Deep Learning-Based Decision Support System for Nurse Staff in Hospitals.

BackgroundArtificial intelligence (AI) applications in health care are expected to provide value for health care organizations, professionals, and patients. However, the implementation of such systems should be carefully planned and organized in order to ensure quality, safety, and acceptance. The gathered view of different stakeholders is a great source of information to understand the barriers and enablers for implementation in a specific context.ObjectiveThis study aimed to understand the context and stakeholder perspectives related to the future implementation of a clinical decision support system for predicting readmissions of patients with heart failure. The study was part of a larger project involving model development, interface design, and implementation planning of the system.MethodsInterviews were held with 12 stakeholders from the regional and municipal health care organizations to gather their views on the potential effects implementation of such a decision support system could have as well as barriers and enablers for implementation. Data were analyzed based on the categories defined in the nonadoption, abandonment, scale-up, spread, sustainability (NASSS) framework.ResultsStakeholders had in general a positive attitude and curiosity toward AI-based decision support systems, and mentioned several barriers and enablers based on the experiences of previous implementations of information technology systems. Central aspects to consider for the proposed clinical decision support system were design aspects, access to information throughout the care process, and integration into the clinical workflow. The implementation of such a system could lead to a number of effects related to both clinical outcomes as well as resource allocation, which are all important to address in the planning of implementation. Stakeholders saw, however, value in several aspects of implementing such system, emphasizing the increased quality of life for those patients who can avoid being hospitalized.ConclusionsSeveral ideas were put forward on how the proposed AI system would potentially affect and provide value for patients, professionals, and the organization, and implementation aspects were important parts of that. A successful system can help clinicians to prioritize the need for different types of treatments but also be used for planning purposes within the hospital. However, the system needs not only technological and clinical precision but also a carefully planned implementation process. Such a process should take into consideration the aspects related to all the categories in the NASSS framework. This study further highlighted the importance to study stakeholder needs early in the process of development, design, and implementation of decision support systems, as the data revealed new information on the potential use of the system and the placement of the application in the care process.

Finding appropriate decision support systems (DSS) development processes and methodologies is a topic that has kept researchers in the decision support community busy for the past three decades at least. Inspired by Gibson and Nolan’s curve (Gibson & Nolan 1974; Nolan, 1979), it is fair to contend that the field of DSS development is reaching the end of its expansion (or contagion) stage, which is characterized by the proliferation of processes and methodologies in all areas of decision support. Studies on DSS development conducted during the last 15 years (e.g., Arinze, 1991; Saxena, 1992) have identified more than 30 different approaches to the design and construction of decision support methods and systems (Marakas, 2003). Interestingly enough, none of these approaches predominate and the various DSS development processes usually remain very distinct and project-specific. This situation can be interpreted as a sign that the field of DSS development should soon enter in its formalization (or control) stage. Therefore, we propose a unifying perspective of DSS development based on the notion of context. In this article, we argue that the context of the target DSS (whether organizational, technological, or developmental) is not properly considered in the literature on DSS development. Researchers propose processes (e.g., Courbon, Drageof, & Tomasi, 1979; Stabell 1983), methodologies (e.g., Blanning, 1979; Martin, 1982; Saxena, 1991; Sprague & Carlson, 1982), cycles (e.g., Keen & Scott Morton, 1978; Sage, 1991), guidelines (e.g., for end-user computer), and frameworks, but often fail to explicitly describe the context in which the solution can be applied.

