Can Ranking Hospitals on the Basis of Patients’ Travel Distances Improve Quality of 

Care? 

Daniel P. Kessler* 

Stanford University, Hoover Institution, and the National Bureau of Economic Research 

* I would like to thank David Becker for exceptional research assistance, Mark 

Satterthwaite for several very helpful conversations, and Daniel Hosken, Paul Pautler, 

and two anonymous referees for helpful comments. Funding from the U.S. National 

Institutes on Aging and the Federal Trade Commission is greatly appreciated. The views 

expressed in this paper do not necessarily represent those of any agency of the U.S. 

Government or of the author’s institutions. 
2

Abstract 

Conventional outcomes report cards– public disclosure of information about the patientbackground-adjusted health outcomes of individual hospitals and physicians -- may help 

improve quality, but they may also encourage providers to “game” the system by 

avoiding sick and/or seeking healthy patients. In this paper, I propose an alternative 

approach: ranking hospitals on the basis of the travel distances of their Medicare 

patients. At least in theory, a distance report card could dominate conventional outcomes 

report cards: a distance report card might measure quality of care at least as well but 

suffer less from selection problems. I use data on elderly Medicare beneficiaries with 

heart attack and stroke from 1994 and 1999 to show that a distance report card would be 

both valid – that is, correlated with true quality – and able to distinguish confidently 

among hospitals – that is, able to reject at conventional significance levels the hypothesis 

that the true quality of a low-ranked hospital was the same as the quality of the average 

hospital. The hypothetical distance report card I propose compares favorably to 

(although does not necessarily dominate) the California AMI outcomes report card. 
3

Introduction 

In the past few years, policy makers and researchers have given considerable 

attention to outcomes report cards in health care – public disclosure of information about 

the patient-background-adjusted health outcomes of individual hospitals and physicians. 

The health policy community disagrees on the merits of report cards. Supporters of 

report cards argue that they enable patients to identify the best physicians and hospitals, 

while simultaneously giving providers powerful incentives to improve quality. Skeptics 

argue that report cards may encourage providers to “game” the system by avoiding sick 

and/or seeking healthy patients in the following way. One medically appropriate factor in 

a provider’s decision how to treat a patient is that patient’s health status at the time of the 

onset of illness. At least for report cards on surgical treatments (such as cardiac bypass 

surgery, or CABG) or nonemergent conditions, this gives providers the opportunity to 

decline to treat more difficult or complicated patients for valid medical reasons. Even 

though outcomes report cards generally adjust reported health outcomes for differences in 

patients’ characteristics (for otherwise providers who treat the most serious cases would 

necessarily appear to have low quality), developers of report cards can only adjust for 

characteristics that they can observe. Because of the complexity of patient care, 

providers are likely to have better information on patients’ conditions than even the most 

clinically detailed database. Providers therefore may be able to improve their outcomes 

report card by selecting patients on the basis of characteristics that are unobservable to 

the analysts but predictive of good outcomes. Furthermore, report cards may encourage 

gaming even if providers do not have superior information on patients’ conditions (for 

example, if providers are risk-averse; see Dranove et al. 2003 for an explanation). 
4

 Previous empirical research suggests that the debate over outcomes report cards is 

of more than academic importance. On one hand, Hannan et al. (1994) report that the 

CABG report card adopted by New York in 1989 played a significant role in the 

observed decline in that state’s in-hospital CABG mortality rate. On the other hand, 

Dranove et al. (2003) show that the CABG report cards adopted by New York and 

Pennsylvania in the early 1990s led to higher levels of resource use and to worse health 

outcomes, particularly for sicker patients. In particular, they find that patients from New 

York and Pennsylvania admitted to the hospital with a heart attack (AMI) experienced 

greater growth in rates of hospital expenditures, greater growth in rates of readmission to 

the hospital with heart failure and recurrent AMI, and (in some specifications) greater 

growth in mortality, as compared to demographically-similar patients from other states 

that did not adopt report cards. 

 Despite the importance of this debate in health care and related contexts like 

education, little work has sought to investigate whether report cards could be more 

constructive if designed in a way that reduced the incentives for and the social costs of 

provider selection behavior. Some report cards have already altered their design to 

eliminate the feature that is most likely responsible for the provider selection in the New 

York and Pennsylvania programs – the requirement to report on all patients receiving an 

elective operative procedure. California, for example, now publishes AMI outcome 

reports, which because of the emergent nature of a significant fraction of AMIs, are likely 

to be more difficult to game than their procedure-based counterparts (California OSHPD 

2002). Yet, because a significant fraction of AMIs are nonemergent (Ho et al. 1989), 
5

even such an illness-based outcomes report card may suffer from the problems associated 

with selection. 

 In this paper, I propose an alternative approach: ranking hospitals on the basis of 

the travel distances of their Medicare patients. Because Medicare is accepted at 

essentially all US hospitals with equal beneficiary deductibles and copayments, 

differences in quality are the primary reason why beneficiaries would incur the costs of 

traveling to a farther versus a nearer hospital. Thus, at least in theory, hospitals that draw 

patients from farther away would be higher quality than hospitals that draw patients from 

nearby. 

