Health Information Technology Spending on the Rise

Health Information Technology Spending on the Rise

Jinhyung Lee (Sungkyunkwan University, South Korea) and Hansil Choi (Sungkyunkwan University, South Korea)
DOI: 10.4018/978-1-5225-5460-8.ch001

Abstract

In this chapter, the authors track health information technology by examining the factors affecting health information technology (IT) expenditure. The authors employed hospital- and patient-level data of the Office of Statewide Health Planning and Development (OSHPD) from 2000 to 2006. The generalized linear model (GLM) was employed with log link and normal distribution and controlled for clustering error. The authors found that not-for-profit and government hospitals, teaching hospitals, competition, and health IT expenditure of neighborhood hospitals were positively associated with health IT expenditure. However, rural hospitals were negatively associated with health IT expenditure. Moreover, the authors found that mean annual health IT expenditure was approximately $7.4 million from 2000-2006. However, it jumped 204% to $15.1 million from 2008-2014.
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Introduction

The benefits of health information technology (IT) have been reported in many studies. The Health IT can increase the quality of care by increasing guideline adherence, improving aggregation, analysis and communication of patient information, supporting therapeutic and diagnostic decision and preventing adverse (Parente & McCullough, 2009; Costa et al., 2015) Moreover, health IT can increase productivity by improving efficiency and expand access to affordable care (Evans et al., 1998; Tierney et al., 1987; Mekhjian et al., 2002; Kuperman & Gibson, 2003, Furukawa et al., 2008; McCullough et al, 2010; Lee et al., 2013; Sharma et al, 2016).

Despite these potential benefits, the adoption rate of health IT has been low in the United States. Only 37 percent of community hospitals reported moderate or high use of health IT in 2005 (AHA, 2007). Only 20-25 percent of hospitals had adopted some version of an electronic medical record (EMR) system (Health Affairs, 2005). About 7 percent of hospitals had installed Computerized Physician Order Entry (CPOE) and another 9 percent had contracted for it, implying that only 16 percent had fully implemented CPOE systems (Fonkych & Taylor, 2005). The American Society of Health-System Pharmacists (AHSP) annual survey shows that fewer than 5 percent of hospitals surveyed have adopted CPOE systems (Pedersen, Schneider, & Scheckelhoff, 2005). More recent study showed that only 1.5 percent of US hospitals had a comprehensive electronic record system and an additional 7.6% had a basic system (Jha et al., 2009). Moreover, the gross revenues of health IT comprise only 2% of health care industry spending, which is scant compared with other information intensive industries, which spend up to 10% (Raymond & Dold, 2001). In general, the U.S. is behind other countries by as much as a dozen years in the adoption of health IT. In 2004, the Office of the National Coordinator (ONC) for Health Information Technology was established in the US, which several organization for economic co-operation and development (OECD) nations such as Germany, Canada, England, Norway and Australia preceded by at least several years (Anderson et al, 2006). However, with the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009, the adoption of health IT increased significantly. Thus, to improve health IT adoption, the factors affecting health IT adoption need to be investigated. This study identifies factors affecting health information technology (IT) investment decision in hospitals using California Hospital and Patient level data of the Office of Statewide Health Planning and Development (OSHPD) from 2000 to 2006.

This study contributes to the literature on health IT adoption in a couple of ways. First, health IT was calculated to include the dollar amount which included all the expenditure related to IT. This measure may be more representative one. Second, the generalized linear model (GLM) with clustering error within hospitals was employed to control for the clustering error. This clustering error makes standard errors quite wrong, leading to incorrect inference (Nichols and Schaffer, 2007).

Key Terms in this Chapter

HIMSS: The Healthcare Information and Management Systems Society (HIMSS) is a nonprofit organization whose goal is to promote the best use of information technology and management systems in the healthcare industry.

OSHPD: California's Office of Statewide Health Planning and Development (OSHPD) is the leader in collecting data and disseminating information about California's healthcare infrastructure.

HITECH Act: The Health Information Technology for Economic and Clinical Health Act (HITECH Act) legislation was created in 2009 to stimulate the adoption of electronic health records (EHR) and supporting technology in the United States.

Health Information Technology: Information technology applied to health and healthcare. It supports health information management across computerized systems and the secure exchange of health information between consumers, providers, payers, and quality monitors.

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