How Can We Bend the Cost Curve? Health ...

How Can We Bend the Cost Curve? William E. Encinosa Jaeyong Bae

Health Information Technology and Its Effects on Hospital Costs, Outcomes, and Patient Safety

Underlying many reforms in the Patient Protection and Affordable Care Act (ACA) is the use of electronic medical records (EMRs) to help contain costs. We use MarketScanH claims data and American Hospital Association information technology (IT) data to examine whether EMRs can contain costs in the ACA’s reforms to reduce patient safety events. We find EMRs do not reduce the rate of patient safety events. However, once an event occurs, EMRs reduce death by 34%, readmissions by 39%, and spending by $4,850 (16%), a cost offset of $1.75 per $1 spent on IT capital. Thus, EMRs contain costs by better coordinating care to rescue patients from medical errors once they occur. Amidst the perfect storm of ever-increasing health care costs, rising underinsurance, and diminishing access to quality care, President Obama signed into law the Patient Protection and Affordable Care Act (ACA) on March 23, 2010 (U.S. Congress 2010). Controlling spending is a particular goal of this landmark legislation. However, controlling costs without reducing quality is an inherent challenge within any such endeavor. Yet, many of the reforms in the ACA do indeed strive to improve quality while reducing costs. An example of this is the ACA’s reforms to reduce hospital-acquired conditions (HACs), such as medical errors and preventable patient safety events (sections 2702 and 3008, U.S. Congress 2010). HACs are medical mistakes and complications that never should occur when patients are receiving care meant to help them. To

create incentives for hospitals to prevent HACs, beginning in October 2014, the ACA calls for a reduction in Medicare payments to hospitals whose rates of HACs are much higher than average. While Medicare already has a ‘‘no-payment policy’’ for HAC cases, this ACA policy will further penalize a hospital with high HAC rates by reducing by 1% payments to that hospital for all diagnosis-related groups (DRGs). The secretary of the Department of Health and Human Services (DHHS) will publicly report on the Hospital Compare website the measures used for each hospital’s payment adjustment.1 The ACA also will extend Medicare’s no-payment policy for HACs to the states’ Medicaid programs (Berwick 2010; Federal Register 2011). HACs can be costly. Encinosa and Hellinger (2008) found that patient safety events

William E. Encinosa, Ph.D., is a senior economist at the Center for Delivery, Organization and Markets, Agency for Healthcare Research and Quality, and an adjunct associate professor at Georgetown Public Policy Institute. Jaeyong Bae, M.A., is with the Department of Health Policy and Management, Rollins School of Public Health, Emory University. This research was funded by the Agency for Healthcare Research and Quality. Address correspondence to Dr. Encinosa at Center for Delivery, Organization and Markets, Agency for Healthcare Research and Quality, 540 Gaither Road, Rockville, MD 20850. Email: [email protected]

288

Inquiry 48: 288–303 (Winter 2011/2012). ’ 2011 Excellus Health Plan, Inc. ISSN 0046-9580 10.5034/inquiryjrnl_48.04.02 www.inquiryjournal.org

Effects of Health IT

Table 1. Patient safety event rates and spending per event under electronic medical records (EMRs) Hospitals with no basic EMRs Hospitals with basic EMRs Preventable surgical-related safety eventsa Event rate (%) Spending per event ($)

2.5 70,336

2.5 64,120*

Likely preventable surgical-related safety eventsb Event rate (%) Spending per event ($)

1.1 63,586

1.1 59,292

Likely preventable nursing-related safety eventsc Event rate (%) Spending per event ($)

2.5 48,654

2.4 43,934**

All safety events Event rate (%) Spending per event ($)

4.99 60,093

5.07 55,810**

Sources: AHRQ Patient Safety Indicators, 2007 MarketScan employer data, and 2007 AHA IT supplement data. a Category includes: anesthesia complications, accidental puncture or laceration during procedure, foreign body left in during procedure, postoperative hemorrhage or hematoma, wound dehiscence, infection due to medical care, postoperative pulmonary embolism and deep vein thrombosis, iatrogenic pneumothorax, postoperative respiratory failure, postoperative sepsis, postoperative physiologic and metabolic derangements, and transfusion reaction. b Category includes: malignant hyperthermia, suture of laceration, nerve compression injury, postoperative acute myocardial infarction, reopening of a surgical site, iatrogenic nervous system complications, and iatrogenic cardiac system complications c Category includes: postoperative decubitus ulcer, postoperative hip fracture, postoperative aspiration pneumonia, postoperative atelectasis, and postoperative urinary tract infection. *** Statistically different from the no basic EMR case at the 99% level. ** Statistically different from the no basic EMR case at the 95% level. * Statistically different from the no basic EMR case at the 90% level.

can increase spending by up to $28,000 (2002 dollars), or 52%, per incident. Overall, Encinosa and Hellinger estimate that 2% of all private insurance spending on surgery patients is due to adverse events, while the DHHS’ Office of Inspector General (OIG) estimates that 3.5% of Medicare inpatient hospital spending is due to adverse events (U.S. DHHS OIG 2010). The Centers for Disease Control and Prevention estimate that reducing the subset of HACs known as healthcare-associated infections (HAIs) alone will decrease spending annually by $45 billion (2007 dollars) (U.S. Centers for Disease Control and Prevention 2009). Reducing HACs not only results in large cost savings, but also improves the quality of care. In particular, Encinosa and Hellinger found that 11% of all privately insured surgery deaths were due to HACs, while the OIG found that a third of Medicare hospital deaths involved HACs. For HAIs, the inpatient death rate is six times higher than for patients without HAIs (Lucado et al. 2010).

This emphasis on the benefits of reducing patient safety events began in 1998, when the Committee on the Quality of Health Care in America was established by the Institute of Medicine (IOM). Its first report estimated that between 44,000 and 98,000 Americans die each year as a result of medical mistakes, with an associated cost of $17 billion to $29 billion (see To Err is Human: Building a Safer Health Care System, Kohn, Corrigan, and Donaldson 1999); this was based on older cost studies from small samples in three states (Johnson et al. 1992; Thomas et al. 1999; Thomas et al. 2000). It is now more than 10 years since the IOM report and patient safety event rates across the country remain high despite modest improvements in overall quality. For example, we found that over the first 10 years following the IOM report, only four of the 12 preventable surgical-related patient safety events in the top panel of Table 1 showed improvements. In fact, the overall average rate of change in these 12 events was a 13% increase over the 10-year 289