To the extent that this is true empirically, a distance report card could dominate 

conventional outcomes report cards, by measuring quality of care almost as well but 

suffering less from selection problems. To see this, decompose measured quality into 

two components: the portion that is correlated with travel distance, and the portion that is 

orthogonal. Distance report cards rank only on the portion of quality correlated with 

distance; conventional report cards rank on both. If hospitals can more easily game the 

portion orthogonal to distances, and the portion correlated with distances is sufficiently 

large to powerfully distinguish among hospitals on the basis of quality, then gaming of a 

distance report card is likely to be less prevalent and less destructive than gaming of an 

outcomes-based report card. 

Gaming is likely to be less prevalent because travel imposes tangible costs on 

patients, and there is no valid medical reason for selection of patients on the basis of 

distance. So, avoiding nearby (or attracting far-away) patients is likely to be more 

difficult than avoiding sick (or attracting healthy) patients. Gaming is likely to be less 
6

destructive because the social losses from any selection induced by a distance report card 

would be likely smaller than the social losses from selection induced by an outcomes 

report card. Since travel distance is, if anything, negatively correlated with health status 

before treatment (Capps et al. (2001)), publication of a distance-based report card would, 

if anything, encourage providers to seek out sicker patients. Given that Dranove et al. 

(2003) report that the greatest losses from the CABG report cards arise out of avoidance 

of sicker patients, the distortion of providers’ incentives that might occur with a distancebased report card is unlikely to be a serious policy problem. 

Finally, rankings on the basis of travel distance offer other advantages over 

outcomes report cards. In contrast to the unidimensional measures of quality generally 

contained in even very clinically detailed outcomes report cards, information about 

patients’ willingness to incur travel costs both incorporates attributes of quality other than 

mortality risk and weights those attributes according to patients’ revealed preferences. 

The purpose of this paper is to estimate the extent of the correlation between 

distance and quality in order to investigate the feasibility of a hypothetical distance report 

card. First, I assess the validity of various types of distance report cards by testing 

whether hospitals with patient populations who travel farther to obtain care have better 

patient health outcomes, controlling for differences in patients’ health status and other 

characteristics. I find that the fraction of patients at a hospital who have traveled a long 

distance is significantly negatively correlated with that hospital’s patients’ expected 

mortality from AMI and stroke, holding other factors constant. Second, I compare the 

ability of a hypothetical distance report card to distinguish confidently among hospitals 

on the basis of expected mortality to the ability of the California AMI report card. The 
7

hypothetical distance-based report card I propose is more powerful than the California 

AMI outcomes report card at distinguishing high-mortality hospitals from the average 

hospital, but less powerful than the California AMI report card at distinguishing lowmortality hospitals from the average hospital. 

The paper proceeds in three parts. In part I, I describe the data and models I use 

to investigate the two hypotheses above. Part II discusses the results, and part III 

concludes with some suggestions for policy reform and further research. 

I. Data and Models 

Data 

 I use data from three sources. First, I use comprehensive individual-level 

longitudinal Medicare claims data from the Centers for Medicare and Medicaid Services 

(CMS) on the health outcomes and medical utilization of virtually all non-rural elderly 

fee-for-service Medicare beneficiaries with a new occurrence of heart attack (AMI) in 

1994 or 1999 or stroke in 1994. I classify strokes into one of three types (in order of 

declining seriousness): an event in which a blood vessel in the brain bursts, spilling 

blood into the spaces surrounding the brain cells (hemorrhagic stroke, primary ICD9 

diagnosis code of 434 or 436); an event in which the blood supply to the part of the brain 

is suddenly interrupted by a blood clot (ischemic stroke, primary ICD9 diagnosis code of 

435 or 362.34); and a transient ischemic attack (an ischemic stroke that lasts only a few 

minutes) or other adverse cerebrovascular event (primary ICD9 diagnosis code of 437). 

I measure outcomes with five variables: mortality within one year of initial admission 

(including deaths out of hospital), readmission for subsequent AMI or heart failure (HF) 

within one year (AMI patients only), and readmission for subsequent stroke or stroke 
8

complications (including paralysis, pneumonia, and urinary tract infections) within one 

year (stroke patients only). Measures of the occurrence of complications were obtained 

by abstracting data on the principal diagnosis for all subsequent admissions (not counting 

transfers and readmissions within 30 days of the index admission) in the year following 

the patient’s initial admission. Treatment of AMI patients is intended to prevent 

subsequent AMIs if possible, and the occurrence of HF requiring hospitalization is 

evidence that the damage to the patient’s heart from ischemic disease has serious 

functional consequences. I also measure a patient’s total hospital utilization with the sum 

of acute and non-acute (mostly skilled nursing) Medicare expenditures (including 

deductibles and copayments) in the year following admission for the study illness. 

Expenditures include all inpatient reimbursements (including copayments and deductibles 

not paid by Medicare) from insurance claims for all hospitalizations in the year following 

each patient’s initial admission. Data on patient demographic characteristics were 

obtained from CMS’s HISKEW enrollment files, with death dates based on death reports 

validated by the Social Security Administration. 

 Second, I use data on U.S. hospital characteristics collected by the American 

Hospital Association (AHA). The response rate of hospitals to the AHA survey is greater 

than 90 percent, with response rates above 95 percent for large hospitals (>300 beds). 

Third, I use a hospital system database constructed from multiple sources (see Madison 

2001 for a detailed discussion). The AHA survey contains extensive year-by-year 

information on hospital system membership status. Validity checking indicated that the 

universe of systems and system hospitals, and the timing of hospitals’ system 

membership, as defined by AHA did not conform to discussion of hospital systems in the 
9

trade press such as Modern Healthcare. We therefore created our own system database 

based on a combination of the AHA and other sources. 

