Arun’s research in the area of Health IT has examined the use of EMR and CPOE systems in the clinical processes of large hospitals, the design of cost-effective telemedicine solutions to broaden access to healthcare in rural India with limited healthcare and technological infrastructures, mobile-health (mHealth) services channel preferences, the cost and quality impacts of health IT capabilities in U.S. hospitals, the search mechanisms that drive IT-enabled innovation in U.S. hospitals, and the role of patient-physician online communities. These projects have been in collaboration with large hospital systems in the United States and India and with scholars across disciplinary traditions and countries.

Selected Publications

Objective:This work seeks to complement and extend prior work by using a multidisciplinary approach to explain electronic medical records (EMR) system use and consequent performance (here, patient satisfaction) among physicians during early stages of the implementation of an EMR.

Design:This was a quantitative study, with data obtained from three distinct sources: individual-level and social-network data from employees; use data from EMR system logs; and patient satisfaction data from patients and/or authorized decision-makers. Responses were obtained from 151 physicians and 8440 patient satisfaction surveys over the course of a 1-year period at the shakedown phase of an EMR system implementation.

Results:Physicians who were better connected, both directly and indirectly, to their peers—that is, other physicians—for advice on their work, used the system less than those who were less connected. In addition to such social network ties, demographic characteristics (gender and age), three personality characteristics (openness to experience, agreeableness and extroversion) and a key technology perception (perceived usefulness) predicted EMR system use.

Conclusions:For hospital administrators and other stakeholders, understanding the contributors to, and the relative importance of, various factors in explaining EMR system use, and its impact on patient satisfaction is of great importance. The factors identified in this work that influence a physician's use of EMR systems can be used to develop interventions and applications that can increase physician buy-in and use of EMR systems.

Background: Consumer use of mobile devices as health service delivery aids (mHealth) is growing, especially as smartphones become ubiquitous. Questions remain, though, as to how consumer traits, health perceptions, situational characteristics, and demographics may impact consumer mHealth usage intentions, assimilation, and channel preferences.

Objective: We examine how consumers’ personal innovativeness toward mobile services (PIMS), perceived health conditions, healthcare availability, healthcare utilization, demographics, and socioeconomic status affect their: 1) mHealth usage intentions and extent of mHealth assimilation, and 2) preference for mHealth as a complement or substitute for in-person doctor visits.

Methods: Leveraging constructs from research in technology acceptance, technology assimilation, consumer behavior, and health informatics, we developed a cross-sectional online survey to study determinants of consumers’ mHealth usage intentions, assimilation, and channel preferences. Data were collected from 1,132 nationally representative U.S. consumers and analyzed using moderated multivariate regressions and analysis of variance (ANOVA).

Results: The results indicate that: 1) 430 consumers in our sample (38%) have started using mHealth, 2) A larger quantity of consumers are favorable to using mHealth as a complement to in-person doctor visits (758 out of 1132 respondents) than as a substitute (532 out of 1132 respondents), and 3) Consumers’ PIMS and perceived health conditions have significant positive direct influences on mHealth usage intentions, assimilation, and channel preferences and significant positive interactive influences on assimilation and channel preferences. The independent variables within the moderated regressions collectively explained 59.70% variance in mHealth usage intentions, 60.41% in mHealth assimilation, 34.29% in preference for complementary use of mHealth, and 45.30% in preference for substitutive use of mHealth. In a follow-up ANOVA examination, we found that those who were more favorable to using mHealth as a substitute to in-person doctor visits than as a complement indicated stronger intentions to use mHealth (F (1, 702)=20.14, P<.001) and stronger assimilation of mHealth (F (1, 702)=41.866, P<.001).

Conclusions: Multiple predictors are shown to have significant impacts on mHealth usage intentions, assimilation, and channel preferences. We suggest that future initiatives to promote mHealth should shift targeting of consumers from coarse demographics to nuanced considerations of: individual dispositions towards mobile service innovations, complementary or substitutive channel use preferences, perceived health conditions, health services availability and utilization, demographics, and socioeconomic characteristics.

