Decision Support
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2022 ◽  
Vol 193 ◽  
pp. 106688
Author(s):  
Christoforos-Nikitas Kasimatis ◽  
Evangelos Psomakelis ◽  
Nikolaos Katsenios ◽  
Giannis Katsenios ◽  
Marilena Papatheodorou ◽  
...  

2022 ◽  
Vol 9 (2) ◽  
pp. 75
Author(s):  
Paul Evangelista ◽  
James Schreiner

This special issue of the Industrial and Systems Engineering Review once again showcases the top papers from the annual General Donald R. Keith memorial capstone conference at the United States Military Academy in West Point, NY. Despite continued COVID restrictions, the truly innovative conference included a mix of in-person presentations with over 50 live and remote judges from across academia and industry to create a high-quality event highlighting the undergraduate student team research. After consideration of over 50 academic papers, the eight listed in this issue were selected for publication in this special issue of the journal. The topics discussed are broad and diverse, however decision support within an uncertain and complex environment emerges as a theme. Much of the work completed by industrial and systems engineers focuses on getting decisions right by means of the tools of our trade. The suite of tools surveyed within these papers represents several state-of-the-art methods as well as time-proven techniques within a unique application domain. Military applications dominated several of the papers. Downey et al. studied massive datasets that represent military operational behaviors in training, seeking to better understand military operational capabilities. Ungrady and Dabkowski tackled the complexities of US Army recruiting through the application of fuzzy cognitive maps, searching for causation. Middlebrooks et al. studied military acquisition system decisions, applying system dynamics modeling. Process improvement represented another sub-theme, with continued focus on decision support. Enos et al. applied lean six sigma techniques to manufacturing processes. Katz et al. explored biomedical machine maintenance scheduling, seeking optimal solutions to a complex scheduling task. Kaloudelis et al. developed a pandemic decision support process for universities. Analytics and machine learning techniques applied to the information domain dominated the third sub-theme. Krueger and Enos developed analytics to support ice hockey strategies. Manzonelli et al. applied natural language processing against information operations, seeking to automate the examination of incredible amounts of narrative data that seek to shape beliefs and attitudes. Please join me in congratulating our authors, especially the young undergraduate scholars that provided the primary intellectual efforts that created the contents of this issue. COL Paul F. Evangelista Chief Data Officer United States Military Academy Taylor Hall, 5th Floor West Point, NY 10996 Email: [email protected] James H. Schreiner, PhD, PMP, CPEM, F.ASEM LTC(P), U.S. Army Associate Professor USMA Academy Professor Director, Engineering Management (EM) Program Department of Systems Engineering Head Officer Representative, Army Softball United States Military Academy Room 420 Mahan Hall West Point, NY 10996 Email: [email protected]


2022 ◽  
Vol 9 (2) ◽  
pp. 117-133
Author(s):  
Demosthenes Kaloudelis ◽  
Ahmed Abdulwahab ◽  
Ayman Fatima ◽  
Zaid Yasin

The global effort to combat the COVID-19 pandemic has changed how people conduct their daily lives. Institutions of higher education have been greatly impacted by these changes and must find ways to adapt to this new environment. Universities are a unique case because they must control disease spread, while maintaining the same or similar quality of education. The University Pandemic Response Decision Support System (UPRDSS) is a system designed to help universities pick the most suitable method for instruction delivery when faced with any pandemic. Using George Mason University as a case study, the goal was to design a system that allows university administrations to make an educated operations decision. The UPRDSS achieves this by simulating the spread of disease, analyzing learning outcome data, and using a multi-attribute utility function to determine the most appropriate method of instruction that enables positive learning and health outcomes.


2022 ◽  
Vol 11 (1) ◽  
pp. 101
Author(s):  
Vinu Sherimon ◽  
P.C. Sherimon ◽  
Rahul V. Nair ◽  
Renchi Mathew ◽  
Sandeep M. Kumar ◽  
...  

Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman.Materials and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested.Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances.Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics.


2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Rebekah Pratt ◽  
Daniel M. Saman ◽  
Clayton Allen ◽  
Benjamin Crabtree ◽  
Kris Ohnsorg ◽  
...  