Finding appropriate decision support systems (DSS) development processes and methodologies is a topic that has kept researchers in the decision support community busy for the past three decades at least. Inspired by Gibson and Nolan’s curve (Gibson & Nolan 1974; Nolan, 1979), it is fair to contend that the field of DSS development is reaching the end of its expansion (or contagion) stage, which is characterized by the proliferation of processes and methodologies in all areas of decision support. Studies on DSS development conducted during the last 15 years (e.g., Arinze, 1991; Saxena, 1992) have identified more than 30 different approaches to the design and construction of decision support methods and systems (Marakas, 2003). Interestingly enough, none of these approaches predominate and the various DSS development processes usually remain very distinct and project-specific. This situation can be interpreted as a sign that the field of DSS development should soon enter in its formalization (or control) stage. Therefore, we propose a unifying perspective of DSS development based on the notion of context. In this article, we argue that the context of the target DSS (whether organizational, technological, or developmental) is not properly considered in the literature on DSS development. Researchers propose processes (e.g., Courbon, Drageof, & Tomasi, 1979; Stabell 1983), methodologies (e.g., Blanning, 1979; Martin, 1982; Saxena, 1991; Sprague & Carlson, 1982), cycles (e.g., Keen & Scott Morton, 1978; Sage, 1991), guidelines (e.g., for end-user computer), and frameworks, but often fail to explicitly describe the context in which the solution can be applied.

The introduction of Industry 4.0 leads to new decision-making scenarios in the construction industry. The construction sector still hesitates to adopt emerging technologies and many construction companies require support in the field of digitalisation and automation of processes. One way to support this sector is the use of decision support systems that perform a careful assessment of the feasibility for replacing traditional systems with automated ones. However, the design and the development of decision support systems is a complex and time-consuming task. In this work we apply Axiomatic Design to define design guidelines for the development of decision support systems in construction equipment selection. The needs of the construction industry for decision support in these scenarios are derived from existing literature in the field of construction and the key characteristic of decision-theoretic expert systems. These needs are analysed and translated into functional requirements. Then, through a mapping and decomposition process, the design solutions for the development of a decision support system are deduced. Functional requirements and design solutions are transformed into design guidelines that support the systematic development and implementation of decision support systems for the construction phase of a building.

A study of the DSS implementation area reveals an increased emphasis on methodologies for DSS development. As the DSS field matures, a larger number of methodological options are becoming available to the DSS developer. Existing methodologies have adopted various methods for performing the important task of DSS requirements analysis and specification. In envisaging a hierarchy of requirements analysis methods, a method for improved requirements analysis is proposed to remedy observed deficiencies within it. This method is based on a view of generic types of inquiries or solicitations made by the DSS user during decision-making. This formalism constitutes the basis of a proposed DSS development methodology that offers several benefits to DSS development. The intended benefits include a sharper and more focused requirements analysis to improve the DSS development process. This paper describes the model and methodology, alongside details of a real-world example of their use in developing a Marketing Decision Support System (MKDSS) for the support of marketing decision-making.

The European cherry fruit fly, Rhagoletis cerasi (Diptera: Tephritidae), is a key pest for the cherry production industry in Europe and west Asia that has recently invaded North America. Insecticide applications are frequently employed to control this devastating pest, often without considering its population trends. We developed a novel decision support system (DSS), and field tested it in commercial sweet cherry orchards in central Greece. The DSS includes two algorithms that predict the timing of adult activity in the wild and support pest management decisions, based on R. cerasi population trends and pesticide properties, respectively. Preparatory monitoring of the testing area during 2014, using adult traps, revealed high population densities of R. cerasi in non-managed sweet cherry orchards and low densities in commercial ones. Implementation of the DSS during 2015 resulted in low R. cerasi adult population densities and zero fruit infestation rates in commercial cherry orchards. Similar population and infestation rates were recorded in conventionally treated plots that received on average two insecticide applications compared to the one-half that the DSS treated plots received. Simultaneously, high population densities and fruit infestation rates were recorded in non-managed cherry orchards. Apparently, the implementation of the simple DSS we developed reduces the cost of R. cerasi management and minimizes the chemical footprint on both the harvested fruit and the environment.

