Educating Students in Healthcare Information Technology: IS Community Barriers, Challenges, and Paths Forward

Healthcare information technology (HIT) is an exciting field to which information systems (IS) scholars have much to contribute. As the IS community continues to tackle enrollment and growth issues across the nation, HIT becomes an attractive topic for the IS educators to embrace. Careful consideration and domain understanding are needed to ensure a suitable depth and balance in curricula. The intent of this article is to provide guidance to the IS community to support and promote successful HIT educational courses and programs by investigating three important questions: (1) Does IS have a role in HIT? (2) Where does an IS educator look to begin with HIT education? (3) How do IS educators frame their vision for HIT curricula leveraging the discipline’s strengths? Our hope is that this article will illuminate HIT curriculum matters for the general IS faculty and generate purposeful debate regarding how best to position HIT education within the IS discipline if IS faculty want to join in the quest to successfully educate and place graduates in the growing health technology sector

Focus groups and critical social IS research: How the choice of method can promote emancipation of respondents and researchers

Critical social research in information systems has been gaining prominence for some time and is increasingly viewed as a valid research approach. One problem of the critical tradition is that there is a lack of empirical research. A contributing factor to this gap in the literature is the lack of agreement on what constitutes appropriate methodologies for critical research. The present paper contributes to this debate by outlining the role that focus group research can play in the critical approach. The paper outlines the main characteristics of critical research with an emphasis on its emancipatory faculties. It then goes on to review the focus group method in general and gives an account of two research projects that used focus groups as a method of data collection. It is argued that focus groups can contribute to emancipation of researchers as well as respondents. This argument is built upon the critical theories of the two most prominent theorists currently relied upon in critical social IS research, namely Jürgen Habermas and Michel Foucault. Focus groups can improve communication and move real discourses closer to the Habermas\u27s ideas speech situation. At the same time, they can contribute to the challenging of prevailing orthodoxy and thereby overcome established regimes of truth in the Foucauldian tradition. The paper ends with a critical reflection of the shortcomings of focus groups as a critical method and of the specific approach chosen in this paper

Basic Classes in Conceptual Modeling: Theory and Practical Guidelines

Since the 1970s, many approaches to representing domains have been suggested. Each approach maintains the assumption that the information about the objects represented in the information system (IS) is specified and verified by domain experts and potential users. Yet, as more IS are developed to support a larger diversity of users such as customers, suppliers, and members of the general public (e.g., in the case of many multiuser online systems), analysts can no longer rely on a stable single group of people for the complete specification of domains; therefore, prior research has questioned the efficacy of conceptual modeling in these heterogeneous settings. This paper aims to address this problem by providing theoretical foundations rooted in psychology research supporting the existence and importance of special classes that are termed basic-level categories. Based on this research, we formulate principles for identifying basic classes in a domain. These classes can guide conceptual modeling, database design, and user interface development in a wide variety of traditional and emergent domains

Focus Groups for Artifact Refinement and Evaluation in Design Research

Focus groups to investigate new ideas are widely used in many research fields. The use of focus groups in design research poses interesting opportunities and challenges. Traditional focus group methods must be adapted to meet two specific goals of design research. For the refinement of an artifact design, exploratory focus groups (EFGs) study the artifact to propose improvements in the design. The cycle of build and evaluate using EFGs continues until the artifact is released for field test in the application environment. Then, the field test of the design artifact may employ confirmatory focus groups (CFGs) to establish the utility of the artifact in field use. Rigorous investigation of the artifact requires multiple CFGs to be run with opportunities for quantitative and qualitative data collection and analyses across the multiple CFGs. In this paper, we discuss the adaptation of focus groups to design research projects. We demonstrate the use of both EFGs and CFGs in a design research project in the health care field

DIGITAL TRACE DATA RESEARCH IN INFORMATION SYSTEMS: OPPORTUNITIES AND CHALLENGES

Digital trace data research is an emerging paradigm in Information Systems (IS). Whether for theory development or theory testing, IS scholars increasingly draw on data that are generated as actors use information technology. Because they are ‘digital’ in nature, these data are particularly suitable for computational analysis, i.e. analysis with the aid of algorithms. In turn, this opens up new possibilities for data analysis, such as process mining, text mining, and network analysis. At the same time, the increasing use of digital trace data for research purposes also raises questions and potential issues that the research community needs to address. For example, one key question is what constitutes a valid contribution to the body of knowledge and how digital trace data research influences our collective identity as a field? In this panel, we will discuss opportunities and challenges associated with digital trace data research. Reflecting on the panelists’ and the audience’s experience, we will point to strategies to mitigate common pitfalls and outline promising research avenues

Editorial

Editorial for this issue

Uncertainty in the information supply chain: Integrating multiple health care data sources

Similar to a product supply chain, an information supply chain is a dynamic environment where networks of information-sharing agents gather data from many sources and utilize the same data for different tasks. Unfortunately, raw data arriving from a variety of sources are often plagued by errors (Ballou et al. 1998), which can lead to poor decision making. Supporting decision making in this challenging environment demands a proactive approach to data quality management, since the decision maker has no control over these data sources (Shankaranarayan et al. 2003). This is true in health care, and in particular in health planning, where health care resource allocation is often based on summarized data from a myriad of sources such as hospital admissions, vital statistic records, and specific disease registries. This work investigates issues of data quality in the information supply chain. It proposes three result-driven data quality metrics that inform and aid decision makers with incomplete and inconsistent data and help mitigate insensitivity to sample size, a well known decision bias. To design and evaluate the result-driven data quality metrics this thesis utilizes the design science paradigm (Simon 1996; Hevner, March et al. 2004). The metrics are implemented within a simple OLAP interface, utilizing data aggregated from several healthcare data sources, and presented to decision makers in four focus groups. This research is one of the first to propose and outline the use of focus groups as a technique to demonstrate utility and efficacy of design science artifacts. Results from the focus groups demonstrate that the proposed metrics are useful, and that the metrics are efficient in altering a decision maker\u27s data analytic strategies. Additionally, results indicate that comparative techniques, such as benchmarking or scenario based approaches, are promising approaches in data quality. Finally, results from this research reveal that decision making literature needs to be considered in the design of BI tools. Participants of the focus groups confirmed that people are insensitive to sample size, but when attention was drawn to small sample sizes, this bias was mitigated