Adopting the 5 core medication management elements of meaningful use electronic medical records reduces adverse drug events and saves costs.
More than one-third of the Centers for Medicare & Medicaid Services core meaningful use (MU) requirements deal with medication management.
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.
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.
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.
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.
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-eSP25Five core measures in the meaningful use electronic medical record (EMR) requirements are medication management functions.
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.
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:
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 .
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 .
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.
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.
In general, an ADE occurred during 6% of Florida hospitalizations in 2010. However, only 1.7% of those ADEs were hospital acquired. By 2010 only 9.9% of the hospitals in Florida had adopted all 5 core meaningful use medication management functions (Table 1). Those hospitals had an ADE rate of 1.36% compared with 1.72% for hospitals adopting none of the 5 functions. The cost savings from averting an ADE was $22,500 in the hospitals that had adopted all 5 core meaningful use medication management functions compared with $13,900 in hospitals without the 5 functions. Since these statistics are unadjusted, in Tables 2 to 4 we present regressions to obtain risk-adjusted results.
No single medication management function alone had an impact on ADEs (data not shown). However, the Table 2 logistic regression shows that bundling the 5 medication management functions together had a significant effect on reducing ADEs. Hospitals with all 5 medication management functions had 62.8% lower odds of a hospital-acquired ADE than hospitals with none of the functions. Hospitals with 3 to 4 functions had 53% lower odds compared with hospitals with no functions. Hospitals with 1 to 2 functions had 58.6% lower odds compared with hospitals with no functions. Converting these results to actual ADE rates (not shown), we see that hospitals with none of the 5 functions had a hospital-acquired ADE rate of 3.6%, compared with 1.56% for hospitals with 1 to 2 functions, 1.76% for hospitals with 3 to 4 functions, and 1.40% for hospitals with all 5 medication management functions. Thus, the use of all 5 functions was more effective at reducing ADEs than the use of 4 or fewer functions.
These results may be overestimated due to unobservable quality measures other than meaningful use (eg, quality improvement efforts) that are correlated with a reduced rate of ADEs; therefore, we did a robustness check (). We examined the impact of meaningful use on ADEs across hospitals of differing quality. We compared hospitals that had large 20% quality improvements (in terms of reduced 30-day Hospital Compare Medicare mortality rates for health attack, heart failure, and pneumonia) from 2008 to 2010 with hospitals that had more than 20% declines in quality (Table 3). Even these poorly performing hospitals had 7% lower odds of an ADE when they adopted all 5 meaningful use medication components. We also compared hospitals based on national benchmarks (not just on Florida data) in terms of 2010 30-day Hospital Compare Medicare cardiac mortality national rankings (Table 3). Even hospitals that were below the national average had 31% lower odds of an ADE when they adopted all 5 meaningful use medication components. Thus, the impact of meaningful use on ADEs is robust across low- and high-quality hospitals.
Finally, we estimated the costs of an ADE using a generalized linear model regression of logged costs regressed on an ADE indicator with the covariates of Table 2 (). We found that the cost of a hospitalization with a hospitalacquired ADE was $14,266 compared with $9474 for a hospitalization with an ADE. Thus, the excess cost of an ADE is $4792 (2012 dollars), resulting in 51% higher costs. The reduction in ADE rates from 3.6% to 1.4% due to adoption of all 5 meaningful use medication functions resulted in a large cost saving. Table 4 shows that adopting all 5 meaningful use medication functions was directly associated with lower costs, with a coefficient of —.239. Therefore, we can conclude that net cost savings result from the lower rate of ADEs under meaningful use. However, the cost savings decrease as the number of EMR functions increase from 1 to 5, indicating that implementing meaningful use does have some offsetting costs. In the right-hand column of Table 4, we see that these results also held in the poorly performing hospitals—those with declining quality over the period 2008 to 2010 (in terms of increased 30-day Hospital Compare mortality rates for health attack, heart failure, and pneumonia). However, there was a much sharper gradient in the drop-off in cost savings among these poorly performing hospitals, with coefficients dropping from –.251 to only –.046 in saving effects savings when moving from 4 functions to 5 functions. Moving from 4 functions to 5 functions in these low-quality hospitals appeared to be more costly than it was for hospitals in general.
