Extrapolating Evidence of Health Information Technology Savings and Costs

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There are a few published estimates of the costs of widespread implementation of EMR systems in the United States. Samuel Wang and colleagues have provided a model for estimating the cost and return on investment for a physician office practice. Exhibit 6 plots the net cumulative and yearly potential savings benefits over costs from EMR systems in hospital and outpatient settings over time. Because we do not take credit for savings from providers already in the adoption process and because process changes and related benefits take time to develop, net savings are initially low and then rise steeply.

This potential net financial benefit could double if the health savings produced by chronic disease prevention and management were included. Our analysis shows that moving the U. Further, these potential sav-ings would outweigh the costs relatively quickly during the adoption cycle. Dollars Also, even if EMR systems were widely adopted, the market might fail to develop interoperability and robust information exchange networks. Given our analysis, we believe that there is substantial rationale for government policy to facilitate widespread diffusion of interoperable HIT.

Actions now, in the early stages of adoption, would provide the most leverage. Taylor and colleagues discuss several alternatives for government action to remove barriers, correct mar-ket failures, and speed the realization of EMR system benefits. We have shown some of the potential benefits of HIT in the current health care system. However, broad adoption of EMR systems and connectivity are necessary but not sufficient steps toward real health care transformation.

For example, adoption of EMR systems and valid comparative performance reporting would en-able the development of value-based competition and quality improvement to drive transformation. HIT also should facilitate system integration for broader op-timization, and comparative benchmarking should encourage development of market-leading examples of ways to better organize, pay for, and deliver care.

It is not known what changes should or will take place after widespread EMR. But it is increasingly clear that a lengthy, uneven adoption of nonstandardized, noninteroperable EMR systems will only delay the chance to move closer to a transformed health care sys-tem. The government and other payers have an important stake in not letting this happen. Thetimetoact is now.

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Laurence Baker ar-gued that these savings were overestimated. Study Data And Methods Here we summarize the methodologies we used to estimate the current adop-tion of EMR systems and the potential savings, costs, and health and safety bene-fits. Our primary data source was the Healthcare Information and Management Systems Society HIMSS —Dorenfest survey, which represents a broad canvassing of acute care hos-pitals, chronic care facilities, and ambulatory practices on their adoption and plans to adopt various HIT components. To examine the factors related to differences in adoption, we merged additional data about the providers and then performed probit regression analysis.

Our lower-boundestimateofHIT adoption assumedanintegratedsystemthathad an EMR, clinical decision support, and a central data repository—from the same vendor to ensure interoperability. We adjusted the estimates according to the known under-representation of smaller providers in this survey. We conducted a broad liter-ature survey to capture evidence of HIT effects.

Extrapolating Evidence of Health Information Technology Savings and Costs

The survey was primarily from peer-reviewed literature, but it included some information from non-peer-reviewed liter-ature. Expert opinion was used to validate some of this evidence. In some cases, such as savings from transcription, reported results covered a broad range, and we used these ranges to estimate a possible distribution of savings. For effects supported by only a few useful articles, we superimposed the same degree of dispersion.

For hospital adoption, we built a model of EMRsystemcosts basedonthe literature andoninformation supplieddirectlytous from hospitals. We included one-time implementation costs, such as provider downtime and hardware costs, and ongoing maintenance costs. Our data allowed us to relate hospital adoption costs to size and operating expenses of hospitals and generally represented the adoption of newer, more complete EMR systems, includ-ing clinical decision support and computerized physician order entry CPOE.

For the acquisition and setup costs of ambulatory systems, we used a publicly available database of commercial systems and excluded products that did not have most of the desirable features of an ambulatory EMR system.

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From these simulations, we report the mean and show sensitivity to assumptions about the initial adoption rate and assumed adoption period. MG Santa Monica, Calif. But the bulk of the savings couldberealizedbyinstallationinhospitals with more than beds. Abouttwo-thirds of the CPOE benefits are attributable to adverse drug events avoided for pa-tients age sixty-five and older. Although this group comprises only 13 percent of the population, it accounts for a much larger fraction of hospital bed days, and its mem-bers are more susceptible than others to adverse drug events.

