Health Information Technology

Health Information Technology Research Curation Team:
Aaron Baird (Georgia State University)
Corey Angst (University of Notre Dame)
Eivor Oborn (The University of Warwick)

Released: June 2018

Updated: September 2020

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1. Focus of the Research Curation
Health information technology (health IT) research is conducted at an intriguing intersection between societies, organizations, and consumers. Health IT is defined as “a broad concept that encompasses an array of technologies to store, share, and analyze health information.”[1]The rapid increase in adoption and use of health IT since the mid-2000s has afforded considerable research opportunities to evaluate and test existing theories (e.g., Paul and McDaniel Jr 2004)as well as to create and refine new ones (e.g., Gao et al. 2015). Such growth comes with challenges for information systems (IS) researchers, particularly with respect to staying up-to-date with the latest advances in the health IT field as well as recalling, cataloging, and understanding how it has developed over the years. In this research curation, we offer insights into how health IT research has thematically advanced over the past two decades within MIS Quarterly(MISQ). 

We followed an inclusive approach for determining the scope of this research curation. Specifically, an article was included in our final dataset if it was published in MISQand if it met one or both of the following criteria: 1) centrally focused on a commonly known health IT artifact (e.g., EHR, telehealth, etc.), or 2) centrally focused on health care as the primary context of interest as assessed by having health IT, health, or medically-related terms in the title, abstract, or keywords. Based on these inclusion criteria and our identification of health and health-related terms (and the semantic roots of these terms) for the search process (e.g., health, medicine, hospital, clinical, patient, doctor, physician, nurse), and after removing articles incongruent with our inclusion criteria (e.g., the article used the term “health” to refer to the health of an IS, for instance, or only referred to health or medical concepts tangentially rather than centrally), the final dataset consisted of 58 articles. These articles represent a census, to our knowledge, of health IT research published in MISQfrom 2003 (the date of the earliest included article) to March 2020 (Volume 44, Issue 1). 

In the following sections, we report on our analyses of the temporal progression (section 2) and thematic advances (section 3) of health IT research in MISQ. After the conclusion (section 4), we have included a table that provides details on the articles included (section 5).

[1]Source: https://www.healthit.gov/patients-families/basics-health-it, Accessed May 2018.

2. Progression of Health IT Research in MISQ
We evaluated the temporal progression of health IT research in MISQ using four time periods: 1) Prior to 2007, 2) 2007 to 2012, 3) 2013 to 2018, and 4) 2019 to 2020 (Volume 44, Issue 1). 

Prior to 2007(Figure 1), much of the health IT research focused on health care as a new context for evaluating traditional IT artifacts. For instance, Dennis and Garfield (2003)considered the use of group support systems by medical project teams. Ray et al. (2005)evaluated the relationship between IT and customer service in the health insurance industry, and Mitchell (2006)examined how application integration in the medical sector could be used to address fragmentation of specialized knowledge. Thus, as seen in the word cloud in Figure 1, many of the primary terms and concepts during this time period were consistent with IS research done in traditional organizational contexts. At the same time, though, IS researchers were also beginning to grapple with how to overcome initial resistance to emerging health IT artifacts and how to better facilitate early health IT adoption processes (e.g., Kohli and Kettinger 2004; Lapointe and Rivard 2005). This is why terms such as resistance, trust, behaviors, and processes also appear prominently in Figure 1, as researchers were working to move beyond a focus on traditional information systems, with a more explicit focus on health-centric IT artifacts. The word cloud also suggests that researchers were grappling with the unique social context and types of informational needs and power dynamics of the health care domain. Such emerging research provided a basis for the gradual transition to more central focus on health IT artifacts in the 2007-2012 time period. 

