Computer Aided Decision Support Tool for Rectal Cancer TNM Staging Using MRI

Abstract This article presents a Computer Aided Facility Management informative system which can output Key Performance Indicators and quantitative parameters about the analysed healthcare facility. The designed system is a self-sufficient application able to manage and analyse digital plans of hospital buildings with no need of third-party plugins or licenses. The system maps hospital’s inner organisation, destinations of use and environmental comforts giving quantitative, qualitative and graphical reports. The core database is linked to other existing hospital databases, so that the system can act as a central control cockpit. Outputs can be used by top-management and decisional staff as a decision-support tool in order to improve hospital’s structure and organisation and to reduce the major workflow risks. Furthermore, many plug-ins and modules have been developed through the years which can be easily linked to the main application thanks to its REST architecture, and which contribute to a complete analysis and management of the healthcare facilities.

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corRECTreatment: A web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree

Applied Clinical Informatics ◽

10.4338/aci-2014-10-ra-0087 ◽

2015 ◽

Vol 06 (01) ◽

pp. 56-74 ◽

Cited By ~ 7

Author(s):

O. Dicle ◽

S. Sökmen ◽

C.C. Çelikoğlu ◽

A. Suner ◽

G. Karakülah

Keyword(s):

Rectal Cancer ◽

Decision Support ◽

Cancer Treatment ◽

Treatment Options ◽

Decision Support Tool ◽

Analytic Hierarchy ◽

Web Based ◽

Support Tool ◽

Rectal Cancer Treatment ◽

The Analytic Hierarchy Process

SummaryBackground: The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians’ decision making.Objective: The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer.Methods: The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set.Results: In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step.Conclusions: The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.Citation: Suner A, Karakülah G, Dicle O, Sökmen S, Çelikoglu CC. corRECTreatment: A web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree. Appl Clin Inf 2015; 6: 56–74http://dx.doi.org/10.4338/ACI-2014-10-RA-0087

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Computer Aided Facility Management (CAFM) as a New Branch of Decision Making Support Technologies in the Field of Facility Management

Encyclopedia of Decision Making and Decision Support Technologies ◽

10.4018/978-1-59904-843-7.ch010 ◽

2011 ◽

pp. 84-92 ◽

Cited By ~ 1

Author(s):

Thomas Madritsch ◽

Michael May ◽

Herwig Ostermann ◽

Roland Staudinger

Keyword(s):

Decision Making ◽

Decision Support ◽

Decision Support Tool ◽

Facility Management ◽

Point Of View ◽

Information And Communications Technology ◽

Industrialized Countries ◽

Support Tool ◽

Computer Aided ◽

Recent Developments

Nowadays facility management (FM) and real estate activities contribute to about 5-10% of the gross domestic product (GDP) of advanced industrialized countries. For example the total value of FM activity including support services is about 8.2% UK GDP (Harris, 2002). Computer aided facility management (CAFM) software is a new class of information and communications technology (ICT) tools to support management in the preparation of relevant data in the decision making process especially in the area of illustration, evaluation, and control of relevant FM structures and processes. Recently, CAFM tools are developing from simple information systems to multifunctional decision support systems (DSSs) for private as well as public organizations. Until now however, little attention has been given to this relevant change in business and academic communities. At the same time numerous software systems with various systematic approaches, functions, and varying success have been established on the market. Despite the multitude of suppliers and users in the different branches uncertainty concerning the procedures and achievable effects still prevails. This is closely related to the lack of well-documented, transparent, and successful case studies. In addition, little is known about how CAFM can be implemented successfully and the factors leading to its sustainable success. From an economic point of view it is very important to support this process in order to avoid wrong decisions and unnecessary investment. In particular, implementation strategies and formulae for success are of great interest (May, 2002). The purpose of this chapter is to describe the relevance of CAFM as a decision support tool in the field of FM. The authors will illustrate the recent developments and market demands of FM and CAFM. The main part will provide an overview on the basic concept as well as building management, for example, CAFM and give detailed insight into the topic and how CAFM may serve as a DSS from an organizational perspective. The next part will introduce some examples of good practices. The chapter closes with an overview of future developments, trends, and research opportunities of CAFM as a decision support tool.

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Demonstration of the Surface Management System: An air traffic control decision support tool

PsycEXTRA Dataset ◽

10.1037/e577012012-001 ◽

2003 ◽

Author(s):

James M. Hitt ◽

Christopher J. Brinton ◽

Deborah H. Walton

Keyword(s):

Decision Support ◽

Traffic Control ◽

Air Traffic Control ◽

Management System ◽

Decision Support Tool ◽

Air Traffic ◽

Support Tool ◽

Control Decision

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Path Associative Networks and Bayesian Belief Networks A career decision support tool

PsycEXTRA Dataset ◽

10.1037/e683322011-113 ◽

2000 ◽

Author(s):

Clarence C. Rohrbaugh

Keyword(s):

Decision Support ◽

Decision Support Tool ◽

Career Decision ◽

Bayesian Belief Networks ◽

Belief Networks ◽

Associative Networks ◽

Support Tool

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Assigning Value to Clinical Information – A Major Limiting Factor in the Implementation of Decision-Support Systems

Methods of Information in Medicine ◽

10.1055/s-0038-1634631 ◽

1996 ◽

Vol 35 (01) ◽

pp. 1-4 ◽

Cited By ~ 5

Author(s):

F. T. de Dombal

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Support Systems ◽

Clinical Information ◽

Limiting Factor ◽

Major Difficulty ◽

Computer Aided ◽

Computer Aided Decision

AbstractThis paper deals with a major difficulty and potential limiting factor in present-day decision support - that of assigning precise value to an item (or group of items) of clinical information. Historical determinist descriptive thinking has been challenged by current concepts of uncertainty and probability, but neither view is adequate. Four equations are proposed outlining factors which affect the value of clinical information, which explain some previously puzzling observations concerning decision support. It is suggested that without accommodation of these concepts, computer-aided decision support cannot progress further, but if they can be accommodated in future programs, the implications may be profound.

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