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The American Journal of Managed Care Special Issue: Health Information Technology - Guest Editor: Farzad Mostashari, MD, ScM
Health Information Technology: On the Cusp of Healthcare Transformation
Ashish K. Jha, MD, MPH
The Data Revolution Comes to Healthcare
Farzad Mostashari, MD, ScM, Visiting Fellow, Brookings Institute, Former National Coordinator for Health IT, US Department of Health and Human Services
The Impact of Electronic Health Record Use on Physician Productivity
Julia Adler-Milstein, PhD; and Robert S. Huckman, PhD
Face Time Versus Test Ordering: Is There a Trade-off?
James E. Stahl, MD, CM, MPH; Mark A. Drew, BID; Jeffrey Weilburg, MD; Chris Sistrom, MD, MPH, PhD; and Alexa B. Kimball, MD, MPH
Evolving Vendor Market for HITECH-Certified Ambulatory EHR Products
Marsha Gold, ScD; Mynti Hossain, MPP; Dustin R. Charles, MPH; and Michael F. Furukawa, PhD
Financial Effects of Health Information Technology: A Systematic Review
Alexander F. H. Low, MBA; Andrew B. Phillips, RN, PhD; Jessica S. Ancker, MPH, PhD; Ashwin R. Patel, MD, PhD; Lisa M. Kern, MD, MPH; and Rainu Kaushal, MD, MPH
Employing Health Information Technology in the Real World to Transform Delivery
Marsha Gold, ScD
Redesigning the Work of Case Management: Testing a Predictive Model for Readmission
Penny Gilbert, MBA, BSM, BSN, RN, CPHQ; Michael D. Rutland, MBA, FHFMA, FACHE, FABC; and Dorothy Brockopp, PhD, RN
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Rohima Begum, MPH; Mandy Smith Ryan, PhD; Chloe H. Winther, BA; Jason J. Wang, PhD; Naomi S. Bardach, MD; Amanda H. Parsons, MD; Sarah C. Shih, MPH; and R. Adams Dudley, MD, MBA
Older Adult Consumers' Attitudes and Preferences on Electronic Patient-Physician Messaging
Richard Lam, MD, MBA; Victor S. Lin BS; Wendy S. Senelick, MPH; Hong-Phuc Tran, MD; Alison A. Moore, MD, MPH; and Brandon Koretz, MD, MBA
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Will Meaningful Use Electronic Medical Records Reduce Hospital Costs?
William E. Encinosa, PhD; and Jaeyong Bae, MA

Will Meaningful Use Electronic Medical Records Reduce Hospital Costs?

William E. Encinosa, PhD; and Jaeyong Bae, MA
Adopting the 5 core medication management elements of meaningful use electronic medical records reduces adverse drug events and saves costs.
Background: More than one-third of the Centers for Medicare & Medicaid Services core meaningful use (MU) requirements deal with medication management.

Objectives: To examine what impact the 5 core medication MU criteria have on hospital-acquired adverse drug events (ADEs) and their costs in 2010, as a baseline for the start of MU implementation in 2011.

Data Sources: 2010 Florida State Inpatient Database (Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality), American Hospital Association (AHA) Healthcare IT Database Supplement to the AHA 2010 Annual Survey of Hospitals, and Hospital Compare.

Methods: We developed one of the first quality indicators to track in-hospital ADEs in administrative data. Controlling for nonresponse selection bias, we used multivariate logit regression analysis to examine the impact of the 5 MU medication elements on the probability of an ADE and on its costs.

Results: A hospital-acquired ADE was noted in 1.7% of hospitalizations. Odds of an ADE were 63% less in hospitals that adopted all 5 core medication MU elements (10% of hospitals in 2010) compared with hospitals that adopted no core elements. This effect was found even among lowperforming hospitals with below-average Hospital Compare quality. Estimated hospital cost savings per averted ADE were $4790. If all hospitals in Florida had adopted all 5 functions, 55,700 ADEs would have been averted and $267 million per year would have been saved.

Conclusions: Adoption of core medication MU elements will cut ADE rates, with cost savings that recoup 22% of information technology costs.

