2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - Risk-adaptive E-triage In Emergency Medicine:
A Prospective Analysis
Scott R Levin, Johns Hopkins School of Medicine,
, Matthew Toerper, Diego A. Martinez,
Heather Gardener, Eric Hamrock, Sean Barnes
Unprecedented levels of crowding and consequential delays in care have
intensified the need for accurate triage in emergency departments (ED). For the
majority of ED patients, the projected clinical course at presentation not is
obvious. Almost half of adult ED visits nationally are triaged to emergency
severity index (ESI) Level 3; the ambiguous midpoint of a 5-Level algorithm
standard in the US. The objective of our electronic (e) triage tool is to improve
differentiation of ED patients by enabling data-driven prognostication of risk of
critical events and illness severity. The tool, prospectively evaluated at multiple
sites, demonstrates improved detection of critically ill patients.
4 - An Evolutionary Computation Approach For Optimizing Multi-level
Data To Predict Individual Patient Outcomes
Sean Barnes, Univ of Maryland-College Park,
, Suchi Saria, Scott R Levin
Widespread adoption of electronic health records and objectives for meaningful
use have increased opportunities for data-driven applications in medicine and
healthcare. Optimally specifying multi-level patient data—which can be defined at
varying levels of granularity—for predictive modeling is a challenge that must be
addressed. We present a general evolutionary computational framework to
optimally specify multi-level data to predict individual patient outcomes. We
evaluate its performance in predicting critical events for emergency department
patients across five populations.
5 - Control System For Electronic Triage In The Emergency
Department: Integrating The User Into Development Loop
Diego A. Martinez, Scott R. Levin, Johns Hopkins University
School of Medicine, Baltimore, MD,
The potential for machine learning systems to improve via exchange of informa-
tion with knowledgeable users has yet to be explored in much detail. In a pilot
study in an emergency department of a large hospital, nurses were presented
with triage level predictions, and they were able to provide feedback through a
real-time communication system. The types of some of this feedback seem prom-
ising for assimilation of clinical gestalt by machine learning systems. The results
show that to benefit from clinical gestalt; machine learning systems must be able
to absorb information in a graceful manner and provide clear explanations of
their predictions.
MB24
109-MCC
Strategy and Uncertainty
Invited: Strategy Science
Invited Session
Chair: Hart Posen, University of Wisconsin, University, Milwaukee, WI,
4, United States,
1 - High On Innovation: The Impact Of Liberalization Policies On
Creative Outcomes
Laurina Zhang, Ivey Business School, Western University, London,
ON, Canada, , Keyvan Vakili
We investigate the impact of two social liberalization policies and one anti-
liberalization policy on innovation. We find that liberalization policies increase
state-level patenting while the anti-liberalization policy reduces patenting.
Liberalization policies increase incumbent inventors’ patenting rate and the rate of
entrance into inventorship. The policies do not impact average innovation quality
but patents filed after liberalization are more likely to be built upon novel
technological recombinations and cite more recent prior art. The findings
highlight the impact of the social context on the rate and direction of innovation.
2 - Seeding The S-curve? The Role Of Early Adopters In Diffusion
Christian Catalini, Massachusetts Institute of Technology,
77 Massachusetts Avenue, Cambridge, MA, 00, United States,
Catherine Tucker
In October 2014, all 4,494 undergraduates at MIT were given access to Bitcoin. As
a unique feature of the experiment, students who would generally adopt only
mature and established technologies were placed into an early-adopter condition:
suddenly they had to decide to either learn more about Bitcoin and try to use it,
to bet on its volatile future by holding it, or to simply cash out and convert it into
US dollars. In this paper, we explore the students’ response to the digital currency,
and in particular how randomly delaying different types of students relative to
their peers affected their adoption decision. Our results point to a novel
mechanism through which early-adopters may influence diffusion.
3 - The AQ Model Of Probabilistic Judgment And Patterns Of Risk
And Return
Ulrik W. Nash, University of Southern Denmark, Odense,
Denmark,
We have long known that uncertainty about the world is crucial for
understanding profit. Moreover, there are reasons to suspect that differences in
the degree of uncertainty that firms perceive about the same situation may be a
fundamental cause of their performance heterogeneity. Here I introduce the AQ
model of probabilistic judgment and use it to predict the flow of money between
firms in factor markets. Heterogeneous distributions of profit that capture
observed patterns of risk and return summarize these flows.
4 - The Impact Of Learning And Overconfidence On Entrepreneurial
Entry And Exit
Hart E Posen, University of Wisconsin-Madison, Madison, WI,
53705, United States, John Chen,
David Croson, Daniel Elfenbein
Research examining entrepreneurial entry and performance highlights the
phenomena of excess entry and delayed exit. We develop a computational model
wherein agents learn from experience both pre- and post-entry making
endogenous entry and exit decisions. The model suggests excess entry and
delayed exit result from a common process — entrepreneurs’ ongoing efforts to
learn about their prospects and act according to their updated information. One
interesting result is that a population of unbiased entrants exhibits beliefs that
overestimate their true success probabilities, providing a rational explanation for
empirical patterns typically explained by individuals’ biases.
MB25
110A-MCC
Project Management Methodologies
Invited: Project Management and Scheduling
Invited Session
Chair: Yael S Grushka-Cockayne, Darden School of Business,
Charlottesville, VA, United States,
1 - Multifarious Project Management Methodologies
Vered Holzmann, Tel Aviv University, ,
Yael S Grushka-Cockayne, Hamutal Weisz, Daniel Zitter
In order for a project manager to deliver an effective and efficient solution to the
customer’s needs, an adaptable methodology for the planning and execution of
the project is to be adopted. Following the paradigm that “one size does not fit
all”, meaning each project has different characteristics that should be taken into
consideration when selecting the appropriate management method for a project,
this study suggests the exploitation of several methodologies in a project to
effectively and efficiently delivery of a successful product. The conceptual
framework is based on an integration of the waterfall, agile, and TOC methods to
be applied in complex projects derived from specific attributes.
2 - Limiting Financial Risk From Catastrophic Events In
Project Management.
Peter D Simonson, North Dakota State University, Fargo, ND,
United States, , Joseph Szmerekovsky
For a project manager, planning for uncertainty is a staple of their jobs and
education. But the uncertainty associated with a catastrophic event presents
difficulties not easily controlled with traditional methods of risk management.
This dissertation proposes to bring and modify the concept of a project schedule as
a bounded “Alphorn of Uncertainty” to the problem of how to reduce the risk of a
catastrophic event wreaking havoc on a project and, by extension. The
dissertation will present new mathematical models underpinning the methods
proposed to reduce risk as well as simulations to demonstrate the accuracy of
those models.
MB24