Figure 1: Word Cloud for Articles Published Prior to 2007

The 2007 to 2012 time period (Figure 2) was characterized by significant upheaval in health IT markets as governmental programs and policies were being debated and implemented to enhance health IT adoption, assimilation, and use. These programs included the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 and Meaningful Use (MU) policies in the U.S. and continuation of the National Programme for Information Technology (NPfIT) in the U.K. Based on the excitement from such programs, and the general nature of substantial growth in health IT markets during this time, research questions tended to focus on health IT investment decision making and governance (e.g, Xue et al. 2008), complementarities and changes necessary to benefit from health IT implementations (e.g., Davidson and Chismar 2007), consumer decision making processes associated with health IT use (e.g., Angst and Agarwal 2009), and overall impacts of health IT investments on performance (e.g., Kohli et al. 2012). Thus, the focus shifted from one of evaluating traditional IT artifacts in a new context, as was often in the case in the previous time period, to one of explicitly considering health IT artifacts and their impacts, within this time period.

Figure 2: Word Cloud for Articles Published between 2007 and 2012

In the five-year period from 2013 to 2018(Figure 3), the topics of interest and stakeholders considered began to significantly diversify. For instance, research in this time period was conducted on how consumers play a role in impacting perceptions of medical provider quality (e.g., Gao et al. 2015), the role of online health communities in reducing disparities (e.g., Goh et al. 2016), and how the use of health information impacted outcomes such as duplicate testing (e.g., Ayabakan et al. 2017). Further, the methods used significantly diversified with application of predictive models (e.g., Lin et al. 2017), sequence analysis (e.g., Angst et al. 2017b), growth-mixture models (e.g., Angst et al. 2017a), and in-depth qualitative efforts (e.g., Singh et al. 2015). Thus, health IT research in MISQ in this time-periodbroadened and deepened and continued to significantly contribute to IS theory and practice by pushing the boundaries of our understandings, explanations, and methodological approaches.

Figure 3: Word Cloud for Articles Published between 2013 and June 2018

In the most recent time-period, 2019 to 2020 Volume 44, Issue 1, as of the writing of this curation, we observe that the focus of health IT articles in MISQis shifting away from only consideration of health care organizations and enterprise IT (e.g., Kwon and Johnson 2018), such as EHRs.Rather, we are now seeing more of an emphasis on the impact of health IT use on patients as well as an emphasis on design and use of algorithms and analytics. For example, notable articles in this time period assess the impact of wearable health IT on personal fitness activity (James et al. 2019)and the impact of health IT and analytic models on chronic disease outcomes (Bardhan et al. 2020). Thus, as depicted in Figure 4, primary areas of emphasis are expanding to include not only digital infrastructure within large enterprises (e.g., hospitals), but also to connections and processes that are more inclusive of additional stakeholders, including patients outside of traditional health provider settings. For example, research is expanding to examine individuals using wearables or engaging in social media and online communities (Liu et al. 2020a; Liu et al. 2020b).

Figure 4: Word Cloud for Articles Published between 2019 and 2020

3. Thematic Advances in Knowledge
In our thematic analysis, we identified five (5) primary themes within health IT research in MISQ: 1) health IT as a strategic asset, 2) health IT adoption and use, 3) health IT security and privacy, 4) health IT for development, and 5) health as a context. We are also now observing the early emergence of a potential sixth theme, impact on patients.

Research within the health IT as a strategic assettheme focuses on evaluating strategic decisions related to health IT investments (e.g., Angst et al. 2017a; Kohli and Tan 2016; Salge et al. 2015), governance (e.g., Xue et al. 2008)and performance-related outcomes (e.g., Ayabakan et al. 2017). Research within this theme has advanced IS theory and practice by demonstrating how technology and information can be leveraged to address heterogeneity in ways not often considered in other contexts. For instance, predicting which customers (patients) are likely to be the highest consumers of resources and seeking to proactively reduce such costs is something unique to health care that provides insights into building and applying predictive models (e.g, predictive modeling of chronic disease risk, Lin et al. 2017). Further, findings within this stream in regard to how to best allocate resources in dynamic health IT processes (e.g., use of telemedicine, Yeow and Goh 2015)and how to apply health IT toward preventing (or reducing) overuse of resources (e.g., reduce duplicate testing, Ayabakan et al. 2017)have advanced our knowledge of how to strategically apply IT toward effectiveness and efficiency. 

