لخّصلي

Technology adoption, and information technology in particular, have been linked to productivity growth in a wide variety of sectors. However, a historical perspective suggests caution is warranted in linking any particular technology to the promise of substantial, sustained productivity growth within a specific industry. Work by the McKinsey Global Institute (2002) argues that the productivity acceleration of the 1990s, widely attributed to information technology (IT), was concentrated in a limited number of sectors, and IT was only one of several factors that combined to create the productivity jump.

In this paper, I analyze the impact of health information technology (HIT) on the costs and quality of medical care, testing whether the technology has demonstrated potential to improve the productivity of the health care sector. Against a backdrop of persistently high growth in health spending, many policymakers are looking to HIT as a key tool to improve the efficiency of the health care sector, by preventing medical errors, cutting redundant tests, and improving health outcomes. The RAND Institute has projected that HIT will spur a $142–$371 billion per year reduction in health spending (Hillestad et al. 2005).

The Health Information Management Systems Society estimates that hospitals will spend approximately $26 billion dollars on IT applications between 2010-2014 (HIMSS Analytics 2009). These expenditures will be driven partly by a federal program, the 2009 HITECH Act, which will implement reimbursement incentives and penalties designed to encourage HIT adoption. These new incentive payments are projected to increase net Medicare and Medicaid spending by $30 billion over nine years (2011-2019). However, the Congressional Budget Office (2008) estimates the total costs of the legislation to be markedly lower, $19 billion, since it predicts that HIT will reduce medical expenditures and thus reduce related federal spending.

This study focuses primarily on two types of health information technology: electronic medical records (EMR) and clinical decision support (CDS). EMR maintain patient information and physician notes in a computerized database rather than a paper chart. EMR allow the provider to track the patient’s health over time and read the input of other consulting physicians. CDS provides timely reminders and information to doctors. CDS may recommend screening tests, flag drug-drug interactions and drug allergy information, or discourage the provider from repeating a test by highlighting a previous result. Together, these systems form the backbone of a basic clinical HIT system.

The paper explores several channels through which HIT adoption may affect the quality and quantity of care provided. First, EMR may reduce the effort cost to the physician of prescribing an extensive medical workup, which may increase the intensity of provided treatment. Second, EMR may improve communication across providers, which may in turn increase reliance on specialists and reduce redundant testing. Last, CDS may reduce medical errors and improve routine care by providing timely reminders to physicians. The net impact of these three channels on total medical expenditures, health outcomes, and quality of care is ambiguous.

I perform a detailed empirical analysis of the the impact of hospital HIT investment, using Medicare claims data. HIT is associated with 1.3 percent higher medical expenditures, with the 95% confidence interval ranging from −0.03 to 2.6 percent. Other results find that length of stay and number of physicians consulted do not change significantly after adoption. Despite the cost increases, HIT is associated with very modest reductions in patient mortality of 0.03 percentage points [95 percent confidence interval: −0.36 to 0.30 percentage points]. Further, there are no significant improvements in the complication rate, adverse drug events or readmission rate, after HIT adoption.

The results fail to measure a social benefit to HIT adoption over this period, although it should be noted that the finding is local both to the basic types of software systems commonly implemented over the study period, from 1998-2005, and the organizational structure of adopting hospitals. I will discuss these limitations further in the penultimate section of the paper.

These findings are estimated in a 20 percent sample of Medicare claims from 1998-2005; the sample includes 2.5 million inpatient admissions at 3880 hospitals. The claims data allows detailed tracking of patients’ health outcomes, services rendered, and medical expenditures. HIT adoption is measured at the hospital level from the Health Information and Management Systems Survey (HIMSS).
A fixed effects econometric model exploits within-hospital across-time variation in HIT adoption status to estimate the effects of adoption. The multi-year panel data along with variation in the timing of HIT adoption allows the inclusion of rich controls for time trends beyond those used in conventional differencein-differences analysis; in particular, I control for state-year fixed effects, adopter-specific time trends, and differential trends that vary according to a hospital’s baseline characteristics. I analyze potential threats to validity, testing for simultaneous changes in other hospital investments and probing the robustness of the results to any changes in patient sorting across hospitals.

Buntin et al. (2011) provide a review of recent literature on health IT, finding in a meta-analysis that 92% of studies suggested positive overall benefit to health IT. My analysis has several advantages over previous research. First, it estimates the impact of HIT over a broad, national sample of hospitals, rather than presenting a case study of a single institution or HMO (cf. Bates et al. 1999; Demakis et al. 2000; Evans et al. 1994; Javitt et al.). Second, it uses panel data to implement a difference-in-differences strategy, instead of relying on cross-sectional evidence (cf. DesRoches et al. 2010, Himmelstein et al. 2010).

My paper builds upon and complements the recent work on HIT with panel data by Miller and Tucker (2011), McCullough et al. (2011) and Furukawa et al. (2010). An advantage of my analysis is that it brings together a large set of outcome variables including medical expenditures and quality of care measures in addition to mortality rates, allowing a rich analysis of adoption costs and benefits; to my knowledge, it is the first large scale analysis of the impact of HIT on billing expenditures. Lastly, I implement a robust empirical strategy that controls for a rich set of state-by-year fixed effects and differential time trends that vary by hospital characteristics, rather than imposing uniform time trends across hospitals. This more flexible approach is particularly important for identifying the impact of HIT adoption on medical expenditures, as described in more detail in Section 3.1.

The paper proceeds as follows. Section 2 describes the data in more detail and discusses the HIT adoption decision. Section 3 presents the empirical strategy and results. Section 4 analyzes the policy implications and interpretation of these findings. The final section summarizes the results and concludes.