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Frame ME, Kaiser J, Kegley J, Armstrong J, Schlessman B. Impacts of decision support systems on cognition and performance for intelligence-gathering path planning. Mil Psychol 2024; 36:323-339. [PMID: 38661460 DOI: 10.1080/08995605.2023.2178210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 01/12/2023] [Indexed: 02/18/2023]
Abstract
Decision Support Systems (DSS) are tools designed to help operators make effective choices in workplace environments where discernment and critical thinking are required for effective performance. Path planning in military operations and general logistics both require individuals to make complex and time-sensitive decisions. However, these decisions can be complex and involve the synthesis of numerous tradeoffs for various paths with dynamically changing conditions. Intelligence collection can vary in difficulty, specifically in terms of the disparity between locations of interest and timing restrictions for when and how information can be collected. Furthermore, plans may need to be changed adaptively mid-operation, as new collection requirements appear, increasing task difficulty. We tested participants in a path planning decision-making exercise with scenarios of varying difficulty in a series of two experiments. In the first experiment, each map displayed two paths simultaneously, relating to two possible routes for the two available trucks. Participants selected the optimal path plan, representing the best solution across multiple routes. In the second experiment, each map displayed a single path, and participants selected the best two paths sequentially. In the first experiment, utilizing the DSS was predictive of adoption of more heuristic decision strategies, and that strategic approach yielded more optimal route selection. In the second experiment, there was a direct effect of the DSS on increased decision performance and a decrease in perceived task workload.
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Affiliation(s)
- Mary E Frame
- Research and Development Department, Parallax Advanced Research, Beavercreek, Ohio
| | | | - John Kegley
- 711th Human Performance Wing (711th HPW), Air Force Research Laboratory, Dayton, Ohio
| | - Jessica Armstrong
- 711th Human Performance Wing (711th HPW), Air Force Research Laboratory, Dayton, Ohio
| | - Bradley Schlessman
- 711th Human Performance Wing (711th HPW), Air Force Research Laboratory, Dayton, Ohio
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Spoladore D, Tosi M, Lorenzini EC. Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review. Artif Intell Med 2024; 151:102859. [PMID: 38564880 DOI: 10.1016/j.artmed.2024.102859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/05/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024]
Abstract
Diabetes is a non-communicable disease that has reached epidemic proportions, affecting 537 million people globally. Artificial Intelligence can support patients or clinicians in diabetes nutrition therapy - the first medical therapy in most cases of Type 1 and Type 2 diabetes. In particular, ontology-based recommender and decision support systems can deliver a computable representation of experts' knowledge, thus delivering patient-tailored nutritional recommendations or supporting clinical personnel in identifying the most suitable diet. This work proposes a systematic literature review of the domain ontologies describing diabetes in such systems, identifying their underlying conceptualizations, the users targeted by the systems, the type(s) of diabetes tackled, and the nutritional recommendations provided. This review also delves into the structure of the domain ontologies, highlighting several aspects that may hinder (or foster) their adoption in recommender and decision support systems for diabetes nutrition therapy. The results of this review process allow to underline how recommendations are formulated and the role of clinical experts in developing domain ontologies, outlining the research trends characterizing this research area. The results also allow for identifying research directions that can foster a preeminent role for clinical experts and clinical guidelines in a cooperative effort to make ontologies more interoperable - thus enabling them to play a significant role in the decision-making processes about diabetes nutrition therapy.
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Affiliation(s)
- Daniele Spoladore
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing - National Research Council, (CNR-STIIMA), Lecco, Italy.
| | - Martina Tosi
- Department of Health Sciences, University of Milan, 20142 Milan, Italy; Institute of Agricultural Biology and Biotechnology - National Research Council (CNR-IBBA), Milan, Italy.
| | - Erna Cecilia Lorenzini
- Department of Biomedical Sciences for Health, University of Milan, I-20133 Milan, Italy.
