The Development of Algorithmically Based Decision-Making Systems in Children’s Protective Services: Is Administrative Data Good Enough?

2019 ◽  
Vol 50 (2) ◽  
pp. 565-580 ◽  
Author(s):  
Philip Gillingham
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Child Maltreatment ◽  
Decision Support Systems ◽  
Administrative Data ◽  
Research Data ◽  
Public Agencies ◽  
Protective Services ◽  
Vulnerable Children

Abstract Big data techniques are being used in children’s protective services to develop algorithmically based decision support systems (DSSs) to identify the most vulnerable children and introduce early and preventive services. These techniques use administrative data from multiple public agencies but are most reliant on data from children’s protective services. Using key principles from representation theory, and drawing from the author’s research, data from children’s protective services are considered in terms of the extent to which they represent and can be interpreted to provide a faithful representation of the phenomenon of child maltreatment. The aim is to address the question of whether children’s protective services data are good enough to develop accurate and practically useful DSS.

Mental Health Clinical Decision Support Exploiting Big Data

2019 ◽  
pp. 160-184 ◽  
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

Mental Health Clinical Decision Support Exploiting Big Data

2021 ◽  
pp. 983-1000
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

Decision Support Systems

2020 ◽  
Author(s):  
Sean B. Eom
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Data Analytics ◽  
Support Systems ◽  
Academic Discipline ◽  
Big Data Analytics ◽  
Academic Field

A decision support system is an interactive human–computer decision-making system that supports decision makers rather than replaces them, utilizing data and models. It solves unstructured and semi-structured problems with a focus on effectiveness rather than efficiency in decision processes. In the early 1970s, scholars in this field began to recognize the important roles that decision support systems (DSS) play in supporting managers in their semi-structured or unstructured decision-making activities. Over the past five decades, DSS has made progress toward becoming a solid academic field. Nevertheless, since the mid-1990s, the inability of DSS to fully satisfy a wide range of information needs of practitioners provided an impetus for a new breed of DSS, business intelligence systems (BIS). The academic discipline of DSS has undergone numerous changes in technological environments including the adoption of data warehouses. Until the late 1990s, most textbooks referred to “decision support systems.” Nowadays, many of them have replaced “decision support systems” with “business intelligence.” While DSS/BIS began in academia and were quickly adopted in business, in recent years these tools have moved into government and the academic field of public administration. In addition, modern political campaigns, especially at the national level, are based on data analytics and the use of big data analytics. The first section of this article reviews the development of DSS as an academic discipline. The second section discusses BIS and their components (the data warehousing environment and the analytical environment). The final section introduces two emerging topics in DSS/BIS: big data analytics and cloud computing analytics. Before the era of big data, most data collected by business organizations could easily be managed by traditional relational database management systems with a serial processing system. Social networks, e-business networks, Internet of Things (IoT), and many other wireless sensor networks are generating huge volumes of data every day. The challenge of big data has demanded a new business intelligence infrastructure with new tools (Hadoop cluster, the data warehousing environment, and the business analytical environment).

scholarly journals RELATIONSHIP BETWEEN BIG DATA AND DECISION SUPPORT SYSTEMS

2020 ◽  
Vol 11 (1) ◽  
pp. 82-93
Author(s):  
Gustavo Grander ◽  
Luciano Ferreira da Silva ◽  
Alan Tadeu Moraes Moraes ◽  
Paulo Sergio Gonçalves de Oliveira
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Analytical Techniques ◽  
Strong Relationship ◽  
Decision Making Processes ◽  
Scopus Database

This article aimed to identify relationships between Big Data and Decision Support Systems. For this, we conducted a search in the Scopus database and as a result, we identified a report according to the increased frequency of publications, frequency of publications in journals and, using the VOSviewer software, we performed an analysis of words co-citation. We identified 5 groups of keywords that suggest different areas of study (e.g., logistics, health and social media), as well as a more recent focus on studies aimed at sustainable development, machine learning, analytical techniques and decision-making processes decision. An important contribution that should also be highlighted was the strong relationship between the keywords Big Data, artificial intelligence and decision making, suggesting studies involving the three terms in a large number of works. 

The Decision to Substantiate Allegations of Child Maltreatment

2020 ◽  
pp. 173-193
Author(s):  
Sarah A. Font ◽  
Kathryn Maguire-Jack ◽  
Rebecca Dillard
Keyword(s):  
Decision Making ◽  
Child Maltreatment ◽  
Child Protective Services ◽  
Objective Assessment ◽  
The United States ◽  
Family Characteristics ◽  
Protective Services ◽  
Vulnerable Children ◽  
Representative Study ◽  
Nationally Representative

In the United States, the Child Protective Services system is responsible for investigating and responding to allegations of child abuse and neglect. At the conclusion of an investigation, caseworkers are expected to decide whether allegations are “substantiated” (demonstrated to be true) or not. How that decision is made—and whether it reflects an objective assessment of the evidence available—is widely debated. This chapter first presents an overview of the decision-making process and the implications of decision-making for vulnerable children and families. Next, it describes how rates of substantiation vary across and within states. The authors then present data from a nationally representative study of child protective services investigations on the factors associated with the decision to substantiate child maltreatment. They find that agency characteristics are predictive of substantiation, net of child and family characteristics. Overall, the authors conclude that substantiation is unlikely to be a valid indicator of the incidence of child maltreatment, and they discuss possible strategies for improving the consistency and utility of the substantiation decision.

Mental Health Clinical Decision Support Exploiting Big Data

2021 ◽  
pp. 341-359
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

IMPLEMENTASI METODE VIKOR DALAM PENERIMAAN JURNALIS PADA KORAN WASPADA MEDAN

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Soraya Rahma Hayati ◽  
Mesran Mesran ◽  
Taronisokhi Zebua ◽  
Heri Nurdiyanto ◽  
Khasanah Khasanah
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Support Systems ◽  
Knowledge Based ◽  
Computer Based ◽  
Support Decision Making ◽  
Made In

The reception of journalists at the Waspada Daily Medan always went through several rigorous selections before being determined to be accepted as journalists at the Waspada Medan Daily. There are several criteria that must be possessed by each participant as a condition for becoming a journalist in the Daily Alert Medan. To get the best participants, the Waspada Medan Daily needed a decision support system. Decision Support Systems (SPK) are part of computer-based information systems (including knowledge-based systems (knowledge management)) that are used to support decision making within an organization or company. Decision support systems provide a semitructured decision, where no one knows exactly how the decision should be made. In this study the authors applied the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) as the method to be applied in the decision support system application. The VIKOR method is part of the Multi-Attibut Decision Making (MADM) Concept, which requires normalization in its calculations. The expected results in this study can obtain maximum decisions.Keywords: Journalist Acceptance, Decision Support System, VIKOR

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