Niam Yaraghi

Health Information Exchange as a Multisided Platform: Adoption, Usage, and Practice Involvement in Service Co-Production

Health Information Exchanges HIE are becoming integral parts of the national healthcare reform efforts, chiefly because of their potential impact on cost reduction and quality enhancement in healthcare services. However, the potential of an HIE platform can only be realized when its multiple constituent users actively participate in using its variety of services. In this research, we model HIE systems as multisided platforms that incorporate self-service technologies whose value to the users depends on both user-specific and network-specific factors. We develop a model of adoption, use, and involvement of clinical practices in the coproduction of the HIE services. This model is grounded in social network theory, service operations theory, and institutional isomorphism theory. A longitudinal study of actual adoption and use behaviors of 2,054 physicians within 430 community medical practices in Western New York over a three-year period has been carried out to evaluate the proposed model. This study has been supported by HEALTHeLINK, the Regional Health Information Organization of Western New York, which has an extensive database comprising over half a million transactions on patient records by the HIE users. We extracted panel data on adoption, use, and service coproduction behaviors from this database and carried out a detailed analysis using metrics derived from the foundational theories. Positioning practices within two distinct but interrelated networks of patients and practitioners, we show that adoption, use, and service coproduction behaviors are influenced by the topographies of the two networks, isomorphic effects of large practices on the smaller ones, and practice labor inputs in HIE use. Our findings provide a comprehensive view of the drivers of HIE adoption and use at the level of medical practices. These results have implications for marketing and revenue management of HIE platforms, as well as public health and national/regional healthcare policy making.

An Empirical analysis of the financial benefits of health information exchange in emergency departments

OBJECTIVE To examine the impact of health information exchange (HIE) on reducing laboratory tests and radiology examinations performed in an emergency department (ED). MATERIALS AND METHODS The study was conducted in an ED setting in Western New York over a period of 2 months. The care of the patients in the treatment group included an HIE query for every encounter, while the care of other patients in the control group did not include such queries. A group of medical liaisons were hired to query the medical history of patients from an HIE and provide it to the ED clinicians. Negative binomial regression was used to analyze the effects of HIE queries on the number of performed laboratory tests and radiology examinations. The log files of the HIE system since 1 year before the ED admission were used to analyze the differences in outcome measures between the 2 groups of patients. RESULTS Ceteris paribus, HIE usage is associated with, respectively, 52% and 36% reduction in the expected total number of laboratory tests and radiology examinations ordered per patient at the ED. CONCLUSIONS The results indicate that access to additional clinical data through the HIE will significantly reduce the number of laboratory tests and radiology examinations performed in the ED settings and thus support the ongoing HIE efforts.

Comparison of AHP and Monte Carlo AHP Under Different Levels of Uncertainty

Despite the extensive application of Monte Carlo analytic hierarchy process (MCAHP) in various fields of decision making, its performance has not been compared with the classic analytic hierarchy process (AHP). Both of these methods are heavily affected by individual or group preferences and thus provide subjective rankings. Since the mere difference between their results does not necessarily warrant the superiority of one against the other, a reliable and robust ranking of alternatives should be available as a comparison basis so that the results of these two methods can be evaluated. In this paper, we use a simulation approach to compare the results of AHP with MCAHP under different levels of uncertainty. We validate our simulation results by comparing the performance of these two alternatives against a real world and reliable ranking of blogs. Our simulation results show that as long as the variation in different pairwise comparisons is less than 0.24, the performance of AHP is not statistically different from the performance of MCAHP. When the uncertainty in terms of variation grows beyond 0.24, MCAHP provides more precise rankings. The findings of this research add to the current body of knowledge in the multicriteria decision analysis as well as Information Systems literature and provide insights for managerial applications of these techniques.

