quality of the procedures accomplished.

Together these reports highlight challenges and opportunities in training residents to perform invasive procedures. Both studies involved the procedural skills of residents. One used an educational intervention, the other featured faculty supervision. Both studies produced outcomes that suggest improved procedural training, but neither improved the actual quality of delivered care. A brief educational intervention increased resident confidence and knowledge but did not increase the quality or number of procedures performed by residents. Opportunities to perform invasive procedures increased dramatically when an experienced attending physician was available to supervise residents. However, more education was not provided, and the quality of procedures performed did not improve.

Given these limitations, how should physicians learn to perform invasive procedures? We endorse a systematic approach to achieve high levels of procedural skills in resident physicians. First, procedures should be carefully selected. Only those essential to future practice should be required. If possible, opportunities should be available for selected trainees to develop skills in performing additional procedures relevant to their future careers. An example would be the opportunity for residents in a hospitalist track to develop proficiency in central venous catheter insertion through clinical experience, didactic education, and rigorous skill assessment. Second, dedicated programs are needed to train and assess residents in procedural skills. Reliance on clinical experience alone is inadequate because of the low frequency at which most procedures are performed and the inability to standardize assessments in routine clinical practice.

Simulation technology is a powerful adjunct to traditional clinical training and has been demonstrated to be highly effective in developing procedural skills in disciplines such as endoscopy9 and laparoscopic surgery.10 At our institution, a simulation‐based training program has been used to help residents achieve11 and maintain12 a high level of skill in performing advanced cardiac life support procedures. We use simulation to provide opportunities for deliberate practice in a controlled environment in which immediate feedback is emphasized and mastery levels are reached. The rigorous curriculum is standardized, but learner progress is individualized depending on the practice time needed to achieve competency standards.

Most important, when training physicians to perform invasive procedures, it is critical to use interventions and training programs that can be linked to improvements in actual clinical care. The studies by Miranda et al. and Lucas et al. highlight the utility of focused educational programs to complement clinical training as well as the positive impact of direct faculty supervision. These results are important starting points for programs to consider as they train and certify residents in required procedural skills. However, much work remains to be done. These studies have revealed that improvements in patient care outcomes are not likely to occur unless robust, learner‐centered educational programs are combined with adequate opportunities for residents to perform procedures under appropriate supervision.

See one, do one, teach one is a refrain familiar to all physicians. Historically, most procedural training has occurred at the bedside. In this model, senior residents, subspecialty fellows, or faculty members would demonstrate procedural skills to junior trainees, who would subsequently practice the procedures on patients, often with uneven, risky results. Acquisition of procedural skills by residents and fellows on inpatient wards is suboptimal for at least 2 reasons beyond the risks to patient safety: (1) clinical priorities are more important than educational priorities in this setting, and (2) the patient, not the medical learner, is the most important person in the room.

Recently, several new factors have challenged the traditional medical education model. For a variety of reasons, general internists currently perform far fewer invasive procedures than they used to.1 A heightened focus on patient safety and quality raises questions about the qualifications needed to perform invasive procedures. Assessment requirements have also become more stringent. The Accreditation Council for Graduate Medical Education (ACGME) now requires the use of measures that yield reliable and valid data to document the competence of trainees performing invasive procedures.2 In 2006 these factors, and the challenge to educate, assess, and certify residents, prompted the American Board of Internal Medicine to revise its certification requirements and remove the need for technical proficiency in several procedures including paracentesis, central venous catheter placement, and thoracentesis.3, 4

Two studies reported in this issue of the Journal of Hospital Medicine highlight important issues about preparing residents to perform invasive procedures. These include the educational limits of routine clinical care and the challenge to design rigorous educational interventions that improve residents' skills. Miranda and colleagues5 designed a clinical trial to evaluate an educational intervention in which residents practiced insertion of subclavian and internal jugular venous catheters under the supervision of a hospitalist faculty member. The goal was to reduce the frequency of femoral venous catheters placed at their institution. Although residents demonstrated increased knowledge and confidence after the educational intervention, the actual number of subclavian and internal jugular venous catheter insertions was lower in the intervention group, and was rare overall. The intervention did not achieve the stated goal of reducing the number of femoral venous catheters placed by residents. This research highlights that residents cannot be trained to perform invasive procedures through clinical experience alone. In addition, it demonstrates that brief educational interventions are also insufficient. Whether a longer and more robust educational intervention might have shown different results is uncertain, but many experts believe that opportunities for deliberate practice6 using standardized and sustained treatments7 can be a powerful tool to boost the procedural skills of physicians.

At the same institution, Lucas and colleagues studied the impact of a procedural service on the number of invasive procedures performed on a general medicine inpatient service.8 They found a 48% increase in procedure attempts when the procedure service staffed by an experienced faulty member was available. However, no improvement in success rate or reduction in complications was demonstrated. Thus, opportunities for trainees to perform procedures increased, but the presence of a faculty member to provide direct supervision did not improve the quality of the procedures accomplished.

Together these reports highlight challenges and opportunities in training residents to perform invasive procedures. Both studies involved the procedural skills of residents. One used an educational intervention, the other featured faculty supervision. Both studies produced outcomes that suggest improved procedural training, but neither improved the actual quality of delivered care. A brief educational intervention increased resident confidence and knowledge but did not increase the quality or number of procedures performed by residents. Opportunities to perform invasive procedures increased dramatically when an experienced attending physician was available to supervise residents. However, more education was not provided, and the quality of procedures performed did not improve.

Given these limitations, how should physicians learn to perform invasive procedures? We endorse a systematic approach to achieve high levels of procedural skills in resident physicians. First, procedures should be carefully selected. Only those essential to future practice should be required. If possible, opportunities should be available for selected trainees to develop skills in performing additional procedures relevant to their future careers. An example would be the opportunity for residents in a hospitalist track to develop proficiency in central venous catheter insertion through clinical experience, didactic education, and rigorous skill assessment. Second, dedicated programs are needed to train and assess residents in procedural skills. Reliance on clinical experience alone is inadequate because of the low frequency at which most procedures are performed and the inability to standardize assessments in routine clinical practice.

Simulation technology is a powerful adjunct to traditional clinical training and has been demonstrated to be highly effective in developing procedural skills in disciplines such as endoscopy9 and laparoscopic surgery.10 At our institution, a simulation‐based training program has been used to help residents achieve11 and maintain12 a high level of skill in performing advanced cardiac life support procedures. We use simulation to provide opportunities for deliberate practice in a controlled environment in which immediate feedback is emphasized and mastery levels are reached. The rigorous curriculum is standardized, but learner progress is individualized depending on the practice time needed to achieve competency standards.

Most important, when training physicians to perform invasive procedures, it is critical to use interventions and training programs that can be linked to improvements in actual clinical care. The studies by Miranda et al. and Lucas et al. highlight the utility of focused educational programs to complement clinical training as well as the positive impact of direct faculty supervision. These results are important starting points for programs to consider as they train and certify residents in required procedural skills. However, much work remains to be done. These studies have revealed that improvements in patient care outcomes are not likely to occur unless robust, learner‐centered educational programs are combined with adequate opportunities for residents to perform procedures under appropriate supervision.