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Tokgöz P, Krayter S, Hafner J, Dockweiler C. Decision support systems for antibiotic prescription in hospitals: a survey with hospital managers on factors for implementation. BMC Med Inform Decis Mak 2024; 24:96. [PMID: 38622595 PMCID: PMC11020884 DOI: 10.1186/s12911-024-02490-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 03/25/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Inappropriate antimicrobial use, such as antibiotic intake in viral infections, incorrect dosing and incorrect dosing cycles, has been shown to be an important determinant of the emergence of antimicrobial resistance. Artificial intelligence-based decision support systems represent a potential solution for improving antimicrobial prescribing and containing antimicrobial resistance by supporting clinical decision-making thus optimizing antibiotic use and improving patient outcomes. OBJECTIVE The aim of this research was to examine implementation factors of artificial intelligence-based decision support systems for antibiotic prescription in hospitals from the perspective of the hospital managers, who have decision-making authority for the organization. METHODS An online survey was conducted between December 2022 and May 2023 with managers of German hospitals on factors for decision support system implementation. Survey responses were analyzed from 118 respondents through descriptive statistics. RESULTS Survey participants reported openness towards the use of artificial intelligence-based decision support systems for antibiotic prescription in hospitals but little self-perceived knowledge in this field. Artificial intelligence-based decision support systems appear to be a promising opportunity to improve quality of care and increase treatment safety. Along with the Human-Organization-Technology-fit model attitudes were presented. In particular, user-friendliness of the system and compatibility with existing technical structures are considered to be important for implementation. The uptake of decision support systems also depends on the ability of an organization to create a facilitating environment that helps to address the lack of user knowledge as well as trust in and skepticism towards these systems. This includes the training of user groups and support of the management level. Besides, it has been assessed to be important that potential users are open towards change and perceive an added value of the use of artificial intelligence-based decision support systems. CONCLUSION The survey has revealed the perspective of hospital managers on different factors that may help to address implementation challenges for artificial intelligence-based decision support systems in antibiotic prescribing. By combining factors of user perceptions about the systems´ perceived benefits with external factors of system design requirements and contextual conditions, the findings highlight the need for a holistic implementation framework of artificial intelligence-based decision support systems.
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Affiliation(s)
- Pinar Tokgöz
- School of Life Sciences, Department Digital Health Sciences and Biomedicine, Professorship of Digital Public Health, University of Siegen, 57068, Siegen, Germany.
| | - Stephan Krayter
- School of Life Sciences, Department Digital Health Sciences and Biomedicine, Professorship of Digital Public Health, University of Siegen, 57068, Siegen, Germany
| | - Jessica Hafner
- School of Life Sciences, Department Digital Health Sciences and Biomedicine, Professorship of Digital Public Health, University of Siegen, 57068, Siegen, Germany
| | - Christoph Dockweiler
- School of Life Sciences, Department Digital Health Sciences and Biomedicine, Professorship of Digital Public Health, University of Siegen, 57068, Siegen, Germany
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Andargoli AE, Ulapane N, Nguyen TA, Shuakat N, Zelcer J, Wickramasinghe N. Intelligent decision support systems for dementia care: A scoping review. Artif Intell Med 2024; 150:102815. [PMID: 38553156 DOI: 10.1016/j.artmed.2024.102815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 12/11/2023] [Accepted: 02/12/2024] [Indexed: 04/02/2024]
Abstract
In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted a comprehensive scoping review of empirical studies that utilised AI-powered clinical decision support systems in dementia care. The results indicate that AI applications in dementia care primarily focus on diagnosis, with limited attention to other aspects outlined in the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia 2017-2025 (GAPD). A trifecta of challenges, encompassing data availability, cost considerations, and AI algorithm performance, emerges as noteworthy barriers in adoption of AI applications in dementia care. To address these challenges and enhance AI reliability, we propose a novel approach: a digital twin-based patient journey model. Future research should address identified gaps in GAPD action areas, navigate data-related obstacles, and explore the implementation of digital twins. Additionally, it is imperative to emphasize that addressing trust and combating the stigma associated with AI in healthcare should be a central focus of future research directions.
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Affiliation(s)
| | | | - Tuan Anh Nguyen
- Swinburne University of Technology, Melbourne, Australia; National Ageing Research Institute, Australia
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Michel J, Manns A, Boudersa S, Jaubert C, Dupic L, Vivien B, Burgun A, Campeotto F, Tsopra R. Clinical decision support system in emergency telephone triage: A scoping review of technical design, implementation and evaluation. Int J Med Inform 2024; 184:105347. [PMID: 38290244 DOI: 10.1016/j.ijmedinf.2024.105347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/09/2024] [Accepted: 01/21/2024] [Indexed: 02/01/2024]
Abstract
OBJECTIVES Emergency department overcrowding could be improved by upstream telephone triage. Emergency telephone triage aims at managing and orientating adequately patients as early as possible and distributing limited supply of staff and materials. This complex task could be improved with the use of Clinical decision support systems (CDSS). The aim of this scoping review was to identify literature gaps for the future development and evaluation of CDSS for Emergency telephone triage. MATERIALS AND METHODS We present here a scoping review of CDSS designed for emergency telephone triage, and compared them in terms of functional characteristics, technical design, health care implementation and methodologies used for evaluation, following the PRISMA-ScR guidelines. RESULTS Regarding design, 19 CDSS were retrieved: 12 were knowledge based CDSS (decisional algorithms built according to guidelines or clinical expertise) and 7 were data driven (statistical, machine learning, or deep learning models). Most of them aimed at assisting nurses or non-medical staff by providing patient orientation and/or severity/priority assessment. Eleven were implemented in real life, and only three were connected to the Electronic Health Record. Regarding evaluation, CDSS were assessed through various aspects: intrinsic characteristics, impact on clinical practice or user apprehension. Only one pragmatic trial and one randomized controlled trial were conducted. CONCLUSION This review highlights the potential of a hybrid system, user tailored, flexible, connected to the electronic health record, which could work with oral, video and digital data; and the need to evaluate CDSS on intrinsic characteristics and impact on clinical practice, iteratively at each distinct stage of the IT lifecycle.
