Download free Advantage Offered By Computer-Based Clinical Decision Support Tools software11/9/2016 Decision Support Systems (DSS) Information Systems Analysis. Topic: Decision Support Systems. Randall E. Louw. 10. University of. Missouri St. Vicky. Sauter. Fall 2. Introduction. 1. 1 A brief history of Decision Support Systems Decision Support Systems have evolved over the past three. MIS theory developments during the 1. Decision Support. Systems evolve into more elaborate computer- based systems that supported. By. early 1. 98. 0’s Decision Support Systems enjoyed more interests from academics and. Decision Support Systems was greatly expanded by the end of.
It was only during the 1. Decision Support Systems and more complex systems, which incorporated, advanced. As many organizations started to upgrade their. Decision Support Systems. The rapid expansion of the. Internet provided additional opportunities for the scope of Decision Support. Systems and consequently many new innovative systems such as OLAP and other. Systems definition and description. According to. Sprague and Watson (1. They define DSS broadly as. DSS provides. varying analysis without much programming effort and is usually directed towards. I strongly believe that patient safety and quality care can be greatly enhanced by the tools offered by computer-assisted decision support. Decision Support and Executive Information Systems 10.1 When Should you use the Decision Support Approach? Decision support systems (DSS). Through it to find sound information to support clinical decisions was difficult. A key advantage to systematic literature searching is that important information regarding. Managers main uses for a DSS includes searching. Users. often search for correlations between data without rewriting the underlying MIS. DSS allows graphic capabilities, which not only. Consequently, DSS supports both tactical and strategic. DSS varies in. scope – some are intended for multiple users (more common nowadays) and other. Although DSS. can be dissected into many different components, I will mainly concentrate on a. The fist. important aspect of DSS is that they provide information which are used in the. The emphasis here is not on the quantity of. There are multiple factors that qualify. It is a common. notion that information can be (and often is. Managers often postpone. They falsely rely. This. curvilinear relationship between information and comprehending information is. The graph suggests that the quantity of. Thus, the user should try to. Only as the user gains “new insights” and “breakthrough. To. make a good decision, one needs not only information about the specific. In other words, one needs a. Better understanding enables. This. not only decreases the complexity of the decision process, but also decreases. A successful business makes good decisions. These abstractions are then re- usable for making. Unlike information, which often relates only to. Many types of Decision Support. Systems. As mentioned. DSS can be classified in many different categories. Amongst the common. Data driven. has file drawer systems, data analysis systems, analysis information systems. Model driven. underlying model that drives the DSS can come from various disciplines or areas. With model drive DSS the. These. systems usually are not data intensive and consequently are not linked to very. Knowledge. systems provide recommendation and/or suggestion schemes which aids the user in. Knowledge driven DSS. They focuses on knowledge and recommends actions to managers. Moreover, it has special. It also access documents such as company. They are a special type of hybrid DSS that emphasizes. GDSS. supports electronic communication, scheduling, document sharing and other group. See table 1 in appendix section for a. Decision Support Systems framework)2. Inter- and Intra- organization. DSSThese systems are driven by the rapid growth of. Internet and other networking technologies such as broadband WAN’s, LAN’s, WIP. Inter- organization DSS are used to serve companies stakeholders (customers. DSS are more directed towards. The latter, because of. New breeds of. Systems, which are combinations units using aspects of more than one different. DSS. A very popular example is Web based DSS, which can be driven by a. Web- based DSS are computerized systems that delivers. Their definition and. Many other Decision Support Systems. Keeping the. various distinctions and classifications of DSS in mind, a DSS should be. The. dominant technology component or model underlying the system. LAN, web- based) An example: a management team might want a. DSS3. Database Management. DBMS)Managerial. Decision Systems. DB supporting. it. Users can manipulate data temporarily and needs special access and/or. DBSIn order to. provide the DSS with the right data, there should be a structure in place for. The. database is the “feeder” of the DSS and good database design is thus crucial in. DSS. Database design and management is. Consequently our discussion on databases will parsimonious at best. Relational. databases are in most cases the system of choice when it comes to designing a. DSS. This is primarily because of the flexibility associated with a relational. DB but also because it allows normalization (reduction in data duplication). The ability of. these databases to identify relationships between entities makes information. DSS. Some hierarchical and. DB platform overshadows the maintenance. DSS, underscoring the need for a. DB design. Because the. DSS its. structure and design should be carefully evaluated and implemented with due. There seems to be a general trend in. DSS, using a task specific. DSS around a thin client fat server environment. This is perhaps a further. Internet is. the most comprehensive network of thousands of interconnected databases and web. The Data Warehouse. DB generally provides current information about the. The data. warehouse fills this gap by capturing operational data and presenting it in a. DB used in the DSS. Thus the data warehouse. DB coexists to provide synergistic outcomes which supports information. DSS superimposed on the systems platform. Development in the. DB has brought about the use of “intelligent agents” which assists in. DB and data warehouses. Management. requirements of the Decision Support Systems. Information. uses and requirements differ at each managerial level. Higher. managerial levels have a greater reliance on external environmental information. Top. management commonly use information to make decisions about long term planning. Gore et. al, 1. 