Inquiry/Volume 48, Winter 2011/2012

period.2 This is far from the 50% reduction in medical errors that the Institute of Medicine ambitiously recommended over a five-year period. To renew this effort, in April 2011, the Department of Health and Human Services, together with the Partnership for Patients,3 announced a goal of reducing HACs by 40% between 2010 and 2013 (U.S. DHHS 2011), saving 60,000 lives and avoiding $20 billion in costs (McCannon and Berwick 2011). What makes such an ambitious goal now possibly attainable has been the recent adoption and utilization of health information technology (HIT), such as electronic medical records (EMRs). The use of HIT has enormous potential to improve the safety of health care (IOM 2001; Bates and Gawande 2003) and to curb the increase in the cost of health care (IOM 2003). A recent Congressional Budget Office (CBO) report estimated that the potential annual net savings from adopting HIT are about $80 billion (2005 dollars) (U.S. CBO 2008). In fact, a key feature of the ACA legislation is the realization that information technology improvements, such as the use of EMRs, must be part of the solution. For example, the ACA requires the Centers for Medicare and Medicaid Services (CMS) to develop a plan to integrate reporting measures for the ‘‘meaningful use’’ of EMRs as established by the Health Information Technology for Economic and Clinical Health (HITECH) Act, which is a part of the American Recovery and Reinvestment Act of 2009. HITECH provides $27 billion in incentives over a 10-year period for hospitals and physicians to adopt the meaningful use of EMRs (Blumenthal 2010). Hospitals can receive up to $11 million for such adoption, or face Medicare payment reductions if they do not adopt by 2015. Moreover, under the ACA, CMS will further encourage HIT adoption by increasing the current 50% federal match to a 90% federal match for HIT investments that states make before 2016 to streamline their Medicaid systems (Berwick 2010). The hopes for these kinds of HIT innovations are high, but, as is the case with many policy reforms, some uncertainty surrounds the ways that HIT ultimately will translate 290

into improved patient safety and reduced costs. First, EMR adoption is still relatively low. Only 1.6% of hospitals in 2009 had EMRs that satisfy the HITECH rules for meaningful use (Jha et al. 2010). There are many barriers to adopting EMRs besides investment costs. For example, in states with e-discovery laws, providers may fear that EMRs will make it easier for complainants to file malpractice lawsuits against them (Miller and Tucker 2010).4 Also, EMRs present a wide range of patient privacy concerns, such as the costs of complying with strict privacy laws in some states (Miller and Tucker 2009), and the risk of hacking into patient EMR data, which recently has become a substantial threat according to two OIG reports (Fiegl 2011). Second, there is weak evidence that EMRs prevent HACs, other than medication errors. Recent papers show a very minimal impact on HAC rates (Amarasinghan et al. 2009; Culler et al. 2007; Menachemi et al. 2008; Parente and McCullough 2009), but these papers are limited. They do not explore the full potential of EMRs in coordinating care both before and after a patient safety event. They ignore the impact of EMRs in terms of rescuing patients from bad outcomes once a HAC occurs, saving costs. Thus, while a few studies have examined the impact of EMRs on patient safety rates, no studies have yet to examine the impact of EMRs on patient safety costs and outcomes. In this study, we address this limitation and extend the seminal patient safety study by Encinosa and Hellinger (2008) on patient safety costs and outcomes by merging EMR data from the American Hospital Association (AHA) to national insurance claims data. We examine not only whether EMRs prevent a wide spectrum of HACs, but also examine whether EMRs prevent death, readmissions, and high spending over the course of 90 days following occurrence of a HAC.

Background According to Bates and Gawande (2003), there are three mechanisms by which EMRs may impact HACs. First, EMRs may better coordinate the front end of medical care to


prevent HACs from occurring. Second, even if a HAC occurs, EMRs may better coordinate the back end of medical care to more quickly detect a HAC and more rapidly rescue the patient from the HAC’s bad outcomes, such as death. Third, EMRs may in the long run reduce HACs by tracking and providing feedback about adverse events, allowing a quicker learning curve. We do not discuss this third, long-term effect in this study, but focus on the first two short-term effects. Studies that have found evidence of the first effect—the direct effect of EMRs preventing HACs—have primarily focused on a medication error or an adverse drug event, which is only one measure of preventable medical error. King et al. (2003) reported that the adoption of computerized prescriber order entry (CPOE), a crucial component of EMRs, decreased medication errors by 40%. Additionally, Bates et al. (2001) found that EMR adoption reduced absolute adverse drug events by 70%. In a review of 12 studies, Shamliyan et al. (2008) concluded that CPOE was associated with a 66% decline in prescribing errors. More recently, several statewide and national studies have been conducted on the impact of EMR adoption on medical errors other than medication errors. In general, the results from these studies have shown that the impact of EMRs on preventing medical errors or adverse patient safety events is minimal. Culler et al. (2007) evaluated the effect of the availability of IT applications on 15 of the Agency for Healthcare Research and Quality’s (AHRQ) Patient Safety Indicators (PSIs) in 66 Georgia hospitals. Information about the IT application was acquired by the Computerized Physician Order Entry and the IT Infrastructure Survey (CPOEITIS). Of all 15 PSIs, the multivariate regressions demonstrated that the availability of an IT application was significantly and negatively associated with only one PSI (postoperative hemorrhage or hematoma). Menachemi, Chukmaitov, and Brooks (2008) analyzed the effect of clinical IT adoption on 20 of the AHRQ PSIs in 98 Florida hospitals. Clinical IT, which provides information on diagnosis, treatment planning, and evaluation of medical outcomes, includes essential components of EMRs. Their multivariate regres-

sions showed that clinical IT adoption decreased the rates of only five of the 20 PSIs (death in low-mortality DRGs, decubitus ulcer, postoperative sepsis, postoperative hemorrhage, and postoperative pulmonary embolism). Amarasingham et al. (2009) analyzed the effect of the Clinical Information Technology Assessment Tool (CITAT) on complications in 72 Texas hospitals. The CITAT measures the degree of the automation of hospital information. Their cross-sectional analysis indicated that higher scores in decision support were associated with a 16% decrease in the adjusted odds for complications. Using four years of nationwide Medicare patient data, Parente and McCullough (2009) evaluated the effect of three health IT applications (EMRs, nurse charts, and picture archiving and communications systems [PACS]) on three PSIs (infection due to medical care, postoperative hemorrhage or hematoma, and postoperative pulmonary embolism or deep vein thrombosis). This study shows that of the three health IT applications, only EMRs had a very modest effect on medical errors. Finally, examining the effect of EMRs on all the AHRQ PSIs over three years in California, Furukawa et al. (2010) found that EMRs actually increased the rate of patient safety events. Thus, evidence on the first effect of EMRs on patient safety—the direct effect of preventing medical errors—is mixed and weak. Moreover, these studies do not control for the endogeneity between EMRs and patient safety events arising due to unobservable hospital quality or patient severity. They also did not control for nonresponse selection bias due to the often low IT survey response rates. In this study, we present methods to avoid these limitations. The second effect of EMRs on patient safety is through the early detection and amelioration of the damages of the patient safety event. As already noted, EMRs may better coordinate the back end of medical care to more quickly detect an event and more rapidly rescue the patient from bad outcomes—such as death—from the medical error. Consider as an analogy that some auto technologies, such as electronic stability controls and pre-crash sensors, may prevent 291


a car wreck, while other technologies such as air bags may better prevent passenger death once a wreck occurs. In this same way, EMRs may aid in preventing death when a medical error occurs, helping to lower costs. Very little is known about this feature of EMRs in rescuing patients from patient safety events. We address this gap in the literature and explore this feature of EMRs in this study.

