Impact of Health Information Technology on Clinical Care

Health information technology (health IT) has great potential to improve health care; however, there is limited information about health IT effects on clinical care, especially with respect to commercially available technology in community-based ambulatory clinics. The dearth of quantitative information is particularly concerning given the significant resources needed for implementation and the limited adoption of health IT in the United States. We evaluated the effects of ambulatory health IT on quality, safety, and resource use between 2004 and 2007. We used a quasi-experimental pre-post design with concurrent controls, within a large, integrated health delivery system (IDS). In this natural experiment, the staggered health IT implementation across 110 primary care teams occurred over 38 months, starting in November 2004. This new commercial health IT included an electronic medical record integrated with an order-entry system and decision-support; the previous system was primarily based on the paper medical record. The IDS's previous automated databases permitted consistent capture of our outcomes and patient-level covariates before and after the new health IT, but are notably distinct from the new health IT with respect to providing real-time clinical data. We focused on a cohort of 780,000 IDS members with at least one of five chronic diseases (asthma, coronary artery disease, heart failure, diabetes mellitus, and hypertension) in January 2004; these patients may be particularly sensitive to health IT-related changes in ambulatory care. The presence of health IT in the primary care team was the main predictor. The quality and safety measures included guideline-adherent drug dispensations and laboratory monitoring, drug adherence, and physiologic disease control (as measured by laboratory tests). The resource-use measures include emergency department visits, nonelective hospitalizations, and office visits. Using Poisson regression models with patient-level random effects, we tested the hypotheses that health IT is associated with improved quality and safety measures, and health IT is associated with lower visit rates. We had the ability to detect small changes in our outcomes, e.g., 80 percent power to detect a 3 percent change in the relative rate of nonelective hospitalizations in the CHF group. We made adjustments for patient, insurance, and organizational factors, including socioeconomic status, case mix, cost-sharing, and care-delivery structure, using automated data and annual surveys. In short, this natural experiment provided an ideal setting to understand the value of health IT in community-based, ambulatory care.