In this study, we developed one of the first quality indicators to track in-hospital ADEs in large administrative data sets. That allowed us to examine the impact of the 5 meaningful use medication management components across all hospitals in Florida. We found that adopting all 5 medication management components of Stage 1 meaningful use reduced the rate of ADEs by 61% compared with not adopting all 5 components, and by 10% compared with adopting only 4 components. If all hospitals in Florida adopted all 5 components, 55,700 ADEs per year would be averted. At $4790 in hospital cost savings per averted ADE, that would save $267 million per year.
Does this cost savings make investing in EMRs worthwhile? In the 2007 AHA IT survey report, the median spending on IT capital per hospital bed was $6112 per year (2012 dollars).15 Using the 2007 AHA survey data and following Miller and Tucker’s methods, we found that $380 million is spent on hospital IT capital in Florida annually, given that Florida has 62,100 beds.16 Thus, adopting IT with the 5 meaningful use medication management components would recoup 70% ($267 million/$380 million) of the IT capital investment due to the averted ADEs. However, the median costs of operating the IT is an additional $13,266 per bed per year.15 Including both IT capital and IT operating costs, the cost savings from the averted ADEs would recoup 22% of all IT costs.
However, note that we did not examine other related cost savings. Even if meaningful use does not avert an ADE, it may help to avert death and disability after the ADE. In fact, Encinosa and Bae17 report a $0.55 cost offset per $1 spent on IT just from averted deaths and readmissions after unaverted nonmedication adverse events. Future research should examine these types of additional cost savings among ADEs.
Finally, it is important to note that our methods controlled for the AHA IT hospital nonresponse bias. However, we did not control for potential endogeneity between EMR adoption and ADEs. It could be that high-quality hospitals with low ADE rates were the ones adopting all 5 meaningful use medication management components, in which case our reported results would be an overestimation. However, the results shown in Tables 3 and 4 indicate that even lowquality hospitals had a reduction in ADEs and costs when they adopted all 5 meaningful use medication functions. Moreover, using earlier 2007 AHA IT survey data, Encinosa and Bae16 controlled for endogeneity between EMR adoption and nonmedication adverse events and found that there was no overestimation bias due to high-quality hospitals with low error rates adopting EMRs. Instead, they found an underestimation bias due to unobservable patient condition severity. Drug errors are also likely to be similarly affected by unobservable severity. Thus, Encinosa and Bae’s results indicate that we may have underestimated the effect of meaningful use on ADEs. Future research should examine this as future AHA IT meaningful use data become available and panel data analyses become possible.
Finally, it should be noted that Stage 2 of meaningful use, which began in October, 2013, makes a few changes. Functions "maintain active medication allergy list" in Stage 1 are now incorporated into "summary of care document at transitions of case and referrals" in Stage 2. "Implement drug-drug and drug-allergy interaction checks" in Stage 1 are also now incorporated into "clinical decision support" in Stage 2. "Capability to exchange key information is eliminated in Stage 2. But much of this is subsumed under "medication reconcilliation" in Stage 2. The new feature, "electronic medication administration record (eMAR)," is added in Stage 2. Future research should examine the effectiveness of this new mix of medication management functions under Stage 2.Author Affiliations: From Agency for Healthcare Research and Quality and Georgetown University (WEE), Rockville, MD; Emory University (JB), Atlanta, GA.
Funding Source: This study was funded by the Agency for Healthcare Research and Quality
Author Disclosures: From AHRQ (WE), Washington, DC; Emory University (JB), Atlanta, GA.