Models 

 I model the determinants of the intensity of treatment and health outcomes of 

individual elderly Medicare patients with AMI and stroke. In zip code z lying in MSA of 

size Mz (Mz is a vector of 5 indicator variables capturing six size categories) during year t 

= 1994 or 1999, observational units in the analysis consist of individuals i=1,. . ., Nzt who 

are initially admitted to hospital j = 1, ..., J with a new occurrence of illness. Each patient 

has observable demographic characteristics Xizt: four age indicator variables (70-74 

years, 75-79 years, 80-89 years, and 90-99 years; omitted group is 65-69 years), gender, 

and black/nonblack race; and a full set of interaction effects between age, gender, and 

race. Each patient has health status upon admission to the hospital Aizt, where Aizt = 1 if 

the patient had acute care hospital utilization in the year prior to his/her illness. The 

patient then receives treatment that results in Medicare expenditures of Rizt. The patient 

has a health outcome Oizt, possibly affected by the intensity of treatment received, where 

a higher value denotes a more adverse outcome (O is binary in all of our outcome 

models). 

 I match to each patient by hospital of admission and year several hospital 

characteristics (Kjt) that may affect treatment and quality of care and are likely to be 

correlated with the willingness of patients to incur travel costs to attend the hospital: 

hospital bed size (indicators included for medium (100-300 beds) and large (300+ beds) 

hospitals, omitted group is small hospitals); hospital teaching status; hospital ownership 
10

status (indicators included for for-profit and public ownership, omitted group is nonprofit 

ownership); whether the hospital had a catheterization (cardiac imaging) laboratory; and 

whether the hospital was high-volume (more than 75 AMI admissions). In addition, I 

match by hospital of admission one of five measures of the relative travel distances of 

that hospital’s Medicare patients (Ljt), where relative travel distance is defined as the 

distance from the patient to the hospital divided by the distance from the patient to his or 

her nearest hospital.1

 These five measures include the 25th percentile of patients’ relative 

travel distances at the hospital; the median patient’s relative travel distance at the 

hospital; the 75th percentile of patients’ relative travel distances at the hospital; the 

fraction of patients at the hospital with a relative travel distance greater than 1 (i.e., for 

whom the hospital was not the closest choice); and the fraction of patients at the hospital 

with a relative travel distance greater than 1.5 (i.e., for whom the hospital was at least 

150% of the distance to the closest). 

 I estimate linear models of expenditures and outcomes as a function of 3-digit 

zip-code-fixed-effects ("z); year-fixed-effects (2t) that vary by MSA size Mz, to capture 

differences in cost and quality due, for example, to differences in the diffusion of 

technology in more versus less populous areas; patient demographic characteristics (Xizt); 

health status (Aizt); hospital characteristics (Kjt); and hospital quality as proxied by 

relative travel distance (Ljt): 

ln(Rizt)

Oizt = "z + Mz*2t + XiztN + 0Aizt + Kjt( + *Ljt + ,izt, (1) 

where ,izt is an independently-distributed error term, with E(,izt |...) = 0. 

 

1

 To reduce measurement error, patients who chose a hospital within 1 mile of their residence are 

categorized as having attended their closest hospital, even if there was an alternative hospital which had a 

calculated distance of less than 1 mile. 
11

II. Results 

Tables 1 and 2 show that a ranking of hospitals based on Medicare beneficiaries’ 

travel distances is negatively correlated with hospitals’ average rates of adverse health 

outcomes and positively correlated with hospitals’ average Medicare expenditures. Table 

1 presents the average rate of all-cause mortality, cardiac complications, and intensity of 

treatment for patients with AMI in 1994 and 1999 (N = 285,367) initially admitted to 

“high-distance” hospitals – those with their median patient’s relative travel distance 

greater than 1 -- and “low-distance” hospitals—those with their median patient’s relative 

travel distance equal to 1. Table 1 analyzes the care of beneficiaries admitted to the 

hospital separately for patients with and without an inpatient hospital admission in the 

year before their AMI. 

The first column of Table 1 shows that patients who were admitted for AMI to a 

high-distance hospital experienced better health outcomes than did patients who were 

admitted to a low-distance hospital. Patients admitted to a high distance hospital 

experienced .71 percentage points (=33.54 - 34.25), or 2.1 percent (=.71/33.54) lower 

one-year all-cause mortality than patients admitted to a low-distance hospital. The effect 

of admission to a high-distance hospital on readmission rates are more mixed, with rates 

of readmission with AMI slightly lower and rates of readmission with CHF slightly 

higher, indicating that the additional survivors were not in markedly poor cardiac health. 

In any event, this result should be interpreted with some caution. Readmission rates 

measure health outcomes only imperfectly; some readmissions to the hospital may 

represent a decision to provide additional treatment conditional on health rather than a 

decline in health. 
12

The second and third columns of Table 1 show that the mortality gain to attending 

a high-distance hospital was slightly greater for sicker patients, consistent with a model in 

which the benefits of quality are larger for the more severely ill. PRIOR-ADM measures 

the health status of a patient prior to admission, where PRIOR-ADM =1 for every patient 

who had an inpatient hospital admission in the year prior to their admission for AMI (the 

population mean of PRIOR-ADM is .30). Among those patients who were in good health 

at the time of their AMI (N= 199,152), those who were admitted to a high-distance 

hospital experienced .7 percentage points lower mortality. Among those patients who 

were in poor health at the time of their AMI (N=86,215), those who were admitted to a 

high-distance hospital experienced .85 percentage points lower mortality. Table 1 also 

suggests one reason why high-distance hospitals have better outcomes: more intensive 

treatment. Patients’ hospital expenditures in the year after their AMI were 4.9 percent (= 

(21130-20149) / 20149) higher at high-distance hospitals. 

