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<prism:eIssn>1526-551X</prism:eIssn>
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<title><![CDATA[Introduction: Applications of Management Science and Operations Research Models and Methods to Problems in Health Care]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/183?rss=1</link>
<description><![CDATA[
<p>Over the last decade or so, the health-care industry in the United States, Canada, and around the world has started to solve important problems using traditional management science and operations research (MS/OR) models and methods, such as mathematical programming, simulation, queueing theory, and decision analysis. In recent years, the results of modeling efforts have been significant and have begun to positively affect the delivery of health care. This special issue documents eight successful applications of MS/OR methods to solving actual problems in health care.</p>
]]></description>
<dc:creator><![CDATA[Carter, M. W., Golden, B. L., Wasil, E. A.]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1090.0445</dc:identifier>
<dc:title><![CDATA[Introduction: Applications of Management Science and Operations Research Models and Methods to Problems in Health Care]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>185</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>183</prism:startingPage>
<prism:section>Articles</prism:section>
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<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/186?rss=1">
<title><![CDATA[Scheduling Medical Residents at Boston University School of Medicine]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/186?rss=1</link>
<description><![CDATA[
<p>The chief residents in the psychiatry program at Boston University School of Medicine (BUSM) must construct a schedule that simultaneously assigns residents to five types of call shifts, spanning three different hospitals, over a 365-day planning horizon. We show how user expertise and heuristic approaches alone fail to find acceptable solutions to this complex combinatorial problem; likewise, mathematical programming techniques alone are inadequate, largely because they lack a clearly definable objective function. However, by combining both approaches, we were able to find high-quality solutions in a very short time. The resulting schedule, which BUSM uses currently, has yielded substantial benefits; the solution quality has improved, and the effort required to develop the solution has been reduced.</p>
]]></description>
<dc:creator><![CDATA[Cohn, A., Root, S., Kymissis, C., Esses, J., Westmoreland, N.]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1080.0369</dc:identifier>
<dc:title><![CDATA[Scheduling Medical Residents at Boston University School of Medicine]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>195</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>186</prism:startingPage>
<prism:section>Articles</prism:section>
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<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/196?rss=1">
<title><![CDATA[Fraser Health Uses Mathematical Programming to Plan Its Inpatient Hospital Network]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/196?rss=1</link>
<description><![CDATA[
<p>Fraser Health (FH), a British Columbia health authority that serves more than 1.5 million people, must increase its acute care capacity significantly over the next 15 years because of anticipated population growth and aging. The distribution of the projected capacity over each of FH's 12 hospitals depends on the mix of clinical services to be provided at each site, a decision guided by population needs and clinical practices. We present a multiperiod mathematical programming model that we developed to provide options for configuring the system, specifically the location of clinical services and allocation of bed capacity across the hospitals. The decisions in the model are based on population access, critical mass standards, and clinical adjacencies. We describe its application in a long-term planning initiative that FH undertook. Extensive scenario analyses allowed administrators, clinicians, and planners to test multiple system configurations, gain a robust understanding of the trade-offs between these configurations, and formalize the planning process for acute care services.</p>
]]></description>
<dc:creator><![CDATA[Santibanez, P., Bekiou, G., Yip, K.]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1080.0405</dc:identifier>
<dc:title><![CDATA[Fraser Health Uses Mathematical Programming to Plan Its Inpatient Hospital Network]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>208</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>196</prism:startingPage>
<prism:section>Articles</prism:section>
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<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/209?rss=1">
<title><![CDATA[Helping Men Decide About Scheduling a Prostate Cancer Screening Exam]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/209?rss=1</link>
<description><![CDATA[
<p>This paper reports on the application of decision counseling based on the analytic hierarchy process (AHP) to assist men in deciding whether or not to schedule a prostate cancer screening exam. The study is based on data that we collected from 129 men enrolled in the intervention arm of two randomized, controlled trials. First, we administered a baseline survey to gather data on participant sociodemographic characteristics and perceptions about prostate cancer and screening. Subsequently, a health educator conducted a session with each man to review an informational booklet on prostate cancer screening. Then, the health educator used an AHP-based decision process that identified the most important factors (both pro or con) that might influence prostate cancer screening preferences, clarified preferences related to scheduling a prostate screening exam, and elicited a scheduling decision. We performed univariable and multivariable logistic regression analyses to identify variables associated with the decision. Of the 129 men enrolled, 85 men (66 percent) decided to schedule a prostate cancer screening exam. Multivariable analyses showed that preference strengths and favorable perceptions of prostate cancer screening predicted the decision to screen.</p>
]]></description>
<dc:creator><![CDATA[Liberatore, M., Nydick, R., Daskalakis, C., Kunkel, E., Cocroft, J., Myers, R.]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1080.0395</dc:identifier>
<dc:title><![CDATA[Helping Men Decide About Scheduling a Prostate Cancer Screening Exam]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>217</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>209</prism:startingPage>
<prism:section>Articles</prism:section>
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<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/218?rss=1">
<title><![CDATA[Decision Making with Prostate Cancer: A Multiple-Objective Model with Uncertainty]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/218?rss=1</link>
<description><![CDATA[
<p>A patient who has been diagnosed with prostate cancer must make a difficult treatment decision. The available alternatives have varying cure rates and probabilities of side effects over a period of many years. This paper describes a well-informed, objective, and personalized model for comparing treatments to help a patient make the best treatment decision.</p>
]]></description>
<dc:creator><![CDATA[Simon, J.]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1080.0406</dc:identifier>
<dc:title><![CDATA[Decision Making with Prostate Cancer: A Multiple-Objective Model with Uncertainty]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>227</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>218</prism:startingPage>
<prism:section>Articles</prism:section>
</item>

