Published Online: June 27, 2014
Abby Swanson Kazley, PhD; Annie N. Simpson, PhD; Kit N. Simpson, DPH; and Ron Teufel, MD
To determine whether advanced electronic health record (EHR) use in hospitals is associated with lower cost of providing inpatient care.
National Inpatient Sample (NIS) and the Health Information Management Systems Society (HIMSS) Annual Survey are combined in the restrospective, cross-sectional analysis. We study patients who are 18 years or older and discharged from a general acute care hospital.
Using 2009 data and a cross-sectional design with a gamma distributed generalized linear model, a patient-level analysis is conducted with propensity scores to control for selection bias. Patient- and organizational-level variables are included as controls. The main outcome measure is total cost per patient admission and represents the amount that it costs the hospital to provide services based on the adjusted charges for an admission.
We include 5,047,089 individuals treated at 550 hospitals in the United States and represent a population-based sample. There are 104 (18.9%) hospitals included that use advanced EHRs. Patients treated in hospitals with advanced EHRs cost, on average, $731, or 9.66%, less than patients admitted to hospitals without advanced EHRs, after controlling for patient and hospital characteristics.
Hospitals that use advanced EHRs have lower cost per patient admission than comparable hospitals with similar case mix.
Am J Manag Care. 2014;20(6):e183-e190
Advanced EHR use in hospitals has the potential to save money on patient care.
This study supports the case for advanced EHR adoption and use, consistent with meaningful use.
Advanced EHR use requires costly investment for hospitals that can be regained through savings in patient care.
Patients treated in hospitals with advanced EHRs cost 9.66% less than those treated in hospitals without advanced EHRs.
Electronic health records (EHRs) have been suggested as a tool for improving the overall quality and cost of care in the United States.1 Proponents and policy makers have created incentives through the Health Information Technology for Clinical and Economic Health (HITECH) Act to offset the cost of purchase in order to encourage the adoption and use of advanced EHR systems in a “meaningful” way.2 Such criteria for use are based on previous studies that report improvements in quality.3 To qualify as a meaningful user and benefit from the related incentives, EHR systems must include electronic prescribing, health information exchange with other providers, automated reporting of quality data, electronic recording of patients’ history (demographics, vital signs, medication and diagnosis lists, and smoking status), created care summary documents, and at least 1 clinical decision support tool.4-6 Such meaningful use requirements are believed to improve the legibility of records, reduce prescription errors, improve adherence to best clinical practice guidelines, improve patient and clinician access to records, and allow exchange of health information.4 In addition to gains in quality, EHRs have been predicted to save $81 billion annually through safety improvement and increased efficiency of care,7 yet little is known about their impact on hospital cost, and no previous studies have examined the relationship between cost per admission and EHR use in a national sample of acute care hospitals for adults.
Cost savings associated with EHRs are expected to come through better coordination of care, reduction of medical errors and adverse drug events (ADEs), and increased efficiency and reduction of duplicate testing; previous studies have demonstrated the potential. Silow-Carroll and colleagues found that at 9 hospitals with comprehensive EHR use, “Faster, more accurate communication and streamlined processes have led to improved patient flow, fewer duplicative tests, faster responses to patient inquiries, redeployment of transcription and claims staff, more complete capture of charges, and federal incentive payments,” which lead to cost specific EHR components, including automated notes and records, order entry, and clinical decision support, are associated with fewer complications, lower mortality rates, and lower costs in Texas hospitals.9 In a single hospital, inpatient EHR with computerized provider order entry (CPOE) use was associated with a decrease in laboratory tests, radiology examinations, monthly transcription costs, medication errors, and paper costs.10 On the other hand, a study performed in California revealed that EHR use was associated with an increase in hospital costs, nursing staff levels, and complications, but a decrease in mortality for some conditions.11 This same study did not find any evidence that advanced EHR use reduced length of stay or demand for nurses. Similarly, another study examining EHR use in physician practices found that electronic access to patient lab and imaging results may actually increase the number of overall tests given to individual patients by 40% to 70%, thus increasing costs.12 In a study of Medicare patient–level billing data from 1998 to 2005, Agha reported that EHR adoption was associated with an initial 1.3% increase in billed charges, and saw no evidence of cost savings over the 5 years postadoption.13
In a previous study of a single hospital, physician inpatient order writing on microcomputers was found to be associated with reduced resource utilization.14 These costs of providing care were approximately 12% lower in the area of overall charges, hospital charges, bed charges, diagnostic test charges, and drug charges, although the system required increased physician time. Similarly, the cost savings of advanced EHR use found in this study may be the result of several practices or benefits associated with advanced EHR use. First, the automated nature of advanced EHRs may reduce errors through overall coordination of care, less duplication of tests, and increased efficiency.