Table 2 presents analogous descriptive statistics on the complications, mortality, 

health status, and utilization of patients with stroke in 1994 (all strokes, N = 252,742; 

hemorrhagic strokes, N = 29,460; ischemic strokes, N = 156,260; transient ischemic 

attack and other cerebrovascular event, N = 67,022). As with AMI patients, mortality of 

high-distance patients is lower; utilization of high-distance patients is higher; and the 

health status of high-distance patients is worse. The average mortality gain from 

admission to a high-distance versus a low-distance hospital is smaller for stroke (.09 

percentage points) than for AMI, but the mortality gain from admission to a high-distance 

hospital for the most severely ill (hemorrhagic) stroke patients, 1.72 percentage points, is 

substantially larger. However, the larger mortality gain for this population is 
13

accompanied by a higher differential rate of readmission with complications. 

Hemorrhagic stroke patients admitted to a high-distance hospital were .89 percentage 

points more likely to be readmitted with stroke and .6 percentage points more likely to be 

readmitted with other complications, as compared to patients admitted to a low-distance 

hospital. For the reasons above, this result should be interpreted with some caution; 

readmissions for patients with stroke frequently represent readmission to nonacute 

facilities for rehabilitation, rather than readmission for treatment of complications. 

Consistent with previous work, there is no evidence that patients admitted to highdistance hospitals are healthier than their low-distance-hospital counterparts; if anything, 

they are slightly sicker. Statistics not presented in Table 1 show that 30.3 percent of 

patients admitted to a high-distance hospital had an inpatient admission in the year prior 

to AMI, as compared to 30.1 percent of patients admitted to a low-distance hospital. 

Patients’ prior year expenditures conditional on PRIOR-ADM = 1 were 5.3 percent (= 

(12668-12031) / 12031) higher for those initially admitted to a high-distance hospital, 

suggesting that these patients were slightly sicker on admission. Differences in the health 

status of patients with stroke admitted to high-distance versus low-distance hospitals are 

similar. Statistics not presented in Table 2 show that 34 percent of patients admitted to a 

high-distance hospital had an inpatient admission in the year prior to stroke, as compared 

to 33.9 percent of patients admitted to a low-distance hospital. For this reason, the 

negative correlation between distance and subsequent health outcomes is not likely due to 

differences in patients’ health status at high-distance versus low-distance hospitals. 

 Table 3 describes the distributions of the five distance-based measures of hospital 

quality that I analyze in the regression models that follow. The top panel of the table 
14

presents the (patient-weighted) distribution of hospitals’ travel distances for AMI patients 

from 1994 and 1999; the bottom panel of the table presents the distribution of hospitals’ 

travel distances for stroke patients. The top three rows of each panel describe the 

distribution across hospitals of the distribution of patients’ relative travel distances. The 

average median relative travel distance is 1.8 – that is, at the average hospital, half the 

patients traveled more than 180% of the distance to their closest hospital, and half 

traveled less. The average median relative travel distance is larger than the median 

median relative travel distance because of a set of hospitals that have very high median 

patient travel distances, i.e., attract most of their patients from afar. According to the 

bottom two rows of each panel, the fraction of patients at a hospital for whom the 

hospital was not their closest is distributed uniformly. For AMI patients, the mean 

(median) of the distribution is .5, the 10th percentile is .1, and the 90th percentile is .91. 

For stroke patients, the distribution is slightly heavier at the top of the distribution (higher 

mean and bottom quantiles, lower top quantiles). Not surprisingly, using a more stringent 

definition of high-distance hospital (i.e., classifying a hospital as high-distance if it was at 

least 150% of the distance to the patient’s closest hospital) shifts the distribution 

downward roughly proportionately. 

 Tables 4 and 5 present estimates of * from equation (1) for patients with AMI and 

stroke, respectively. The top three rows of table 4 present estimates of the effect of the 

25th percentile, median, and 75th percentile of a hospital’s AMI patients’ travel distance 

on mortality, cardiac complications, and Medicare expenditures. The estimated effects of 

these three quantiles of a hospital’s patients’ relative travel distance on both outcomes 

and expenditures are small and statistically insignificant. The bottom two rows of Table 
15

4 present estimates of the effect of the proportion of AMI patients at a hospital for whom 

the hospital was high-distance, where a high-distance hospital is defined either as a 

hospital that is not a patient’s closest hospital or as a hospital that is at least 150% of the 

distance to a patient’s closest hospital. These two measures of patient travel distance are 

significantly negatively correlated with adverse outcomes and positively correlated with 

expenditures. Moving from a hospital that was the closest choice for all of its patients to 

a hospital that was the closest choice for none of its patients leads, in expectation, to 1.03 

percentage points lower mortality and approximately 2.9 percent higher Medicare 

expenditures, holding other factors constant. These effects are of the same order of 

magnitude as the raw differences in Table 1. Using the more stringent definition of a 

high-distance hospital leads to greater mortality and expenditure effects. The effects of 

these measures of travel distance on complications rates are small and statistically 

insignificant. 