The health IT adoption and usetheme focuses on more granular (i.e., tactical and operational) decisions and processes related to the adoption and use of health IT.[1]Research within this theme has explored and evaluated challenges associated with leveraging technology to inform users (physicians) regarding use practices and outcomes (e.g., Kohli and Kettinger 2004), how to overcome user resistance (e.g., Lapointe and Rivard 2005), and how health IT impacts structures and practices in health care provider organizations (e.g., Romanow et al. 2018). More recently, this stream has diversified by considering health IT artifacts used by consumers including online intermediaries (e.g., Chan and Ghose 2014), online health care provider ratings (Gao et al. 2015), and online health communities (e.g., Goh et al. 2016). Such research has significantly advanced our understandings particularly by considering impacts of technology adoption and use on professionals (e.g., Kohli and Kettinger 2004), consumers (e.g., Chan and Ghose 2014), and even society (e.g., Goh et al. 2016). Further, this research has helped to broaden the constructs considered in IT adoption and use research and the conditions under which such constructs emerge or are most effectively applied (e.g., employee work practices and experiences of the adopting firm and technology vendor, Avgar et al. 2018).

The health IT security and privacytheme focuses on strategies for managing risks associated with health IT use and information sharing.[2]The health care context is an excellent context for such research due to the strong emphasis within this industry in maintaining the confidentiality, integrity, and availability of protected health information. Thus, it should come as no surprise that research within this stream has considered what drives health care institutions to invest in security and privacy (e.g., Angst et al. 2017a; Kwon and Johnson 2014), how consumers view the privacy protections in place (e.g., Angst and Agarwal 2009), and techniques for enhancing privacy (e.g., Li and Sarkar 2014). Such research has advanced our understandings of privacy and security investments, application, and perceptions, particularly by showing that the framing of messages about the value of health IT can alleviate privacy concerns of consumers (patients) (e.g., Angst and Agarwal 2009), and that voluntary adoption of protections (e.g., Kwon and Johnson 2014)and semi-collaborative networks (Menon 2018)are essential predictors of security and privacy initiative success.

The health IT for developmenttheme focuses on how health IT artifacts and innovations are being applied in developing countries and markets. Research within this stream has examined how the application of health IT and related innovations is contingent on the local context and requires attention to regional conditions and available resources when considering how to effectively pilot, scale, diffuse, and sustain health IT implementation and use (e.g., Miscione 2007; Srivastava and Shainesh 2015; Venkatesh et al. 2016). This has advanced theory and practice by evaluating health IT implementation and use under conditions of limited resources and capability gaps and has also demonstrated how new generations of technologies, such as mobile technologies, can be leveraged to overcome such barriers (e.g., Ganju et al. 2016).

In regard to articles that leverage the health care context to contribute to IS in general, we established the health as a contexttheme with the health context either in the foreground or background. In categorizing these articles, we drew insight from other scholars in developing our understanding regarding the role of context.[3]Within this theme, the ‘foreground context’ articles develop their analyses by drawing out the distinctiveness of the setting details. These foregrounded aspects of the context were found to shape the study findings, such as having remote or geographically dispersed regions (or catchment areas) (e.g., Paul and McDaniel Jr 2004; Serrano and Karahanna 2016), or non-traditional organizational settings, such as home health care (e.g., Nielsen et al. 2014). Other articles foregrounded the unique details of the health technologies being theorized (e.g. Jones 2014)or specific inter-professional tasks and features (e.g. Paul and McDaniel Jr 2004; Sergeeva et al. 2017)critical to developing the paper’s contributions. Context papers themed as ‘background context,’ did not draw out the situational details of the study’s health context in developing their research question or contribution. These studies took a more generalized approach to health organizations as a work context, for example examining integration of knowledge across dispersed units (Mitchell 2006)and group IS proficiency (Kane and Borgatti 2011). More recently, an emphasis on chronic disease has emerged, particularly due to the Special Issue on “The Role of Information Systems and Analytics in Chronic Disease Prevention and Management” (Bardhan et al. 2020).