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Hospitals are now faced with delivering value-based care (high quality patient care at a reduced cost) rather than volume-based care. To investigate the impact of IT on value-creation in health care, we identify and theorize how the extent of use and rate of growth in use for three HIT capabilities (Clinical Process Management, Patient Engagement, and Patient Transition) may independently and jointly affect cost and patient quality outcomes in the context of the U.S. health care industry. Our empirical data is based on multiple archival sources from 2008-2013, including data on implementation and use of HIT functionalities, hospital characteristics, quality of patient care outcomes, and cost of care outcomes. We identify measures for our constructs and propose analysis methods to test our model and hypotheses. We seek to contribute to our understanding of how portfolios of HIT capabilities and associated complementarities may contribute to the delivery of value-based care.

Mobile health (mHealth) are touted to have huge potential to broaden access, at low cost, to quality healthcare. We examine how awareness and use of mHealth develops among consumers in urban and rural India through a combination of individual traits related to mobile services and individual health characteristics. We conducted a survey in several parts of urban and rural India to develop a diversified sample that approximates the 2011 Indian Census. We find consumers appraisals of mobile service-enabled empowerment, affects mHealth awareness/use through innovativeness toward mobile services. We also find that this mediation mechanism is stronger (1) for rural consumers who perceive themselves less vulnerable to chronic diseases and (2) for urban consumers who exhibit a higher regularity of preventive monitoring. Our study has implications on how mHealth awareness and use can be developed among consumers in urban and rural areas and in developing country contexts.

In the United States, the Centers for Medicare & Medicaid Services (CMS) has begun instituting pay-for-performance incentives that reward hospitals based on patient-centric outcomes such as patient satisfaction. Further, to promote the “meaningful use” of health information technology (HIT), CMS has been prompting hospitals to adopt and use HITs. Computerized provider order entry (CPOE) is one such HIT and is designed to improve coordination in patient care teams and consequently patient outcomes. We explore the impact of CPOE-enabled coordination on patient satisfaction with the care team. In a departure from prior research that has tended to treat the team as all clinicians within a hospital unit/clinic, we conceptualize (and operationalize) patient care teams as ad hoc and patient-specific and thus comprised of those clinicians having direct contact with the patient. In a further departure from prior research that has employed lean measures of IS use (e.g., use intentions, duration, or frequency of use), we respond to the call for rich measures of IS use by conceptualizing deep structure use (DSU) of CPOE as patient care team-level usage of CPOE features. We draw upon adaptive structuration theory (AST) to identify faithfulness of appropriation (FOA) and consensus on appropriation (COA) as two related, but distinct, aspects of CPOE appropriation by patient care teams that affect DSU. We also draw on relational coordination theory to conceptualize communicative coordination (CC) as team communication for coordination purposes and theorize that DSU affects patient satisfaction through CC and informating the patient differentially across high/low patient mortality risk conditions. Based on data from 224 patient care teams caring for both low and high patient mortality risk conditions, our results indicate that FOA and COA are salient predictors of DSU, and that the effect of COA on DSU is mediated by FOA. We also observed a significant indirect effect of DSU on patient satisfaction (as mediated by communicative coordination and patient informating), but only for high patient mortality risk conditions. Our findings are important because they show that by using CPOE in a comprehensive manner, patient care team members are better able to coordinate patient care and are able to better inform the patient about their care, ultimately leading to improved patient satisfaction. Additional implications for HIT research and practice are discussed.

The mobile health (mHealth) channel has been suggested to be effective in assisting with chronic disease management. However, little is known about the mHealth channel preferences of consumers who may be vulnerable to chronic disease. Integrating the lens of approach-avoidance beliefs with regulatory focus theory, we: 1) focus on mHealth channel preference (CHANNEL) as our dependent variable, 2) identify perceived mHealth usefulness (PU) as an approach belief and perceived mHealth risk (RISK) as an avoidance belief, and 3) develop hypotheses pertaining to the how the regulatory focus of the individual (operationalized as perceived vulnerability to chronic disease, VULN) moderates the impacts of PU and RISK on CHANNEL. Based on analyses using structural equation modeling (SEM) of survey data collected from 954 individuals in the U.S., we find that, compared to a promotion regulatory focus (low VULN), a prevention regulatory focus (high VULN) amplifies the effect of RISK on CHANNEL and suppresses the effect of PU on CHANNEL. We discuss the implications of our findings for theory, practice, and future research related to mHealth channel preferences.