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Association of Electronic Health Records With Cost Savings in a National Sample
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Association of Electronic Health Records With Cost Savings in a National Sample

Abby Swanson Kazley, PhD; Annie N. Simpson, PhD; Kit N. Simpson, DPH; and Ron Teufel, MD
The authors examine the association between advanced electronic health record (EHR) use and cost in hospitals. Patients treated in hospitals with advanced EHRs cost 9.66% less.
When controlling for patient and hospital characteristics in the multivariable model, the mean cost per patient admission for hospitals without advanced EHRs was $7938. The mean cost per patient admission for hospitals with advanced EHRs was $7207. Based on the generalized linear regression model, presence of an advanced EHR was significantly associated with cost of admission, and the beta of –0.0966 indicates that patients treated in hospitals with advanced EHRs have costs that are on average 0.0966%, or $731, lower than those for patients treated in hospitals without an advanced EHR. Other significant predictors of cost include, as expected, patient age, race, gender, severity of illness, risk of death, transfer status, case mix, and insurance type, as well as hospital size, type of hospital, teaching status, rural or urban location, and region of the country. The estimates of the multivariable model are presented in Table 2. When the propensity score adjustment was included, the association between advanced EHR use and cost remained statistically significant, with cost estimates remaining the same, indicating that selection bias was likely minimal. The GEE model controlling for potential correlation between patients seen at the same hospital remained significant (P of advanced EHR =.04) and garnered the same results as seen in the generalized linear model.

Sensitivity Analyses

We developed a 5% sample of the data using propensity score matching based on the nearest neighbor-matching greedy algorithm approach. We limited the sample to 72,002 randomly selected observations from each group to keep computational time below 8 hours. Propensity scores in the original sample ranged from 0.012 to 0.921. The heterogeneity between the EHR and non-EHR admissions was well controlled by the matching, as illustrated by the decrease in the absolute standardized difference in the means for all the propensity score model variables before and after the match (Figure). The cost differences between the EHR and non-EHR admissions in the matched groups were slightly larger than the cost difference observed for the total data set (Table 3). The use of the GEE approach on the main data analysis did not make a significant difference in the results. We explored whether the observed relationship between advanced EHR and cost of care was also present for other measures of hospital resource use, and found $3305 lower total charges per admission when mean charges were modeled. When we explored length of stay (LOS), adjusting for covariates similar to the cost model, we found a mean LOS of 4.1 days for both groups (P =.84). The resource input data available in the data set were limited. However, hospitals with advanced EHRs appear to have had a slightly higher rate of nursing fulltime– equivalent personnel per 1000 adjusted admissions (RNFTEs). The mean number of RNFTEs was 4.27 for the advanced EHR admissions and 4.15 for the comparison group in the propensity score–matched sample, for a difference of 0.09 (P <.0001) RNFTEs after controlling for covariates in the model.

When assessing hidden bias due to unmeasured variables, we used methods described by Rosenbaum to estimate the sensitivity parameter, Γ, the degree of departure from a study that is free of hidden bias.20 In order to attribute the lower hospital cost in patients in an advanced EHR hospital to an unmeasured factor, a hidden bias or unobserved covariate would need to increase the odds of being in an advanced EHR hospital by more than a factor of gamma >100. The inferential finding of lower admission cost in patients seen in advanced EHR hospitals was, therefore, very insensitive to hidden bias.

DISCUSSION

Hospitals have struggled to adopt and use EHRs because they are costly to purchase, implement, and maintain, especially in the current environment of cost containment and increased efforts to be “lean.”21 Thus, the HITECH incentives were created to help offset the costs by offering hospitals hundreds of thousands of dollars in payments for purchase and “meaningful use” of EHRs. This analysis provides evidence that advanced EHR use may result in cost savings for hospitals in addition to meaningful use incentives, and supports the business case for the costly implementation and maintenance of an EHR system. Such implementation should include plans for advanced EHR use.22