We note that a new theme appears to be emerging focused on the impact of health IT and analytics on patient behaviors and outcomes (i.e., impact on patients). For instance, some of the most recent studies evaluate impacts on asthma management(Son et al. 2020; Zhang and Ram 2020)and general self-management of health (Jiang and Cameron 2020; Savoli et al. 2020).

[1]We note that another MISQ Research Curation is available on the topic of IS Use (https://www.misqresearchcurations.org/), which overlaps with this theme and provides additional insights into IS Use research.

[2]Also refer to MISQ Research Curations on the topics of Privacy, Trust, and Securing Digital Assets (https://www.misqresearchcurations.org/).

[3]Burton-Jones, A. and Volkoff, O. 2017. How can we Develop Contextualized Theories of Effective Use? A Demonstration in the Context of Community-Care Electronic Health Records. Information Systems Research(28:3), pp. 468-489.

Hong, W., Chan, F. K., Thong, J. Y., Chasalow, L. C., and Dhillon, G. 2013. A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research. Information Systems Research(25:1), pp. 111-136.

Johns, G. 2006. The Essential Impact of Context on Organizational Behavior. Academy of Management Review(31:2), pp. 386-408.

4. Conclusion
Health IT research has significantly advanced IS theory and practice at-large. Our evaluation of health IT research published within MISQ provides insights into the progression and thematic advances of this research stream. Looking to the future, health IT research shows no signs of abating, as calls for additional research continue to be published (e.g., Kohli and Tan 2016)and we continue to build upon prior efforts (e.g., Romanow et al. 2012). Based on these trends, we see the future of health IT research as significant, impactful, and beneficial to the IS community at large.

Please cite this curation as follows: Baird , A., Angst, C., Oborn, E., “Health Information Technology,” in MIS Quarterly Research Curations, Ashley Bush and Arun Rai, Eds., http://misq.org/research-curations, June 20, 2018; updated September 2020. doi: 10.25300/06212018

References
Angst, C. M., Wowak, K. D., Handley, S. M., and Kelley, K. 2017. “Antecedents of Information Systems Sourcing Strategies in U.S. Hospitals: A Longitudinal Study,” MIS Quarterly (41:4), pp. 1129–1152. Also available at https://aisel.aisnet.org/misq/vol41/iss4/8/.

Angst, C. M., and Agarwal, R. 2009. “Adoption of Electronic Health Records in the Presence of Privacy Concerns: The Elaboration Likelihood Model and Individual Persuasion,” MIS Quarterly (33:2), pp. 339–370. Also available at https://aisel.aisnet.org/misq/vol33/iss2/8/.

Angst, C. M., Block, E. S., D’Arcy, J., and Kelley, K. 2017. “When Do IT Security Investments Matter? Accounting for the Influence of Institutional Factors in the Context of Healthcare Data Breaches,” MIS Quarterly (41:3), pp. 893–916. Also available at https://aisel.aisnet.org/misq/vol41/iss3/12/.

Avgar, A., Tambe, P., and Hitt, L. M. 2018. “Built to Learn: How Work Practices Affect Employee Learning During Healthcare Information Technology Implementation,” MIS Quarterly (Forthcomin:Forthcoming), pp. 1–15. Also available at https://aisel.aisnet.org/misq/vol42/iss2/15/.

Ayabakan, S., Bardhan, I., Zheng, Z. E., and Kirksey, K. 2017. “The Impact of Health Information Sharing on Duplicate Testing,” MIS Quarterly (41:4), pp. 1083–1103. Also available at https://aisel.aisnet.org/misq/vol41/iss4/6/.

Bardhan, I., Chen, H., and Karahanna, E. 2020. “Connecting Systems, Data, and People: A Multidisciplinary Research Roadmap for Chronic Disease Management,” MIS Quarterly (44:1), pp. 185–200. Also available at https://aisel.aisnet.org/misq/vol44/iss1/9/.

