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Book ChapterDOI: 10.1007/978-1-4419-1153-7_609

Health Care Management

01 Jan 2013-pp 679-684
About: The article was published on 2013-01-01. It has received 1 citation(s) till now.

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Journal ArticleDOI: 10.1017/DMP.2015.55
Abstract: Given the importance of evaluation in an organization and considering the objectives and missions of military hospitals, we aimed to extract some indexes (in addition to common evaluation indexes) for use in evaluating military hospitals. This was an applied-type qualitative study. The participants were 15 health experts who were first chosen by a purposeful sampling, which was then continued by theoretical sampling. The data obtained were analyzed by using MAXQDA11 software and the content analysis method. After 290 obtained codes were analyzed, 17 indexes in 6 domains were extracted, including capacity development for crisis periods, equipment and facilities, training and research, passive defense, treatment, and services, from which 8 indexes were related to capacity development for crisis periods and equipment and facilities (4 indexes each), 3 indexes were related to services, and 6 indexes were related to training and research, passive defense, and treatment (2 indexes each). The results of the present research, as a supplement to current evaluation methods such as accreditation, can be used for the comprehensive evaluation of military hospitals.

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3 Citations

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Open accessJournal ArticleDOI: 10.1016/0377-2217(78)90138-8
Abstract: A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs. A scalar measure of the efficiency of each participating unit is thereby provided, along with methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs. Equivalences are established to ordinary linear programming models for effecting computations. The duals to these linear programming models provide a new way for estimating extremal relations from observational data. Connections between engineering and economic approaches to efficiency are delineated along with new interpretations and ways of using them in evaluating and controlling managerial behavior in public programs.

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22,924 Citations


Journal ArticleDOI: 10.1287/MNSC.27.6.668
01 Jun 1981-Management Science
Abstract: A model for measuring the efficiency of Decision Making Units =DMU's is presented, along with related methods of implementation and interpretation. The term DMU is intended to emphasize an orientation toward managed entities in the public and/or not-for-profit sectors. The proposed approach is applicable to the multiple outputs and designated inputs which are common for such DMU's. A priori weights, or imputations of a market-price-value character are not required. A mathematical programming model applied to observational data provides a new way of obtaining empirical estimates of extrernal relations-such as the production functions and/or efficient production possibility surfaces that are a cornerstone of modern economics. The resulting extremal relations are used to envelop the observations in order to obtain the efficiency measures that form a focus of the present paper. An illustrative application utilizes data from Program Follow Through =PFT. A large scale social experiment in public school education, it was designed to test the advantages of PFT relative to designated NFT =Non-Follow Through counterparts in various parts of the U.S. It is possible that the resulting observations are contaminated with inefficiencies due to the way DMU's were managed en route to assessing whether PFT as a program is superior to its NFT alternative. A further mathematical programming development is therefore undertaken to distinguish between "management efficiency" and "program efficiency." This is done via procedures referred to as Data Envelopment Analysis =DEA in which one first obtains boundaries or envelopes from the data for PFT and NFT, respectively. These boundaries provide a basis for estimating the relative efficiency of the DMU's operating under these programs. These DMU's are then adjusted up to their program boundaries, after which a new inter-program envelope is obtained for evaluating the PFT and NFT programs with the estimated managerial inefficiencies eliminated. The claimed superiority of PFT fails to be validated in this illustrative application. Our DEA approach, however, suggests the additional possibility of new approaches obtained from PFT-NFT combinations which may be superior to either of them alone. Validating such possibilities cannot be done only by statistical or other modelings. It requires recourse to field studies, including audits e.g., of a U.S. General Accounting Office variety and therefore ways in which the results of a DEA approach may be used to guide such further studies or audits are also indicated.

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1,462 Citations



Open accessJournal ArticleDOI: 10.1287/OPRE.43.4.570
Abstract: Two medical applications of linear programming are described in this paper. Specifically, linear programming-based machine learning techniques are used to increase the accuracy and objectivity of breast cancer diagnosis and prognosis. The first application to breast cancer diagnosis utilizes characteristics of individual cells, obtained from a minimally invasive fine needle aspirate, to discriminate benign from malignant breast lumps. This allows an accurate diagnosis without the need for a surgical biopsy. The diagnostic system in current operation at University of Wisconsin Hospitals was trained on samples from 569 patients and has had 100% chronological correctness in diagnosing 131 subsequent patients. The second application, recently put into clinical practice, is a method that constructs a surface that predicts when breast cancer is likely to recur in patients that have had their cancers excised. This gives the physician and the patient better information with which to plan treatment, and may elimin...

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Topics: Breast lumps (65%), Breast cancer (61%)

730 Citations


Journal ArticleDOI: 10.1016/0004-3702(78)90014-0
Peter Szolovits1, Stephen G. Pauker2Institutions (2)
Abstract: Medical decision making can be viewed along a spectrum, with categorical (or deterministic) reasoning at one extreme and probabilistic (or evidential) reasoning at the other. In this paper we examine the flowchart as the prototype of categorical reasoning and decision analysis as the prototype of probabilistic reasoning. Within this context we compare PIP, INTERNIST, CASNET, and MYCIN—four of the present programs which apply the techniques of artificial intelligence to medicine. Although these systems can exhibit impressive expert-like behavior, we believe that none of them is yet capable of truly expert reasoning. We suggest that a program which can demonstrate expertise in the area of medical consultation will have to use a judicious combination of categorical and probabilistic reasoning—the former to establish a sufficiently narrow context and the latter to make comparisons among hypotheses and eventually to recommend therapy.

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Topics: Deductive reasoning (66%), Model-based reasoning (66%), Reasoning system (64%) ...read more

444 Citations


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