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.

Group Support Systems as Tools for HR Decision Making

2011 ◽  
pp. 1087-1095
Author(s):  
James Yao ◽  
John Wang
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Human Resource ◽  
Financial Management ◽  
Support Systems ◽  
Group Support Systems ◽  
Group Support ◽  
Soft Data

In the late 1960s, a new type of information system came about: model-oriented DSS or management decision systems. By the late 1970s, a number of researchers and companies had developed interactive information systems that used data and models to help managers analyze semistructured problems. These diverse systems were all called decision support systems (DSS). From those early days, it was recognized that DSS could be designed to support decision-makers at any level in an organization. DSS could support operations, financial management, and strategic decision making. Group decision support systems (GDSS) which aim at increasing some of the benefits of collaboration and reducing the inherent losses are interactive information technology-based environments that support concerted and coordinated group efforts toward completion of joint tasks (Dennis, George, Jessup, Nunamaker, & Vogel, 1998). The term group support systems (GSS) was coined at the start of the 1990s to replace the term GDSS. The reason for this is that the role of collaborative computing was expanded to more than just supporting decision making (Patrick & Garrick, 2006). For the avoidance of any ambiguities, the latter term shall be used in the discussion throughout this article. Human resources (HR) are rarely expected like other business functional areas to use synthesized data because HR groups have been primarily connected with transactional processing of getting data into the system and on record for reporting and historical purposes (Dudley, 2007). For them soft data do not win at the table; hard data do. Recently, many quantitative or qualitative techniques have been developed to support human resource management (HRM) activities, classified as management sciences/operations research, multiattribute utility theory, multicriteria decision making, ad hoc approaches, and human resource information systems (HRIS) (Byun, 2003). More importantly, HRIS can include the three systems of expert systems (ES), decision support systems (DSS), and executive information systems (EIS) in addition to transaction processing systems (TPS) and management information systems (MIS) which are conventionally accepted as an HRIS. As decision support systems, GSS are able to facilitate HR groups to gauge users’ opinions, readiness, satisfaction, and so forth, increase their HRM activity quality, and generate better group collaborations and decision makings with current or planned HRIS services. Consequently, GSS can help HR professionals exploit and make intelligent use of soft data and act tough in their decision-making process.

A Framework for Data and Mined Knowledge Interoperability in Clinical Decision Support Systems

2012 ◽  
pp. 84-110 ◽  
Author(s):  
Reza S. Kazemzadeh ◽  
Kamran Sartipi ◽  
Priya Jayaratna
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Guideline ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Healthcare Personnel

Due to reliance on human knowledge, the practice of medicine is subject to errors that endanger patients’ health and cause substantial financial loss to healthcare institutions. Computer-based decision support systems assist healthcare personnel to improve quality of clinical practice. Currently, most clinical guideline modeling languages represent decision-making knowledge in terms of basic logical expressions. In this paper, we focus on encoding, sharing, and using results of data mining analyses to influence decision making within Clinical Decision Support Systems. A knowledge management framework is proposed that addresses the issues of data and knowledge interoperability by adopting healthcare and data mining modeling standards. In a further step, data mining results are incorporated into a guideline-based decision support system. A prototype tool has been developed to provide an environment for clinical guideline authoring and execution. Also, three real world case studies have been presented, one of which is used as a running example throughout the paper.

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.