Ben-Assuli, O., and Padman, R. 2020. “Trajectories of Repeated Readmissions of Chronic Disease Patients: Risk Stratification, Profiling, and Prediction,” MIS Quarterly (44:1), MIS Quarterly, pp. 201–227. Also available at https://aisel.aisnet.org/misq/vol44/iss1/10/.

Bernardi, R., Sarker, S., and Sahay, S. 2019. “The Role of Affordances in the Deinstitutionalization of a Dysfunctional Health Management Information System in Kenya: An Identity Work Perspective,” MIS Quarterly (43:4), pp. 1177–1200. Also available at https://aisel.aisnet.org/misq/vol43/iss4/10/.

Braa, J., Hanseth, O., Heywood, A., Mohammed, W., and Shaw, V. 2007. “Developing Health Information Systems in Developing Countries: The Flexible Standards Strategy,” MIS Quarterly (31:2), pp. 381–402. Also available at https://aisel.aisnet.org/misq/vol31/iss2/9/.

Braa, J., Monteiro, E., and Sahay, S. 2004. “Networks of Action: Sustainable Health Information Systems Across Developing Countries,” MIS Quarterly (28:3), pp. 337–362. Also available at https://aisel.aisnet.org/misq/vol28/iss3/3/.

Brohman, K., Addas, S., Dixon, J., and Pinsonneault, A. 2020. “Cascading Feedback: A Longitudinal Study of a Feedback Ecosystem for Telemonitoring Patients with Chronic Disease,” MIS Quarterly (44:1), pp. 421–450. Also available at https://aisel.aisnet.org/misq/vol44/iss1/17/.

Burtch, G., and Chan, J. 2019. “Investigating the Relationship Between Medical Crowdfunding and Personal Bankruptcy in the United States: Evidence of a Digital Divide,” MIS Quarterly (43:1), pp. 237–262. Also available at https://aisel.aisnet.org/misq/vol43/iss1/13/.

Chan, J., and Ghose, A. 2014. “Internet’s Dirty Secret: Assessing the Impact of Online Intermediaries on HIV Transmission,” MIS Quarterly (38:4), pp. 955–976. Also available at https://aisel.aisnet.org/misq/vol38/iss4/3/.

Davidson, E., and Chismar, W. G. 2007. “The Interaction of Institutionally Triggered and Technology-Triggered Social Structure Change: An Investigation of Computerized Physician Order Entry (CPOE),” MIS Quarterly (31:4), pp. 739–758. Also available at https://aisel.aisnet.org/misq/vol31/iss4/6/.

Dennis, A. R., and Garfield, M. 2003. “The Adoption and Use of GSS in Project Teams: Toward More Participative Processes and Outcomes,” MIS Quarterly (27:2), pp. 289–323. Also available at https://aisel.aisnet.org/misq/vol27/iss2/8/.

Essén, A., and Värlander, S. W. 2019. “How Technology-Afforded Practices at the Micro-Level Can Generate Change at the Field Level: Theorizing the Recursive Mechanism Actualized in Swedish Rheumatology 2000-2014,” MIS Quarterly (43:4), MIS Quarterly, pp. 1155–1176. Also available at https://aisel.aisnet.org/misq/vol43/iss4/9/.

Ganju, K. K., Pavlou, P. A., and Banker, R. D. 2016. “Does Information and Communication Technology Lead to the Well-Being of Nations? A Country-Level Empirical Investigation,” MIS Quarterly (40:2), pp. 417-- A10. Also available at https://aisel.aisnet.org/misq/vol40/iss2/10/.

Gao, G., Greenwood, B. N., Agarwal, R., and McCullough, J. S. 2015. “Vocal Minority and Silent Majority: How Do Online Ratings Reflect Population Perceptions of Quality?,” MIS Quarterly (39:3), MIS Quarterly, pp. 565–590. Also available at https://aisel.aisnet.org/misq/vol39/iss3/5/.

