Introduction to be added here.
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.
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.