Data We use three data sets. First, as in Encinosa and Hellinger (2008), which used 2001–2002 MarketScanH data, our source of patient outcome data is the 2007 MarketScanH Commercial Claims and Encounter Database; this contains claims data for inpatient care, outpatient care, and prescription drugs for 35 million enrollees in employer-sponsored benefit plans for large employers across all 50 states. This database is described in detail in Encinosa and Hellinger (2008). The unit of observation is any adult, nonelderly major surgery admission between March 1, 2007, and October 1, 2007, which did not follow another major surgery admission within the previous 90 days for that patient. We examine all hospital claims incurred within 90 days after the surgery admission date. Second, our source of electronic medical record data is the Information Technology (IT) Supplement to the American Hospital Association’s 2007 Annual Survey. (See Jha, DesRoches, Campbell, et al. 2009 for a description of this survey.) Overall, 3,049 (63.1%) acute care general hospitals responded to the IT survey. We merged the AHA EMR data to the MarketScan patient data. We have a total of 92,853 nonelderly, adult major surgeries at 2,619 hospitals with nonmissing AHA EMR data. Third, we use the AHA 2007 Annual Survey to obtain hospital characteristics other than EMR status. We also use this data to obtain information on the hospitals that did not report EMR information, as discussed in the next section. Methods We use multivariate regression analyses to examine four relationships: 1) the relationship 292

between basic EMRs and the probability that a surgery will have a patient safety event; 2) the relationship between basic EMRs and the probability of inpatient death within 90 days following surgery in surgeries with a patient safety event versus those without an event; 3) the relationship between basic EMRs and the probability of a 90-day readmission for surgeries with a patient safety event versus those without an event; and 4) the relationship between basic EMRs and total 90-day hospital expenditures in surgeries with a patient safety event versus those without an event. Basic EMRs We follow Jha, DesRoches, Campbell, et al. (2009) in their use of the AHA EMR data and code 24 EMR functionalities at each hospital; we then use their definition of basic EMRs. A hospital is coded as having ‘‘basic EMRs’’ if it has the following eight basic EMR functionalities in at least one major clinical unit: demographic characteristics of patients, problem lists, medication lists, discharge summaries, laboratory reports, radiologic reports, diagnostic test results, and computerized prescriber order entry (CPOE) for medications. Basic EMRs is a binary zero-one variable. Note that some hospitals may have some elements of electronic medical records, but still may be coded as having no basic EMRs if they do not have at least the eight basic elements listed previously. Potentially Preventable Patient Safety Events Following Encinosa and Bernard (2005), we examine a composite measure of 24 potentially preventable adverse medical events, as listed in Table 1. There are three basic groups of indicators. First, ‘‘surgical-related patient safety events’’ contain 12 indicators that are related to the actual surgery, not to postoperative nursing care (see the appendix in Encinosa and Bernard 2005 and Bernard and Encinosa 2005 for coding details). These 12 are defined by the PSI methodology as developed by the AHRQ.5 Second, ‘‘nursingrelated patient safety events’’ are made up of five indicators commonly used in the nursing literature: postoperative hip fracture, decubitus ulcer, aspiration pneumonia, atelectasis


(i.e., iatrogenic lung collapse), and urinary tract infection. (For details, see Furukawa et al. 2010; Cho et al. 2003; Unruh 2003; Needleman et al. 2002; Aiken et al. 2002; and Kovner et al. 2002.) Third, we consider seven other surgical complications that AHRQ found to be ‘‘likely preventable patient safety events’’ (see p. 70 of McDonald et al. 2002): malignant hyperthermia, suture of laceration, nerve compression injury, postoperative acute myocardial infarction (AMI), reopening of a surgical site, iatrogenic nervous system complications, and iatrogenic cardiac system complications. Outcomes Death refers to any inpatient hospital death occurring within 90 days after the index discharge, including the initial surgery. Readmissions refer to any overnight stays at an inpatient hospital within 90 days of the index discharge. Expenditures are transacted prices, including all inpatient hospital, physician, drug, and lab payments for any inpatient stay occurring up to 90 days after the index discharge, including the initial surgery. We adjust all payments for the local wage rate to control for variations in medical prices across markets. Regressions use the log of expenditures to control for the skewed distribution of expenditures. After the regressions, the log of expenditures then is retransformed into dollars using the Duan smearing estimator to adjust for the bias arising under the log retransformation (Duan 1983). Endogeneity of EMRs and Outcomes Both our EMR and outcome variables may be correlated with an unobservable characteristic of the hospital (e.g., quality) or with an unobservable characteristic of the patient (e.g., patients with unobservable severity may choose to go to hospitals with EMRs). This would bias our estimates of the impact of EMRs on outcomes. To resolve this issue, for the first three regressions dealing with the probability of a patient safety event, death, and readmission, we use a bivariate probit system of two equations (Greene 1990, p. 689; Buchmueller et al. 2004). The first equation we estimate is the probability that the hospital adopts EMRs (or, alternatively, the probabil-

ity that the patient selects an EMR hospital for surgery). The second equation is the probability that the patient has the given outcome (e.g., death). Bivariate probit jointly estimates both equations, allowing the error terms in each equation to be correlated across equations, thus controlling for the endogeneity. The first equation (the EMR adoption equation) is required by bivariate probit to contain an instrument that is excluded from the second equation (outcome equation) and which predicts EMR adoption in the first regression, but which is not correlated with the outcome variable of the second equation. In our bivariate probit regressions, the instruments are the binary variables ‘‘hacking’’ and ‘‘fundraising.’’ The AHA IT survey data ascertained whether a hospital had a fear of its potential EMR system being hacked by outsiders. This hacking variable was a strong predictor of no EMR adoption. The general AHA survey provided a variable for fundraising that indicated whether a hospital used its money to fund community charity fundraising events. This type of fundraising would mean having less money to invest in EMR adoption. We indeed found that the fundraising variable was a strong predictor of no EMR adoption. These two instruments, fundraising and hacking, also satisfy the exclusion restriction, which requires that they have no effect on patient safety, death, and readmissions, so they can be legitimately excluded from the outcome equations. To test the validity of the exclusion restriction for the two instruments, we use a standard over identification test often employed with bivariate probits (Bollen, Guilkey, and Mroz 1995; Reichman, Corman, and Noonan 2003; Rashad and Kaestner 2004). We estimate the just-identified bivariate probit model using only one instrument in the EMR adoption equation and with the remaining instruments moved to the outcome equation. We then use a Wald test to assess the null hypothesis that these remaining instruments are jointly equal to zero in predicting the outcome. If these instruments are jointly significant in the outcome equation, then the exclusion restrictions are not valid. All exclusion restrictions were found to be valid in all three outcome equations, with the tested instruments never being statistically significant 293


at the 10% level in predicting the outcome (p5.93 in the patient safety equation, p5.56 in the death equation, and p5.72 in the readmission equation). As a robustness check, we also ran an alternative multinomial probit model with four equations combined: the EMR adoption equation, patient safety event equation, death equation, and readmission equation (Ashford and Sowden 1970). This was based on ‘‘simulated maximum likelihood’’ using the Geweke-Hajivassiliou-Keane simulator found in the STATA module mvprobit designed by Lorenzo Cappellari and Stephen Jenkins.6 The results did not differ between this four-probit and the three bivariate probits, so we only report the bivariate results. Ideally, to capture the full effect of EMRs on all variables simultaneously, we also would like to estimate EMR adoption in the first equation and then separately estimate outcome equations for both the subset of hospitals with EMRs and the subset of hospitals without EMRs. This would not constrain coefficients to be the same across both types of hospitals, so the full effect of EMRs could be seen. However, since the three outcomes are relatively rare, we do not have a large enough sample size to feasibly do this. But because the log of expenditures is a continuous variable, we can do this analysis with expenditures. First, we use the full maximum likelihood Heckman model in Stata 12, with bootstrapped standard errors (Green 1990, p. 744). We estimate two versions of the expenditure model. The first-stage equation in both versions is an EMR adoption equation with excluded instruments ‘‘hacking’’ and ‘‘no adequate IT staff.’’ In the first version of the model, the second-stage equation is the expenditure equation for all hospitals with EMRs. In a second version of the model, the second-stage expenditure equation is conducted on hospitals without EMRs. Both instruments are valid, negatively predicting EMR adoption and having no effect on expenditures, passing the over identification test discussed earlier (p5.11 for the expenditure equation for hospitals with EMRs, and p5.56 for the expenditure equation for hospitals without EMRs). In both versions, an inverse Mills ratio is computed from the first stage and then inserted in the second-stage logged expenditure equation. The 294

