Influence of Institutional Forces on Managerial Beliefs and Healthcare Analytics Adoption

The 2010 passage of the Affordable Care Act provided strong motivation for healthcare organizations to improve operational efficiency and enhance organizational performance. Large and medium-sized organizations nationwide have adopted healthcare analytics to obtain operational benefits, with the Cleveland Clinic as a prime example of a successful adoption. Both Delos M. Cosgrove, chief executive officer, and James Merlino, chief experience officer, were instrumental in the adoption and use of healthcare analytics to improve patient satisfaction scores (Merlino and Raman, 2013). Another example is Forest Laboratories, which, in collaboration with Converge Health and Intermountain Healthcare, developed a rapid-learning system based on data analytics to improve outcomes for patients with respiratory diseases (PR Newswire, 2014). Jeff Elton, managing director of Accenture Life Sciences, claims that to improve patient outcomes, healthcare requires accurate data and predictive analytics from a range of resources available in the organization (Accenture, n.d.). Data analytics is of high value to healthcare organizations because big data in healthcare is overwhelming in terms of volume, the diversity of data types, and the speed at which it must be managed (EMC, 2012). Healthcare analytics has the potential to transform the way healthcare providers use sophisticated technologies to gain insights from their data repositories and make informed managerial decisions (An, 2013; Raghupathi and Raghupathi, 2014). The information healthcare analytics provides can be used to allocate resources, distribute funds for healthcare services, and guide policy formulation and implementation (Fos and Zuniga, 1999; Srinivasan and Arunasalam, 2013). One of the best examples of using healthcare analytics for improving firm performance is Premier, the U.S. healthcare alliance network. Premier has more than 2,700 members, hospitals, and health systems; 90,000 non-acute facilities; and 400,000 physicians in its network. Healthcare analytics is employed to analyze clinical, financial, patient, and supply chain data, to gain a comprehensive understanding of resource utilization, clinical outcome measures, and transaction-level cost data. Using healthcare analytics, Premier saved 29,000 lives, resulting in a cost savings of $7 billion through 2013 (IBM, 2013). However, a high level of variance is associated with the level of healthcare analytics adopted by U.S. hospitals and clinics ((Raghupathi and Raghupathi, 2014). Some research indicates that top management beliefs may play a role in the adoption of technology, such as healthcare analytics in hospitals and clinics (Chatterjee et al., 2002). Less understood, however, is the potential influence of institutional pressures on framing top management beliefs, particularly the level at which technology (e.g., healthcare analytics) is adopted. Therefore, the current research examines the influence of institutional pressures (i.e., mimetic, coercive, and normative pressure) on top management beliefs and level of healthcare analytics adoption in U.S. hospitals and clinics. This study integrates institutional forces and the influence of top management beliefs on the level at which healthcare analytics is adopted, thus reconciling previous research's presumption of independency. This study demonstrates that some external institutional forces manifest their influence on top management beliefs while others do so on the level of healthcare analytics adoption itself. As more healthcare organizations contemplate the adoption of data analytics, understanding how institutional forces influence top management beliefs and subsequent adoption of healthcare analytics in organizations also increases in importance. LITERATURE REVIEW Research has extensively used institutional theory to explain organizational form and adoption of practices (DiMaggio and Powell, 1983; Grob and Benn, 2014; Messerschmidt and Hinz, 2013; Verbeke and Tung, 2013). …