Goh, J. M., Gao, G., and Agarwal, R. 2016. “The Creation of Social Value: Can an Online Health Community Reduce Rural–Urban Health Disparities?,” MIS Quarterly (40:1), pp. 247–263. Also available at https://aisel.aisnet.org/misq/vol40/iss1/13/.

Hanseth, O., Jacucci, E., Grisot, M., and Aanestad, M. 2006. “Reflexive Standardization: Side Effects and Complexity in Standard Making,” MIS Quarterly (30: Special Issue on Standard Making), pp. 563–581.

Huang, K.-Y., Chengalur-Smith, I., and Pinsonneault, A. 2019. “Sharing Is Caring: Social Support Provision and Companionship Activities in Healthcare Virtual Support Communities,” MIS Quarterly (43:2), pp. 395–424. Also available at https://aisel.aisnet.org/misq/vol43/iss2/5/.

James, T. L., Wallace, L., and Deane, J. K. 2019. “Using Organismic Integration Theory to Explore the Associations Between Users’ Exercise Motivations and Fitness Technology Feature Set Use,” MIS Quarterly (43:1), pp. 287–312. Also available at https://aisel.aisnet.org/misq/vol43/iss1/15/.

Jiang, J., and Cameron, A.-F. 2020. “IT-Enabled Self-Monitoring for Chronic Disease Self-Management: An Interdisciplinary Review,” MIS Quarterly (44:1), pp. 451–508. Also available at https://aisel.aisnet.org/misq/vol44/iss1/18/.

Jones, M. 2014. “A Matter of Life and Death: Exploring Conceptualizations of Sociomateriality in the Context of Critical Care,” MIS Quarterly (38:3), pp. 895--U351. Also available at https://aisel.aisnet.org/misq/vol38/iss3/15/.

Kane, G. C., and Borgatti, S. P. 2011. “Centrality-IS Proficiency Alignment and Workgroup Performance,” MIS Quarterly (35:4), pp. 1063–1078. Also available at https://aisel.aisnet.org/misq/vol35/iss4/14/.

Karahanna, E., Chen, A., Liu, Q. Ben, and Serrano, C. 2019. “Capitalizing on Health Information Technology to Enable Advantage in U.S. Hospitals,” MIS Quarterly (43:1), pp. 113–140. Also available at https://aisel.aisnet.org/misq/vol43/iss1/8/.

Kohli, R., and Kettinger, W. J. 2004. “Informating the Clan: Controlling Physicians’ Costs and Outcomes,” MIS Quarterly (28:3), pp. 363–394. Also available at https://aisel.aisnet.org/misq/vol28/iss3/4/.

Kohli, R., Devaraj, S., and Ow, T. T. 2012. “Does Information Technology Investment Influence a Firm’s Market Value? A Case of Non-Publicly Traded Healthcare Firms,” MIS Quarterly (36:4), pp. 1145–1163. Also available at https://aisel.aisnet.org/misq/vol36/iss4/15/.

Kohli, R., and Tan, S. S.-L. 2016. “Electronic Health Records: How Can IS Researchers Contribute to Transforming Healthcare?,” MIS Quarterly (40:3), pp. 553–573. Also available at https://aisel.aisnet.org/misq/vol40/iss3/4/.

Kwon, J., and Johnson, M. E. 2018. “Meaningful Healthcare Security: Does ‘Meaningful-Use’ Attestation Improve Information Security Performance?,” MIS Quarterly (42:4), pp. 1043–1067. Also available at https://aisel.aisnet.org/misq/vol42/iss4/4/.

Kwon, J., and Johnson, M. E. 2014. “Proactive Versus Reactive Security Investments in the Healthcare Sector,” MIS Quarterly (38:2), pp. 451–471. Also available at https://aisel.aisnet.org/misq/vol38/iss2/8/.

Lapointe, L., and Rivard, S. 2005. “A Multilevel Model of Resistance to Information Technology Implementation,” MIS Quarterly (29:3), pp. 461–491. Also available at https://aisel.aisnet.org/misq/vol29/iss3/6/.