 Moving an AMI patient from a low-distance to a high-distance hospital is a costeffective way to improve quality of care. For example, moving a patient from a hospital 

that was the closest choice for all versus none of its patients leads to an increase in 

Medicare expenditures of approximately $632 (=.0286*$22,119 average 1999 AMI 

Medicare expenditures) and a decrease in mortality of 1.03 percentage points, which 

implies that the additional treatment at high-distance versus low-distance hospitals is 

efficient assuming a cost per year of life saved of at least $61,419 (=632/.0103). This is 

well below the value of an added year of life that would be inferred from most published 

studies (Viscusi 1993; Duke University Center for Health Policy, Law, and Management 

2004). Using the more stringent definition of a high-distance hospital leads to virtually 
16

the same implied cost-effectiveness of treatment at a high-distance versus low-distance 

hospital. The same implied cost-effectiveness ratios hold for the additional treatment 

obtained by moving a patient to an incrementally higher-distance hospital, although the 

absolute expenditure increase and mortality decrease is smaller (multiply numerator and 

denominator of ratio by an arbitrarily small number T). 

 Table 5 presents estimates of the effect of the proportion of stroke patients at each 

hospital for whom the hospital was high distance on the outcomes and expenditures for 

stroke. The top panel of table 5 presents estimates of the effect of travel distance on all 

stroke patients grouped together; the bottom three panels present estimates for each type 

of stroke patient separately. The effects of travel distance on stroke patients’ outcomes 

and Medicare expenditures are larger than the effect of distance on AMI patients. 

According to the top panel of the table, moving from a hospital that was high distance for 

none versus all of its patients leads, in expectation, to 1.1 to 1.83 percentage points lower 

stroke mortality, and to approximately 5.1 to 7.4 percent higher Medicare expenditures, 

holding other factors constant. In cost-effectiveness terms, moving a stroke patient from 

a hospital at which none versus all of its patients were high-distance leads to additional 

Medicare treatments that are efficient assuming a value per year of life saved of at least 

$68,212 (=.0737*$16,937/.0183), which is very similar to the effect for AMI. For 

patients with transient ischemic attack and other adverse cerebrovascular events, choice 

of hospital based on its patients’ travel distance translates into a maximum mortality gain 

of 13 percent (= 1.75 / 13.45 percentage points average mortality) which is substantial. 

This mortality effect is especially striking given that it is accompanied by a decreased 

rate of subsequent readmission for stroke and by insignificantly higher Medicare 
17

expenditures. In other words, moving a patient with transient ischemic attack to a higherdistance hospital is necessarily welfare-improving. For sicker (hemorrhagic) stroke 

patients, the substantial mortality gain of admission to a high-distance hospital is 

accompanied by an increased rate of readmission rate both for stroke and for other 

complications, indicating that they may be in more marginal health. However, in results 

not presented in the table, if the readmission variables are defined to exclude all nonacute 

care hospital (largely skilled nursing) admissions, the estimated effect of distance on 

readmission declines and becomes statistically insignificant. Because nonacute 

admission after stroke may measure both the existence of complications and the supply of 

additional services conditional on health status, this finding mitigates the negative 

implications of the estimated effect of distance on complications in this population. 

In order to investigate whether the estimated effects of a hospital’s patients’ travel 

distance are due to unobserved differences in patients’ health status, I estimated * with 

instrumental variables (IV) methods, using as instruments functions of patients’ distances 

to high-distance hospitals. Intuitively, these methods compare the outcomes of patients 

who live nearby to a high-distance hospital versus those who live far away from one. 

Under the assumption that patient residential location decisions are uncorrelated with 

their health status, IV estimates of the effect of treatment at a high-distance hospital will 

be consistent, regardless of differences in the characteristics of patients who are actually 

treated at high-distance versus low-distance hospitals. 

 I experimented with several different specifications. One specification used as 

instruments the proportion of patients at the nearest hospital for whom the hospital was 

not the closest choice; the relative distance to the nearest hospital that was not the closest 
18

choice (i.e., the distance to the nearest high-distance hospital divided by the distance to 

the nearest hospital) for at least 75 percent its patients; the relative distance to the nearest 

hospital that was not the closest choice for at least 50 percent of its patients; and the 

relative distance to the nearest hospital that was not the closest choice for at least 25 

percent of its patients. An alternative specification estimated the effect of admission to a 

hospital that was not the closest choice for at least 50 percent of its patients, using as an 

instrument each patient’s relative distance to the nearest hospital that was not the closest 

choice for at least 50 percent of its patients. Estimates of * from these models were very 

sensitive to choice of specification and had generally large standard errors 

Table 6 investigates the second key concern about distance-based report cards: 

can they distinguish confidently between hospitals on the basis of quality? For each of 

the five patient populations I examine (AMI, all stroke, and three types of strokes 

separately), table 6 reports the expected deviation from average mortality for a patient 

admitted to hospitals of different qualities, and the 98 percent confidence intervals around 

the expected deviations. The first column of the table reproduces the estimate of * from 

equation (1) from tables 4 and 5. The second column reports the expected mortality for a 

reference patient admitted to the hospital at the 10th percentile of the distribution, i.e., the 

