Use Case of Providing Decision Support for Product Developers in Product Improvement Processes

Author(s):  
Michael Abramovici ◽  
Andreas Lindner ◽  
Susanne Dienst
2020 ◽  
pp. 744-766
Author(s):  
Erich Ortner ◽  
Marco Mevius ◽  
Peter Wiedmann ◽  
Florian Kurz

Nowadays, the number of human to application system interactions is dramatically increasing. For instance, citizens interact with the help of the internet to organize meetings spontaneously. Furthermore, standards such as Business Process Model and Notation (BPMN) and the Decision Modeling Notation (DMN) allow the creation of graphical models to document the (interaction) processes. Moreover, simulations and automations can be set up to encounter new technical challenges. Smart Cities aim at enabling their citizens to use these digital services. However, looking beyond technology, there is still a significant lack of interaction and support between “normal” citizens and the public administration. This article introduces an approach, which describes the design of enhanced interactional applications for decision support in Smart Cities based on Dialogical Logic process patterns. The authors demonstrate the approach with the help of a use case concerning a budgeting scenario as well as a summary and outlook on further research.


2020 ◽  
Vol 27 (1) ◽  
pp. e100084
Author(s):  
Lydia Oakey-Neate ◽  
Geoff Schrader ◽  
Jörg Strobel ◽  
Tarun Bastiampillai ◽  
Yasmin van Kasteren ◽  
...  

IntroductionNon-adherence to antipsychotic medications for individuals with serious mental illness increases risk of relapse and hospitalisation. Real time monitoring of adherence would allow for early intervention. AI2 is a both a personal nudging system and a clinical decision support tool that applies machine learning on Medicare prescription and benefits data to raise alerts when patients have discontinued antipsychotic medications without supervision, or when essential routine health checks have not been performed.Methods and analysisWe outline two intervention models using AI2. In the first use-case, the personal nudging system, patients receive text messages when an alert of a missed medication or routine health check is detected by AI2. In the second use-case, as a clinical decision support tool, AI2 generated alerts are presented as flags through a dashboard to the community mental health professionals. Implementation protocols for different scenarios of AI2, along with a mixed-methods evaluation, are planned to identify pragmatic issues necessary to inform a larger randomised control trial, as well as improve the application.Ethics and disseminationThis study protocol has been approved by The Southern Adelaide Clinical Human Research Ethics Committee. The dissemination of this trial will serve to inform further implementation of the AI2 into daily personal and clinical practice.


2021 ◽  
Vol 11 (23) ◽  
pp. 11415
Author(s):  
Carmen Marcher ◽  
Andrea Giusti ◽  
Dominik T. Matt

The construction sector is one of the major global economies and is characterised by low productivity and high inefficiencies, but could highly benefit from the introduction of robotic equipment in terms of productivity, safety, and quality. As the development and the availability of robotic solutions for the construction sector increases, the evaluation of their potential benefits compared to conventional processes that are currently adopted on construction sites becomes compelling. To this end, we exploit Bayesian decision theory and apply an axiomatic design guideline for the development of a decision-theoretic expert system that: (i) evaluates the utility of available alternatives based on evidence; (ii) accounts for uncertainty; and (iii) exploits both expert knowledge and preferences of the users. The development process is illustrated by means of exemplary use case scenarios that compare manual and robotic processes. A use case scenario that compares manual and robotic marking and spraying is chosen for describing the development process in detail. Findings show how decision making in equipment selection can be supported by means of dedicated systems for decision support, developed in collaboration with domain experts.


UNISTEK ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 1-4
Author(s):  
Syahriani Syam

ABSTRAKSistem pendukung keputusan adalah salah satu sistem untuk menunjang keputusan dalam suatu organisasi baik dilingkungan pendidikan, kantor, maupun pemerintahan. Masalah administrasi yang masih manual mengakibatkan kurang efisien terhadap kegiatan seleksi calon siswa-siswi baru di MTs Al Husna .Metode  Weighted product adalah salah satu metode untuk melakukan pengambilan keputusan dengan sistem multi kriteria, dimana kriteria tersebut telah ditentukan oleh pihak sekolah. Dalam penelitian ini digunakan indikator kriteria yaitu nilai ujian nasional, nilai semester akhir, baca tulis al-qur’an, wawancara, tes tertulis dan penilaian terhadap prestasi non akademik. Dengan pemodelan fungsionalnya menggunakan use case diagram. Penelitian ini menghasilkan suatu aplikasi pendukung keputusan terhadap pemilihan calon siswa siswi baru.Kata Kunci : Sistem Pendukung Keputusan, Weighted Product, Mts Al Husna, ABSTRACTDecision support system is one of the systems to support decisions in an organization both in the education, office and government environments. Administrative problems are still manual resulting in less efficient selection of prospective new students in MTs Al Husna. Weighted product method is one method for making decisions with a multi-criteria system, where the criteria have been determined by the school. In this study the criteria used are national exam scores, final semester scores, al-quran writing, interviews, written tests and assessments of non-academic achievements. With functional modeling using use case diagrams. This research resulted in a decision support application to the selection of prospective new students.Keywords: Decision Support System, Weighted Product, Mts Al Husna