inverse Mills ratios control for endogeneity between EMRs and expenditures. To further minimize endogeneity issues, all our regressions control for the following covariates. We include 18 collapsed chronic condition variables derived from 30 chronic conditions developed by Elixhauser et al. (1998) in the AHRQ Comorbidity Software. Baldwin et al. (2006) have shown that these are the best performing comorbidity measures. We also include the following variables to control for potential confounding effects of patient severity: five age categories, sex, and an indicator for emergency room admission. Next, to control for demand-side factors that may influence the patient’s degree of utilization and costs, we control for the type of the health plan (HMO, which is either a capitated HMO or a capitated point-of-service plan), and for whether the patient is an hourly wage worker (versus salaried), which may proxy for low income. The costs of PSI events should decline with HMO enrollment and low income since this is a segment of the population that spends less on health care. To control for market characteristics we include region fixed effects. Finally, we include a variable indicating whether a hospital is a teaching hospital. The means of all the covariates are found in Table 2. Identification of the Differential Impact of EMRs in Patient Safety Events Our first bivariate regression estimates the direct impact of EMRs on the probability of a patient safety event during the surgery. However, our main interest is the effect that EMRs have on recovery once a patient safety event occurs. That is, what effect EMRs have on the probability of death and readmission once a patient safety event occurs. To identify this effect, in our bivariate regressions on death and readmission, respectively, we include an interaction term, EMR*patient safety event. This term will capture any additional effect that EMRs have on patient safety cases beyond the effects they have on surgeries without patient safety events. In our Heckman model of expenditures, we do not include interactions, but we include two separate analyses, one on surgeries with EMRs and one on surgeries without EMRs. Comparing the coefficients on the patient safety variable


Table 2. Descriptive statistics of surgery patients across hospitals Variables

All hospitals

Hospitals with no basic EMRs

Hospitals with basic EMRs

Patient safety event rate 90-day inpatient death rate 90-day readmission rate 90-day inpatient payments Basic EMRs Fundraising Hacking fears No IT staff Teaching hospital HMO Age Female Hourly worker ER admission Congestive heart failure Valvular disease Peripheral vascular disease Hypertension Other neurological disorders Chronic pulmonary disease Diabetes Diabetes w/ chronic complications Hypothyroidism Renal failure Liver disease Solid tumor w/out metastasis Rheumatoid arthritis/collagen Obesity Weight loss Fluid and electrolyte disorders Chronic blood loss anemia Depression Northeast North Central South West N

5.01% .98% 14.69% $29,483 ($28,547) 21.28% 71.18% 8.32% 24.37% 73.12% 22.33% 49.54 (11.07) 57.62% 27.48% 12.54% 2.92% 4.19% 2.72% 18.36% 2.38% 5.29% 6.50% 1.49% 2.03% 1.32% 1.46% 12.84% 2.06% 4.66% .61% 2.47% 4.15% 2.76% 13.48% 21.99% 51.66% 12.87% 92,853

4.99% .93% 14.47% $29,296 ($28,438) 0 74.81% 9.52% 24.29% 75.44% 22.22% 49.64 (11.03) 57.78% 27.84% 12.38% 2.88% 4.05% 2.73% 18.62% 2.36% 5.44% 6.62% 1.50% 2.06% 1.29% 1.37% 12.28% 2.02% 4.74% .60% 2.54% 4.18% 2.84% 13.40% 23.10% 49.96% 13.54% 73,091

5.07% 1.14%* 15.49%*** $29,967** ($28,942) 1 57.90%*** 3.90%*** 24.65% 64.63%*** 22.72%*** 49.17*** (11.17) 57.05% 26.16%*** 13.13% 3.09% 4.68%** 2.68% 17.40%*** 2.45% 4.73%*** 6.08%** 1.46% 1.95% 1.44%** 1.79%*** 14.88%*** 2.23%* 4.39% .65% 2.24%* 4.06%* 2.48%* 13.76%*** 17.93%*** 57.88%*** 10.44% 19,762

Note: Standard deviations are in parentheses. *** Statistically different from the no basic EMR case at the 99% level. ** Statistically different from the no basic EMR case at the 95% level. * Statistically different from the no basic EMR case at the 90% level.

across the two regressions provides insight into the differential impact of EMRs when there is a patient safety event. Finally, we use post-estimation simulations to compute the impact of EMRs on the excess outcome rates due to patient safety events. To estimate the ‘‘excess rates’’ of the outcomes (death, readmission, and expenditures) that are due to the PSIs, we first predict the outcome’s rate after the regression assuming every surgery had a PSI, and then predict the outcome’s rate assuming every surgery had no PSI. The difference between the two rates is the excess

rate due to PSIs. We then compute the impact of EMRs on the excess rates first by assuming every surgery had EMRs and predicting the excess outcome rates, and then assuming every surgery did not have EMRs. The result is the differential impact that EMRs had on the excess rates due to patient safety events. The standard errors of the excess rates were computed with 1,000 bootstrapping replications. Selection Bias Since only 63% of hospitals responded to the AHA’s Annual Survey’s Information Tech295


nology Supplement, there is the potential for a nonresponse bias in our results. To control for this bias, we followed Little and Vartivarian (2003) and developed hospital weights consisting of the inverse of the estimated probability of responding to the survey. We obtained information on nonresponding and responding hospitals from data from the AHA’s full 2007 annual hospital survey data of all 5,022 acute care general hospitals. Using the psmatch2 routine in Stata 12, we created each hospital’s propensity score with a logit regression of the probability of responding to the IT survey, controlling for 26 hospital characteristics. Hospital weights then were constructed by the inverse of the propensity score. To assess the performance of the weights in reducing nonresponse bias, we matched nonresponding hospitals to responding hospitals using the nearest-neighbor method (Becker and Ichino 2002), which balanced propensity scores across the 26 hospital characteristics. No hospitals lacked a common region of support (Becker and Ichino 2002). While 22 of the 26 hospital characteristics in the raw data predicted a response to the survey (within a 95% level of statistical significance), only two hospital characteristics still predicted a response to the survey after matching. The average absolute value of the bias in the 26 hospital characteristics between responding and nonresponding hospitals was reduced from 22.7 to 2.7 due to the propensity score matching.7 Thus, these constructed sample weights substantially reduce any selection bias. All of our analyses use the weighted sample.

