scholarly journals Standards Related to Interoperability in EHR & HS

2013 ◽  
pp. 19-44 ◽  
Author(s):  
Mário Macedo ◽  
Pedro Isaías
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Clinical Data ◽  
Business Processes ◽  
Support Systems ◽  
Open Architecture ◽  
Health Record ◽  
Major Step ◽  
Efficiency And Effectiveness ◽  
Clinical Terminology

The standardization of clinical data represents a major step in the development of information and organizational knowledge of health services. The evolution of information systems from a model of different database owners to a different open software based model is a major challenge. For this reason it is essential to adopt models of metadata based on archetypes to improve the development of information systems and simultaneously integrate all applications. The adoption of clinical terminology that can translate existing knowledge and enhance its growth is a necessary goal. Accessibility, ubiquity, completeness, consistency and durability of the clinical data are essential objectives for efficiency and effectiveness gains in organizations. This chapter presents the concepts and technologies needed to implement a model of EHR (Electronic Health Record) based on a standard, open architecture. It also presents some concepts of decision support systems and business processes that can be integrated with the EHR.

scholarly journals Perencanaan Strategis Sistem Informasi Dan Teknologi Informasi Pada PT. Matahari Department Store TBK.

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Chandra Indira S ◽  
Audi Ramadhan ◽  
Achmad Fauzi Saputra ◽  
Nita Yalina
Keyword(s):  
Quality Control ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Digital Signature ◽  
Business Processes ◽  
Department Store ◽  
Digital Marketing ◽  
Market Place ◽  
Efficiency And Effectiveness

PT. Matahari Department Store Tbk. adalah sebuah perusahaan ritel di Indonesia yang menawarkan berbagai produk fashion dan kosmetik untuk berbagai kalangan. Sampai saat ini, PT. Matahari Department Store Tbk membutuhkan maintenance perencanaan strategis sistem informasi untuk mengikuti tren pasar. Sehingga hal tersebut menjadi kesenjangan penulisan penelitian ini. penelitian ini bertujuan untuk merancang dan merencanakan teknologi strategis yang dapat digunakan PT. Matahari Department Store untuk meningkatkan efensiensi dan efektifitas operasional dan proses bisnisnya dalam meningkatkan laba. Metode yang digunakan dalam penelitian perencanaan strategis sistem informasi pada PT. Matahari Department Store Tbk. adalah menggunakan metode Ward & Peppard. Hasil dari penelitian ini adalah rekomendasi sistem informasi yang memungkinkan meningkatkan efisiensi dan efektifitas proses bisnis PT. Matahari Department Store Tbk. seperti Digital Marketing, Aplikasi Digital Signature dan Kuitansi Pegawai, Sistem Pendukung Keputusan Ukuran Baju, Sistem Informasi Integrasi Market Place, Sistem Informasi Integrasi Rantai Pasok, Sistem Pendukung Keputusan Tren Fashion, Aplikasi Quality Control dan Sistem Informasi Kepatuhan dengan rencana implementasi selama 5 tahun. Dengan adanya penelitian ini diharapkan dapat meningkatkan efisiensi dan efektifitas proses bisnis PT. Matahari Department Store Tbk. PT. Matahari Department Store Tbk. is a retail company in Indonesia that offers a variety of fashion and cosmetic products for various groups. Until now, PT. Matahari Department Store Tbk requires maintenance of information system strategic planning to keep up with market trends. So that this is a gap in the writing of this study. This study aims to design and plan strategic technology that can be used by PT. Matahari Department Store to improve the efficiency and effectiveness of its operations and business processes in increasing profits. The method used in the strategic planning research of information systems at PT. Matahari Department Store Tbk. is to use the Ward & Peppard method. The results of this study are recommendations for information systems that allow increasing the efficiency and effectiveness of PT. Matahari Department Store Tbk. such as Digital Marketing, Digital Signature and Employee Receipt Applications, Dress Size Decision Support Systems, Market Place Integration Information Systems, Supply Chain Integration Information Systems, Fashion Trend Decision Support Systems, Quality Control Applications and Compliance Information Systems with a 5-year implementation plan. With this research, it is hoped that the efficiency and effectiveness of the business processes of PT. Matahari Department Store Tbk.