10th percentile of the quality measure**, less the expected mortality for a reference 

patient admitted to the average hospital, with upper and lower 98 percent confidence 

intervals. The third column reports the expected mortality for a reference patient 

admitted to the hospital at the 90th percentile of the distribution less the expected 

mortality for a reference patient admitted to the average hospital. 
19

Table 6 shows that a hypothetical distance report card can distinguish confidently 

the worst decile of hospitals from the average hospital. For every patient population and 

each of the two distance measures, the lower bound of the 98 percent confidence interval 

around the expected deviation from average mortality for a patient admitted to the 10th

percentile hospital is greater than zero. In fact, for AMI patients, calculations not in table 

6 show that the lower bound of the 98 percent confidence interval around the expected 

deviation from average mortality for a patient admitted to the 25th percentile hospital is 

greater than zero as well. The hypothetical distance report card is less able to powerfully 

distinguish the best hospitals from the average hospital. For no patient population is the 

upper bound of the 98 percent confidence interval around the expected deviation from 

average mortality for a patient admitted to the 90th percentile hospital less than zero. 

In comparison to the California AMI outcomes report card, the hypothetical 

distance-based report card I propose is more powerful at distinguishing low-quality 

hospitals from the average hospital, but less powerful at distinguishing high-quality 

hospitals from the average hospital. According to the table on page 16 of the 1996-98 

report (California OSHPD 2002), the AMI outcomes report card found that 8.0 percent of 

hospitals had risk-adjusted mortality rates that were better than expected in at least one 

model, and 10.6 percent of hospitals had risk-adjusted mortality rates that were worse 

than expected in at least one model. This is the same as the number of hospitals in the 

report for which the upper (lower) bound of the 98 percent confidence interval of the 

risk-adjusted mortality rate fell below (above) the state average. In other words, the 

California AMI outcomes report card can distinguish the top 8.0 percent and the bottom 

10.6 percent of hospitals from the mean. The hypothetical distance-based report card can 
20

distinguish confidently up to the 25th percentile from the mean; however, it can not 

confidently distinguish the top decile (or even the top 8 percent of hospitals) from the 

mean. 

III. Conclusion 

 How useful would be a hospital report card based on patients’ travel distances? A 

report card based on patients’ willingness to travel for treatment could dominate 

conventional outcomes report cards, if it suffered less from selection problems but 

measured quality at least as well. Although the theoretical case for a distance report card 

on grounds of selection is clear, the ability of a distance report card to measure quality is 

an unresolved empirical issue. For a distance report card to be useful in practice, it would 

need to be both valid – that is, correlated with true quality – and able to distinguish 

confidently among hospitals – that is, able to reject at conventional significance levels the 

hypothesis that the true quality of a low-ranked hospital was the same as the quality of 

the average hospital. 

In this paper, I propose a specific distance report card, and document empirically 

that it would be both valid and powerful. I assign to each non-rural general 

medical/surgical hospital in the US a ranking based on the fraction of Medicare patients 

at the hospital with one or more specific illnesses for whom the hospital was not their 

closest choice (and variants of this, such as the fraction of patients for whom the hospital 

was at least 150% of the distance to their closest choice). I use longitudinal claims data 

on elderly Medicare beneficiaries admitted to the hospital with cardiac and 

cerebrovascular illnesses in 1994 and 1999, matched with data on the characteristics of 
21

all general medical/surgical hospitals. These data include information on each patients’ 

demographic characteristics, type and severity of illness on admission to the hospital, 

subsequent Medicare expenditures, and health outcomes, measured by all-cause one-year 

mortality and readmission to the hospital with complications. 

I report four key findings. First, hospitals with patient populations who travel 

farther to obtain care have statistically significantly better outcomes, holding other factors 

constant. For example, for a patient with heart attack, moving from a hospital that was 

the closest choice for all of its patients to a hospital that was the closest choice for none 

of its patients leads, in expectation, to about a percentage point significantly lower 

mortality with no measurable increase in cardiac complications – a small, but nontrivial 

effect. 

Second, the implied cost-effectiveness of the incremental treatment at a highdistance versus a low-distance hospital is high by conventional standards. The better 

outcomes at high-distance versus low-distance hospitals are due in part to more intensive 

treatment; at least for some populations, patients admitted to high-distance hospitals have 

slightly higher average Medicare expenditures in the year following their illness than do 

patients admitted to low-distance hospitals. For AMI and stroke patients in aggregate, 

moving from a hospital at which none to one at which all of its patients were highdistance leads to additional Medicare treatments that are efficient assuming a value per 

year of life saved of $60,000 to $70,000 – by conventional standards, a low threshold to 

meet. Furthermore, for less severely ill patients with stroke (those with transient 

ischemic attack and other adverse cerebrovascular events), choice of hospital based on its 

patients’ travel distance translates into a mortality gain of 13 percent (= 1.75 percentage 
22

points/ 13.45) percentage points average mortality) which is substantial. This mortality 

effect is especially striking given that it is accompanied by a decreased rate of subsequent 

readmission for stroke and by insignificantly higher Medicare expenditures. At least for 

these patients, choice of hospital based on distance would be unambiguously welfareimproving. 

Third, there is no evidence that the better outcomes of patients at high-distance 

hospitals are due to more their favorable health status on admission; if anything, the 

opposite is (weakly) true. Patients admitted to a high distance hospital are slightly more 

likely to have had an inpatient admission in the year prior to the onset of illness, and 

conditional on an admission, had higher Medicare hospital expenditure during that prior 

year. 