Am J Manag Care. 2013;19(11 Spec No. 10):eS19-eSP25
Five core measures in the meaningful use electronic medical record (EMR) requirements are medication management functions.
  • In 2010, 10% of Florida hospitals adopted all 5 of these functions. 
  • Adopting all 5 functions reduced hospital-acquired ADEs from 3.6% to 1.4%.
  • If all hospitals in Florida adopted all 5 functions, $267 million per year would be saved, recouping 22% of the information technology capital costs.
To encourage the adoption of electronic medical records (EMRs), in 2011 the Centers for Medicare & Medicaid Services (CMS) implemented an incentive system that rewards hospitals for implementation of the meaningful use regulations. This program was established by the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. HITECH provides about $27 billion in incentives over a 10-year period for hospitals and physicians to adopt the meaningful use of EMRs.1 Prior to HITECH, meaningful use adoption was relatively low. Only 1.6% of hospitals in 2009 had EMRs that satisfy the HITECH rules for meaningful  use.2 As of June 2013, more than 60% (n = 3481) of hospitals had received a HITECH incentive payment, for a total of $5.5 billion paid out to hospitals.

However, many doubts about the cost benefits of EMRs are now emerging. In a recent Wall Street Journal editorial, Soumerai and Koppel surmised that the United States will spend $1 trillion on health information technology (HIT) without much in cost savings.3 Much of this skepticism was exacerbated by a recent Agency for Healthcare Research and Quality (AHRQ) evidence report, Enabling Medication Management Through Health Information Technology, which examined almost 36,000 HIT medication studies and found 76 studies that focused on clinical outcomes. About 34% of those studies found a significant beneficial impact of HIT on  outcomes. However, only 31 articles studied cost outcomes, and only 5 of those provided full economic analyses. As a result, the report concluded that “given the uncertainty that surrounds the costs and outcomes data, and limited study designs, it is difficult to  reach any definitive conclusion as to whether the additional costs and benefits represent value for money.”4

A major limitation of this EMR literature, as the AHRQ evidence report concluded, is that most of the studies examined only 1 EMR medication function at a time. For example, 19 of the 31 cost studies examined only computer decision support functions. Only 2  studies examined the joint use of computer decision support functions and computerized physician order entry in hospitals.5 Of the 14 core measures that hospitals must satisfy for a Stage 1 meaningful use incentive payment, 5 in 2011-2013 deal with medication management functions in the EMR system. Yet no study has examined all 5 functions bundled together.

The reason for this lack of research is that most of the studies usually took place at 1 hospital and involved only a few thousand

patients. Such low power makes it almost impossible to study more than 1 EMR function. A simple power analysis prior to our research indicated that we would need 654,000 patients to detect a 10% change in adverse drug event (ADE) rates truly due to the  5 meaningful use medication management components. Such a large patient sample is beyond the scope of many studies, which may be the reason why the AHRQ evidence report found little evidence of the effect of EMRs on costs.

However, even if researchers have a large enough database to examine all 5 EMR functions, another problem is that an ADE measure that can be applied to large administrative data sets does not yet exist. In fact, because of this data measurement problem, Medicare has yet to establish a zero-payment policy for hospital-acquired ADEs among its list of never events, even though the National Quality Forum has mandated death orsevere disability due to medication errors as 1 of its 28 never events. Such an ADE measure for large databases is much needed under the Affordable Care Act, particularly since the Act launched the $1 billion Partnership for Patients initiative to reduce the incidence of 10 patient safety events (1 of which is ADEs) by 40% through the 26 Hospital Engagement Networks.

In this study, we attempted to overcome these major limitations. First, we examined all 5 meaningful use medication management components together. Second, instead of a few hospitals, we examined a wide array of hospitals in the real world, both advantaged and disadvantaged. We included all hospitals in Florida. Third, since we had all 2.6 million hospitalizations in Florida, we had the statistical power to detect the impact of full meaningful use medication management adoption on rare ADEs and their costs. We did this by developing one of the first measures of hospital-acquired ADEs that can be used on large-scale administrative data sets.



First, we used the 2010 Florida State Inpatient Database from the Agency for Healthcare Research and Quality’s Healthcare Cost and Utilization Project (HCUP).6 This database consists of all 2.64 million inpatient hospitalizations in Florida. We selected Florida because it has very accurately coded “present on admission” (POA) diagnosis data, especially for International Classification of Diseases, Ninth Revision E-codes (injury codes), compared with other states. Second, our source of EMR data linked to the Florida data was the American Hospital Association (AHA) Healthcare Information Technology (IT) Database Supplement to the AHA 2010 Annual Survey of Hospitals. (See Jha and colleagues7 for a description of this survey.) This survey first asked questions about meaningful use in 2010. Third, we linked the 2008 to 2010 Medicare Hospital Compare data to the Florida data in order to use Medicare’s hospital quality measures.