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Affiliation(s)
- Julie Michel
- SAMU 93-UF Recherche-Enseignement-Qualité, Université Paris 13, Sorbonne Paris Cité, Inserm U942, Hôpital Avicenne, 125, rue de Stalingrad, 93009 Bobigny, France
| | - Aurélia Manns
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France.
| | - Sofia Boudersa
- Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France
| | - Côme Jaubert
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France
| | - Laurent Dupic
- Régulation Régionale Pédiatrique, SAMU de Paris, Hôpital Necker - Enfants Malades, AP-HP, Paris, France
| | - Benoit Vivien
- Digital Health Program of Université de Paris Cité, Paris, France; Régulation Régionale Pédiatrique, SAMU de Paris, Hôpital Necker - Enfants Malades, AP-HP, Paris, France
| | - Anita Burgun
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France
| | - Florence Campeotto
- Digital Health Program of Université de Paris Cité, Paris, France; Régulation Régionale Pédiatrique, SAMU de Paris, Hôpital Necker - Enfants Malades, AP-HP, Paris, France; Faculté de Pharmacie, Université de Paris Cité, Inserm UMR S1139, Paris, France
| | - Rosy Tsopra
- Université Paris Cité, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, F-75006 Paris, France; Department of Medical Informatics, AP-HP, Hôpital Européen Georges-Pompidou et Hôpital Necker-Enfants Malades, F-75015 Paris, France
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Renfrew D, Vasilaki V, Katsou E. Indicator based multi-criteria decision support systems for wastewater treatment plants. Sci Total Environ 2024; 915:169903. [PMID: 38199342 DOI: 10.1016/j.scitotenv.2024.169903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 12/17/2023] [Accepted: 01/02/2024] [Indexed: 01/12/2024]
Abstract
Wastewater treatment plant decision makers face stricter regulations regarding human health protection, environmental preservation, and emissions reduction, meaning they must improve process sustainability and circularity, whilst maintaining economic performance. This creates complex multi-objective problems when operating and selecting technologies to meet these demands, resulting in the development of many decision support systems for the water sector. European Commission publications highlight their ambition for greater levels of sustainability, circularity, and environmental and human health protection, which decision support system implementation should align with to be successful in this region. Following the review of 57 wastewater treatment plant decision support systems, the main function of multi-criteria decision-making tools are technology selection and the optimisation of process operation. A large contrast regarding their aims is found, as process optimisation tools clearly define their goals and indicators used, whilst technology selection procedures often use vague language making it difficult for decision makers to connect selected indicators and resultant outcomes. Several recommendations are made to improve decision support system usage, such as more rigorous indicator selection protocols including participatory selection approaches and expansion of indicators sets, as well as more structured investigation of results including the use of sensitivity or uncertainty analysis, and error quantification.
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Affiliation(s)
- D Renfrew
- Department of Civil & Environmental Engineering, Institute of Environment, Health and Societies, Brunel University London, Uxbridge Campus, Middlesex, UB8 3PH Uxbridge, UK
| | - V Vasilaki
- Department of Civil & Environmental Engineering, Institute of Environment, Health and Societies, Brunel University London, Uxbridge Campus, Middlesex, UB8 3PH Uxbridge, UK
| | - E Katsou
- Department of Civil & Environmental Engineering, Imperial College London, London SW7 2AZ, UK.