scholarly journals The Effect of Perceived Factors of Decision Support Systems on Actual Usage: Behavioral Intention of Using Decision Support Systems as a Mediating Variable: “An Empirical Study of Jordanian Higher Education Institutions”

2016 ◽  
Vol 12 (1) ◽  
pp. 201
Author(s):  
Bilal Mohammed Salem Al-Momani
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Behavioral Intention ◽  
Support Systems ◽  
Higher Education Institutions ◽  
Quality Of Information

Decision support systems (DSS) are interactive computer-based systems that provide information, modeling, and manipulation of data. DSS are clearly knowledge-based information systems to capture, Processing and analysis of information affecting or aims to influence the decision making process, performed by people in scope professional job appointed by a user. Hence, this study describes briefly the key concepts of decision support systems such as perceived factors with a focus on quality  of information systems and quality of information variables, behavioral intention of using DSS, and actual DSS use by adopting and extending the technology acceptance model (TAM) of Davis (1989); and Davis, Bagozzi and Warshaw (1989).There are two main goals, which stimulate the study. The first goal is to combine Perceived DSS factors and behavioral intention to use DSS from both the social perspective and a technology perspective with regard to actual DSS usage, and an experimental test of relations provide strategic locations to organizations and providing indicators that should help them manage their DSS effectiveness. Managers face the dilemma in choosing and focusing on most important factors which contributing to the positive behavioral intention of use DSS by the decision makers, which, in turn, could contribute positively in the actual DSS usage by them and other users to effectively solve organizational problems. Hence, this study presents a model which should provide the useful tool for top management in the higher education institutions- in particular-to understand the factors that determine using behaviors for designing proactive interventions and to motivate the acceptance of TAM in order to use the DSS in a way that contributes to the higher education decision-making plan and IT policy.To accomplish or attain the above mentioned objectives, the researcher developed a research instrument (questionnaire) and distributed it amongst the higher education institutions in Jordan to collect data in order to empirically study hypothesis testing (related to the objectives of study). 341 questionnaires were returned from the study respondents. Data were analyzed by utilizing both SPSS (conducted descriptive analysis) and AMOS (conducting structural equation modelling).Findings of the study indicate that some hypotheses were supported while the others were not. Contributions of the study were presented. In addition, the researcher presented some recommendations. Finally, this study has identified opportunities for further study which has progressed greatly advanced understanding constantly of DSS usage, that can help formulate powerful strategies Involving differentiation between DSS perceived factors.

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

Outsourcing and Information Systems Development

2008 ◽  
pp. 1866-1876
Author(s):  
Julie E. Kendall ◽  
Kenneth E. Kendall
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Support Systems ◽  
Systems Development ◽  
Information Systems Development ◽  
Quality Systems ◽  
Executive Information Systems ◽  
Partner Relationship ◽  
Competitive Systems ◽  
Types Of Information

Many firms outsource creation of program code for management information systems, but not all experiences are successful. Although some researchers and practitioners are quick to blame failures on differing country cultures, this does not appear to be the reason. Rather it is the compatibility or differences in corporate cultures between the client company and the outsourcing partner that may help or hinder the development of quality systems. In this chapter we examine the metaphors found in the language of client corporations and outsourcing partners and explain how to look for compatibility when designing various types of information systems including traditional MIS, decision support systems, expert systems and AI, executive information systems, cooperative systems, and competitive systems. We explain how the development of certain types of systems can benefit from situations where more positive metaphors exist and offer some guidelines for the MIS practitioner, thereby minimizing risk and increasing the likelihood of a more successful client company-outsourcing partner relationship.

Outsourcing and Information Systems Development

2011 ◽  
pp. 225-241
Author(s):  
Julie E. Kendall ◽  
Kenneth E. Kendall
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Support Systems ◽  
Systems Development ◽  
Information Systems Development ◽  
Quality Systems ◽  
Executive Information Systems ◽  
Partner Relationship ◽  
Competitive Systems ◽  
Types Of Information

Many firms outsource creation of program code for management information systems, but not all experiences are successful. Although some researchers and practitioners are quick to blame failures on differing country cultures, this does not appear to be the reason. Rather it is the compatibility or differences in corporate cultures between the client company and the outsourcing partner that may help or hinder the development of quality systems. In this chapter we examine the metaphors found in the language of client corporations and outsourcing partners and explain how to look for compatibility when designing various types of information systems including traditional MIS, decision support systems, expert systems and AI, executive information systems, cooperative systems, and competitive systems. We explain how the development of certain types of systems can benefit from situations where more positive metaphors exist and offer some guidelines for the MIS practitioner, thereby minimizing risk and increasing the likelihood of a more successful client company-outsourcing partner relationship.