98. Also at top- level decision- making, conjoint analysis are. However, reports and analyses generated by lower. DSS supports the lower management just as much. In short, the information needs for. See appendix 1 for a more elaborate. Moreover, because the DSS are employed to improve. Recorded experience. MIS called. knowledge management. Keeping these. benefits in mind, it is not surprising that Decision Support Systems has enjoyed. Evaluation. of an appropriate Decision Support Systems. The DSS design. and development process involves input from every aspect of the business process. Business executives and managers. DSS so that they can more accurately. DSS. 3 The following steps can be. DSS: 1. Firm’s. strategic focus First and. DSS needs to clearly outline its. This will in turn provide the. DSS and will give a. This team will perform an. A multi- disciplinary team approach is necessary with feedback. DSS. Team members should be made responsible to communicate with other members. DSS and they should try. It is. crucial to elicit “buy- in” from all individuals who will use or will be affected. Decision Support Systems since non- compliance by some parties might. Also clarify issues such. Service contracto. Price of packageo. Time frame of deliverableso. Review vendors offerings and compare with other. Vendors involvement with implementation/deployment of. Training of users and after implementation support. However, DSS can. DSS are there to. Managers can sometimes be over- optimistic. DSS and develop a unrealistic reliance on the system. Power, C. J; Caveat. Emperor). 1. 3 Also, if managers. When managers have preconceived notions and. DSS can magnify the harm. This area of analysis, design and implementation should. However. Decision Support Systems remains a tool that can provide firms with a. Decision. Support Systems are the rule rather than the exception. As. technologies evolve, Decision Support Systems will change and adapt to the. Information Systems. Appendix 1: Uses and requirements of information. Management. Level. Information. Use. Information. Requirements. Top. Management. 1. Publishers. M (1. Management Decision Systems; Boston, MA; Division of. Research; GSB Harvard University. H (1. 98. 2); Developing computer solutions for your business problems. Englewood, NJ; Prentice- Hall Inc. Decision support system - Wikipedia. A decision support system (DSS) is a computer- based information system that supports business or organizational decision- making activities. DSSs serve the management, operations, and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance. Unstructured and Semi- Structured decision problems. Decision support systems can be either fully computerized, human- powered or a combination of both. While academics have perceived DSS as a tool to support decision making process, DSS users see DSS as a tool to facilitate organizational processes. A properly designed DSS is an interactive software- based system intended to help decision makers compile useful information from a combination of raw data, documents, and personal knowledge, or business models to identify and solve problems and make decisions. Typical information that a decision support application might gather and present includes: inventories of information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),comparative sales figures between one period and the next,projected revenue figures based on product sales assumptions. DSSs are often contrasted with more automated decision- making systems known as Decision Management Systems. In the middle and late 1. EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model- oriented DSS. According to Sol (1. In the 1. 97. 0s DSS was described as . In the late 1. 97. DSS movement started focusing on . In the 1. 98. 0s DSS should provide systems . This decision support system is credited with significantly reducing travel delays by aiding the management of ground operations at various airports, beginning with O'Hare International Airport in Chicago and Stapleton Airport in Denver. Colorado. As the turn of the millennium approached, new Web- based analytical applications were introduced. The advent of more and better reporting technologies has seen DSS start to emerge as a critical component of management design. Examples of this can be seen in the intense amount of discussion of DSS in the education environment. DSS also have a weak connection to the user interface paradigm of hypertext. Both the University of Vermont. PROMIS system (for medical decision making) and the Carnegie Mellon ZOG/KMS system (for military and business decision making) were decision support systems which also were major breakthroughs in user interface research. Furthermore, although hypertext researchers have generally been concerned with information overload, certain researchers, notably Douglas Engelbart, have been focused on decision makers in particular. Taxonomies. A passive DSS is a system that aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions. An active DSS can bring out such decision suggestions or solutions. A cooperative DSS allows the decision maker (or its advisor) to modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation. The system again improves, completes, and refines the suggestions of the decision maker and sends them back to them for validation. The whole process then starts again, until a consolidated solution is generated. Another taxonomy for DSS has been created by Daniel Power. Using the mode of assistance as the criterion, Power differentiates communication- driven DSS, data- driven DSS, document- driven