During the period of study, only a minority of hospitals were using advanced EHR systems.23 The additional value of this study compared specific EHR components that are associated with stage 3, or meaningful use, and can be related to cost savings, including CPOE, clinical decision support systems, automation of ancillary services including a clinical data repository, pharmacy, laboratory, and radiology information, and automation of nursing work flow with electronic nursing documentation and medication administration records. The staging model that was used demonstrates that cost savings may not be realized until multiple features are included and implemented. Since EHR systems are complex and costly to implement, it is often a multistage process to adopt and use EHRs.24 Thus, hospitals must anticipate that the financial savings may not exist until advanced, “meaningful” use is attained. The majority of hospitals have yet to reach the stage of implementation where cost savings are possible, since they are not using advanced EHRs.

The cost differences observed in this study were measured based on costs estimated from charges using the hospital’s reported mean cost-to-charge ratio for the year of the data, and it is possible that this estimate of cost could bias cost estimates for an individual hospital. However, it is unlikely that systematic bias from this approach would have skewed our results. Given the findings of this study, patients and payer groups may consider selecting hospitals for care based on advanced EHR use, especially if they are paying for care based on a formula related to charges. This may be especially true if the efforts to promote consumer-driven healthcare are successful and if these hospitals demonstrate high levels of quality. However, given the current arrangement where hospital prices are not related to what patients have to pay, the cost savings of advanced EHR use may be most valuable to third-party payers. Our business case also does not include the costs of implementation or maintenance of an EHR system, which can be significant: they range from hundreds of thousands to millions of dollars.

This study had several limitations. First, EHR use is not randomly assigned, and thus there is the potential for selection bias. To overcome this, we included a propensity score adjustment. Second, the NIS data did not include a fully representative sample, and some of the states included in the sample did not provide hospital identifiers for patient-level data. Those cases were excluded from the study, as they could not be linked to EHR use data. Also, because the data are from 2009, we could not be certain that the systems classified as “advanced” in the adoption model absolutely meet the requirements for meaningful use according to the HITECH Act. However, given the definitions and components included in our definition, they are most consistent with meaningful use. There is a possibility that hospitals with EHRs are better able to capture charges. Better charge capture will result in higher estimated cost per admission in our data. Thus, if EHR use improves charge capture, costs (estimated as charges adjusted by the cost-to-charge ratio) will appear higher, resulting in a higher cost per admission in the advanced EHR group in the current study. Finally, some individual cases were excluded because the NIS data did not provide a hospital identifier to allow EHR use to be determined. While we did not find evidence that the excluded cases were systematically different than the included case, this does present a potential weakness.

CONCLUSIONS

Hospitals that use advanced EHRs report lower costs per patient admission than hospitals that do not use advanced EHRs. These cost savings will benefit many thirdparty payers, hospitals, and patients, and incentives such as those provided through the HITECH Act to promote EHR adoption and use will benefit hospitals. Since many previous studies have shown that EHRs can improve the safety and quality of care in hospitals, the projected cost savings in this study provides additional motivation and builds the business case for hospitals to make the large investment in adopting and maintaining an EHR system. This study is a very large multistate study that is likely representative of national trends and builds upon previous work of cost savings associated with EHR use. The results provide support for the continued adoption and use of EHRs to improve healthcare through cost savings and quality improvements.

Author Affiliations: Department of Health Care Leadership and Management (ASK, ANS, KNS); Department of Pediatrics, Medical University of South Carolina, Charleston (RT).

Source of Funding: None reported.

Author Disclosures: The authors 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 (ASK, KNS, RT); acquisition of data (ASK, KNS, RT); analysis and interpretation of data (ASK, ANS, KNS); drafting of the manuscript (ASK, ANS, KNS, RT); critical revision of the manuscript for important intellectual content (ASK, KNS, RT); statistical analysis (ANS, KNS); administrative, technical, or logistic support (ASK).

Address correspondence to: Abby Swanson Kazley, PhD, Department of Health Care Leadership and Management, Medical University of South Carolina, 151 Rutledge Ave, Charleston, SC 29425. E-mail: swansoaj@ musc.edu.
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