DSS and Multiple Perspectives of Complex Problems

2011 ◽  
pp. 286-295 ◽  
Author(s):  
David Paradice ◽  
Robert A. Davis
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Support Systems ◽  
Collaborative Work ◽  
Vantage Point ◽  
Decision Makers ◽  
Complex Problems ◽  
Knowledge Based ◽  
Spreadsheet Models

Decision support systems have always had a goal of supporting decision-makers. Over time, DSS have taken many forms, or many forms of computer-based support have been considered in the context of DSS, depending on one’s particular perspective. Regardless, there have been decision support systems (DSS), expert systems, executive information systems, group DSS (GDSS), group support systems (GSS), collaborative systems (or computer-supported collaborative work (CSCW) environments), knowledge-based systems, and inquiring systems, all of which are described elsewhere in this encyclopedia. The progression of decision support system types that have emerged follows to some degree the increasing complexity of the problems being addressed. Some of the early DSS involved single decision-makers utilizing spreadsheet models to solve problems. Such an approach would be inadequate in addressing complex problems because one aspect of problem complexity is that multiple stakeholders typically exist. Baldwin (1993) examined the need for supporting multiple views and provides the only attempt found in the information systems literature to operationalize the concept of a perspective. In his work, a view is defined as a set of beliefs that partially describe a general subject of discourse. He identified three major components of a view: the belief or notion to convey, a language to represent the notion, and a subject of discourse. He further described notions as comprising aspects and a vantage point. Aspects are the characteristics or attributes of a subject or situation that a particular notion emphasizes. A vantage point is described by the level of detail (i.e., overview or detailed analysis). Assuming the subject of discourse can be identified with the notion, Baldwin described how differences in views may occur via differences in the notion, the language, or both.

scholarly journals Decision Support: Time for Collaboration

2011 ◽  
Vol 20 (01) ◽  
pp. 102-104
Author(s):  
A. Guardia ◽  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Research Articles ◽  
Health Record ◽  
Excellent Research ◽  
Support Time ◽  
Selection Of

SummaryTo summarize current outstanding research in the field of decision support.A selection of excellent research articles published in 2010 in the field of computerized clinical decision support systems.This selection of articles shows that deci- sion support systems (DSS) are getting better integrated into the electronic health record systems (EHR) and into the clinician’s workflow. As a result, there is a better collaboration between physicians and DSS, which improves the care of patients.

scholarly journals Clinical Decision Support Systems Governance and the Pharmacy Department

2013 ◽  
Vol 48 (7) ◽  
pp. 607-608
Author(s):  
Brent I. Fox ◽  
Bill G. Felkey
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Meaningful Use ◽  
Clinical Decision ◽  
Health Record ◽  
Pharmacy Department ◽  
Core Requirements

The Meaningful Use criteria include a variety of core requirements that all electronic health record systems must demonstrate. An increasing level of functionality from clinical decision support systems is one of those requirements. In this article, we discuss general and specific aspects of clinical decision support systems, including potential roles for pharmacy in governance.

scholarly journals Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Jannik Schaaf ◽  
Martin Sedlmayr ◽  
Johanna Schaefer ◽  
Holger Storf
Keyword(s):  
Decision Support ◽  
Data Integration ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Data ◽  
Rare Diseases ◽  
Scoping Review ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Abstract Background Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. Methods We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items “Objective and background of the publication/project”, “System or project name”, “Functionality”, “Type of clinical data”, “Rare Diseases covered”, “Development status”, “System availability”, “Data entry and integration”, “Last software update” and “Clinical usage”. Results The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: “Analysis or comparison of genetic and phenotypic data,” “machine learning” and “information retrieval”. Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. Conclusions Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

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