Authorship Information: The authors (JB, WE) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (JB, WE); acquisition of data (WE); analysis and interpretation of data (JB, WE); drafting of the manuscript (JB, WE); critical revision of the manuscript for important intellectual content (JB, WE); statistical analysis (JB, WE); provision of study materials or patients (JB, WE); obtaining funding (WE); administrative, technical, or logistic support (JB, WE); and supervision (WE).
Address correspondence to: William E. Encinosa, PhD, Center for Delivery, Organization and Markets, Agency for Healthcare Research and Quality, 540 Gaither Rd, Rockville, MD 20850. Email: firstname.lastname@example.org. Blumenthal D. Launching HITECH. N Engl J Med. 2010;362(5): 382-385.
2. Jha A, DesRoches C, Kralovec PD, Joshi MS. A progress report on electronic health records in U.S. hospitals. Health Aff (Millwood). 2010; 29(10):1-7.
3. Soumera, S, Koppel R. A major glitch for digitized health-care records: savings promised by the government and vendors of information technology are little more than hype. The Wall Street Journal.http://online.wsj.com/article/SB10000872396390443847404577627041964831020.html?mod=WSJ_Opinion_LEFTTopOpinion. Published September 18, 2010. Accessed October 4, 2012.
4. McKibbon KA, Lokker C, Handler SM, et al. Enabling Medication Management Through Health Information Technology. Evidence Report/Technology Assessment No. 201. Prepared by the McMaster University Evidence-based Practice Center under Contract HHSA 290-2007-10060-I. Rockville MD: Agency for Healthcare Research and Quality; April 2011. AHRQ publication 11-E008-EF.
5. O’Reilly D, Tarride JE, Goeree R, Lokker C, McKibbon KA. The economics of health information technology in medication management: a systematic review of economic evaluations. J Am Med Inform Assoc 2012;19(3):423-438.
6. Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality. SID Database Documentation. http://www hcup-us.ahrq.gov/db/state/siddbdocumentation.jsp. Published July 2013. Accessed October 3, 2013.
7. Jha AK, DesRoches CM, Campbell EG, et al. Use of electronic health records in U.S. hospitals. N Engl J Med. 2009;360(16):1628-1638.
8. Lucado J, Paez K, Elixhauser A. Medication-Related Adverse Outcomes in U.S. Hospitals and Emergency Departments, 2008. HCUP Statistical Brief #109. Agency for Healthcare Research and Quality.http://www.hcup-us.ahrq.gov/reports/statbriefs/sb109.pdf. Published April 2011. Accessed October 3, 2013.
9. Coffey R, Milenkovic M, Andrews RM. The Case for the Present-on-Admission (POA) Indicator. HCUP Methods Series Report # 2006-01. Agency for Healthcare Research and Quality. http://www.hcup-us.ahrq.gov/reports/methods/2006_1.pdf. Published June 26, 2006. Accessed October 3, 2013.
10. Houchens RL, Elixhauser A, Romano PS. How often are potential patient safety events present on admission? Jt Comm J Qual Patient Saf. 2008;34(3):154-163.
11. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8-27.
12. Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ. 2001;20(4):461-494.
13. Little RJ, Vartivarian S. On weighting the rates in non-response weights. Stat Med. 2003;22(9):1589-1599.
14. Becker SO, Ichino A. Estimation of average treatment effects based on propensity scores. Stata J. 2002;2(4):358-377.
15. American Hospital Association. Continued Progress: Hospital Use of Information Technology. http://www.aha.org/aha/content/2007/pdf/070227-continuedprogress.pdf. Published 2007. Accessed August 30, 2011.
16. Miller AR, Tucker CE, Can health care information technology save babies? Journal of Political Economy. 2011;119(2):289-324.
17. Encinosa WE, Bae J. Health information technology and its effect on hospital costs, outcomes, and patient safety. Inquiry 2012;48(4): 288-303.