Results Unadjusted Outcomes and Costs During the study period, about 21% of surgeries were in hospitals with basic EMRs. About 5% of the adult major surgeries had at least one of the 24 potentially preventable adverse medical events. As shown in Table 1, EMR hospitals did not have any statistically significant difference in rates of patient safety events compared to hospitals without EMRs, across all types of events. Thus, EMR hospitals did not differ in the mix of patient safety events compared to hospitals without EMRs. However, the spending per patient safety event 296

seemed to be systematically lower in the EMR hospitals in Table 1, across all types of events. For all safety events, the average spending on a patient safety event was $55,810 under EMRs, compared to $60,093 without EMRs. However, over all surgeries, event or no event, EMR surgeries were more expensive; in Table 2, total 90-day spending for surgeries with EMRs was $29,967 on average, versus $29,296 for surgeries without EMRs. Thus, the raw data indicates that EMRs only had a cost-saving differential effect on spending once a patient safety event occurred, which was not due to a difference in the mix of types of patient safety events. Adjusted EMR Impact on Patient Safety Controlling for the 27 covariates in the bivariate probit models of Table 3, basic EMRs had no impact on the probability of a patient safety event (column 2, Table 3). Note that the estimated correlation (rho) between the error terms of the EMR adoption equation and the patient safety equation is negative (last row, Table 3). This indicates that any selection bias is due to unobservable high-quality hospitals adopting EMRs and having low patient safety rates, rather than unobservable high-severity patients selecting into EMR hospitals. However, rho was not statistically significant, indicating that there was no significant selection bias arising from this possible endogeneity between EMRs and patient safety. The excluded instruments (hacking and fundraising) of the EMR adoption equation (column 1, Table 3) performed well, strongly predicting non-adoption of EMRs. Deaths Due to Patient Safety Events Similarly, in column 3 of Table 3, EMRs had no statistically significant impact on death, while the occurrence of a patient safety event was a strong predictor of death. However, the coefficient for the interaction EMR*patient safety event was 2.249, statistically significant at the 95% level. This indicates that EMRs reduce the probability of death once a patient safety event occurs. In Table 5, the magnitude of this effect is simulated. In the middle row and first column of Table 5, the excess death rate due to patient safety events in hospitals without basic EMRs is 2.6% (3.4%2.8%). In the second column, the excess death rate due


Table 3. Bivariate probit estimates of the impact of electronic medical records (EMRs) on outcomes Dependent variables Basic EMRs Patient safety event Basic EMR * patient safety event Teaching hospital HMO Age 41–49 Age 50–55 Age 56–60 Age 61–64 Female Hourly worker ER admission Congestive heart failure Valvular disease Peripheral vascular disease Hypertension Other neurological disorders Chronic pulmonary disease Diabetes Diabetes w/ chronic complications Hypothyroidism Renal failure Liver disease Solid tumor w/out metastasis Rheumatoid arthritis/collagen Obesity Weight loss Fluid and electrolyte disorders Chronic blood loss anemia Depression Hospital fundraising Hospital fear of IT hacking Rho

Basic EMRs – – – 2.322*** .058*** 2.003 2.041** 2.039** 2.060*** 2.011 2.038*** .019 .051 .070*** .006 2.040*** 2.011 2.061** 2.036 2.025 .022 .075* .127*** .114*** .063* 2.025 2.013 2.071** 2.026 2.059* 2.487*** 2.609*** –

(.012) (.013) (.016) (.017) (.017) (.018) (.011) (.012) (.016) (.032) (.026) (.033) (.014) (.034) (.024) (.022) (.045) (.038) (.045) (.042) (.016) (.036) (.026) (.067) (.034) (.027) (.033) (.013) (.023)

Patient safety event .148 – – 2.074*** 2.033 .053* .219*** .306*** .342*** 2.109*** .013 .310*** .468*** .638*** .133*** 2.133*** .447*** .220*** .152*** .011 2.181*** .068 .087 2.161*** .339*** 2.260*** .415*** .353*** .046 2.051 – – 2.096

(.117) (.021) (.021) (.029) (.028) (.028) (.029) (.017) (.018) (.022) (.036) (.029) (.044) (.023) (.041) (.032) (.030) (.063) (.067) (.067) (.062) (.026) (.045) (.051) (.083) (.043) (.042) (.053) (.067)

Death .103 .660*** 2.249** 2.163*** 2.052 .018 .098 .197*** .203*** 2.063 2.021 .326*** .394*** .217*** .278*** 2.209*** .580*** .082 2.131 .166 2.545** .541*** .567*** .314*** .517*** 2.280* .430*** .378*** .008 .005 – – 2.005

Readmission

(.278) 2.052 (.092) (.056) .351*** (.031) (.119) 2.116* (.067) (.049) 2.143*** (.017) (.047) .005 (.017) (.067) .003 (.021) (.065) .112*** (.022) (.066) .137*** (.022) (.067) .163*** (.023) (.039) 2.089*** (.014) (.042) .021 (.015) (.046) .246*** (.019) (.070) .360*** (.035) (.067) .143*** (.031) (.083) .388*** (.037) (.057) 2.068*** (.018) (.068) .503*** (.037) (.073) .143*** (.028) (.084) .138*** (.027) (.109) .489*** (.048) (.221) .000 (.050) (.096) .496*** (.050) (.095) .307*** (.050) (.048) .348*** (.019) (.073) .183*** (.043) (.150) .001 (.036) (.137) .614*** (.074) (.073) .274*** (.039) (.086) .196*** (.032) (.111) .259*** (.037) – – (.162) .046 (.054)

Note: The first stage of the bivariate probit is the first column, estimating EMR adoption. Second stages estimate the outcomes in the last three columns. Rho is the correlation between the first and second stage. Standard errors are in parentheses. Region dummies are not shown. *** Statistically different from zero at the 99% level. ** Statistically different from zero at the 95% level. * Statistically different from zero at the 90% level.

to patient safety events in hospitals with basic EMRs is 1.7% (2.5%2.8%). Thus, the excess death rate due to patient safety events declines by 34% due to EMRs {(2.621.7) / 2.6}. Note that the estimated correlation (rho) between the error terms of the EMR adoption equation and the death equation was not statistically significant, indicating that there was no endogeneity issue between EMR and death. Readmissions Due to Patient Safety Events In the final column 4 of Table 3, EMRs had no statistically significant impact on readmissions, while the occurrence of a patient safety

event was a strong predictor of readmission. However, the coefficient for the interaction EMR*patient safety event was 2.116, statistically significant at the 90% level. This indicates that EMRs reduce the probability of readmission once a patient safety event occurs. In Table 5, the magnitude of this effect is simulated. In the last row and first column of Table 5, the excess readmission rate due to patient safety events in hospitals without basic EMRs is 8.9% (23.4%2.14.5%). In the second column, the excess readmission rate due to patient safety events in hospitals with basic EMRs is 5.4% (18.8%213.4%). Thus, the 297


excess readmission rate due to patient safety events declines by 39% due to EMRs {(8.925.4)/8.9}. Note that the estimated correlation (rho) between the error terms of the EMR adoption equation and the readmission equation was not statistically significant, indicating that there was no endogeneity issue between EMRs and readmission. Expenditures Due to Patient Safety Events In Table 4, we present the Heckman estimates of the impact of EMR on spending. In column 1 (EMR hospitals), the coefficient for a patient safety event is .70, while in hospitals without EMRs (column 2), the coefficient for patient safety is much larger, .76. This indicates that EMRs cause patient safety events to have a smaller effect on spending. We simulate the magnitude of this effect in the first row of Table 5. In the first row and first column of Table 5, the excess spending due to patient safety events in hospitals without basic EMRs is $31,297 ($57,5832$26,286). In the second column, the excess spending due to patient safety events in hospitals with basic EMRs is $26,448 ($52,4652$26,017). Thus, the excess spending due to patient safety events declines by $4,849, or 16%, due to basic EMRs {($31,2972$26,448)/$31,297}. Note that the coefficient on the inverse Mills ratio in column 2 of Table 4 is statistically significant, indicating that the regression controlled for a potential endogeneity issue between EMRs, patient safety, and spending.

