As the United States expends extraordinary efforts toward the digitization of its health-care system, and as policy makers across the globe look to information technology (IT) as a means of making health-care systems safer, more affordable, and more accessible, a rare and remarkable opportunity has emerged for the information systems research community to leverage its in-depth knowledge to both advance theory and influence practice and policy. Although health IT (HIT) has tremendous potential to improve quality and reduce costs in healthcare, significant challenges need to be overcome to fully realize this potential. In this commentary, we survey the landscape of existing studies on HIT to provide an overview of the current status of HIT research. We then identify three major areas that warrant further research: (1) HIT design, implementation, and meaningful use; (2) measurement and quantification of HIT payoff and impact; and (3) extending the traditional realm of HIT. We discuss specific research questions in each domain and suggest appropriate methods to approach them. We encourage information systems scholars to become active participants in the global discourse on health-care transformation through IT.

Research Commentary---The Digital Transformation of Healthcare: Current Status and the Road Ahead

The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 that was signed into law as part of the “stimulus package” represents the largest US initiative to date that is designed to encourage widespread use of electronic health records (EHRs). In light of the changes anticipated from this policy initiative, the purpose of this paper is to review and summarize the literature on the benefits and drawbacks of EHR systems. Much of the literature has focused on key EHR functionalities, including clinical decision support systems, computerized order entry systems, and health information exchange. Our paper describes the potential benefits of EHRs that include clinical outcomes (eg, improved quality, reduced medical errors), organizational outcomes (eg, financial and operational benefits), and societal outcomes (eg, improved ability to conduct research, improved population health, reduced costs). Despite these benefits, studies in the literature highlight drawbacks associated with EHRs, which include the high upfront acquisition costs, ongoing maintenance costs, and disruptions to workflows that contribute to temporary losses in productivity that are the result of learning a new system. Moreover, EHRs are associated with potential perceived privacy concerns among patients, which are further addressed legislatively in the HITECH Act. Overall, experts and policymakers believe that significant benefits to patients and society can be realized when EHRs are widely adopted and used in a “meaningful” way.

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Benefits and drawbacks of electronic health record systems

Information politics

Much research in the last two decades has demonstrated that human responses deviate from the performance deemed normative according to various models of decision making and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be interpreted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational limitations, (3) the wrong norm being applied by the experimenter, and (4) a different construal of the task by the subject. In the debates about the viability of these alternative explanations, attention has been focused too narrowly on the model response. In a series of experiments involving most of the classic tasks in the heuristics and biases literature, we have examined the implications of individual differences in performance for each of the four explanations of the normative/descriptive gap. Performance errors are a minor factor in the gap; computational limitations underlie non-normative responding on several tasks, particularly those that involve some type of cognitive decontextualization. Unexpected patterns of covariance can suggest when the wrong norm is being applied to a task or when an alternative construal of the task should be considered appropriate.

Individual differences in reasoning: Implications for the rationality debate?-Open Peer Commentary-Differences, games, and pluralism

Digital transformation in healthcare: Analyzing the current state-of-research

This article proposes a novel approach, called data snapshots, to generate real-time probabilities of winning for National Basketball Association (NBA) teams while games are being played. The approach takes a snapshot from a live game, identifies historical games that have the same snapshot, and uses the outcomes of these games to calculate the winning probabilities of the teams in this game as the game is underway. Using data obtained from 20 seasons worth of NBA games, we build three models and compare their accuracies to a baseline accuracy. In Model 1, each snapshot includes the point difference between the home and away teams at a given second of the game. In Model 2, each snapshot includes the net team strength in addition to the point difference at a given second. In Model 3, each snapshot includes the rate of score change in addition to the point difference at a given second. The results show that all models perform better than the baseline accuracy, with Model 1 being the best model.

A Data Snapshot Approach for Making Real-Time Predictions in Basketball.

As more and more people use online auctions as a means of buying and selling on the Internet, cross-listing, defined as simultaneous auctions of identical products listed by the same seller, has come into increased prominence. However, to date, this phenomenon remains understudied in the academic literature. In this paper, we examine six exploratory questions related to cross-listing: (1) what proportion of online auctions sellers engage in cross-listing, (2) how many simultaneous auctions do these sellers list, (3) how many of these auctions are successful, (4) are cross-listing sellers systematically different from those that do not cross-list, (5) how do closing prices differ between cross-listed and non cross-listed auctions, and (6) how does bidder participation vary between cross-listed and non cross-listed auctions? Using detailed data collected from 21,714 Apple iPod auctions on eBay, we provide answers to the above questions. Implications for practice and research are discussed.

An Empirical Study of Cross-Listing in Online Auctions

This study investigates how people use content from websites when there are mechanisms that validate content on those sites. Specifically, we examine the effects of two governance mechanisms, expert- and community-governance, on content usage from two web pages. We draw upon the elaboration-likelihood model as the theoretical basis to postulate hypotheses for empirical testing. Data collected from student subjects through a web-based experiment show that credibility of a governance mechanism has a positive effect on individuals' use of content from web pages, while also influencing their perceptions of content quality. Research and practical implications of our findings are discussed.

Content Use from Websites: Effects of Governance Mechanisms

Prior work shows that confirmation bias, defined as the tendency to seek confirming evidence, is prevalent on the Web as well. While this has been attributed to individuals' psychological needs or cognitive limitations, the roles of search engines and search contexts have largely been neglected. The goals of this study are to examine how search contexts may change the composition of search results, and how – if at all – search engines may contribute to confirmation bias. Results of two studies show that search engines may exacerbate confirmation bias by generating results that consist only of confirming evidence for search contexts where disconfirming evidence is identified using different terms or phrases. This induces individuals to make biased decisions. Findings of this study deepen our understanding of the ways in which confirmation bias unfolds on the Web when individuals use search engines.

Confirmation Bias: Roles of Search Engines and Search Contexts

Two mechanisms employed by organizations to ensure information quality in organizational knowledge repositories is using experts to control or edit users’ contributions (such as in a refereed repository), or using a community of users to review, rate, or edit existing contributions (such as in a community-driven wiki). The goal of this paper is to explore these two mechanisms and study their impact on knowledge contributions by organizational members. Propositions are suggested by drawing upon self-determination theory.

Varol O. Kayhan

Papers

A Data Snapshot Approach for Making Real-Time Predictions in Basketball.

An Empirical Study of Cross-Listing in Online Auctions

Content Use from Websites: Effects of Governance Mechanisms

Confirmation Bias: Roles of Search Engines and Search Contexts

Contributing Knowledge to Organizational Repositories: The Role of Governance Mechanisms