Abstract Background In this paper we describe the use of the Consolidated Framework for Implementation Research (CFIR) to study implementation of a web-based, point-of-care, EHR-linked clinical decision support (CDS) tool designed to identify and provide care recommendations for adults with prediabetes (Pre-D CDS). Methods As part of a large NIH-funded clinic-randomized trial, we identified a convenience sample of interview participants from 22 primary care clinics in Minnesota, North Dakota, and Wisconsin that were randomly allocated to receive or not receive a web-based EHR-integrated prediabetes CDS intervention. Participants included 11 clinicians, 6 rooming staff, and 7 nurse or clinic managers recruited by study staff to participate in telephone interviews conducted by an expert in qualitative methods. Interviews were recorded and transcribed, and data analysis was conducted using a constructivist version of grounded theory. Results Implementing a prediabetes CDS tool into primary care clinics was useful and well received. The intervention was integrated with clinic workflows, supported primary care clinicians in clearly communicating prediabetes risk and management options with patients, and in identifying actionable care opportunities. The main barriers to CDS use were time and competing priorities. Finally, while the implementation process worked well, opportunities remain in engaging the care team more broadly in CDS use. Conclusions The use of CDS tools for engaging patients and providers in care improvement opportunities for prediabetes is a promising and potentially effective strategy in primary care settings. A workflow that incorporates the whole care team in the use of such tools may optimize the implementation of CDS tools like these in primary care settings. Trial registration Name of the registry: Clinicaltrial.gov. Trial registration number: NCT02759055. Date of registration: 05/03/2016. URL of trial registry record: https://clinicaltrials.gov/ct2/show/NCT02759055 Prospectively registered.


2022 ◽  
Vol 6 (GROUP) ◽  
pp. 1-14
Author(s):  
Angela Mastrianni ◽  
Lynn Almengor ◽  
Aleksandra Sarcevic

In this study, we explore how clinical decision support features can be designed to aid teams in caring for patients during time-critical medical emergencies. We interviewed 12 clinicians with experience in leading pediatric trauma resuscitations to elicit design requirements for decision support alerts and how these alerts should be designed for teams with shared leadership. Based on the interview data, we identified three types of decision support alerts: reminders to perform tasks, alerts to changes in patient status, and suggestions for interventions. We also found that clinicians perceived alerts in this setting as coordination mechanisms and that some alert preferences were associated with leader experience levels. From these findings, we contribute three perspectives on how alerts can aid coordination and discuss implications for designing decision support alerts for shared leadership in time-critical medical processes.


2022 ◽  
Vol 17 (s1) ◽  
Author(s):  
Agung Syetiawan ◽  
Mira Harimurti ◽  
Yosef Prihanto

With 25% confirmed cases of the country’s total number of coronavirus disease 2019 (COVID-19) on 31 January 2021, Jakarta has the highest confirmed cases of in Indonesia. The city holds a significant role as the centre of government and national economic activity for which pandemic have had a huge impact. Spatiotemporal analysis was employed to identify the current condition of disease transmission and to provide comprehensive information on the COVID-19 outbreak in Jakarta. We applied space-time analysis to visualise the pattern of COVID-19 hotspots in each time series. We also mapped area capacity of the referral hospitals covering the entire area of Jakarta to understand the hospital service range. This research was conducted in 4 stages: i) disease mapping; ii) spatial autocorrelation analysis; iii) space-time pattern analysis; and iv) areal capacity mapping. The analysis resulted in 144 sub-districts categorised as high vulnerability. Autocorrelation studies by Moran’s I identified cluster patterns and the emerging hotspot results indicated successful interventions as the number of hotspots fell in the first period of social restrictions. The results presented should be beneficial for policy makers.


2022 ◽  
Vol 6 (1) ◽  
pp. 10
Author(s):  
Matej Vuković ◽  
Stefan Thalmann

Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sustainability. ML outperforms traditional approaches in many cases, but its complexity leads to unclear bases for decisions. Thus, acceptance of ML and, concomitantly, Industry 4.0, is hindered due to increasing requirements of fairness, accountability, and transparency, especially in sensitive-use cases. ML does not augment organizational knowledge, which is highly desired by manufacturing experts. Causal discovery promises a solution by providing insights on causal relationships that go beyond traditional ML’s statistical dependency. Causal discovery has a theoretical background and been successfully applied in medicine, genetics, and ecology. However, in manufacturing, only experimental and scattered applications are known; no comprehensive overview about how causal discovery can be applied in manufacturing is available. This paper investigates the state and development of research on causal discovery in manufacturing by focusing on motivations for application, common application scenarios and approaches, impacts, and implementation challenges. Based on the structured literature review, four core areas are identified, and a research agenda is proposed.


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