Fourth, a hypothetical distance report card can distinguish confidently the worst 

hospitals from the average hospital. For every patient population and each of the two 

distance measures, the lower bound of the 98 percent confidence interval around the 

expected deviation from average mortality for a patient admitted to the 10th percentile 

hospital is greater than zero. For AMI patients, moreover, a distance-based report card 

can distinguish the bottom quarter of hospitals from the average. The hypothetical 

distance report card is less able to powerfully distinguish the best hospitals from the 

average hospital, although from a policy perspective, this may be less important. This 

compares favorably to (although does not necessarily dominate) existing outcomes report 

cards, such as the California AMI outcomes report card. According to the California 

Office of Statewide Health Planning and Development, the AMI outcomes report card 
23

can confidently distinguish the top 8.0 percent and the bottom 10.6 percent of hospitals 

from the mean. 

For these reasons, I conclude that distance based report cards can serve as a useful 

measure of hospital quality. Future research might explore the power of other distancebased reporting mechanisms and the validity and power of using distance report cards for 

other illnesses and other patient populations. 
24

 

Table 1: Health outcomes, health expenditures, and health on admission of 

elderly medicare beneficiaries with AMI 

Admitted to high-distance and low-distance hospitals, 1994 and 1999 

All patients and those with and without an inpatient admission in the year prior to 

AMI 

 Total Good health on admission: Poor health on admission:

 PRIOR-ADM = 0 PRIOR-ADM = 1 

365 day mortality 

High Distance Hospital 33.54% 29.23% 43.44% 

Low Distance Hospital 34.25% 29.93% 44.29% 

 

365 day CHF readmission 

High Distance Hospital 8.75% 6.95% 12.86% 

Low Distance Hospital 8.61% 6.85% 12.68% 

 

365 day AMI readmission 

High Distance Hospital 5.44% 4.75% 7.03% 

Low Distance Hospital 5.56% 4.95% 6.96% 

 

365 day prior to admission inpatient expenditures 

High Distance Hospital $3,844 $0 $12,668 

Low Distance Hospital $3,618 $0 $12,031 

 

365 day total inpatient expenditures 

High Distance Hospital $21,130 $20,562 $22,433 

Low Distance Hospital $20,149 $19,805 $20,949 
25

Table 2: Health outcomes, health expenditures, and health on admission of 

elderly Medicare beneficiaries with stroke 

Admitted to high-distance and low-distance hospitals, 1994 

 All strokes Hemorrhagic Occlusive 

Transient 

Ischemic 

 Strokes Strokes Attack/Other 

365 day mortality

High Distance Hospital 29.67% 51.85% 32.19% 13.19% 

Low Distance Hospital 29.76% 53.57% 32.58% 13.76% 

 

365 day Stroke readmission

High Distance Hospital 15.00% 13.46% 16.10% 13.13% 

Low Distance Hospital 14.79% 12.57% 15.70% 13.59% 

 

365 day Other Complication Readmission 

High Distance Hospital 7.36% 6.61% 8.03% 6.13% 

Low Distance Hospital 7.25% 5.96% 7.86% 6.38% 

 

365 day prior to admission inpatient expenditures

High Distance Hospital $4,059 $3,734 $3,899 $4,593 

Low Distance Hospital $3,802 $3,511 $3,656 $4,255 

 

365 day total inpatient expenditures

High Distance Hospital $17,411 $21,892 $19,032 $11,462 

Low Distance Hospital $16,327 $20,026 $17,977 $11,052 
26

Table 3: Patient-weighted distributions 

of hospitals’ distance-based quality measures for 

elderly Medicare beneficiaries with AMI and stroke, 1994-1999 

Average 10th percentile Median 90th percentile 

AMI, 1994 and 1999 

25th pctile of relative 

travel distance of 

patients at your hospital 1.18 1.00 1.00 1.60 

Median relative travel 

distance of patients at 

your hospital 1.82 1.00 1.02 2.57 

75th pctile of relative 

travel distance of 

patients at your hospital 4.14 1.00 1.91 6.57 

Fraction of patients at 

your hospital for whom 

hospital was not their 

closest 50.6% 10.2% 50.0% 91.4% 

Fraction of patients at 

your hospital for whom 

hospital was > 150% of 

distance to their closest 38.7% 7.1% 34.6% 78.1% 

Stroke, 1994 

25th pctile of relative 

travel distance of 

patients at your hospital 1.18 1.00 1.00 1.50 

Median relative travel 

distance of patients at 

your hospital 1.79 1.00 1.11 2.64 

75th pctile of relative 

travel distance of 

patients at your hospital 4.21 1.00 2.21 6.88 

Fraction of patients at 

your hospital for whom 

hospital was not their 

closest 52.9% 13.5% 53.9% 90.3% 

Fraction of patients at 

your hospital for whom 

hospital was > 150% of 

distance to their closest 38.5% 9.2% 36.5% 71.2% 
27

Table 4: Effect of distance-based hospital quality measures on 

health outcomes and health expenditures of 

elderly Medicare beneficiaries with AMI, 1994 and 1999 

(standard errors in parentheses) 

1year mortality

1year AMI 

readmit rate 

1year CHF 

readmit rate 

Ln(1yr total 

expenditure) 

25th pctile of relative 

travel distance of 

patients at your hospital 

0.05 

(0.05) 

-0.01 

(0.02) 

0.00 

(0.03) 

0.16 

(0.10) 

Median relative travel 

distance of patients at 

your hospital 

0.00 

(0.01) 

0.00 

(0.01) 

0.00 

(0.01) 

