The effects of health information technology on the costs and quality of medical care

Abstract

Information technology has been linked to productivity growth in a wide variety of sectors, and health information technology (HIT) is a leading example of an innovation with the potential to transform industry-wide productivity. This paper analyzes the impact of health information technology (HIT) on the quality and intensity of medical care. Using Medicare claims data from 1998 to 2005, I estimate the effects of early investment in HIT by exploiting variation in hospitals' adoption statuses over time, analyzing 2.5 million inpatient admissions across 3900 hospitals. HIT is associated with a 1.3% increase in billed charges (p-value: 5.6%), and there is no evidence of cost savings even five years after adoption. Additionally, HIT adoption appears to have little impact on the quality of care, measured by patient mortality, adverse drug events, and readmission rates.

Keywords: 033; Health information technology; Hospital productivity; I10.

Figure 1

Impact of HIT on Expenditures and Mortality Notes: Each panel of this figure plots regression coefficients and 95 percent confidence intervals from a single regression where the dependent variable is indicated in the caption. The regression includes a series of explanatory dummy variables indicating the year relative to initial HIT adoption for hospitals that change their adoption status over the study period. Adoption occurred in year 0. The outcome variable in panel A is total medical expenditures over 1 year following initial admission, and the outcome in panel B is 1-year mortality. Regressions control for or hospital fixed effects, state-year fixed effects, a differential time tread amongst HIT adopting hospitals, time trends that vary by quartile of hospital size, trauma hospital status, hospital investment in CT, PET and MRI scanners, as well as patient age (in 1-year bins), sex, race, and primary diagnosis code. An observation is a hospital-year, 1998-2004. There are 27,317 observations in total. Regressions are weighted by number of patients, and standard errors are clustered by hospital.

Figure 2

Impact of HIT on Intensity Quality of Care Note: Each panel plots regression coefficients and 95 percent confidence intervals (in grey) from a single regression. The regression includes a series of explanatory dummy variables indicating the year relative to initial HIT adoption. Adoption occurred in year 0. There are 27,317 observations. See noted to Figure 1 for further details.