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Ohno Y, Aoki T, Endo M, Koyama H, Moriya H, Okada F, Higashino T, Sato H, Oyama-Manabe N, Haraguchi T, Arakita K, Aoyagi K, Ikeda Y, Kaminaga S, Taniguchi A, Sugihara N. Machine learning-based computer-aided simple triage (CAST) for COVID-19 pneumonia as compared with triage by board-certified chest radiologists. Jpn J Radiol 2024; 42:276-290. [PMID: 37861955 PMCID: PMC10899374 DOI: 10.1007/s11604-023-01495-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/22/2023] [Indexed: 10/21/2023]
Abstract
PURPOSE Several reporting systems have been proposed for providing standardized language and diagnostic categories aiming for expressing the likelihood that lung abnormalities on CT images represent COVID-19. We developed a machine learning (ML)-based CT texture analysis software for simple triage based on the RSNA Expert Consensus Statement system. The purpose of this study was to conduct a multi-center and multi-reader study to determine the capability of ML-based computer-aided simple triage (CAST) software based on RSNA expert consensus statements for diagnosis of COVID-19 pneumonia. METHODS For this multi-center study, 174 cases who had undergone CT and polymerase chain reaction (PCR) tests for COVID-19 were retrospectively included. Their CT data were then assessed by CAST and consensus from three board-certified chest radiologists, after which all cases were classified as either positive or negative. Diagnostic performance was then compared by McNemar's test. To determine radiological finding evaluation capability of CAST, three other board-certified chest radiologists assessed CAST results for radiological findings into five criteria. Finally, accuracies of all radiological evaluations were compared by McNemar's test. RESULTS A comparison of diagnosis for COVID-19 pneumonia based on RT-PCR results for cases with COVID-19 pneumonia findings on CT showed no significant difference of diagnostic performance between ML-based CAST software and consensus evaluation (p > 0.05). Comparison of agreement on accuracy for all radiological finding evaluations showed that emphysema evaluation accuracy for investigator A (AC = 91.7%) was significantly lower than that for investigators B (100%, p = 0.0009) and C (100%, p = 0.0009). CONCLUSION This multi-center study shows COVID-19 pneumonia triage by CAST can be considered at least as valid as that by chest expert radiologists and may be capable for playing as useful a complementary role for management of suspected COVID-19 pneumonia patients as well as the RT-PCR test in routine clinical practice.
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Affiliation(s)
- Yoshiharu Ohno
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan.
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan.
| | - Takatoshi Aoki
- Department of Radiology, University of Occupational and Environmental Health School of Medicine, Kitakyusyu, Fukuoka, Japan
| | - Masahiro Endo
- Division of Diagnostic Radiology, Shizuoka Cancer Center, Sunto-Gun, Nagaizumi-Cho, Shizuoka, Japan
| | - Hisanobu Koyama
- Department of Radiology, Advanced Diagnostic Medical Imaging, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Hiroshi Moriya
- Department of Radiology, Ohara General Hospital, Fukushima, Fukushima, Japan
| | - Fumito Okada
- Department of Radiology, Oita Prefectural Hospital, Oita, Oita, Japan
| | - Takanori Higashino
- Department of Radiology, National Hospital Organization Himeji Medical Center, Himeji, Hyogo, Japan
| | - Haruka Sato
- Department of Radiology, Oita University Faculty of Medicine, Yufu, Oita, Japan
| | - Noriko Oyama-Manabe
- Department of Radiology, Jichi Medical University Saitama Medical Center, Saitama, Saitama, Japan
| | - Takafumi Haraguchi
- Department of Advanced Biomedical Imaging and Informatics, St. Marianna University School of Medicine, Kawasaki, Kanagawa, Japan
| | | | - Kota Aoyagi
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | | | | | | | - Naoki Sugihara
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
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Smet S, Verhaeghe S, Beeckman D, Fourie A, Beele H. The process of clinical decision-making in chronic wound care: A scenario-based think-aloud study. J Tissue Viability 2024:S0965-206X(24)00027-5. [PMID: 38461069 DOI: 10.1016/j.jtv.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 03/01/2024] [Indexed: 03/11/2024]
Abstract
AIMS To undertake a comprehensive investigation into both the process of information acquisition and the clinical decision-making process utilized by primary care nurses in the course of treating chronic wounds. DESIGN Scenario-based think-aloud method, enriched by the integration of information processing theory. The study was conducted within the framework of home care nursing organizations situated in [placeholder]. A cohort of primary care nurses (n = 10), each possessing a minimum of one year of nursing experience, was recruited through the collaboration of three home care nursing organizations. METHODS Two real-life clinical practice scenarios were employed for the interviews, with the researcher adopting the roles of either the patient or another clinician to enhance the realism of the think-aloud process. Each think-aloud session was promptly succeeded by a subsequent follow-up interview. The Consolidated criteria for Reporting Qualitative research checklist was followed to guarantee a consistent and complete report of the study. RESULTS Amidst noticeable variations, a discernible pattern surfaced, delineating three sequential concepts: 1. gathering overarching information, 2. collecting and documenting wound-specific data, and 3. interpreting information to formulate wound treatment strategies. These concepts encompassed collaborative discussions with stakeholders, while the refinement of wound treatment strategies was interwoven within both concepts 2 and 3. CONCLUSIONS Evident variations were identified in chronic wound care clinical decision-making, regardless of educational background or experience. These insights hold the potential to inform the development of clinical decision support systems for chronic wound management and provide guidance to clinicians in their decision-making endeavours.