<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/228?rss=1">
<title><![CDATA[Pilot Model: Judging Alternate Modes of Dispensing Prophylaxis in Los Angeles County]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/228?rss=1</link>
<description><![CDATA[
<p>In large metropolitan areas such as Los Angeles County (LAC), public health departments must supplement their traditional methods of dispensing prophylaxis to the general public in their jurisdictions during mass biological and chemical events. Although many alternate modes of dispensing are being discussed, a quantitative model to compare their relative advantages and disadvantages does not exist. We use multicriteria decision analysis to help the LAC Department of Public Health (DPH) assess the trade-offs associated with these alternate modes of dispensing oral prophylaxis. We found that one of the most hyped methods (drive-thru dispensing) is not a good choice for LAC. Our baseline analysis shows that the top two alternatives, dispensing through commercial pharmacies and dispensing through the United States Postal Service, are equally effective. Either alternative would be acceptable; the final decision could be made on either legal or political grounds. Our analysis provided the DPH with much insight into to its decision problem. It could become part of the justification of proposals for alternate dispensing plans to the Centers for Disease Control and Prevention. In addition, the DPH has hired a consulting firm to pursue business participation, including commercial pharmacies, for dispensing mass prophylaxis.</p>
]]></description>
<dc:creator><![CDATA[Richter, A., Khan, S.]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1080.0427</dc:identifier>
<dc:title><![CDATA[Pilot Model: Judging Alternate Modes of Dispensing Prophylaxis in Los Angeles County]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>240</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>228</prism:startingPage>
<prism:section>Articles</prism:section>
</item>