Li, X.-B., and Sarkar, S. 2014. “Digression and Value Concatenation to Enable Privacy-Preserving Regression,” MIS Quarterly (38:3), pp. 679–698. Also available at https://aisel.aisnet.org/misq/vol38/iss3/5/.

Lin, Y.-K., Chen, H., Brown, R. A., Li, S.-H., and Yang, H.-J. 2017. “Healthcare Predictive Analytics for Risk Profiling in Chronic Care: A Bayesian Multitask Learning Approach,” MIS Quarterly (41:2), pp. 473- A3. Also available at https://aisel.aisnet.org/misq/vol41/iss2/9/.

Liu, Q. Ben, Liu, X., and Guo, X. 2020. “The Effects of Participating in a Physician-Driven Online Health Community in Managing Chronic Disease: Evidence from Two Natural Experiments,” MIS Quarterly (44:1), pp. 391–419. Also available at https://aisel.aisnet.org/misq/vol44/iss1/16/.

Liu, X., Zhang, B., Susarlia, A., and Padman, R. 2020. “Go to You Tube and Call Me in the Morning: Use of Social Media for Chronic Conditions,” MIS Quarterly (44:1), pp. 257–283. Also available at https://aisel.aisnet.org/misq/vol44/iss1/12/.

Menon, N. M. 2018. “Information Spillover and Semi-Collaborative Networks in Insurer Fraud Detection,” MIS Quarterly (Forthcoming), pp. 1–20. Also available at https://aisel.aisnet.org/misq/vol42/iss2/5/.

Miscione, G. 2007. “Telemedicine in the Upper Amazon: Interplay with Local Health Care Practices,” MIS Quarterly (31:2), pp. 403–425. Also available at https://aisel.aisnet.org/misq/vol31/iss2/10/.

Mitchell, V. L. 2006. “Knowledge Integration and Information Technology Project Performance,” MIS Quarterly (30:4), pp. 919–939. Also available at https://aisel.aisnet.org/misq/vol30/iss4/8/.

Nielsen, J., Mathiassen, L., and Newell, S. 2014. “Theorization and Translation in Information Technology Institutionalization: Evidence from Danish Home Care,” MIS Quarterly (38:1), pp. 165–186. Also available at https://aisel.aisnet.org/misq/vol38/iss1/10/.

Park, I., Sharman, R., and Rao, H. R. 2015. “Disaster Experience and Hospital Information Systems: An Examination of Perceived Information Assurance, Risk, Resilience, and HIS Usefulness,” MIS Quarterly (12:4), pp. 382–405. Also available at https://aisel.aisnet.org/misq/vol39/iss2/5/.

Paul, D. L., and McDaniel Jr, R. R. 2004. “A Field Study of the Effect of Interpersonal Trust on Virtual Collaborative Relationship Performance,” MIS Quarterly (28:2), pp. 183–227. Also available at https://aisel.aisnet.org/misq/vol28/iss2/4/.

Ray, G., Muhanna, W. A., and Barney, J. B. 2005. “Information Technology and the Performance of the Customer Service Process: A Resource-Based Analysis,” MIS Quarterly (29:4), pp. 625–652. Also available at https://aisel.aisnet.org/misq/vol29/iss4/4/.

Romanow, D., Cho, S., and Straub, D. 2012. “Editor’s Comments: Riding the Wave: Past Trends and Future Directions for Health IT Research,” MIS Quarterly (36:3), iii-x. Also available at https://aisel.aisnet.org/misq/vol36/iss3/2/.

Romanow, D., Rai, A., and Keil, M. 2018. “CPOE-Enabled Coordination: Appropriation for Deep Structure Use and Impacts on Patient Outcomes,” MIS Quarterly (42:1). Also available at https://aisel.aisnet.org/misq/vol42/iss1/12/.

Salge, T. O., Kohli, R., and Barrett, M. 2015. “Investing in Information Systems: On the Behavioral and Institutional Search Mechanisms Underpinning Hospitals’ IS Investment Decisions,” MIS Quarterly (39:1), pp. 61–90. Also available at https://aisel.aisnet.org/misq/vol39/iss1/6/.