Hospital-Acquired Adverse Drug Events

To identify ADEs, we use the algorithm developed by Lucado and colleagues8 for flagging ADEs in HCUP.8 However, many of the ADEs identified by the algorithm may have occurred outside of the hospitalization. To identify only those ADEs that occurred during that hospitalization, we redesigned the algorithm to flag ADEs that were coded as not being POA. The POA codes used in HCUP have been validated.9 Following Houchens and colleagues10 and the HCUP POA report,9 we screened out a few hospitals (5% of the hospitalizations) that had either more than 95% of their diagnoses coded as POA or more than 20% with POA missing.

Meaningful Use

In the AHA IT supplement, we could identify hospitals that by 2010 had implemented the 5 Stage 1 meaningful use core measures that pertain to medication management:

  1. Use computerized physician order entry for medication orders.
  2. Implement decision support system for drug-drug and drug-allergy interaction checks.
  3. Capability to exchange key clinical information (for example, problem list, medication list, medication allergies, and diagnostic test results) among providers electronically.
  4. Maintain active medication list.
  5. Maintain active medication allergy list.

We required functions 1, 2, and 5 above to be “fully implemented across all units” of the hospital. Unit implementation for functions 3 and 4 could not be ascertained from the AHA data. The adoption rates for these 5 measures are reported in Table 1.

Regression Analyses

We used multivariate logistic regression analyses to examine 2 main relationships: (1) the relationship between each of the 5 core meaningful use medication management measures and the probability that a hospitalization will have an ADE and (2) the relationship between the number of the meaningful use medication management measures adopted (0, 1-2, 3-4, all 5) and the costs of a hospitalization. To further minimize endogeneity issues, all our regressions controlled for the following covariates. We included 26 collapsed chronic condition variables derived from 30 chronic conditions developed by Elixhauser and colleagues.11 We also included the following variables to control for potential confounding effects of patient severity: 6 age categories, sex, race, an indicator for emergency department admission, an indicator for surgery, and an indicator for whether the patient had more than 5 chronic conditions. We also controlled for the type of insurance (private, uninsured, Medicare, Medicaid) and for the median household income of the patient’s zip code. Finally, we controlled for hospital characteristics such as bed size, for-profit status, not-for-profit status, public hospital, teaching hospital, and the hospital’s percentage of missing POA codes. We also controlled for whether the hospital had a 20% or more quality improvement from 2008 to 2010 in terms of reduced 30-day Hospital Compare Medicare mortality rates for health attack, heart failure, and pneumonia. Finally, to control for the effects of other HIT components, we used an indicator of “advanced EMR” following Jha and colleagues’7 definition of comprehensive EMRs as a hospital adopting all 24 AHA EMR functionalities in all major clinical units. The means of all the covariates are found in Table 2.

The HCUP hospital costs included all costs except physician and laboratory costs. The HCUP cost information was obtained from the hospital accounting reports collected by CMS. To estimate the hospital costs of an ADE, we regressed the log of costs on ADEs. We used the log of expenditures to control for the skewed distribution of expenditures. To avoid bias due to retransforming the log, we used a generalized linear model regression with a log link and a gamma distribution, following Manning and Mullahy.12 We included 100 diagnosis-related group fixed effects in the cost regressions.

Selection Bias

Because only 58% of the Florida hospitalizations linked to the AHA IT supplement due to survey nonresponse, there is the potential for a nonresponse bias in our results. To control for this bias, we followed Little and Vartivarian13 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 2010 annual hospital survey. Using the psmatch2 routine in Stata 12 (StataCorp LP, College Station, Texas), we created each hospital’s propensity score weight with a logit regression of the probability of responding to the IT survey, controlling for 23 hospital characteristics. 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 Ichino14), which balanced propensity scores across the 23 hospital characteristics. No hospitals lacked a common region of support (Becker and Ichino14). Although 12 of the 23 hospital characteristics in the raw data predicted a response to the survey (within a 95% level of statistical significance), none of the hospital characteristics still predicted a response to the survey after matching. The average absolute value of the bias in the 23 hospital characteristics between responding and nonresponding hospitals was reduced from 20.4 to 7.8 due to the propensity score matching. Thus, these constructed sample weights substantially reduced any selection bias. All of our analyses used the weighted sample. The final weighted sample had 2,533,091 hospitalizations as observations.


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