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Affiliation(s)
- Steven Smet
- Wound Care Centre, Ghent University Hospital, Ghent, Belgium; Skin Integrity Research Group (SKINT), University Centre for Nursing and Midwifery (UCVV), Department of Public Health and Primary Care, Ghent University, Ghent, Belgium.
| | - Sofie Verhaeghe
- University Centre for Nursing and Midwifery (UCVV), Department of Public Health and Primary Care, Ghent University, Ghent, Belgium.
| | - Dimitri Beeckman
- Skin Integrity Research Group (SKINT), University Centre for Nursing and Midwifery (UCVV), Department of Public Health and Primary Care, Ghent University, Ghent, Belgium; Swedish Centre for Skin and Wound Research (SCENTR), School of Health Sciences, Örebro University, Örebro, Sweden. https://twitter.com/DimitriBeeckman
| | - Anika Fourie
- Skin Integrity Research Group (SKINT), University Centre for Nursing and Midwifery (UCVV), Department of Public Health and Primary Care, Ghent University, Ghent, Belgium. https://twitter.com/anika_fourie
| | - Hilde Beele
- Wound Care Centre, Ghent University Hospital, Ghent, Belgium; Department of Dermatology, Ghent University Hospital, Ghent, Belgium.
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Navarro J, Aguarón J, Moreno-Jiménez JM, Turón A. Social mood during the Covid-19 vaccination process in Spain. A sentiment analysis of tweets and social network leaders. Heliyon 2024; 10:e23958. [PMID: 38332867 PMCID: PMC10851300 DOI: 10.1016/j.heliyon.2023.e23958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/04/2023] [Accepted: 12/19/2023] [Indexed: 02/10/2024] Open
Abstract
In accordance with the cognitive orientation contemplated in the resolution of complex problems posed in public decision-making using decision support systems and social networks, this work studies the possibility of identifying the state of mind of society through the state of mind of network leaders. Using sentiment and emotion analysis as research techniques and Twitter as a representative social network, the study corpus considers tweets and retweets in Spanish about COVID-19 in the period from February 27, 2020 to December 31, 2021. As cognitive orientation claims, the proposed techniques will allow us to extract the arguments that support the different positions and decisions from the analysis of the tweets issued exclusively by social leaders. In the case study considered, the COVID-19 vaccination process in Spain, the reduction in the number of tweets' authors (more than 8,000) to the network leaders (just 8) was greater than 99 %; and the subsequent reduction in the number of associated tweets was greater than 88 % from the 18,193 tweets in society to the 2,145 tweets of the eight social leaders. The impressive degree of information compression achieved may be useful to establish new directions of social mood analysis applied to healthcare and business management.
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Affiliation(s)
- Jorge Navarro
- Grupo Decisión Multicriterio Zaragoza (GDMZ), Department of Applied Economics, Faculty of Economics and Business, University of Zaragoza, Gran Vía 2, 50005, Zaragoza, Spain
| | - Juan Aguarón
- Grupo Decisión Multicriterio Zaragoza (GDMZ), Department of Applied Economics, Faculty of Economics and Business, University of Zaragoza, Gran Vía 2, 50005, Zaragoza, Spain
| | - José María Moreno-Jiménez
- Grupo Decisión Multicriterio Zaragoza (GDMZ), Department of Applied Economics, Faculty of Economics and Business, University of Zaragoza, Gran Vía 2, 50005, Zaragoza, Spain
| | - Alberto Turón
- Grupo Decisión Multicriterio Zaragoza (GDMZ), Department of Applied Economics, Faculty of Economics and Business, University of Zaragoza, Gran Vía 2, 50005, Zaragoza, Spain
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Marzouk M, Azab S. Modeling climate change adaptation for sustainable coastal zones using GIS and AHP. Environ Monit Assess 2024; 196:147. [PMID: 38221585 PMCID: PMC10788322 DOI: 10.1007/s10661-023-12287-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 12/29/2023] [Indexed: 01/16/2024]
Abstract
The world is currently confronting one of its biggest environmental challenges: combating climate change. Coastal zones are one of the areas thought to be most sensitive to current and future climate change threats. The paper integrates Remote Sensing (RS), Geographic Information System (GIS) techniques, and Multi-Criteria Decision Analysis (MCDA) to detect vulnerable areas from climate change impacts in coastal zones in order to recommend adaptation systems in new coastal zones that can withstand various climatic changes. The proposed decision-making framework was developed in three phases: 1) climate data collection and processing; 2) Coastal Climate Impact Assessment (CCIA) model development; and 3) implementation and adaptation system selection. The climate data collection and processing phase involves determining the most significant climate change parameters and their indicators that affect coastal zone stability, extracting climatic data indicators from different climate database sources, and prioritizing the selected indicators. The indicators' weights were estimated using the Analytical Hierarchy Process (AHP) through a questionnaire survey shared with experts in climate change impacts. A CCIA model development phase involves the formulation of the proposed model using GIS technique to discover the vulnerable areas according to the most dominant impact. The implementation and adaptation system selection phase involves the application of the framework to Al-Alamein New City in Egypt. A sensitivity analysis was conducted to measure the behavior of several climate change parameters to identify the most critical parameter for climate change in Al-Alamein New City. The results showed that the geology of the region is the most crucial component influenced by climate change. It is capable of producing a very sensitive area in the coastal zone while also taking other factors into account. When creating new urban neighborhoods, the erosion of the shoreline is the least important factor to consider. This is because coastal deterioration is caused by both the influence of metrological data on the region and the impact of human activity. Shoreline deterioration will be reduced if climate conditions are maintained while limiting the impact of human activities. To adapt to the long-term effects of climate change on coastal zones, a combination of soft and hard protection systems should be considered.