Conclusion The Patient Protection and Affordable Care Act provides many reasons to reduce the rates of hospital-acquired conditions such as medical errors: reduced reimbursement to hospitals with above average HAC rates, no payments for the extra costs of HAC cases, and large subsidies for investments in electronic medical records. However, there is not yet empirical evidence of a strong business case for the use of EMRs in the reduction of patient safety events. Like most recent research, in this paper we find no impact of EMRs on preventing HACs. However, in this study, we argue that researchers have been examining a limited 298

aspect of IT with respect to HACs. Instead of IT being ‘‘front-loaded’’ in the health care system to prevent HACs, we show that health IT has been ‘‘back-loaded’’ to improve the recovery from HACs. By coordinating care, IT may have an impact on this recovery. In this study, we find a large impact of health IT on the recovery from HACs. We find that EMRs reduced the excess costs of HACs that occurred by 16%, decreased excess readmissions due to HACs by 39%, and reduced excess deaths due to HACs by 34%. In perspective, the 24 patient safety events, or HACs, considered in this study were responsible for 13.3% of all 90-day deaths after major surgery when the hospital had no basic EMRs. In contrast, if the hospital did indeed have basic EMRs, HACs were responsible for only 8.7% of all 90-day deaths after major surgery. Extrapolating this to all adult surgeries in the U.S., basic EMR adoption would save 4.6% (13.328.7) of all surgery deaths, or 5,140 adult lives a year. Extrapolating the EMR costs savings of $4,850 per patient safety event to the national level, basic EMRs would save $2.8 billion per year among adult surgeries. Does this cost savings make investing in EMRs worthwhile? Using our results, we show that investing in IT is indeed cost effective. In the 2007 AHA IT survey report, the median spending on IT capital per hospital bed was $5,556 per year (American Hospital Association 2007). Using the 2007 AHA survey data, and following Miller and Tucker’s (2009) methods, we find that the average number of beds dedicated to adult surgeries were 45 beds per hospital, with an average of 2,006 adult surgeries per hospital. This amounts to $125 spent on IT capital per adult surgery per year {(45*$5,556)/2006}. In contrast, the median savings during patient safety events under EMRs is $4,388 per event (the unshown median of the mean $4,850 in savings from Table 5). This amounts to a median savings of $219 per adult surgery {$4,388*(.05*N)/N}, where N is the number of national adult surgeries and .05*N is the number of surgeries with medical errors). Thus, the cost offset is $1.75 per $1 spent on IT capital per adult surgery per year (219/ 125). The cost offset may be even higher since


Table 4. Heckman estimation of the impact of patient safety events on hospital expenditures under electronic medical records (EMRs) Expenditures with basic EMRs Patient safety event Teaching hospital HMO Age 41–49 Age 50–55 Age 56–60 Age 61–64 Female Hourly ER admission Congestive heart failure Valvular disease Peripheral vascular disease Hypertension Other neurological disorders Chronic pulmonary disease Diabetes Diabetes w/ chronic complications Hypothyroidism Renal failure Liver disease Solid tumor w/out metastasis Rheumatoid arthritis/collagen Obesity Weight loss Fluid and electrolyte disorders Chronic blood loss anemia Depression Inverse Mills ratio

.702*** 2.214*** 2.205*** .052** .195*** .236*** .249*** 2.214*** 2.057*** .086*** .536*** .397*** .320*** 2.022 .658*** .101*** .074** .164** 2.012 .425*** .335*** .128*** .464*** .100*** .923*** .407*** .133** .177*** .110

(.047) (.064) (.034) (.026) (.032) (.033) (.037) (.020) (.019) (.024) (.056) (.055) (.048) (.022) (.060) (.035) (.033) (.082) (.056) (.092) (.095) (.036) (.071) (.036) (.104) (.065) (.053) (.050) (.437)

Expenditures with no basic EMRs .761*** 2.171*** 2.109*** .108*** .237*** .293*** .315*** 2.199*** .055*** .170*** .524*** .375*** .277*** .027*** .481*** .145*** .071*** .243*** .025 .417*** .221*** .157*** .288*** .173*** .814*** .438*** .203*** .201*** .520**

(.022) (.024) (.013) (.013) (.013) (.015) (.016) (.009) (.009) (.013) (.024) (.021) (.027) (.009) (.027) (.020) (.018) (.040) (.028) (.036) (.042) (.018) (.036) (.018) (.062) (.030) (.021) (.025) (.207)

Notes: Maximum likelihood Heckman model. The inverse Mills ratios are from two first-stage estimations of EMR adoption that are not shown. Shown second stages estimate logged expenditures. Region dummies are not shown. Bootstrapped standard errors are in parentheses. *** Statistically different from the no basic EMR case at the 99% level. ** Statistically different from zero at the 95% level. * Statistically different from zero at the 90% level.

27% of the EMRs were not just basic, but more expensive comprehensive systems. Thus, the savings from reducing patient safety costs with EMRs in surgeries is costeffective with respect to the IT capital investment costs per year allocated to the surgeries. However, the median costs of operating the IT is an additional $12,060 per year (AHA 2007). Including both IT capital and IT operating costs, the cost offset is 55 cents per $1 spent on all IT costs per surgery. However, note that we only included 24 patient safety indicators. There may be many more preventable safety events (as well as near misses) that occurred but were not included in our analyses, such as medication errors. In fact, we do not consider drug-

related errors, diagnostic errors, and errors in choice of therapy, all of which accounted for 12% of surgical errors in the Colorado-Utah study underlying the IOM report (Gawande et al 1999). It has been shown that such drug errors are substantially reduced by EMRs (Shamliyan et al. 2008). Moreover, we do not include nonmedical costs, such as days of lost work due to patient safety events. Thus, our expenditure and cost-offset results are an underestimate of the total expenditures attributable to all preventable adverse events. Finally, it is important to note that our methods controlling for both the AHA IT hospital nonresponse bias and the potential endogeneity between EMRs and outcomes did improve our estimates. That is, both selection biases and sample biases would have 299


Table 5. Ninety-day risk-adjusted expenditures and outcomes after patient safety events under electronic medical records (EMRs) Hospitals with no basic EMRs Hospitals with basic EMRs

Difference

Expenditures ($) Patient safety event No patient safety event Difference

57,583 26,286 31,297

52,465 26,017 26,448

5,118*** 269 4,849*** (16%)

Death (%) Patient safety event No patient safety event Difference

3.4 .8 2.6

2.5 .8 1.7

.9** 0 .9* (34%)

Readmissions (%) Patient safety event No patient safety event Difference

23.4 14.5 8.9

18.8 13.4 5.4

4.6* 1.1 3.5* (39%)

Notes: Estimates are simulated from the Table 3 and 4 regressions, with bootstrapped standard errors. Difference shows excess spending and excess death and readmission rates. *** Statistically different from zero at the 99% level. ** Statistically different from zero at the 95% level. * Statistically different from zero at the 90% level.

biased our estimates had we not controlled for them. For example, without using our methods (bivariate probit, Heckman model, and propensity score reweighting of sample) to control for these biases, our cost estimate for the EMR savings of $4,849 (Table 5) per patient safety event would have been reduced to $3,757. In our estimate of the EMRs’ impact on the excess death rate due to patient safety events, the close to one-percentagepoint EMR reduction in the excess death rate (.9 in Table 5) would have been reduced to .3. Thus, the sample selection bias and the nonresponse sample bias would have resulted in the underestimation of the impact of EMRs on rescuing patients from medical errors. This underestimation indicates that unobservable high-quality hospitals tend to adopt EMRs, have better outcomes, and are more apt to respond to the AHA IT survey.

