Damage Caps and Defensive Medicine: Reexamination with Patient‐Level Data

• Author: Ali Moghtaderi,Steven Farmer,Bernard Black
• Date: 01 March 2019
• Published date: 01 March 2019
• DOI: http://doi.org/10.1111/jels.12208

Journal of Empirical Legal Studies

Volume 16, Issue 1, 26–68, March 2019

Damage Caps and Defensive Medicine:

Reexamination with Patient-Level Data

Ali Moghtaderi,*Steven Farmer, and Bernard Black

Physicians often claim that they practice “defensive medicine,” including ordering extra

imaging and laboratory tests, due to fear of malpractice liability. Caps on noneconomic

damages are the principal proposed remedy. Do these caps in fact reduce testing, overall

health-care spending, or both? We study the effects of “third-wave” damage caps, adopted

in the 2000s, on specific areas that are expected to be sensitive to med mal risk: imaging

rates, cardiac interventions, and lab and radiology spending, using patient-level data, with

extensive fixed effects and patient-level covariates. We find heterogeneous effects. Rates

for the principal imaging tests rise, as does Medicare Part B spending on laboratory and

radiology tests. In contrast, cardiac intervention rates (left-heart catheterization, stenting,

and bypass surgery) do not rise (and likely fall). We find some evidence that overall Medi-

care Part B rises, but variable results for Part A spending. We find no evidence that caps

affect mortality.

I. Introduction

Physicians often claim that they practice “defensive medicine,” notably ordering extra

imaging and laboratory tests, due to fear of medical malpractice (“med mal”) liability,

which drives up health-care costs. The concept of defensive medicine has no precise defi-

nition, but includes conducting tests and procedures with no (or even negative) clinical

value, or whose value is too low to justify the associated cost. Imaging and laboratory tests

are widely believed to be overused, partly for defensive reasons. An often proposed rem-

edy is caps on noneconomic damages.

*Address correspondence to Ali Moghtaderi, George Washington University School of Medicine and Health Sci-

ences, 2100 Pennsylvania Ave., NW, Washington DC 20037; email: moghtaderi@gwu.edu. Moghtaderi is Assistant

Research Professor at George Washington University, School of Medicine and Health Sciences, Department of

Clinical Research and Leadership; Farmer is Associate Professor of Medicine and Public Health at George

Washington University, School of Medicine and Health Science; Black is Nicholas J. Chabraja Professor at North-

western University, Pritzker School of Law, Institute for Policy Research, and Kellogg School of Management .

We thank participants in workshops at the American Society of Health Economics 2016 Annual Meeting, Amer-

ican Law and Economics Association (2016), Conference on Empirical Legal Studies (2016), Conference on

Empirical Legal Studies—Asia (2017), Southern Economics Association (2016), University of Wisconsin at Milwau-

kee, Economics Department, University of Paris II Law School, Michael Frakes and Kosali Simon (discussants),

William Sage, and Keith Telster for comments and suggestions. The National Heart, Lung, and Blood Institute

provided funding for this project (5 R01 HL113550).

26

We study whether damage caps affect imaging rates, cardiac interventions, and lab

and radiology spending, using patient-level data, with extensive fixed effects and patient-

level covariates. Relative to prior research on defensive medicine, much of which princi-

pally studies overall spending, we innovate in two principal ways. First, we study specific

areas that are likely to be sensitive to med mal risk. Second, we use a very large longitudi-

nal dataset (the 5 percent Medicare random sample, covering around 2 M patients), with

zip code fixed effects (FE), plus extensive covariates.

We study “third-wave” damage caps, adopted during 2002–2005. We use a

difference-in-differences (DiD) research design. We compare nine “New-Cap” states

that adopted caps during this period to a narrow control group of 20 “No-Cap” states,

with no caps in effect during our sample period, and a broad control group that also

includes the 22 “Old-Cap” states, with caps in effect throughout our sample period.

We study rates for the principal cardiac stress tests (stress electrocardiogram [stress

ECG], stress echocardiography [stress echo], and single-photon emission computed

tomography [SPECT]); other computed tomography (CT) scans, and magnetic reso-

nance imaging (MRI). We also study the principal invasive cardiac procedures:

left-heart catheterization (LHC, also called coronary angiography); percutaneous

intervention (PCI, often called stenting); cardiac artery bypass grafting (CABG); and

any revascularization (PCI or CABG). For spending, we study two categories that are

generally thought to be sensitive to malpractice risk—outpatient laboratory (“lab”)

and radiology spending (including stress tests, MRI, and CT scans) (e.g., Baicker

et al. 2007). We also study overall Medicare Part A and Part B spending, for compari-

son to prior studies.