Dranove and colleagues examined the implementation of EHR as a business process innovation.15 They used data from Medicare cost reports combined with survey data on EHR adoption to assess the economic implications of EHR adoption on mean cost per admission. They reported that EHR adoption was associated with a rise in cost, but that cost reduction for hospitals in favorable locations—where there was high availability of expertise and complementary services—resulted in cost reductions after 3 years, while hospitals with unfavorable conditions had increased costs, even after 6 years.
The objective of this study was to determine if advanced EHR use is associated with lower cost of care in acute care general hospitals. In an era of increased pressure to adopt EHRs and other health information technology, it is important to understand the benefits and challenges of EHR use. Our study adds to the work by Agha and Dranove by using more recent individual-level patient data that includes all payers.13,15 Our cost measure reflects the variation in cost per admission observed at the level of the individual, as opposed to the mean for the hospital, which allowed us to control for variations in case mix. We limited our analysis to contrasting hospitals with advanced EHRs to all other levels of implementation based on the finding by Dranove and colleagues that cost savings due to EHR implementation are most likely observed for institutions with a critical mass of EHR expertise.
The data for this cross-sectional patient-level analysis were obtained from the National Inpatient Sample (NIS) 2009 and Health Information Management Systems Society (HIMSS) 2009. The NIS includes discharge data from more than 1000 hospitals in 45 states, which encompasses 96% of the United States population. The HIMSS 2009 data were used to measure hospital EHR use. The HIMSS data “represent a broad canvassing of acute care hospitals, chronic care facilities, ambulatory practices on their adoption and plans to adopt various HIT components” and have been widely used in previous research.16 Patients were included in the analysis if they were 18 years or older. In the NIS, some states do not release the American Hospital Association identifiers, and thus the individual patient cases cannot be included because EHR use cannot be determined. Costs were calculated for each admission using the total charges reported multiplied by the hospital-specific cost-to-charge ratio for 2009. Admissions with zero charges were excluded from the analysis.
The generalized linear model and propensity models controlled for patient age, gender, race, All Patient Refined Diagnosis Related Groups (APDRGs) mortality and severity, neonatal or maternal status, private insurance coverage, Medicare or Medicaid coverage, Diagnosis Related Group (DRG) case mix group, and whether the patient arrived as a transfer. Both models also controlled for the following hospital-level variables: teaching status, urban location, bed size, and geographical region. The main outcome measure was total cost of hospital admission per patient-billed hospitalization. Outcomes such as length of stay and the effects of nurse staffing variables on outcomes were explored using similar modeling approaches.
EHR use is measured using stages based on individual applications reported in the hospitals. These include stage 0 (no automation), stage 1 (automation of ancillary services including a clinical data repository, and pharmacy, laboratory, and radiology information systems), stage 2 (stage 1 + automation of nursing work flow with electronic nursing documentation, and medication administration records), and stage 3 (advanced EHR including: stages 1 and 2 + CPOE and clinical decision support). Since meaningful use criteria are consistent with stage 3 adoption of EHR, we chose to compare hospitals that have advanced EHRs with all others, and this staging system has been used in previous research.17
Generalized linear modeling techniques were used to test the hypothesis that the total costs per admission were different between hospitals with and without advanced EHRs. To correct for the non-normal distribution of costs, gamma-distributed generalized linear models using a logarithmic transformation18 were analyzed using the PROC GENMOD module in the SAS statistical software (version 9.2; SAS Institute Inc, Cary, North Carolina). The use of a gamma-distributed generalized linear model with a log-transformed link function has been shown to be an accurate method to estimate healthcare cost distributions that are generally right-skewed, especially when the logtransformed dependent variables do not have heavy tails or excessive heteroscedasticity such as was found to be true in these data.19
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