Savoli, A., Barki, H., and Paré, G. 2020. “Examining How Chronically Ill Patients’ Reactions to and Effective Use of Information Technology Can Influence How Well They Self-Manage Their Illness,” MIS Quarterly (44:1), pp. 351–389. Also available at https://aisel.aisnet.org/misq/vol44/iss1/15/.

Sergeeva, A., Huysman, M., Soekijad, M., and van den Hooff, B. 2017. “Through the Eyes of Others: How Onlookers Shape the Use of Technology at Work,” MIS Quarterly (41:4), pp. 1153–1178. Also available at https://aisel.aisnet.org/misq/vol41/iss4/9/.

Serrano, C., and Karahanna, E. 2016. “The Compensatory Interaction between User Capabilities and Technology Capabilities in Influencing Task Performance: An Empirical Assessment in Telemedicine Consultations,” MIS Quarterly (40:3), pp. 597–621. Also available at https://aisel.aisnet.org/misq/vol40/iss3/6/.

Silva, L., and Hirschheim, R. A. 2007. “Fighting Against Windmills: Strategic Information Systems and Organizational Deep Structures,” MIS Quarterly (31:2), pp. 327–354. Also available at https://aisel.aisnet.org/misq/vol31/iss2/7/.

Singh, R., Mathiassen, L., and Mishra, A. 2015. “Organizational Path Constitution in Technological Innovation: Evidence from Rural Telehealth,” MIS Quarterly (39:3), pp. 643–666. Also available at https://aisel.aisnet.org/misq/vol39/iss3/8/.

Son, J., Flatley Brennan, P., and Zhou, S. 2020. “A Data Analytics Framework for Smart Asthma Management Based on Remote Health Information Systems with Bluetooth-Enabled Personal Inhalers,” MIS Quarterly (44:1), pp. 285–303. Also available at https://aisel.aisnet.org/misq/vol44/iss1/13/.

Srivastava, S. C., and Shainesh, G. 2015. “Bridging the Service Divide Through Digitally Enabled Service Innovations: Evidence from Indian Healthcare Service Providers,” MIS Quarterly (39:1), pp. 245-- A19. Also available at https://aisel.aisnet.org/misq/vol39/iss1/14/.

 Thompson, S., Whitaker, J., Kohli, R., and Jones, C. 2020. “Chronic Disease Management: How IT and Analytics Create Healthcare Value Through the Temporal Displacement of Care,” MIS Quarterly (44:1), pp. 227–256. Also available at https://aisel.aisnet.org/misq/vol44/iss1/11/.

Venkatesh, V., Rai, A., Sykes, T. A., and Aljafari, R. 2016. “Combating Infant Mortality in Rural India: Evidence from a Field Study of EHealth Kiosk Implmentations,” MIS Quarterly (40:2), pp. 353–380. Also available at https://aisel.aisnet.org/misq/vol40/iss2/7/.

Xue, Y., Liang, H., and Boulton, W. R. 2008. “Information Technology Governance in Information Technology Investment Decision Processes: The Impact of Investment Characteristics, External Environment, and Internal Context,” MIS Quarterly (32:1), pp. 67–96. Also available at https://aisel.aisnet.org/misq/vol32/iss1/6/.

Yeow, A., and Goh, K. H. 2015. “Work Harder or Work Smarter? Information Technology and Resource Allocation in Healthcare Processes,” MIS Quarterly (39:4), pp. 763–785. Also available at https://aisel.aisnet.org/misq/vol39/iss4/4/.

Zhang, W., and Ram, S. 2020. “A Comprehensive Analysis of Triggers and Risk Factors for Asthma Based on Machine Learning and Large Heterogeneous Data Sources,” MIS Quarterly (44:1), pp. 305–349. Also available at https://aisel.aisnet.org/misq/vol44/iss1/14/. 

Figure 5: Health IT Infographic