<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/241?rss=1">
<title><![CDATA[Bringing Robustness to Patient Flow Management Through Optimized Patient Transports in Hospitals]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/241?rss=1</link>
<description><![CDATA[
<p>Intrahospital transports are often required for diagnostic or therapeutic reasons. Depending on the hospital layout, transportation between nursing wards and service units is either provided by ambulances or by trained personnel who accompany patients on foot. In many large German hospitals, the patient transport service is poorly managed and lacks work-flow coordination. This contributes to higher hospital costs (e.g., because a patient is not delivered to the operating room on time) and to patient inconvenience (e.g., because of long waiting times). We designed a computer-based planning system, Opti-<I>TRANS</I><sup>&copy;</sup>, that supports all phases of the transportation flow, including travel booking, dispatching transport requests, and monitoring and reporting trips in real time. The methodology, which we developed to solve the underlying optimization problem&mdash;a dynamic dial-a-ride problem with hospital-specific constraints, draws on fast heuristic methods to ensure the efficient and timely provision of transports. We illustrate the strong impact of Opti-<I>TRANS</I> on the daily performance of the patient transportation service of a large German hospital. The major benefits of Opti-<I>TRANS</I> include streamlined transportation processes and work flow, significant savings, and improved patient satisfaction. Moreover, it has contributed to increased awareness among hospital staff of the importance of implementing efficient logistics practices.</p>
]]></description>
<dc:creator><![CDATA[Hanne, T., Melo, T., Nickel, S.]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1080.0379</dc:identifier>
<dc:title><![CDATA[Bringing Robustness to Patient Flow Management Through Optimized Patient Transports in Hospitals]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>255</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>241</prism:startingPage>
<prism:section>Articles</prism:section>
</item>

<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/256?rss=1">
<title><![CDATA[A Simulation Model to Compare Strategies for the Reduction of Health-Care-Associated Infections]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/256?rss=1</link>
<description><![CDATA[
<p>Cook County Hospital, like many hospitals in the United States and worldwide, is pursuing a strategy to combat health-care&ndash;associated infections (HAIs). In the United States, approximately two million people are infected each year and over 100,000 die. In this paper, an interdisciplinary team of researchers from Georgia Tech and Cook County Hospital, with backgrounds in engineering, economics, and medicine, analyze the flow of pathogens. We combine infection rates and cost data to build a discrete-event simulation model to capture the complex relationships between hand hygiene, isolation, demand, and costs. We find that both hand hygiene and isolation policies have a significant impact on rates of infection, and that a complex interplay between factors exists. This suggests that a systems-level approach to infection-control procedures will be required to contain health-care&ndash;associated infections.</p>
]]></description>
<dc:creator><![CDATA[Hagtvedt, R., Griffin, P., Keskinocak, P., Roberts, R.]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1090.0435</dc:identifier>
<dc:title><![CDATA[A Simulation Model to Compare Strategies for the Reduction of Health-Care-Associated Infections]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>270</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>256</prism:startingPage>
<prism:section>Articles</prism:section>
</item>

<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/271?rss=1">
<title><![CDATA[Overbooking Increases Patient Access at East Carolina University's Student Health Services Clinic]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/271?rss=1</link>
<description><![CDATA[
<p>The health-care clinic presented in this study experienced significant numbers of patients who failed to arrive for their scheduled appointments (no-shows). The cost of reducing patient access at this clinic because of no-shows is estimated to exceed $400,000 annually. An interdisciplinary quality-improvement team developed a novel health-care overbooking model that includes the effects of employee burnout. This model estimates the nonlinear nature of the costs associated with medical-provider burnout caused by overbooked appointments that exceed clinic capacity. Several key East Carolina University clinical staff members had been skeptical about the value of overbooking. The model was instrumental in convincing them that implementing an overbooking process would benefit patients and the organization. The clinic, which subsequently implemented such a process, attributes a savings of $95,000 per semester to the initiative.</p>
]]></description>
<dc:creator><![CDATA[Kros, J., Dellana, S., West, D.]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1090.0437</dc:identifier>
<dc:title><![CDATA[Overbooking Increases Patient Access at East Carolina University's Student Health Services Clinic]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>287</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>271</prism:startingPage>
<prism:section>Articles</prism:section>
</item>