Network Effects in Health Information Exchange Growth

The importance of the Healthcare Information Exchange (HIE) in increasing healthcare quality and reducing risks and costs has led to greater interest in identifying factors that enhance adoption and meaningful use of HIE by healthcare providers. In this research we study the interlinked network effects between two different groups of physicians -- primary care physicians and specialists -- as significant factors in increasing the growth of each group in an exchange. An analytical model of interlinked and intragroup influences on adoption is developed using the Bass diffusion model as a basis. Adoption data on 1,060 different primary and secondary care physicians over 32 consecutive months was used to test the model. The results indicate not only the presence of interlinked effects, but also that their influence is stronger than that of the intragroup. Further, the influence of primary care physicians on specialists is stronger than that of specialists on primary care physicians. We also provide statistical evidence that the new model performs better than the conventional Bass model, and the assumptions of diffusion symmetry in the market are statistically valid. Together, the findings provide important guidelines on triggers that enhance the overall growth of HIE and potential marketing strategies for HIE services.

Professional and geographical network effects on healthcare information exchange growth: does proximity really matter?

BACKGROUND AND OBJECTIVE We postulate that professional proximity due to common patients and geographical proximity among practice locations are significant factors influencing the adoption of health information exchange (HIE) services by healthcare providers. The objective of this study is to investigate the direct and indirect network effects of these drivers on HIE diffusion. DESIGN Multi-dimensional scaling and clustering are first used to create different clusters of physicians based on their professional and geographical proximities. Extending the Bass diffusion model to capture direct and indirect network effects among groups, the growth of HIE among these clusters is modeled and studied. The network effects among the clusters are investigated using adoption data over a 3-year period for an HIE based in Western New York. MEASUREMENT HIE adoption parameters-external sources of influence as well as direct and indirect network coefficients-are estimated by the extended version of the Bass diffusion model. RESULTS Direct network effects caused by common patients among physicians are much more influential on HIE adoption as compared with previously investigated social contagion and external factors. Professional proximity due to common patients does influence adoption decisions; geographical proximity is also influential, but its effect is more on rural than urban physicians. CONCLUSIONS Flow of patients among different groups of physicians is a powerful factor in HIE adoption. Rather than merely following the market trend, physicians appear to be influenced by other physicians with whom they interact with and have common patients.

The Current and Future State of the Sharing Economy

This paper finds that the sharing economy is “the peer-to-peer based activity of obtaining, giving, or sharing access to good and services”. Alternative names for this phenomenon include gig economy, platform economy, access economy, and collaborative consumption. The sharing economy is estimated to grow from

Do Health Information Exchanges Deter Repetition of Medical Services

14 billion in 2014 to

Drivers of information disclosure on health information exchange platforms: insights from an exploratory empirical study

335 billion by 2025. This estimate is based on the rapid growth of Uber and Airbnb as indicative. Data shows that private vehicles go unused for 95 per cent of their lifetime. Together with the fact that there are fewer requirements to drive for Lyft, Ola and Uber than for a taxi company means greater supply of rides. Prices of shared services are also falling as indicated by Airbnb rates that are between 30 and 60 per cent cheaper than hotel rates around the world. More information shared on an online platform can lead to greater trust between users, but it can also lead to racial and gender bias. Sharing economy companies must work to combat bias on their platforms, both in their algorithms and their users. Removing some identifying information from profiles lowers risk of bias. It is difficult for any one company to form a monopoly since the cost for customers to switch between sharing economy services is quite low.

Winning at All Costs: Analysis of Inflation in Nursing Homes’ Rating System

Repetition of medical services by providers is one of the major sources of healthcare costs. The lack of access to previous medical information on a patient at the point of care often leads a physician to perform medical procedures that have already been done. Multiple healthcare initiatives and legislation at both the federal and state levels have mandated Health Information Exchange (HIE) systems to address this problem. This study aims to assess the extent to which HIE could reduce these repetitions, using data from Centers for Medicare 8 Medicaid Services and a regional HIE organization. A 2-Stage Least Square model is developed to predict the impact of HIE on repetitions of two classes of procedures: diagnostic and therapeutic. The first stage is a predictive analytic model that estimates the duration of tenure of each HIE member-practice. Based on these estimates, the second stage predicts the effect of providers’ HIE tenure on their repetition of medical services. The model incorporates moderating effects of a federal quality assurance program and the complexity of medical procedures with a set of control variables. Our analyses show that a practices tenure with HIE significantly lowers the repetition of therapeutic medical procedures, while diagnostic procedures are not impacted. The medical reasons for the effects observed in each class of procedures are discussed. The results will inform healthcare policymakers and provide insights on the business models of HIE platforms.