0.01 

(0.03) 

75th pctile of relative 

travel distance of 

patients at your hospital 

0.01 

(0.01) 

0.00 

(0.00) 

0.00 

(0.00) 

0.02 

(0.01) 

Fraction of patients at 

your hospital for whom 

hospital was not their 

closest 

-1.03 

(0.38) 

-0.31 

(0.19) 

0.00 

(0.23) 

2.86 

(0.85) 

Fraction of patients at 

your hospital for whom 

hospital was > 150% of 

distance to their closest 

-1.33 

(0.41) 

-0.25 

(0.21) 

0.13 

(0.25) 

3.67 

(0.92) 
28

 

 

Table 5: Effect of distance-based hospital quality measures on 

health outcomes and health expenditures of 

elderly Medicare beneficiaries with stroke, 1994 

(standard errors in parentheses) 

1year mortality

1year stroke 

readmit rate 

1year other 

complication 

readmit rate 

Ln(1yr total 

expenditure) 

All Strokes 

Fraction of patients at 

your hospital for whom 

hospital was not their 

closest 

-1.10 

(0.41) 

-0.08 

(0.34) 

0.14 

(0.25) 

5.05 

(0.88) 

Fraction of patients at 

your hospital for whom 

hospital was > 150% of 

distance to their closest 

-1.83 

(0.50) 

0.11 

(0.41) 

0.25 

(0.30) 

7.37 

(1.06) 

Hemorrhagic Strokes 

Fraction of patients at 

your hospital for whom 

hospital was not their 

closest 

-2.93 

(1.44) 

1.57 

(0.99) 

0.96 

(0.71) 

12.31 

(2.79) 

Fraction of patients at 

your hospital for whom 

hospital was > 150% of 

distance to their closest 

-4.17 

(1.70) 

2.48 

(1.16) 

1.59 

(0.84) 

19.65 

(3.29) 

Occlusive Strokes 

Fraction of patients at 

your hospital for whom 

hospital was not their 

closest 

-0.87 

(0.56) 

0.28 

(0.45) 

0.11 

(0.33) 

5.56 

(1.08) 

Fraction of patients at 

your hospital for whom 

hospital was > 150% of 

distance to their closest 

-1.49 

(0.67) 

0.36 

(0.54) 

0.14 

(0.40) 

6.86 

(1.30) 

Transient Ischemic 

Attack/Other 

Fraction of patients at 

your hospital for whom 

hospital was not their 

closest 

-0.93 

(0.62) 

-1.51 

(0.63) 

-0.03 

(0.45) 

1.04 

(1.77) 

Fraction of patients at 

your hospital for whom 

hospital was > 150% of 

distance to their closest 

-1.75 

(0.75) 

-1.53 

(0.76) 

-0.07 

(0.54) 

2.55 

(2.14) 
29

Table 6: Expected deviation from average mortality for a patient admitted to hospitals of 

various qualities, elderly Medicare beneficiaries with AMI and stroke 

(standard errors in parentheses) 

[98 percent confidence intervals in brackets]

Expected deviation from average 

mortality for a patient admitted to... 

Estimated effect of a 

unit change in 

quality index 

(standard error) 

from table 4-5 

The 10th percentile 

hospital 

[confidence interval] 

The 90th percentile 

hospital 

[confidence interval]

AMI 

Fraction of patients at your hospital 

for whom hospital was not their 

closest 

-1.03 

(0.38) 

0.41 

[0.32,0.50] 

-0.42 

[-1.23,0.38] 

Fraction of patients at your hospital 

for whom hospital was > 150% of 

distance to their closest 

-1.33 

(0.41) 

0.42 

[0.35,0.48] 

-0.53 

[-1.27,0.21] 

All Strokes 

Fraction of patients at your hospital 

for whom hospital was not their 

closest 

-1.10 

(0.41) 

0.43 

[0.30,0.56] 

-0.41 

[-1.27,0.45] 

Fraction of patients at your hospital 

for whom hospital was > 150% of 

distance to their closest 

-1.83 

(0.50) 

0.53 

[0.42,0.64] 

-0.60 

[-1.43,0.22] 

Hemorrhagic Strokes 

Fraction of patients at your hospital 

for whom hospital was not their 

closest 

-2.93 

(1.44) 

1.16 

[0.64,1.67] 

-1.07 

[-4.12,1.99] 

Fraction of patients at your hospital 

for whom hospital was > 150% of 

distance to their closest 

-4.17 

(1.70) 

1.27 

[0.86,1.68] 

-1.42 

[-4.37,1.53] 

Occlusive Strokes 

Fraction of patients at your hospital 

for whom hospital was not their 

closest 

-0.87 

(0.56) 

0.35 

[0.18,0.52] 

-0.33 

[-1.50,0.85] 

Fraction of patients at your hospital 

for whom hospital was > 150% of 

distance to their closest 

-1.49 

(0.67) 

0.43 

[0.29,0.58] 

-0.49 

[-1.59,0.62] 

Transient Ischemic Attack/Other 

Fraction of patients at your hospital 

for whom hospital was not their 

closest 

-0.93 

(0.62) 

0.36 

[0.17,0.56] 

-0.35 

[-1.64,0.95] 

Fraction of patients at your hospital 

for whom hospital was > 150% of 

distance to their closest 

-1.75 

(0.75) 

0.51 

[0.35,0.67] 

-0.56 

[-1.78,0.66] 
30

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