scholarly journals A Framework for Developing Distributed Cooperative Decision Support Systems - Inception Phase

10.28945/2369 ◽  
2001 ◽  
Author(s):  
Alexandre Gachet
Keyword(s):  
Decision Support ◽  
Distributed Computing ◽  
Human Factors ◽  
Decision Support Systems ◽  
Development Process ◽  
Support Systems ◽  
Technical Factors

This paper describes the This paper describes the inception phase of the development process of a Framework for Developing Distributed Cooperative Decision Support Systems (DSSs). It analyzes the reasons why the broad use of DSSs has not occurred yet and makes propositions to improve this situation. It shows that, for the most part, modern distributed computing architectures could solve many of the presented issues. In the first section, this paper gives an overview of DSSs, based on definitions, history, taxonomies and DSS architectures. In the second section, it covers three categories of problems in the DSS area: human factors, conceptual factors and technical factors. To finish, it proposes possible solutions to these problems using concepts borrowed from new distributed computing architectures.

Clinical Decision Support Systems in Nursing

2010 ◽  
pp. 1056-1070
Author(s):  
Dawn Dowding ◽  
Rebecca Randell ◽  
Natasha Mitchell ◽  
Rebecca Foster ◽  
Valerie Lattimer ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Roles And Responsibilities ◽  
Technical Factors ◽  
The Impact

Increasingly, new and extended roles and responsibilities for nurses are being supported through the introduction of clinical decision support systems (CDSS). This chapter provides an overview of research on nurses’ use of CDSS, considers the impact of CDSS on nurse decision making and patient outcomes, and explores the socio-technical factors that impact the use of CDSS. In addition to summarising previous research, both on nurses’ use of CDSS and on use of CDSS more generally, the chapter presents the results of a multi-site case study that explored how CDSS are used by nurses in practice in a range of contexts. The chapter takes a socio-technical approach, exploring the barriers and facilitators to effective CDSS use at a level of the technology itself, the ways people work, and the organisations in which they operate.

Clinical Decision Support Systems in Nursing

2009 ◽  
pp. 26-40 ◽  
Author(s):  
Dawn Dowding ◽  
Rebecca Randell ◽  
Natasha Mitchell ◽  
Rebecca Foster ◽  
Valerie Lattimer ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Roles And Responsibilities ◽  
Technical Factors ◽  
The Impact

Increasingly, new and extended roles and responsibilities for nurses are being supported through the introduction of clinical decision support systems (CDSS). This chapter provides an overview of research on nurses’ use of CDSS, considers the impact of CDSS on nurse decision making and patient outcomes, and explores the socio-technical factors that impact the use of CDSS. In addition to summarising previous research, both on nurses’ use of CDSS and on use of CDSS more generally, the chapter presents the results of a multi-site case study that explored how CDSS are used by nurses in practice in a range of contexts. The chapter takes a socio-technical approach, exploring the barriers and facilitators to effective CDSS use at a level of the technology itself, the ways people work, and the organisations in which they operate.

Towards a Traceable Clinical Guidelines Application

2010 ◽  
Vol 49 (06) ◽  
pp. 571-580 ◽  
Author(s):  
E. Domínguez ◽  
M. Zapata ◽  
B. Pérez
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Human Resource ◽  
Clinical Guidelines ◽  
Development Process ◽  
Clinical Guideline ◽  
Support Systems ◽  
Tool Chain ◽  
Application Data ◽  
Resource Costs

Summary Objectives: The goal of this research is to provide an overall framework to enable modelbased development of clinical guideline-based decision support systems (GBDSSs). The automatically generated GBDSSs are aimed at providing guided support to the physician during the application of guidelines and automatically storing guideline application data for traceability purposes. Methods: The development process of a GBDSS for a guideline is based on modeldriven development (MDD) techniques which allow us to carry out such a process automatically, making development more agile and saving on human resource costs. We use UML Statecharts to represent the dynamics of guidelines and, based on this model, we use a MDD-based tool chain to generate the guideline-dependent components of each GBDSS in an automatic way. In particular, as for the traceability capabilities of each GBDSS, MDD techniques are combined with database schema mappings for metadata management in order to automatically generate the GBDSS-persistent component as one of the main contributions of this paper. Results: The complete framework has been implemented as an Eclipse plug-in named GBDSSGenerator which, starting from the statechart representing a guideline, allows the development process to be carried out automatically by only selecting different menu options the plug-in provides. We have successfully validated our overall approach by generating the GBDSS for different types of clinical guidelines, even for laboratory guidelines. Conclusions: The proposed framework allows the development of clinical guideline-based decision support systems in an automatic way making this process more agile and saving on human resource costs.

scholarly journals Metode Bayes dalam Penentuan Lokasi Strategis pada Sistem Pendukung Keputusan Pembangunan Perumahan

KREA-TIF ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 47
Author(s):  
Puspa Eosina ◽  
Muhamad Lutfi ◽  
Mohamad Ridwan
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Housing Development ◽  
Bayes Method ◽  
Management Level ◽  
Strategic Location ◽  
Technical Factors

<h1 align="center"><strong>Abstrak</strong></h1><p><em>Sistem Pendukung Keputusan (SPK) banyak dikembangkan di perusahaan-perusahaan atau organisasi untuk kebutuhan level manajemen. Nilai probalilitas untuk setiap lokasi pada </em><em>S</em><em>PK Penentuan Lokasi Strategis Pembangunan Perumahan yang dibangun pada penelitian ini, dihasilkan berdasarkan perhitungan menggunakan metode Bayes</em><em>. </em><em>Kriteria yang digunakan pada dalam perhitungan ada sembilan yaitu </em><em>kompatibilitas, sarana dan prasarana, faktor teknis, aksesibilitas, fleksibilitas, estetika, masyarakat, fasilitas pelayanan, biaya.</em><em> </em><em>Penerapan pada data lokasi yang digunakan, yaitu tiga data lokasi,lokasi  </em><em>Taman Argo Subur, lokasi Taman Kirana, dan lokasi Perumahan Kirana Cikarang, diperoleh nilai probabilitas tertinggi  rekomendasi sebesar 97% untuk lokasi Perumahan Kirana Cikarang</em>.<em></em></p><p align="center"><strong><em>Abstract</em></strong></p><p><em>Decision Support Systems </em><em>(DSS) </em><em>are widely developed in companies or organizations for </em><em>support</em><em> of management level. </em><em>The r</em><em>ecommended </em><em>of </em><em>location</em><em> from</em><em> </em><em>the </em><em>D</em><em>SS</em><em> Determining Strategic Location of Housing Development in this study, produced based on calculations using the Bayes method. </em><em>Were </em><em>used </em><em>of criteria</em><em>, namely compatibility, facilities and infrastructure, technical factors, accessibility, flexibility, aesthetics, society, service facilities, costs. </em><em>Three location have used in t</em><em>he application, namely </em><em> the Taman </em><em>Argo Subur, the </em><em>Taman</em><em> Kirana, and the </em><em>Perumahan </em><em>Kirana Cikarangng, obtained the highest probability value of 97% for the Kirana Cikarang Housing location.</em></p>

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