scholarly journals Decision-Support System-based Service Delivery in the Product-Service System Context: Literature Review and Gap Analysis

Procedia CIRP ◽  
2019 ◽  
Vol 83 ◽  
pp. 126-131 ◽  
Author(s):  
Roberto Sala ◽  
Giuditta Pezzotta ◽  
Fabiana Pirola ◽  
George Q. Huang
Keyword(s):  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Service Delivery ◽  
Support System ◽  
Gap Analysis ◽  
Service System ◽  
Product Service System ◽  
Product Service ◽  
System Context

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review

2019 ◽  
Vol 892 ◽  
pp. 274-283
Author(s):  
Mohammed Ashikur Rahman ◽  
Afidalina Tumian
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Cardiac Care ◽  
Learning Techniques

Now a day, clinical decision support systems (CDSS) are widely used in the cardiac care due to the complexity of the cardiac disease. The objective of this systematic literature review (SLR) is to identify the most common variables and machine learning techniques used to build machine learning-based clinical decision support system for cardiac care. This SLR adopts the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) format. Out of 530 papers, only 21 papers met the inclusion criteria. Amongst the 22 most common variables are age, gender, heart rate, respiration rate, systolic blood pressure and medical information variables. In addition, our results have shown that Simplified Acute Physiology Score (SAPS), Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) are some of the most common assessment scales used in CDSS for cardiac care. Logistic regression and support vector machine are the most common machine learning techniques applied in CDSS to predict mortality and other cardiac diseases like sepsis, cardiac arrest, heart failure and septic shock. These variables and assessment tools can be used to build a machine learning-based CDSS.

A DSS User is a Happy Negotiator

2016 ◽  
pp. 590-611
Author(s):  
Patrick Hippmann
Keyword(s):  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Support Systems ◽  
Research Framework ◽  
Analysis And Design ◽  
The Impact ◽  
Research And Design

The present work states that the analysis and design of decision support systems should consider their impacts on the emotional behaviors of negotiators. This paper provides a brief literature review with respect to this issue, and an outline of a research framework, which explains how to assess and analyze the dynamics of emotional behaviors in text-based negotiations. Subsequently, it provides some results, which show that a decision support system does not mitigate but intensifies emotional behaviors, toward the end of successful as well as failed text-based online negotiations. It is concluded that the research and design of decision support systems should focus more on the impact such systems have on the emotional behaviors of the supported negotiators.

scholarly journals Kualitas Sistem Informasi Berdasarkan ISO/IEC 25010: Literature Review

2021 ◽  
Vol 20 (1) ◽  
pp. 15
Author(s):  
Made Dwi Mulyawan ◽  
I Nyoman Satya Kumara ◽  
Ida Bagus Alit Swamardika ◽  
Komang Oka Saputra
Keyword(s):  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Product Quality ◽  
Support System ◽  
Mobile Application ◽  
Quality Model ◽  
Software Product ◽  
Quality In Use

Intisari— Sistem informasi saat ini telah menjadi bagian penting dalam meningkatkan efektifitas dan efisiensi suatu proses bisnis, sehingga diperlukan suatu pengukuran kualitas perangkat lunak untuk mengetahui sejauh mana sistem dapat menghasilkan informasi yang berkualitas. Dalam melakukan pengukuran kualitas ada beberapa model yang dapat digunakan sebagai panduan dalam melakukan penilaian perangkat lunak. ISO/IEC 25010 merupakan salah satu model kualitas yang dapat digunakan sebagai standar dalam melakukan pengukuran kualitas perangkat lunak. ISO/IEC 25010 terdiri dari software product quality model dan quality in use model. Artikel ini menelaah beberapa literature yang membahas mengenai pengukuran kualitas perangkat lunak yang menggunakan model ISO/IEC 25010. Saat ini ISO/IEC 25010 telah diterapkan untuk menilai kualitas pada sistem informasi akademik, sistem informasi pemerintah dan lembaga swasta, game, mobile application, dan decision support system. Hasil dari penilaian kualitas perangkat lunak dapat ditentukan melalui pengukuran terhadap aspek penting yang dipilih berdasarkan kebutuhan dari setiap perangkat lunak. Selain itu cara pengujian dan pengumpulan data yang digunakan dalam penilaian dapat berpengaruh terhadap tingkat akurasi dari pengukuran kualitas perangkat lunak.

scholarly journals APLIKASI PENDUKUNG KEPUTUSAN PENENTUAN PROMOSI JABATAN MENGGUNAKAN METODE PROFILE MATCHING (STUDI KASUS DI PT. PETROKIMIA GRESIK)

2019 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Wimpi Sancaka
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Large Scale ◽  
Gap Analysis ◽  
Assessment System ◽  
Decision Making Process ◽  
Matching Method ◽  
Profile Matching

Human resources play an essential role in helping companies to achieve their vision and mission. As a large scale company, PT Petrokimia Gresik obviously needs to invest in Employees’ Performance Assessment System. It could act as a decision-making tool or a measure to evaluate and assess employees' performance at work so that theemployees’ promotion would be moreobjective and organized. Decision support system could be used to reduce the subjectivity in decision-making process. The decision support system that uses profile matching method or competency gap analysis was created based on the data which refers to the decree of the board of directors issued by PT Petrokimia Gresik. This system analyzes and assesses employee’s competencies by grouping and calculating core factors and secondary factors in each variable. The output of the calculation is a ranking of the candidates. By implementing decision support system which uses Profile Matching method, it assists company decision-making process in promotion decision based on employees’ competency scores more optimally.

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