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Affiliation(s)
- Mohamed Marzouk
- Structural Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt.
| | - Shimaa Azab
- Environmental Planning and Development Center, Institute of National Planning, (INP), Cairo, Egypt
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Tatasciore M, Loft S. Can increased automation transparency mitigate the effects of time pressure on automation use? Appl Ergon 2024; 114:104142. [PMID: 37757606 DOI: 10.1016/j.apergo.2023.104142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/12/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023]
Abstract
A factor that can potentially negatively impact the accuracy of automated decision aid use, and increase perceived workload, is time pressure. Increased automation transparency can increase the accuracy of automation use. We examined the extent to which increased transparency can mitigate the negative effects of time pressure on the accuracy of automation use and perceived workload. Participants completed an uninhabited vehicle (UV) management task where they assigned the best UV to complete missions by either accepting or rejecting automated advice. Participants made a decision after either 25s (low time pressure) or 12s (high time pressure). The accuracy of automation use decreased, and perceived workload increased, when under higher time pressure. Higher transparency benefited the accuracy of automation use and increased perceived trust and usability. However, high transparency did not mitigate the negative impacts of high time pressure, indicating that increased time pressure can influence the processing of highly transparent information.
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Affiliation(s)
| | - Shayne Loft
- The University of Western Australia, Australia
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12
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Abstract
Clinical decision support system (CDSS) plays an essential role nowadays and CDSS for treatment provides clinicians with the clinical evidence of candidate prescriptions to assist them in making patient-specific decisions. Therefore, it is essential to find a partition of patients such that patients with similar clinical conditions are grouped together and the preferred prescriptions for different groups are diverged. A comprehensive clinical guideline often provides information of patient partition. However, for most diseases, the guideline is not so detailed that only limited circumstances are covered. This makes it challenging to group patients properly. Here we proposed an approach that combines clinical guidelines with medical data to construct a nested decision tree for patient partitioning and treatment recommendation. Compared with pure data-driven decision tree, the recommendations generated by our model have better guideline adherence and interpretability. The approach was successfully applied in a real-world case study of patients with hyperthyroidism.
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Affiliation(s)
- Wei Zhao
- Ping An Health Technology, Beijing, China
| | | | - Ke Wang
- Ping An Health Technology, Beijing, China
| | | | - Gang Hu
- Ping An Health Technology, Beijing, China
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13
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Françozo RV, Junior LSVU, Carrapateira ES, Pacheco BCS, Oliveira MT, Torsoni GB, Yari J. A web-based software for group decision with analytic hierarchy process. MethodsX 2023; 11:102277. [PMID: 37519948 PMCID: PMC10372897 DOI: 10.1016/j.mex.2023.102277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/29/2023] [Indexed: 08/01/2023] Open
Abstract
The Analytic Hierarchy Process (AHP) is a multi-criteria decision support method and is widely applied in many areas. The original AHP method proposed by Thomas L. Saaty in the 1970s requires (n²-n)/2 comparisons. The number of required comparisons can make using this method challenging for maintaining consistent judgments in problems involving many criteria and/or alternatives. Furthermore, the available software is platform-dependent and generally does not support group decision-making. In this paper, we present software for AHP that demands n-1 comparisons. Additionally, the software supports group decision-making using individual aggregation of priorities with arithmetic and geometric means. The system is available at http://ahpweb.net/ and is accessible from any internet-connected device. It currently has more than 100 users and dozens of decision problems in various areas.•The original AHP formulation requires (n²-n)/2 comparisons per cluster which makes it difficult to make consistent judgments.•AHP avaliable software does not enable group decision making.•The proposed system AHP-WEB fills these gaps. The method demands n-1 comparisons per cluster without any inconsistency and allows group decision making on a web system.