<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/288?rss=1">
<title><![CDATA[Practice Abstracts]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/288?rss=1</link>
<description><![CDATA[
<p>The goal of "Practice Abstracts" is to present interesting, topical, and novel applications of operations research methodology to a wide range of industrial applications. "Practice Abstracts" are intended to provide <I>Interfaces</I> readers with short (2&ndash;4 page) descriptions of the most relevant aspects of operations research-based projects, in a form that is accessible to academics and practitioners in other organizations. Contributions should be sent for evaluation to the editor of "Practice Abstracts," Brian T. Denton, Edward P. Fitts Department of Industrial and Systems Engineering, 376 Daniels Hall, North Carolina State University, 111 Lampe Drive, Campus Box 7906, Raleigh, North Carolina 27695-7906, bdenton@ncsu.edu.</p>
]]></description>
<dc:creator><![CDATA[Denton, B. T.]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1080.0426</dc:identifier>
<dc:title><![CDATA[Practice Abstracts]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>290</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>288</prism:startingPage>
<prism:section>Articles</prism:section>
</item>

<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/291?rss=1">
<title><![CDATA[Book Reviews]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/291?rss=1</link>
<description><![CDATA[
<p>In <I>Book Reviews</I>, we review an extensive and diverse range of books. They cover theory and applications in operations research, statistics, management science, econometrics, mathematics, computers, and information systems. In addition, we include books in other fields that emphasize technical applications. However, we do not review software. To submit a book for review, please send it to me at the above address. Although we cannot review all books because of space limitations, we do list all books that we receive. We commission all book reviews and do not accept unsolicited reviews. To become a reviewer, please send me your name, address, and specific areas of expertise. We encourage readers to suggest books for review or to ask publishers to send copies of such books.</p>
<p>The authors or editors of books we review in this issue are Frada Burstein, Clyde W. Holsapple, John W. Chinneck, Michael C. Ferris, Olvi L. Mangasarian, and Stephen J. Wright.</p>
]]></description>
<dc:creator><![CDATA[Lev, B.]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1080.0416</dc:identifier>
<dc:title><![CDATA[Book Reviews]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>297</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>291</prism:startingPage>
<prism:section>Articles</prism:section>
</item>

<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/298?rss=1">
<title><![CDATA[Contributors]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/298?rss=1</link>
<description><![CDATA[
<p>No abstract available.</p>
]]></description>
<dc:creator><![CDATA[]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1090.0449</dc:identifier>
<dc:title><![CDATA[Contributors]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>301</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>298</prism:startingPage>
<prism:section>Articles</prism:section>
</item>

<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/302?rss=1">
<title><![CDATA[INFORMS MEETING CALENDAR]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/302?rss=1</link>
<description><![CDATA[
<p>No abstract available.</p>
]]></description>
<dc:creator><![CDATA[]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.39.3.302</dc:identifier>
<dc:title><![CDATA[INFORMS MEETING CALENDAR]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>302</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>302</prism:startingPage>
<prism:section>Articles</prism:section>
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<item rdf:about="http://interfaces.journal.informs.org/cgi/content/short/39/3/303?rss=1">
<title><![CDATA[Call for Papers--Interfaces Special Issue: Humanitarian Applications: Doing Good with Good OR]]></title>
<link>http://interfaces.journal.informs.org/cgi/content/short/39/3/303?rss=1</link>
<description><![CDATA[
<p>No abstract available.</p>
]]></description>
<dc:creator><![CDATA[Ergun, O., Keskinocak, P., Swann, J.]]></dc:creator>
<dc:date>2009-06-09</dc:date>
<dc:identifier>info:doi/10.1287/inte.1090.0451</dc:identifier>
<dc:title><![CDATA[Call for Papers--Interfaces Special Issue: Humanitarian Applications: Doing Good with Good OR]]></dc:title>
<dc:publisher>INFORMS</dc:publisher>
<prism:number>3</prism:number>
<prism:volume>39</prism:volume>
<prism:endingPage>303</prism:endingPage>
<prism:publicationDate>2009-05-01</prism:publicationDate>
<prism:startingPage>303</prism:startingPage>
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