Anna Ye Du

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.

Risk Management and Optimal Pricing in Online Storage Grids

Online storage service providers grant a way for companies to avoid spending resources on maintaining their own in-house storage infrastructure and thereby allowing them to focus on their core business activities. These providers, however, follow a fixed, posted pricing strategy that charges the same price in each time period and thus bear all the risk arising out of demand uncertainties faced by their client companies. We examine the effects of providing a spot market with dynamic prices and forward contracts to hedge against future revenue uncertainty. We derive revenue-maximizing spot and forward prices for a single seller facing a known set of buyers. We perform a simulation study using publicly available traffic data regarding Amazon S3 clients from Alexa.com to validate our analytical results. Our field study supports our analysis and indicates that spot markets alone can enhance revenues to Amazon, but this comes at the cost of increased risks due to the increased market share in the spot markets. Furthermore, adding a forward contract feature to the spot markets can reduce risks while still providing the benefits of enhanced revenues. Although the buyers incur an increase in costs in the spot market, adding a forward contract does not cause any additional cost increase while transferring the risk to the buyers. Thus, storage grid providers can greatly benefit by applying a forward contract alongside the spot market.

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.

Capacity Provision Networks: Foundations of Markets for Sharable Resources in Distributed Computational Economies

With the rapid growth of rich-media content over the Internet, content and service providers (SP) are increasingly facing the problem of managing their service resources cost-effectively while ensuring a high quality of service (QoS) delivery at the same time. In this research we conceptualize and model an Internet-based storage provisioning network for rich-media content delivery. This is modeled as a capacity provision network (CPN) where participants possess service infrastructures and leverage their topographies to effectively serve specific customer segments. A CPN is a network of SPs coordinated through an allocation hub. We first develop the notion of discounted QoS capabilities of storage resources. We then investigate the stability of the discount factors over time and the network topography using a test-bed on the Internet through a longitudinal empirical study. Finally, we develop a market maker mechanism for optimal multilateral allocation and surplus sharing in a network. The proposed CPN is closely tied to two fundamental properties of Internet service technology: positive network externality among cooperating SPs and the property of effective multiplication of capacity allocation among several distributed service sites. We show that there exist significant incentives for SPs to engage in cooperative allocation and surplus sharing. We further demonstrate that intermediation can enhance the allocation effectiveness and that the opportunity to allocation and surplus sharing can play an important role in infrastructure planning. In conclusion, this study demonstrates the practical business viability of a cooperative CPN market.

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.

Topographically discounted Internet infrastructure resources: a panel study and econometric analysis

The Internet infrastructure is increasingly utilized for the provision and delivery of a variety of services. The rapid growth and substantial demand fluctuations of the supported services are placing strain on the service providers to effectively manage their infrastructure resources. Capacity Provision Network (CPN) is a market-driven mechanism that enables the service providers to efficiently allocate, share and trade service capabilities. Central to the operation of CPN is the notion of discounted capacities which capture the underlying topology of the Internet. It serves as a practical instrument for service providers to evaluate the utility of capacity services available at remote sites vis-à-vis the capacities available locally and from the content providers. We conducted an Internet based longitudinal field experiment to empirically assess and validate the critical properties of discount factors. Our analysis reveals that discount factors exhibit markedly lower volatility than network delays, and are more stable over seasonal and temporal trend patterns. The results related to size effects suggest that sharing and trade agreements with larger content can be particularly effective. Our finding on the existence of the concavity property points to the ability of intermediation services to enhance the overall gains from resource sharing arrangements.

Availability-aware energy-efficient virtual machine placement

Availability, as a part of Service Level Agreement (SLA), is a critically important issue in cloud services, as an application may not be able to run after certain server or network failures. Cloud service providers seek to not only fulfill the SLA, but also simultaneously minimize their operating costs, which are dominated by the energy consumption. In order to minimize the impact of a server/switch failure inside the datacenter on a single application, one would like to spread out the Virtual Machines (VM) for the application across different racks. However, in doing so, the power consumption may increase significantly. In this paper, we develop a variance-based metric to measure the risk of violating the availability requirement. We then propose two heuristic algorithms to place VMs in online and offline manners, respectively. These algorithms aim to strike a balance between minimizing the risk of violating the availability requirement and minimizing the energy, in order to reduce the overall cost.

Risk hedging in storage grid markets: Do options add value to forwards?

Internet storage services allow businesses to move away from maintaining their own internal storage networks. Service providers currently follow a utility pricing model which translates to them absorbing all the risk that arises from the fluctuating storage needs of their customers. The risk borne by the Internet storage service providers has large revenue implications as Internet startups and smaller companies, which face significant demand stochasticity, constitute an important segment of their clientele. We develop an option pricing mechanism to hedge against this risk and evaluate its effectiveness vis-à-vis forward contracts. We obtain the conditions under which options dominate forward contracts and the trade-offs involved when the provider has to decide on appropriate pricing mechanisms. Our empirical study uses publicly obtainable traffic data of Amazon S3 clients to validate the analytical results. We show that providers can significantly benefit from including options in their risk-hedging portfolio, especially when there is less variation in the costs faced by the buyers in building their own data networks as opposed to using cloud services.

Availability-Aware Energy-Efficient Virtual Machine Placement Algorithm

—Availability, as a part of Service Level Agreement (SLA), is a critically important issue in cloud services, which are affected by server or network failures in datacenters. Cloud service providers seek to not only fulfill the SLA, but also simultaneously minimize their operating costs, which are dominated by the energy consumption. In order to minimize the impact of a server/switch failure on a single application, one spread out the VMs for the application across different racks. Although a higher availability can be achieved, the power consumption may increase significantly. In this paper, we develop a variance-based metric to measure the risk of either over provisioning or under provisioning availability by means of VM placement. We then propose algorithms to place VMs in online and offline manners, respectively. These algorithms aim to strike a balance between maximizing the availability and minimizing the energy, in order to reduce the operating cost for the service providers.

Predicting Transient Downtime in Virtual Server Systems: An Efficient Sample Path Randomization Approach

A central challenge in developing cloud datacenters Service Level Agreements is the estimation of downtime distribution of a set of provisioned servers over a service window, which is compounded by three facts. First, while steady-state probabilities have been derived for birth-death processes involving server failures and repairs, they could be highly inaccurate under transience. Furthermore, steady-state cannot be assured under typical service windows. Therefore, estimation of transient distributions is essential. Second, the processes of failures and repairs may follow any distribution and hence need to be extracted using system log data and modeled using appropriate general distributions. Third, downtime distributions over service windows depend on the number of servers and their deployment structure for a contract. We develop an efficient and generalized sample path randomization approach to precisely estimate transient probabilities under three different checkpointing strategies and three flexible failure distribution models. The estimators are unbiased, consistent, efficient and sufficient. Their asymptotic convergence is established. The estimation algorithms are computationally efficient in solving practical problems and yield rich information on transient system behaviors. The methodology is general and extensible to various server failure and repair processes characterized using birth-death modeling.