Organizational Integration of Decision Analytic Methods in Healthcare Settings

Organizational Integration of Decision Analytic Methods in Healthcare Settings

Christopher L. Pate (Brook Army Medical Center, USA) and Mark D. Swofford (Brook Army Medical Center, USA)
DOI: 10.4018/978-1-4666-9961-8.ch003
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Healthcare organizations face a range of external and internal factors that both enable and hinder the organization's ability to provide safe, quality, timely and reliable healthcare services. The accessibility of data coupled with effectively integrated analytic methods can provide healthcare organizations with essential components of a solid framework for improving performance across the full spectrum of organizational contexts. However, data, methods, and a robust information infrastructure are only part of the solution. Healthcare organizations must consider characteristics of the organization and its strategy in order to effectively integrate analytic methods. Conceptual ideas from organizational theory, economics and strategic management can provide structure to the integration process.
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The integration of analytics in healthcare settings can be a challenging undertaking for any organization desiring to improve decision making and organizational performance. Yet, effective integration of analytics requires an understanding of the organization, the organization’s goals, the organizational structure, and other aspects of an organization’s essence. This chapter bridges theoretical and practical lessons related to integration of analytics in healthcare settings.

Chapter Objectives

After completing the chapter, readers will be able to:

  • 1.

    Define the concept of analytics.

  • 2.

    Identify essential theoretical concepts related to the integration of analytics.

  • 3.

    Discuss the importance of analytic integration in healthcare settings.

  • 4.

    Discuss approaches to improving the integration of analytics in healthcare settings.



More than twenty years ago, Drucker observed that healthcare organizations are among the most complex of any organizational form (Drucker, as cited in Golden, 2006). Over these past two decades, changes in the healthcare landscape have only added to the complexity facing healthcare organizations across the full range of organizational forms encountered in the industry. These changes include growth of new technologies, demographic shifts, an aging population, efforts to decrease healthcare costs (Bernstein, Hing, Moss, Allen, Siller & Tiggle, 2003; Lee, 2009), increasing emphasis on accountability (Tabar, 2012), investment in and growth of health information technology, and increased requirements for data monitoring and management (Shortliffe, 2005). In terms of data content, the healthcare industry “is facing a tsunami of health and healthcare related content generated from numerous patient care points of contact, sophisticated medical instruments, and web-based health communities” (Chen, Chiang, & Storey, 2012, p. 1171). Krumholz (2014, p. 1169) notes that “massive repositories of potential knowledge, populated by data from health care visits, devices, administrative claims, and biospecimens, are increasingly available...(yet)...the promise of massive data assets lies not merely in their size, but in the way they are used.”

The growth of data does necessarily translate into improvements in organizational decision making in healthcare contexts. In fact, the proliferation and accessibility of data can complicate the decision making process. Creation of an effective decision support framework, which would include specific analytic methods as well as an approach to implementation (Austin & Boxerman, 1998), can enhance decision making and ultimately improve performance in multiple contexts within healthcare organizations. In fact, if knowledge creation and acquisition is “an important requirement for survival” (Davis, 1995, p. 115) and if establishment of effective analytic framework supports knowledge creation, organizations are facing an operational imperative in developing and implementing a robust decision analytic framework.

Organizations face a wide range of related concepts and approaches related to decision support and analytics. Many of these concepts lack definitional consensus and consistency across academic and practitioner-focused publications. Defining decision support as “using an organization's data to aid in management decisions” (Sempeles, as cited in Austin & Boxerman, 1998, p. 310) can provide, however, a starting point for healthcare organizations to begin the process of thinking about the process of integration. Healthcare organizations must understand that a variety of frameworks exist in order to develop an effective analytic framework, yet they must also eschew the Tayloristic notion of “one best way” (Kanigel, 1997, p. 1) in crafting and implementing an analytic framework.

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