Disruptive-Technology Avoidance in Healthcare: A Revealed Causal Mapping (RCM) Approach

Disruptive-Technology Avoidance in Healthcare: A Revealed Causal Mapping (RCM) Approach

Bahae Samhan (Illinois State University, Normal, USA) and K.D. Joshi (Washington State University, Pullman, USA)
DOI: 10.4018/IJHISI.2019040103

Abstract

Disruptive innovation has transformed business activities as well as individuals throughout a variety of industries. In healthcare, the implementation of electronic health records (EHR) innovation has changed the way healthcare organizations handle patient records. Despite the potential benefits EHR can bring to healthcare organizations, there is evidence to show that healthcare providers are avoiding EHR innovations. Little research in information system mainstream research has addressed this phenomenon. To understand EHR avoidance, a mid-range theory is evoked from this textual analysis of responses gathered from healthcare providers at a large international hospital. The data was analyzed by applying a revealed causal mapping technique (RCM). Results of the study revealed not only the key constructs surrounding EHR avoidance, but also the underlying concepts that are shaping each of these constructs. This study demonstrated that the use of the RCM methodology yielded concepts and constructs of EHR avoidance that are not suggested by generalized theory, and revealed main interactions and linkages between these constructs.
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1. Introduction

Electronic health records (EHRs), one of the well-valued disruptive technologies in health care to date (Grossma, 2008), are essential in “improving care and future regulation” (Fisher 2015, p. 1). Accordingly, EHR implementation can reform the healthcare industry, especially in how patient data are handled and managed across healthcare organizations. EHRs also disrupt care providers and health systems spanning across the country. In brief, such a system displaces the established paper record system and offers healthcare organizations a groundbreaking product that can effectively modernize the industry.

Prior to EHR innovations, healthcare providers had to maintain paper-based patient records (Samhan & Joshi, 2017). These records comprise largely unstructured data, have limited sharing capability with computerized data systems (Rosenbloom et al., 2011), are expensive to copy, easy to destroy, and also difficult to analyze, store, manage, retrieve, and secure (Tsai & Bond, 2008). Moreover, as these records are completed manually, the handwriting may be illegible in many cases (Hamilton et al., 2003). With EHRs serving as a new medium to store, retrieve, and share patients’ data, the healthcare industry has experienced disruption in multiple ways, both positively and negatively. On the positive side, using EHRs can decrease medical errors, increase the quality of care, and facilitate availability of patient medical history (Jeansson, 2013; Hillestad et al., 2005; Samhan & Joshi, 2017), which helps healthcare providers to assess and diagnose patients faster and more accurately (Samhan & Joshi, 2017). Conversely, evidence shows that EHRs simultaneously disrupt the provision of care that resulted in some healthcare providers avoiding the systems if and when possible (Kane & Labianca, 2011). It is imperative therefore to understand this avoidance phenomenon to help healthcare providers take better advantage of benefits that come with EHR innovations when used meaningfully. To date, mainstream information systems (IS) research in this domain has been limited with mixed and unclear findings (Kellermann & Jones, 2013; Samhan & Joshi, 2017). Prior research has focused mainly on EHR adoption (e.g. Gan & Cao, 2014; Hewitt & McLeod, 2011; Hung, 2013; Jha et al., 2008; Kemper et al., 2006) with some work focusing on various resistance aspects (e.g., Markus, 1983; Poon et al., 2004; Lapointe & Rivard, 2005; Bhattacherjee et al., 2008; Samhan & Joshi, 2017). However, little attention has been given to technology avoidance, specifically towards EHR systems.

The current study aims to rationalize the main concepts shaping the antecedents of the avoidance construct. It is based on the underpinning concepts of the threat avoidance feedback loop (Liang & Xue, 2009). Here, the nature of perceived disruptions that result in various forms of EHR avoidance behaviors, such as technical problems and unnecessary efforts that threaten effective provision of care, will be explored. Qualitative interviews with care providers from a large public hospital located in a developing country to capture perceptions of an implemented EHR system will be conducted with their responses analyzed using the Revealed Causal Mapping (RCM) methodology. RCM is a qualitative method commonly used to identify constructs and linkages revealed from respondents’ statements (Nelson et al., 2000). It has proven to be useful in studying emerging phenomena that require rich and contextualized understanding (Narayanan & Armstrong, 2005). Given that our goal is to understand the perceptions of individuals interacting directly with a newly implemented disruptive technology, the RCM method is applicable to an investigation of these perceptions.

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