Controlling for this bias is important. This underestimation bias may help to explain the weak EMR impact on medical errors often reported in the literature. Future research should examine why computerized prescriber order entry prevents medication errors and adverse drug events, while EMRs have little impact on preventing patient safety events, yet do an excellent job of rapidly recovering the patient from the harm of the medical error. In our study, we see that EMRs behave much like car air bags, saving lives after a wreck, while the CPOE systems behave like pre-crash sensors to prevent an accident. Future research should examine how the whole spectrum of hospital IT devices might better work together to improve the coordination of care over the entire duration of health care, instead of in segments at the front end and back end of health care.

Notes The authors thank conference participants at the First Workshop on Health IT and Economics at the University of Maryland, the 16th Congress of the Confederation of Medical Record Organizations, and the 8th World Congress on Health Economics. This research was funded by the Agency for Healthcare 300

Research and Quality. The views herein do not necessarily reflect the views or policies of AHRQ, nor the U.S. Department of Health and Human Services. 1 See http://www.hospitalcompare.hhs.gov/ (accessed August 27, 2011).


2 Authors’ calculations using the AHRQ 1997 and 2007 Healthcare Utilization and Cost Project’s National Inpatient Sample. 3 The Obama administration launched the Partnership for Patients: Better Care, Lower Costs, a public-private partnership that will help improve the quality, safety, and affordability of health care. In 2011, there were more than 6,200 partners, including 2,800 hospitals. The Partnership for Patients brings together hospitals, employers, physicians, nurses, and patient advocates along with state and federal governments in a shared effort to make hospital care safer, more reliable, and less costly. See http://www.healthcare.gov/compare/part nership-for-patients/ (accessed October 17, 2011).

4 E-discovery laws allow both defendant and plaintiff to automatically obtain relevant electronic records from the other party without objection in pre-trial proceedings. Otherwise, only paper records can be obtained under discovery. At least 13 states have ediscovery laws. 5 http://www.qualityindicators.ahrq.gov/Modules/ psi_overview.aspx (accessed August 27, 2011). 6 http://EconPapers.repec.org/RePEc:boc:bocode: s432601 (accessed August 27, 2011). 7 The bias is the difference in the sample means between response and nonresponse hospitals as a percentage of the square root of the average of the sample variances in the response and nonresponse hospitals. See Rosenbaum and Rubin (1985).

References Aiken, L. H., S. P. Clarke, D. M. Sloane, J. Sochalski, and J. H. Silber. 2002. Hospital Nurse Staffing and Patient Mortality, Nurse Burnout, and Job Dissatisfaction. Journal of the American Medical Association 288(16): 1987–1993. Amarasingham, R., L. Plantinga, M. DienerWest, D. J. Gaskin, and N. Powe. 2009. Clinical Information Technologies and Inpatient Outcomes: A Multiple Hospital Study. Archives of Internal Medicine 169(2):108–114. American Hospital Association (AHA). 2007. Continued Progress: Hospital Use of Information Technology. http://www.aha.org/aha/ content/2007/pdf/070227-continuedprogress.pdf. Accessed Aug 30, 2011. Ashford, J. R., and R. R. Sowden. 1970. Multivariate Probit Analysis. Biometrics 26: 535–546. Baldwin, L., C. Klabunde, P. Green, W. Barlow, and G. Wright. 2006. In Search of the Perfect Comorbidity Measure for Use with Administrative Claims Data: Does it Exist? Medical Care 44(8):745–753. Bates, D. W., and A. A. Gawande. 2003. Improving Safety with Information Technology. New England Journal of Medicine 348:2526–2534. Bates, D., M. Cohen, L. Leape, et al. 2001. Reducing the Frequency of Errors in Medicine Using Information Technology. Journal of the American Medical Informatics Association 8(4):299–308. Becker, S., and A. Ichino. 2002. Estimation of Average Treatment Effects Based on Propensity Scores. The Stata Journal 2(4):358–377. Bernard, D., and W. Encinosa. 2005. Financial and Demographic Influences on Medicare Patient Safety Events. Advances in Patient Safety: From Research to Implementation 1:437–451.

Berwick, D. 2010. Personal statement before the U.S. Senate Committee on Finance, Hearing on Strengthening Medicare and Medicaid: Taking Steps to Modernize America’s Health Care System. November 17, 2010. http://finance. senate.gov/imo/media/doc/FINAL%20Donald % 20Berwick%20Testimony%2011.15.101.pdf. Accessed August 27, 2010. Blumenthal, D. 2010. Launching HITECH. New England Journal of Medicine 362(5):382–385. Bollen, K. A., D. K. Guilkey, and T. A. Mroz. 1995. Binary Outcomes and Endogenous Explanatory Variables: Tests and Solutions with an Application to the Demand for Contraception Use in Tunisia. Demography 32:111–131. Buchmueller, T., A. Couffinhal, M. Grignon, and M. Perronnin. 2004. Access to Physician Services: Does Supplemental Insurance Matter? Evidence from France. Health Economics 13:669–687. Chaudhry, B., J. Wang, S. Wu, M. Maglione, W. Mojica, E. Roth, S. C. Morton, and P. G. Shekelle. 2006. Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care. Annals of Internal Medicine 144:742–752. Cho, S., S. Ketefian, V. Barkauskas, and D. Smith. 2003. The Effects of Nurse Staffing on Adverse Events, Morbidity, Mortality, and Medical Costs. Nursing Research 52(2):71–79. Culler, S. D., J. N. Hawley, V. Naylor, and K. J. Rask. 2007. Is the Availability of Hospital IT Applications Associated with a Hospital’s Risk of Adjusted Incidence Rate for Patient Safety Indicators: Results from 66 Georgia Hospitals. Journal of Medical Systems 31(5):319–327. Duan, N. 1983. Smearing Estimate: A Nonparametric Retransformation Method. Journal of the American Statistical Association 78: 605–610. 301


Elixhauser, A., C. Steiner, D. Harris, and R. Coffey. 1998. Comorbidity Measures for Use with Administrative Data. Medical Care 36(1):8–27. Encinosa, W., and D. Bernard. 2005. Hospital Finances and Patient Safety Outcomes. Inquiry 42(1):60–72. Encinosa, W., and F. Hellinger. 2008. The Impact of Medical Errors on Ninety-Day Costs and Outcomes: An Examination of Surgical Patients. Health Services Research 43(6): 2067–2085. Federal Register. 2011. Medicaid Program: Payment Adjustment for Provider-Preventable Conditions Including Health Care-Acquired Conditions. 76(108), June 6. http://www.gpo. gov/fdsys/pkg/FR-2011-06-06/pdf/2011-13819. pdf. Accessed August 31, 2011. Fiegl, C. 2011. Audit Finds Hospital EMRs Vulnerable to Data Breaches. American Medical News May 26. http://www.ama-assn.org/ amednews/2011/05/23/gvse0526.htm. Accessed November 2, 2011. Furukawa, M. F., T. S. Raghu, and B. B. M. Shao. 2010. Electronic Medical Records, Nurse Staffing, and Nurse-Sensitive Patient Outcomes: Evidence from California Hospitals, 1998–2007. Health Services Research 45(4):941–962. Gawande, A., E. J. Thomas, M. J. Zinner, and T. A. Brennan. 1999. The Incidence and Nature of Surgical Adverse Events in Colorado and Utah in 1992. Surgery 126(1):66–75. Greene, W. 1990. Econometric Analysis. New York: MacMillan. Institute of Medicine (IOM). 2001. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, D.C.: National Academies Press. ———. 2003. Key Capabilities of an Electronic Health Record System. Washington, D.C.: National Academies Press. Jha, A. K., C. M. DesRoches, E. G. Campbell, K. Donelan, S. R. Rao, T. G. Ferris, A. Shields, S. Rosenbaum, and D. Blumenthal. 2009. Use of Electronic Health Records in U.S. Hospitals. New England Journal of Medicine 360(16): 1628–1638. Jha, A. K., C. M. DesRoches, A. E. Shields, P. D. Miralles, J. Zheng, S. Rosenbaum, and E. G. Campbell. 2009. Evidence of an Emerging Digital Divide among Hospitals that Care for the Poor. Health Affairs 28(6):w1160–1170. Jha, A., C. DesRoches, P. Kralovec, and M. Joshi. 2010. A Progress Report on Electronic Health Records in U.S. Hospitals. Health Affairs 29(10):1–7. Johnson, W. G., T. A. Brennan, J. P. Newhouse, L. L. Leape, A. G. Lawthers, H. H. Hiatt, and P. C. Weiler. 1992. The Economic Consequences of Medical Injuries: Implications for 302