The Intelligent Management Information System (IMIS) has the potential to transform human decision making by combining research in Artificial Intelligence, Information Technology, and Systems Engineering. The field of Intelligent Decision Making (IDM) is expanding rapidly, due in part to advances in artificial intelligence and networkcentric environments that can deliver the technology. Communication and coordination between dispersed systems can deliver just-in-time information, real-time processing, collaborative environments, and globally up-to-date information to the human decision maker. At the same time, artificial intelligence techniques have demonstrated that they have matured sufficiently to provide computational assistance to humans in practical applications. It is the development direction of modern management science and technology. In this chapter, firstly we introduce the introduction and background of IMIS, and briefly, the related design conception. Subsequently, the Intelligent Decision Support System (IDSS) is depicted, which is the most significant technology of IMIS and related activities in the manufacturing process. The applications of IDSS and two cases for industrial manufacturing are then presented, representing the future development direction of manufacturing management. Lastly, a summary of this chapter is given. IMIS researchers and technologists have built and investigated Decision Support Systems (DSS) for more than 35 years. The developments in DSS began with building model-oriented DSS in the late 1960s which were followed by theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-1980s. During the mid-1980s, Intelligent DSS were implemented through combining knowledge systems with DSS. These developments are discussed below, as well as the origins of Executive Information Systems, On-line Analytical Processing (OLAP), Business Intelligence, and the implementation of Web-based DSS in the mid-1990s, which quickly became a topic for active discussion, and whose influence spread widely.

The selection process for new students in the field of education is crucial and warrants careful consideration. IT Telkom Purwokerto has a dedicated division, the Admission Unit, responsible for the selection of new students. However, this process often encounters errors, such as miscalculations in the average scores of three subjects, discrepancies between new student data and graduation guideline data, and prolonged simulation processes for graduation. This study proposes a solution to these issues through the implementation of an MVC-based Decision Support System (DSS) for determining the eligibility of new student admissions. The Prototype methodology was chosen to develop an MVC-based system as a resolution to these issues. The criteria used in this research to determine new student admissions involve various factors, including the chosen high school major, interest in the offered majors, average mathematics scores, and the average scores of three main subjects: mathematics, Bahasa Indonesia, and English. The outcomes of this research include the development of an MVC-based decision support system that aims to determine the admission status of new students. It is anticipated that the implementation of this decision support system based MVC will not only aid relevant personnel in the admission decision process but also mitigate potential issues that may arise. The research contributes to the enhancement of the efficiency and accuracy of the new student selection process at IT Telkom Purwokerto.

Finding appropriate decision support systems (DSS) development processes and methodologies is a topic that has kept researchers in the decision support community busy for the past three decades at least. Inspired by Gibson and Nolan’s curve (Gibson & Nolan 1974; Nolan, 1979), it is fair to contend that the field of DSS development is reaching the end of its expansion (or contagion) stage, which is characterized by the proliferation of processes and methodologies in all areas of decision support. Studies on DSS development conducted during the last 15 years (e.g., Arinze, 1991; Saxena, 1992) have identified more than 30 different approaches to the design and construction of decision support methods and systems (Marakas, 2003). Interestingly enough, none of these approaches predominate and the various DSS development processes usually remain very distinct and project-specific. This situation can be interpreted as a sign that the field of DSS development should soon enter in its formalization (or control) stage. Therefore, we propose a unifying perspective of DSS development based on the notion of context. In this article, we argue that the context of the target DSS (whether organizational, technological, or developmental) is not properly considered in the literature on DSS development. Researchers propose processes (e.g., Courbon, Drageof, & Tomasi, 1979; Stabell 1983), methodologies (e.g., Blanning, 1979; Martin, 1982; Saxena, 1991; Sprague & Carlson, 1982), cycles (e.g., Keen & Scott Morton, 1978; Sage, 1991), guidelines (e.g., for end-user computer), and frameworks, but often fail to explicitly describe the context in which the solution can be applied.