2016 ◽  
Vol 1 (1) ◽  
pp. 73
Author(s):  
Yudho Yudhanto ◽  
Fadlul Ilmi Khairun ◽  
Winita Sulandari

<p><em>Boarding house is a residence for rent for certain immigrants who settled diarea within a certain period . There have been many technologies that offer information about the boarding house but is still considered to be less efficient due to search for boarding in accordance with the desired criteria , boarding seekers still have to compare one by one facility as well as the criteria that owned the boarding house . Use of Simple Additive weighting method ( SAW ) on a decision support system is one of the solutions to deal with such matters , where the boarding seekers will find it helpful because it can give recommendations boarding places corresponding to the desired criteria .</em></p><p><em>Research methodology to design and create this application is to use research methods waterfall that is by collecting data, analyzing system (define functional requirements and non functional), do the design (ERD, use case diagrams, use case text, sequence diagrams, and class diagram), and implementation (coding and testing). Marketplace information system is created using the programming language PHP CodeIgniter-based framework 2 and the MySQL database.</em></p><p><em>Applications are focused in finding a boarding recommendation in accordance with the criteria corresponding to the booking to boarding room can be done with this application . With the app is expected to help seekers boarding house to get the best boarding recommendation and can assist in the boarding room reservations and provide benefits to the owner of the boarding house to be able to market his boarding house .</em></p>


Author(s):  
Giuseppe Timperio ◽  
Gajanan Bhanudas Panchal ◽  
Avinash Samvedi ◽  
Mark Goh ◽  
Robert De Souza

Purpose The purpose of this paper is to provide a decision support framework for locations identification to address network design in the domain of disaster relief supply chains. The solution approach is then applied to a real-life case about Indonesia. Design/methodology/approach An approach integrating geographic information system technology and fuzzy analytical hierarchy process has been used. Findings For the Indonesian case, distribution centers should be located in Pekanbaru, Surabaya, Banjarmasin, Ambon, Timika, and Manado. Research limitations/implications The main limitation of this work is that facilities being sited are incapacitated. Inclusion of constraints over capacity would elevate the framework to a further level of sophistication, enabling virtual pool of inventory that can be used to adsorb fluctuation in the demand due to disasters. Practical implications The use case provided in this paper shows a practical example of applicability for the proposed framework. This study is able to support worldwide decision makers facing challenges related with disaster relief chains resilience. In order to achieve efficiency and effectiveness in relief operations, strategic logistics planning in preparedness is key. Hence, initiatives in disaster preparedness should be enhanced. Originality/value It adds value to the previous literature on humanitarian logistics by providing a real-life case study as use case for the proposed methodology. It can guide decision makers in designing resilient humanitarian response, worldwide. Moreover, a combination of recommendations from humanitarian logistics practitioners with established models in facility location sciences provides an interdisciplinary solution to this complex exercise.


2020 ◽  
Author(s):  
Heather Leslie

BACKGROUND Despite electronic health records being in existence for over 50 years, our ability to exchange health data remains frustratingly limited. Commonly used clinical content standards, and the information models that underpin them, are primarily related to health data exchange, and so are usually document- or message-focused. In contrast, over the past 12 years, the Clinical Models program at openEHR International has gradually established a governed, coordinated, and coherent ecosystem of clinical information models, known as openEHR archetypes. Each archetype is designed as a maximal data set for a universal use-case, intended for reuse across various health data sets, known as openEHR templates. To date, only anecdotal evidence has been available to indicate if the hypothesis of archetype reuse across templates is feasible and scalable. As a response to the COVID-19 pandemic, between February and July 2020, 7 openEHR templates were independently created to represent COVID-19–related data sets for symptom screening, confirmed infection reporting, clinical decision support, and research. Each of the templates prioritized reuse of existing use-case agnostic archetypes found in openEHR International's online Clinical Knowledge Manager tool as much as possible. This study is the first opportunity to investigate archetype reuse within a range of diverse, multilingual openEHR templates. OBJECTIVE This study aims to investigate the use and reuse of openEHR archetypes across the 7 openEHR templates as an initial investigation about the reuse of information models across data sets used for a variety of clinical purposes. METHODS Analysis of both the number of occurrences of archetypes and patterns of occurrence within 7 discrete templates was carried out at the archetype or clinical concept level. RESULTS Across all 7 templates collectively, 203 instances of 58 unique archetypes were used. The most frequently used archetype occurred 24 times across 4 of the 7 templates. Total data points per template ranged from 40 to 179. Archetype instances per template ranged from 10 to 62. Unique archetype occurrences ranged from 10 to 28. Existing archetype reuse of use-case agnostic archetypes ranged from 40% to 90%. Total reuse of use-case agnostic archetypes ranged from 40% to 100%. CONCLUSIONS Investigation of the amount of archetype reuse across the 7 openEHR templates in this initial study has demonstrated significant reuse of archetypes, even across unanticipated, novel modeling challenges and multilingual deployments. While the trigger for the development of each of these templates was the COVID-19 pandemic, the templates represented a variety of types of data sets: symptom screening, infection report, clinical decision support for diagnosis and treatment, and secondary use or research. The findings support the openEHR hypothesis that it is possible to create a shared, public library of standards-based, vendor-neutral clinical information models that can be reused across a diverse range of health data sets.


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