a No-Fault Insurance Plan. Journal of the American Medical Association 267(18): 2487–2492. King, W., N. Paice, J. Rangrej, et al. 2003. The Effect of Computerized Physician Order Entry on Medication Errors and Adverse Drug Events in Pediatric Inpatients. Pediatrics 112(3):506. Kohn, L., J. Corrigan, M. Donaldson, Committee on Quality of Health Care in America, Institute of Medicine. 2000. To Err is Human: Building a Safer Health System. Washington, D.C.: National Academies Press. Kovner, C., C. Jones, C. Zhan, P. Gergen, and J. Basu. 2002. Nurse Staffing and Postsurgical Adverse Events: An Analysis of Administrative Data from a Sample of U.S. Hospitals, 1990–1996. Health Services Research 37(3): 611–629. Levinson, D. R., U. S. Department of Health and Human Services, Office of Inspector General. 2010. Adverse Events in Hospitals: National Incidence among Medicare Beneficiaries. Washington, D.C.: DHHS. http://oig.hhs.gov/ oei/reports/oei-06-09-00090.pdf. Accessed August 26, 2011. Little, R. J., and S. Vartivarian. 2003. On Weighting the Rates in Non-response Weights. Statistics in Medicine 22(9):1589–1599. Lucado, J., K. Paez, R. Andrews, and C. Steiner. 2010. Adult Hospital Stays with Infections Due to Medical Care, 2007. HCUP Statistical Brief #94. August. Rockville, Md.: Agency for Healthcare Research and Quality. http:// www. hcup-us.ahrq.gov/reports/statbriefs /sb94.pdf. Accessed August 26, 2011. McCannon, J., and D. Berwick. 2011. A New Frontier in Patient Safety. Journal of the American Medical Association 305(21):2221– 2222. McDonald, K., P. Romano, J. Geppert, et al. 2002. Measures of Patient Safety Based on Hospital Administrative Data-The Patient Safety Indicators. Technical Review 5. AHRQ Publication No. 02-0038. Rockville, Md.: Agency for Healthcare Research and Quality. Menachemi, N., C. Saunders, A. Chukmaitov, et al. 2008. Hospital Adoption of Information Technologies and Improved Patient Safety: A Study of 98 Hospitals in Florida. Journal of Healthcare Management 52(6):398. Menachemi, N., A. Chukmaitov, and R. G. Brooks. 2008. Hospital Quality of Care: Does Information Technology Matter? The Relationship Between Information Technology Adoption and Quality of Care. Health Care Management Review 33(1):55–59. Miller, A., and C. Tucker. 2009. Privacy Protection and Technology Diffusion: The Case of Electronic Medical Records. Management Science 55(7):1077–1093.


Miller, A. 2010. Electronic Discovery and the Adoption of Information Technology. January 18. http://ssrn.com/abstract51421244. Accessed August 27, 2011. ———. 2011. Can Healthcare IT Save Babies? http://papers.ssrn.com/sol3/papers.cfm?abstract _id51080262. Accessed August 27, 2011. Needleman, J., P. Buerhaus, M. Stewart, K. Zelevinsky, and S. Mattke. 2002. Nurse Staffing in Hospitals: Is There a Business Case for Quality? Health Affairs 25(1):204–211. Needleman, J., P. Buerhaus, S. Mattke, M. Stewart, and K. Zelevinsky. 2002. NurseStaffing Levels and the Quality of Care in Hospitals. New England Journal of Medicine 346(22):1715–1722. Parente, S., and J. McCullough. 2009. Health Information Technology and Patient Safety: Evidence from Panel Data. Health Affairs 28(2):357. Rashad, I., and R. Kaestner. 2004. Teenage Sex, Drugs and Alcohol Use: Problems Identifying the Cause of Risky Behaviors. Journal of Health Economics 23:493–503. Reichman, N., H. Corman, and K. Noonan. 2003. Effects of Child Health on Parents’ Relationship Status. NBER Working Paper No. 9610. Cambridge, Mass.: National Bureau of Economic Research. Rosenbaum, P. R., and D. B. Rubin. 1985. Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score. The American Statistician 39(1):33–38. Scott, R. D., U. S. Centers for Disease Control and Prevention. 2009. The Direct Medical Costs of Healthcare-Associated Infections in

U.S. Hospitals and the Benefits of Prevention. http://www.cdc.gov/ncidod/dhqp/pdf/Scott_ CostPaper.pdf. Accessed August 26, 2011. Shamliyan, T. A., S. Duval, J. Du, and R. L. Kane. 2008. Just What the Doctor Ordered. Review of the Evidence of the Impact of Computerized Physician Order Entry System on Medication Errors. Health Services Research 43(1 Pt 1):32–53. Thomas, E. J., D. M. Studdert, J. P. Newhouse, B. I. Zbar, K. M. Howard, E. J. Williams, and T. A. Brennan. 1999. Costs of Medical Injuries in Utah and Colorado. Inquiry 36(3):255–264. Thomas, E. J., D. M. Studdert, H. R. Burstin, E. J. Orav, T. Zeena, E. J. Williams, K. M. Howard, P. C. Weiler, and T. A. Brennan. 2000. Incidence and Types of Adverse Events and Negligent Care in Utah and Colorado. Medical Care 38(3):261–271. U. S. Congress. 2010. Patient Protection and Affordable Care Act. January 5, 2010. Public Law 111–148. Washington, D.C.: U.S. Congress. http://dpc.senate.gov/dpcdoc-sen_health_care_ bill.cfm. Accessed June 30, 2010. U. S. Congressional Budget Office (CBO). 2008. Budget Options. Volume I. Health Care. Washington, D.C.: CBO. http://www.cbo.gov/ftpdocs/ 99xx/doc9925/12-18-HealthOptions.pdf. Accessed August 26, 2011. U. S. Department of Health and Human Services (DHHS). 2011. HHS Action Plan to Prevent Healthcare-Associated Infections. 2011. Washington, D.C.: DHHS. http://www.hhs.gov/ash/initia tives/hai/index.html. Accessed August 26, 2011. Unruh, L. 2003. Licensed Nurse Staffing and Adverse Events in Hospitals. Medical Care 41(1):142–152.

303