DSS, knowledge- driven DSS, and model- driven DSS. Model- driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; they are not necessarily data- intensive. Dicodess is an example of an open source model- driven DSS generator. An enterprise- wide DSS is linked to large data warehouses and serves many managers in the company. A desktop, single- user DSS is a small system that runs on an individual manager's PC. Components. Such a framework includes people, technology, and the development approach. This is the part of the application that allows the decision maker to make decisions in a particular problem area. The user can act upon that particular problem. Generator contains Hardware/software environment that allows people to easily develop specific DSS applications. This level makes use of case tools or systems such as Crystal, Analytica and i. Think. Tools include lower level hardware/software. DSS generators including special languages, function libraries and linking modules. An iterative developmental approach allows for the DSS to be changed and redesigned at various intervals. Once the system is designed, it will need to be tested and revised where necessary for the desired outcome. Classification. Not every DSS fits neatly into one of the categories, but may be a mix of two or more architectures. Holsapple and Whinston. It is a hybrid system that includes two or more of the five basic structures described by Holsapple and Whinston. There are four stages in the evolution of clinical decision support system (CDSS). The primitive version is standalone which does not support integration. The second generation of CDSS supports integration with other medical systems. The third generation is standard- based while the fourth is service model- based. Executive dashboard and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources. Due to DSS all the information from any organization is represented in the form of charts, graphs i. For example, one of the DSS applications is the management and development of complex anti- terrorism systems. For example, the DSSAT4 package. Precision agriculture seeks to tailor decisions to particular portions of farm fields. There are, however, many constraints to the successful adoption on DSS in agriculture. All aspects of Forest management, from log transportation, harvest scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. In this context the consideration of single or multiple management objectives related to the provision of goods and services that traded or non- traded and often subject to resource constraints and decision problems. The Community of Practice of Forest Management Decision Support Systems provides a large repository on knowledge about the construction and use of forest Decision Support Systems. A problem faced by any railroad is worn- out or defective rails, which can result in hundreds of derailments per year. Under a DSS, CN managed to decrease the incidence of derailments at the same time other companies were experiencing an increase. See also. Sloan School of Management. Sprague, R; (1. 98. Taylor, James (2. Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics. Boston MA: Pearson Education. ISBN 9. 78- 0- 1. Decision support systems: an organizational perspective. Reading, Mass., Addison- Wesley Pub. ISBN 0- 2. 01- 0. Henk G. Expert systems and artificial intelligence in decision support systems: proceedings of the Second Mini Euroconference, Lunteren, The Netherlands, 1. Aronson; Ting- Peng Liang (2. Decision Support Systems and Intelligent Systems. Neues anwenderfreundliches Konzept der Entscheidungsunterst. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 1. Power, D. Decision support systems: concepts and resources for managers. Westport, Conn., Quorum Books.^Stanhope, P. Get in the Groove: building tools and peer- to- peer solutions with the Groove platform. New York, Hungry Minds^Gachet, A. Building Model- Driven Decision Support Systems with Dicodess. Zurich, VDF.^Power, D. The On- Line Executive Journal for Data- Intensive Decision Support 1(3).^ ab. Sprague, R. Building effective decision support systems. ISBN 0- 1. 3- 0. 86. Haag, Cummings, . Mc. Graw- Hill Ryerson Limited: 1. ISBN 0- 0. 7- 2. 81. Marakas, G. Decision support systems in the twenty- first century. Upper Saddle River, N. J., Prentice Hall.^ ab. Holsapple, C. W., and A. Decision Support Systems: A Knowledge- Based Approach. Paul: West Publishing. ISBN 0- 3. 24- 0. Hackathorn, R. Handbook on Decision Support Systems. Berlin: Springer Verlag. Journal of Biomedical Informatics. Decision Support Systems. Why has the uptake of Decision Support Systems been so poor? In: Crop- soil simulation models in developing countries. Matthews and William Stephens). Wallingford: CABI.^Community of Practice Forest Management Decision Support Systems, http: //www. Further reading. Decision Support Systems - A Bibliography 1. Borges, J. G, Nordstr. The experience and the expertise world- wide. Dept of Forest Resource Management, Swedish University of Agricultural Sciences. Sweden. Delic, K. A., Douillet,L. Risk Assessment and Management, Vol. Gomes da Silva, Carlos; Cl. European Journal of Operational Research. Ender, Gabriela; E- Book (2. Download http: //www. Open. Space- Online. Computers & Operations Research. Jintrawet, Attachai (1. A Decision Support System for Rapid Assessment of Lowland Rice- based Cropping Alternatives in Thailand. Agricultural Systems 4. Matsatsinis, N. F. Siskos (2. 00. 2), Intelligent support systems for marketing decisions, Kluwer Academic Publishers. Omid A. Sianaki, O Hussain, T Dillon, AR Tabesh - . Web- based and model- driven decision support systems: concepts and issues. Decision Support Systems., Nov. Vol. 4. 1 Issue 1, p. Sauter, V. Decision support systems: an applied managerial approach. New York, John Wiley. Silver, M. Systems that support decision makers: description and analysis. Chichester ; New York, Wiley. Sprague, R. Decision support systems: putting theory into practice. Englewood Clifts, N. J., Prentice Hall.
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