Medication er-rors and adverse drug events in ambulatory settings have been studied much less than in hospitals. The available data suggest that roughly eight million outpatient events occur each year, of which one-third to one-half are preventable. About two-thirds of preventable adverse drug events might be avoided through widespread use of ambulatory CPOE.

EMR systems can integrate evi-dence-based recommendations for preventive services such as screening exams with patient data such as age, sex, and family history to identify patients needing specific services. The system can remind providers to offer the service during rou-tine visits and remind patients to schedule care. Preventive Services Task Force recommendation. We conclude that all of these measures, except for pneumococcal vac-cination, will increase health care use and spending modestly.

The U. In one study, fifteen chronic condi-tions accounted for more than half of the growth in health care spending between and , and just five diseases accounted for 31 percent of the increase. The term EMR systems as used here includes the electronic medical record EMR , containing current and historical patient information; clinical decision support CDS , which provides reminders and best-prac-tice guidance for treatment; and a central data repository CDR , for the information. Taylor and R. Fonkych and R.

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Taylor et al. The online supplement is available at content. We examined the effects of health IT expenses on revenue at the hospital level using Texas AHA data from to The GEE model was employed to control for variance-covariance error in financial information and clustering error within hospitals. We found a significant and positive effect of health IT expenses on hospital revenue.

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Our findings are consistent with those of previous studies [ 13 , 21 , 50 ]. Health IT makes patient information easily accessible where it is needed, supports better health care decision making, promotes better coordination of care, increases processing speed and eliminates duplicate or unnecessary tests [ 51 ].

The Value of Health Information Technology: Filling the Knowledge Gap

Accordingly, health IT may result in increased revenue in a hospital setting. The most evident explanation for this includes improved capabilities facilitated by health IT such as an EMR system that allows capture of previously lost revenue by eliminating inefficiency [ 13 ]. As expected, we found that hospital utilization showed difference pattern on hospital revenue. Specifically, while inpatient days and outpatient visits were positively associated with revenue, emergency room visits and government admissions were negatively associated with revenue.

Extrapolating Evidence of Health Information Technology Savings and Costs

This suggests that hospitals lose money on emergency room visits and government admissions [ 52 ]. Patients admitted to the emergency room are cared for regardless of their ability to pay because emergency care is the last safety net. Emergency physicians thus provide uncompensated care to uninsured patients, which may reduce hospital revenue [ 53 ].

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Also, a recent study found that the bad debt provision, as the one component of uncompensated care was negatively associated with operating margin among private non-profit hospitals [ 37 ]. Hospital characteristics also played a significant role in hospital revenue.

Teaching status and a competitive environment were important factors in modeling total revenue. We found that teaching status was positively associated with hospital revenue. Moreover, a more competitive environment was positively associated with hospital revenue, suggesting that competition may improve efficiency, which can lead to improved revenue generation. Moreover, we investigated whether the effect of health IT expenses differs between inpatient and outpatient settings. We found that health IT had a stronger association with gross outpatient revenue than inpatient revenue. However, by utilizing all IT expenses in the hospitals in our model, we revealed that health IT expenses were more associated with the outpatient setting revenue.

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  • Compared to the inpatient setting, the outpatient setting is more complex with numerous procedures, surgeries and tests [ 54 ]. It is important that information is acquired quickly, there is efficient feedback about appropriateness of the procedure and costs of medication is readily accessible. Another explanation is related to the current trend of shifting inpatient procedures to outpatient procedures [ 54 ].

    With this prevailing trend, hospital health IT behavior might be optimized in the outpatient setting where the effect of health IT on revenues is greater. There are several limitations to consider when interpreting our findings. The first limitation pertains to the limited external validity as we used data from hospitals in Texas.