Our base specification uses No-Cap states as the control group, and includes zip

code and calendar year FE, plus extensive patient-level and county-level covariates. Thus,

we ask whether caps affect testing rates, cardiac intervention rates, and lab and imaging

spending, in the same location, with controls for patient age, comorbidities, and other

time-varying factors that could affect clinical decisions. To this base specification, we also

add either patient *zip code FE (“patient FE,” which control for unobserved but time-

constant patient health characteristics) or physician *zip code FE (“physician FE,” which

control for unobserved but time-constant physician FE). There are advantages and costs

to either patient or physician FE; we cannot feasibly use both. The choice of whether to

prefer the narrower or broader control group is also a close one. We also assess the sensi-

tivity of our results to a number of alternative specifications, including using more or

fewer covariates, controlling for tort reforms other than damage caps, and adding linear

state trends.

Note that physicians may respond to malpractice risk in two distinct ways. They

may order tests and other procedures with little or no health benefit that reduce malprac-

tice risk—sometimes called “assurance behavior.” Physicians may also avoid risky patients

or risky procedures—sometimes called “avoidance behavior.” If risk declines, physicians

may engage in both less assurance behavior (hence fewer tests and lower spending), and

less avoidance behavior (hence higher spending, and perhaps more testing as well). Pro-

viders may also order tests and perform procedures with limited clinical value for reasons

other than liability risk, including economic incentives, patient preferences, desire to be

Damage Caps and Defensive Medicine: Reexamination with Patient-Level Data 27

thorough, and local norms. If physicians have multiple reasons to “do more,” tort reform

could have only a modest impact on clinical decisions and spending. Thus, the effect of

caps on imaging rates and other clinical decisions is an empirical question. The balance

between the effect of caps on assurance versus their effect on avoidance behavior could

vary across physicians, patients, and procedures.

We find heterogeneous results, consistent with the balance between assur-

ance and avoidance behavior varying across patients and procedures. Point esti-

mates are directionally consistent with patient and physician FE. Cardiac stress

testing rates rise and, in most specifications, MRI and CT rates also rise. Using a

distributed lag specification, which allows the effect of caps to phase in during the

postcap period, point estimates for percentage increases in testing rates are gener-

ally in the mid-single digits with both patient FE and physician FE. These results

are not entirely robust, however. The increases in stress testing and MRI are statis-

tically significant or marginally significant across specifications, but could reflect

continuation of pretreatment trends. The rise in CT scan rates is significant across

most specifications, with flat pretreatmenttrends,butweakensifweincludestate-

specific linear trends.

The conventional wisdom is that physicians conduct more testing in response to

malpractice risk. Our results for stress tests, MRI, and CT scans provide no support for

this view. They instead provide evidence of, if anything, modestly higher testing rates fol-

lowing damage cap adoptions.

In contrast, cardiac intervention rates appear to fall. With physician FE, which is

our preferred specification for cardiac procedures, all point estimates are negative and

statistically significant, and of substantial economic magnitude—9–20 percent depending

on the procedure. With patient FE, the percentage point estimates are smaller and statis-

tically insignificant, but still meaningful at around 4–6 percent.

Turning from specific tests and procedures to spending, we find evidence for mod-

est increases in radiology and lab spending. Radiology spending (which includes stress

tests, MRIs, and CT scans, among other tests) rises by 6 percent with patient FE and by

10 percent with physician FE, with strong statistical significance and flat pretreatment

trends. Lab spending rises insignificantly, but this is relative to declining pretreatment

trends. Combined lab and radiology spending rises by a significant 4 percent with patient

FE and by 6 percent with physician FE, again with flat pretreatment trends. Here, too,

our results are contrary to conventional wisdom.

For broad spending categories, we can use physician FE only for Part B spend-

ing; physician identities are not available for Part A (hospital) spending. The point

estimates for Part B spending are positive andsignificantat+3.8percentwithpatient

FE, and significant but smaller in magnitude at +1.9 percent with physician FE; the

estimates are significant or marginally significant in most specifications, but become

smaller and lose significance when we include linear state trends. The coefficient for

Part A spending with patient FE is similar in magnitude at +3.5 percent, but is insig-

nificant in all specifications, and is near zero (indeed, slightly negative) without

patient FE. Combined Part A and Part B spending is positive and marginally signifi-

cant at +3.6 percent with patient FE, but near zero and insignificant without patient

28 Moghtaderi et al.