Browsing by Author "Helm, Jonathan E."

Now showing 1 - 3 of 3
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    Coordination of Autonomous Healthcare Entities: Emergency Response to Multiple Casualty Incidents
    (Wiley, 2017) Mills, Alex F.; Helm, Jonathan E.; Jola-Sanchez, Andres F.; Tatikonda, Mohan V.; Courtney, Bobby A.; Kelley School of Business – Indianapolis
    In recent years, many urban areas have established healthcare coalitions (HCCs) composed of autonomous (and often competing) hospitals, with the goal of improving emergency preparedness and response. We study the role of such coalitions in the specific context of response to multiple-casualty incidents in an urban setting, where on-scene responders must determine how to send casualties to medical facilities. A key function in incident response is multi-agency coordination. When this coordination is provided by an HCC, responders can use richer information about hospital capacities to decide where to send casualties. Using bed availability data from an urban area and a suburban area in the United States, we analyze the response capability of healthcare infrastructures under different levels of coordination, and we develop a stress test to identify areas of weakness. We find that improved coordination efforts should focus on decision support using information about inpatient resources, especially in urban areas with high inter-hospital variability in resource availability. We also find that coordination has the largest benefit in small incidents. This benefit is a new value proposition for HCCs, which were originally formed to improve preparedness for large disasters.
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    The joint structure–function dynamics of glaucoma progression
    (Taylor and Francis, 2015) Racette, Lyne; Helm, Jonathan E.; Dul, Mitchell; Marin-Franch, Iván; Department of Ophthalmology, IU School of Medicine
    While the presence and rate of glaucoma progression influence treatment decisions, the methods currently available to detect and monitor progression are imprecise and do not allow clinicians to make accurate assessments of the status of their patients. Models that focus on combining structural and functional parameters may improve our ability to detect and monitor glaucoma progression. Several of these models, however, are limited by their reliance on population statistics and on the static assumptions they make about the nature of glaucoma progression. Dynamic modeling of glaucoma progression may lead to a better understanding of glaucoma progression that could eventually translate into making individualized treatment decisions.
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    Operations (management) warp speed: Rapid deployment of hospital‐focused predictive/prescriptive analytics for the COVID‐19 pandemic
    (Wiley, 2022) Shi, Pengyi; Helm, Jonathan E.; Chen, Christopher; Lim, Jeff; Parker, Rodney P.; Tinsley, Troy; Cecil, Jacob; Indiana University Health
    At the onset of the COVID-19 pandemic, hospitals were in dire need of data-driven analytics to provide support for critical, expensive, and complex decisions. Yet, the majority of analytics being developed were targeted at state- and national-level policy decisions, with little availability of actionable information to support tactical and operational decision making and execution at the hospital level. To fill this gap, we developed a multi-method framework leveraging a parsimonious design philosophy that allows for rapid deployment of high-impact predictive and prescriptive analytics in a time-sensitive, dynamic, data-limited environment, such as a novel pandemic. The product of this research is a workload prediction and decision support tool to provide mission-critical, actionable information for individual hospitals. Our framework forecasts time-varying patient workload and demand for critical resources by integrating disease progression models, tailored to data availability during different stages of the pandemic, with a stochastic network model of patient movements among units within individual hospitals. Both components employ adaptive tuning to account for hospital-dependent, time-varying parameters that provide consistently accurate predictions by dynamically learning the impact of latent changes in system dynamics. Our decision support system is designed to be portable and easily implementable across hospital data systems for expeditious expansion and deployment. This work was contextually grounded in close collaboration with IU Health, the largest health system in Indiana, which has 18 hospitals serving over one million residents. Our initial prototype was implemented in April 2020 and has supported managerial decisions, from the operational to the strategic, across multiple functionalities at IU Health.