Group Support Systems as Tools for HR Decision Making

2009 ◽  
pp. 413-419
Author(s):  
James Yao ◽  
John Wang
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Human Resource ◽  
Financial Management ◽  
Support Systems ◽  
Group Support Systems ◽  
Group Support ◽  
Soft Data

In the late 1960s, a new type of information system came about: model-oriented DSS or management decision systems. By the late 1970s, a number of researchers and companies had developed interactive information systems that used data and models to help managers analyze semistructured problems. These diverse systems were all called decision support systems (DSS). From those early days, it was recognized that DSS could be designed to support decision-makers at any level in an organization. DSS could support operations, financial management, and strategic decision making. Group decision support systems (GDSS) which aim at increasing some of the benefits of collaboration and reducing the inherent losses are interactive information technology-based environments that support concerted and coordinated group efforts toward completion of joint tasks (Dennis, George, Jessup, Nunamaker, & Vogel, 1998). The term group support systems (GSS) was coined at the start of the 1990s to replace the term GDSS. The reason for this is that the role of collaborative computing was expanded to more than just supporting decision making (Patrick & Garrick, 2006). For the avoidance of any ambiguities, the latter term shall be used in the discussion throughout this article. Human resources (HR) are rarely expected like other business functional areas to use synthesized data because HR groups have been primarily connected with transactional processing of getting data into the system and on record for reporting and historical purposes (Dudley, 2007). For them soft data do not win at the table; hard data do. Recently, many quantitative or qualitative techniques have been developed to support human resource management (HRM) activities, classified as management sciences/operations research, multiattribute utility theory, multicriteria decision making, ad hoc approaches, and human resource information systems (HRIS) (Byun, 2003). More importantly, HRIS can include the three systems of expert systems (ES), decision support systems (DSS), and executive information systems (EIS) in addition to transaction processing systems (TPS) and management information systems (MIS) which are conventionally accepted as an HRIS. As decision support systems, GSS are able to facilitate HR groups to gauge users’ opinions, readiness, satisfaction, and so forth, increase their HRM activity quality, and generate better group collaborations and decision makings with current or planned HRIS services. Consequently, GSS can help HR professionals exploit and make intelligent use of soft data and act tough in their decision-making process.

Group Support Systems as Tools for HR Decision Making

2009 ◽  
pp. 537-545
Author(s):  
James Yao ◽  
John Wang
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Human Resource ◽  
Financial Management ◽  
Support Systems ◽  
Group Support Systems ◽  
Group Support ◽  
Soft Data

In the late 1960s, a new type of information system came about: model-oriented DSS or management decision systems. By the late 1970s, a number of researchers and companies had developed interactive information systems that used data and models to help managers analyze semistructured problems. These diverse systems were all called decision support systems (DSS). From those early days, it was recognized that DSS could be designed to support decision-makers at any level in an organization. DSS could support operations, financial management, and strategic decision making. Group decision support systems (GDSS) which aim at increasing some of the benefits of collaboration and reducing the inherent losses are interactive information technology-based environments that support concerted and coordinated group efforts toward completion of joint tasks (Dennis, George, Jessup, Nunamaker, & Vogel, 1998). The term group support systems (GSS) was coined at the start of the 1990s to replace the term GDSS. The reason for this is that the role of collaborative computing was expanded to more than just supporting decision making (Patrick & Garrick, 2006). For the avoidance of any ambiguities, the latter term shall be used in the discussion throughout this article. Human resources (HR) are rarely expected like other business functional areas to use synthesized data because HR groups have been primarily connected with transactional processing of getting data into the system and on record for reporting and historical purposes (Dudley, 2007). For them soft data do not win at the table; hard data do. Recently, many quantitative or qualitative techniques have been developed to support human resource management (HRM) activities, classified as management sciences/operations research, multiattribute utility theory, multicriteria decision making, ad hoc approaches, and human resource information systems (HRIS) (Byun, 2003). More importantly, HRIS can include the three systems of expert systems (ES), decision support systems (DSS), and executive information systems (EIS) in addition to transaction processing systems (TPS) and management information systems (MIS) which are conventionally accepted as an HRIS. As decision support systems, GSS are able to facilitate HR groups to gauge users’ opinions, readiness, satisfaction, and so forth, increase their HRM activity quality, and generate better group collaborations and decision makings with current or planned HRIS services. Consequently, GSS can help HR professionals exploit and make intelligent use of soft data and act tough in their decision-making process.

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