The watershed ecosystem is a complex system in which there are numerous transportation and transfer mechanisms of mass and energy. To make management more effective in all national critical watersheds, there needs to be more scientific, evidence-based policymaking that is based on an understanding of the system and mechanisms of the socio-hydrological processes of the watershed. The availability of Decision Support System (DSS) technology can be the appropriate approach to this need because DSS can be an interface between scientific and practical needs (easy-to-use, easy-to-access, user-friendly). However, the availability and implementation of DSS as an important tool in the optimal design of sustainable watershed management in Indonesia are still very limited. This study aims to create a prototype DSS that practitioners and policymakers can use to identify priority areas and optimize technical solutions for controlling surface runoff and soil erosion at various scales in the internal watershed. Herein, the spatial-based numerical modeling system and process mechanism; the database and knowledge; and the Graphical User Interface (GUI) are the three main components that have been used as a framework for model-based DSS development. A distributed rainfall-runoff-erosion model (EcoHydro) is the main engine of DSS for spatially quantifying dimensions of hydrological responses, erosion rate, and sediment production according to the user’s specifications and providing design options for control measures of them. The upper Citarum river basin in West Java, which is a critical and first national priority watershed, has been selected as the case study for DSS development and to demonstrate its application. Furthermore, the resulting DSS can later be developed for use in watersheds and other nationally critical lakes.

: Workshops in the late 1990's launched the commitment of the U.S. Geological Survey's Biological Resources Division (BRD) to develop and implement decision support systems (DSS) applications. One of the primary goals of this framework document is to provide sufficient background and information for Department of the Interior (DOI) bureau stakeholders and other clients to determine the potential for DSS development. Such an understanding can assist them in carrying out effective land planning and management practices. This document provides a definition of DSS and its characteristics and capabilities. It proceeds to describe issues related to meeting resource managers needs, such as the needs for specific applications, customer requirements, information and technology transfer, user support, and institutionalization. Using the decision process as a means to guide DSS development and determine users needs is also discussed. We conclude with information on methods to evaluate DSS development efforts and recommended procedures for verification and validation.

Chapter 4 - Stepping stone to smarter supervision: a human-centered multidisciplinary framework

It is generally accepted that adaptive design methods and flexible constructs facilitate the design and implementation of decision support systems. Flexible constructs in the form of decision support system generators have been proposed for the timely construction of specific decision support systems. Effective communication, however, underlies the success of applying adaptive design methods and creating and modifying decision support system generators. Accordingly, we propose to investigate communication as a mode of action that allows the participants to structure and to exert control over the design environment. A descriptive model is presented that describes how different modes of communicative action are used to enhance the prospects of success when building decision support systems.

Decision Support Systems (DSS) have special merits for organisations in developing countries. Conditions in which DSS development and implementation became viable and effective in developed countries such as the US are described. The lack of DSS applications in the Republic of China (Taiwan) are discussed and DSS implementation recommendations are presented for organisations in Taiwan and other countries with similar economic and cultural conditions. In formulating these recommendations, introducing DSS into an organisation is considered as an internal transfer of technology.

Barcode-assisted medication administration (BCMA) is technology with demonstrated benefit in reducing medication administration errors in hospitalized patients; however, it is not routinely used in emergency departments (EDs). EDs may benefit from BCMA, because ED medication administration is complex and error-prone. A naïve observational study was conducted at an academic medical center implementing BCMA in the ED. The rate of medication administration errors was measured before and after implementing an integrated electronic medical record (EMR) with BCMA capacity. Errors were classified as wrong drug, wrong dose, wrong route of administration, or a medication administration with no physician order. The error type, severity of error, and medications associated with errors were also quantified. A total of 1,978 medication administrations were observed (996 pre-BCMA and 982 post-BCMA). The baseline medication administration error rate was 6.3%, with wrong dose errors representing 66.7% of observed errors. BCMA was associated with a reduction in the medication administration error rate to 1.2%, a relative rate reduction of 80.7% (p < 0.0001). Wrong dose errors decreased by 90.4% (p < 0.0001), and medication administrations with no physician order decreased by 72.4% (p = 0.057). Most errors discovered were of minor severity. Antihistamine medications were associated with the highest error rate. Implementing BCMA in the ED was associated with significant reductions in the medication administration error rate and specifically wrong dose errors. The results of this study suggest a benefit of BCMA on reducing medication administration errors in the ED.