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Affiliation(s)
| | | | | | | | | | | | - Jiyan Yari
- Federal Institute of Education Science and Technology of Mato Grosso do Sul, Brazil
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14
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Diaz C JL, Villa-Tamayo MF, Moscoso-Vasquez M, Colmegna P. Simulation-driven optimization of insulin therapy profiles in a commercial hybrid closed-loop system. Comput Methods Programs Biomed 2023; 242:107830. [PMID: 37806122 DOI: 10.1016/j.cmpb.2023.107830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/19/2023] [Accepted: 09/23/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND Automated insulin delivery (AID) has represented a breakthrough in managing type 1 diabetes (T1D), showing safe and effective glucose control extensively across the board. However, metabolic variability still poses a challenge to commercial hybrid closed-loop (HCL) solutions, whose performance depends on customizable insulin therapy profiles. In this work, we propose an Identification-Replay-Optimization (IRO) approach to optimize gradually and safely such profiles for the Control-IQ AID algorithm. METHODS Closed-loop data are generated using the full adult cohort of the UVA/Padova T1D simulation platform in diverse glycemic scenarios. For each subject, daily records are processed and used to estimate a personalized model of the underlying insulin-glucose dynamics. Every two weeks, all identified models are integrated into an optimization procedure where daily basal and bolus profiles are adjusted so as to minimize the risks for hypo- and hyperglycemia. The proposed strategy is tested under different scenarios of metabolic and behavioral variability in order to evaluate the efficacy and convergence of the proposed strategy. Finally, glycemic metrics between cycles are compared using paired t-tests with p<0.05 as the significance threshold. RESULTS Simulations reveal that the proposed IRO approach was able to improve glucose control over time by safely mitigating the risks for both hypo- and hyperglycemia. Furthermore, smaller changes were recommended at each cycle, indicating convergence when simulation conditions were maintained. CONCLUSIONS The use of reliable simulation-driven tools capable of accurately reproducing field-collected data and predicting changes can substantially shorten the process of optimizing insulin therapy, adjusting it to metabolic changes and leading to improved glucose control.
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Affiliation(s)
- Jenny L Diaz C
- Center for Diabetes Technology, University of Virginia, Charlottesville, 22903, VA, USA.
| | - María F Villa-Tamayo
- Center for Diabetes Technology, University of Virginia, Charlottesville, 22903, VA, USA
| | | | - Patricio Colmegna
- Center for Diabetes Technology, University of Virginia, Charlottesville, 22903, VA, USA
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15
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Zsidai B, Hilkert AS, Kaarre J, Narup E, Senorski EH, Grassi A, Ley C, Longo UG, Herbst E, Hirschmann MT, Kopf S, Seil R, Tischer T, Samuelsson K, Feldt R. A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges. J Exp Orthop 2023; 10:117. [PMID: 37968370 PMCID: PMC10651597 DOI: 10.1186/s40634-023-00683-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/21/2023] [Indexed: 11/17/2023] Open
Abstract
Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV.
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Affiliation(s)
- Bálint Zsidai
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden.
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
| | - Ann-Sophie Hilkert
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Medfield Diagnostics AB, Gothenburg, Sweden
| | - Janina Kaarre
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine Center, University of Pittsburgh, Pittsburgh, USA
| | - Eric Narup
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eric Hamrin Senorski
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Sportrehab Sports Medicine Clinic, Gothenburg, Sweden
| | - Alberto Grassi
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- IIa Clinica Ortopedica E Traumatologica, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Christophe Ley
- Department of Mathematics, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
| | - Umile Giuseppe Longo
- Department of Orthopaedic and Trauma Surgery, Campus Bio-Medico University, Rome, Italy
| | - Elmar Herbst
- Department of Trauma, Hand and Reconstructive Surgery, University Hospital Münster, Münster, Germany
| | - Michael T Hirschmann
- Department of Orthopedic Surgery and Traumatology, Head Knee Surgery and DKF Head of Research, Kantonsspital Baselland, 4101, Bruderholz, Switzerland
| | - Sebastian Kopf
- Center of Orthopaedics and Traumatology, University Hospital Brandenburg a.d.H., Brandenburg Medical School Theodor Fontane, 14770, Brandenburg a.d.H., Germany
- Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, 14770, Brandenburg a.d.H., Germany
| | - Romain Seil
- Department of Orthopaedic Surgery, Centre Hospitalier Luxembourg and Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Thomas Tischer
- Clinic for Orthopaedics and Trauma Surgery, Malteser Waldkrankenhaus St. Marien, Erlangen, Germany
| | - Kristian Samuelsson
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Orthopaedics, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Robert Feldt
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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16
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Eckstein J. [Artificial intelligence in internal medicine : From the theory to practical application in practices and hospitals]. Inn Med (Heidelb) 2023; 64:1017-1022. [PMID: 37847260 PMCID: PMC10602942 DOI: 10.1007/s00108-023-01604-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/19/2023] [Indexed: 10/18/2023]
Abstract
The integration of artificial intelligence (AI) technologies has the potential to improve both the efficiency and the quality of medical care. Applications of AI have already become established in various specialized fields in internal medicine, whereas in other fields the applications are still in various phases of development. An aspect that is important to elucidate is the effects of AI on the interaction between patients and healthcare personnel. A further factor is the comprehensibility of the mode of functioning of the AI-based algorithms involved. In addition to the necessary confidence-building measures, an integration of the technology into existing systems must be strived for to achieve an appropriate acceptance and widespread availability and to relieve pressure on the personnel at the administrative level.
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Affiliation(s)
- Jens Eckstein
- Klinik für Innere Medizin, Universitätsspital Basel, Basel, Schweiz.
- Innovationsmanagement, Universitätsspital Basel, Hebelstr. 10, 4031, Basel, Schweiz.
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17
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Pitt E, Bradford N, Robertson E, Sansom-Daly UM, Alexander K. The effects of cancer clinical decision support systems on patient-reported outcomes: A systematic review. Eur J Oncol Nurs 2023; 66:102398. [PMID: 37633024 DOI: 10.1016/j.ejon.2023.102398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/09/2023] [Accepted: 07/15/2023] [Indexed: 08/28/2023]
Abstract
PURPOSE The implementation of high-quality decision-making support are integral to ensuring the delivery of quality cancer care and subsequently achieving positive patient outcomes. Decision Support Systems (DSS) are increasingly used, however it is not known what the effects are beyond supporting the decision-making process. We aimed to identify and synthesize the available literature regarding the effects of DSS on patient-reported outcomes both during and after cancer treatment. METHODS A systematic review was conducted using dual processes to identify empirical literature that reported an evaluation of DSS interventions and patient-reported outcomes. We appraised study quality using the Mixed Methods Appraisal Tool (MMAT). Data were narratively synthesized. RESULTS We included 15 studies, categorized as symptom assessment interventions or interactive educational interventions. Findings were mixed regarding the effectiveness of DSS interventions in improving total symptom distress and severity, whereas the majority were effective in reducing mean scores for worst and usual pain. Interventions were not effective in improving other health-related patient-reported outcomes including quality of life, global distress, depression, or self-efficacy and there were mixed effects for reducing decisional conflict. There was moderate to high patient adherence to the interventions and generally high satisfaction and acceptability, yet minimal evidence for the effect of DSS interventions in clinician adherence to intervention recommendations. CONCLUSIONS Including patient-reported outcomes in the evaluation of DSS is critical to understand their impact. Inconsistencies in reporting of interventions may, however, be a contributing factor to heterogeneous effects of clinical DSS regarding a broad range of patient-reported outcomes.
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Affiliation(s)
- Erin Pitt
- Cancer and Palliative Care Outcomes Centre and Centre for Healthcare Transformation, Queensland University of Technology (QUT), 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia; Faculty of Health, Queensland University of Technology (QUT), Victoria Park Rd, Kelvin Grove, QLD, 4059, Australia; Centre for Children's Health Research, Children's Health Queensland Hospital and Health Service, 62 Graham St, South Brisbane, QLD, 4101, Australia.
| | - Natalie Bradford
- Cancer and Palliative Care Outcomes Centre and Centre for Healthcare Transformation, Queensland University of Technology (QUT), 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia; Faculty of Health, Queensland University of Technology (QUT), Victoria Park Rd, Kelvin Grove, QLD, 4059, Australia; Centre for Children's Health Research, Children's Health Queensland Hospital and Health Service, 62 Graham St, South Brisbane, QLD, 4101, Australia.
| | - Eden Robertson
- School of Women's and Children's Health, UNSW Medicine, UNSW Sydney, High St, Kensington, NSW, 2052, Australia.
| | - Ursula M Sansom-Daly
- School of Women's and Children's Health, UNSW Medicine, UNSW Sydney, High St, Kensington, NSW, 2052, Australia; Behavioural Sciences Unit, Kids Cancer Centre, Sydney Children's Hospital, High St, Randwick, NSW, 2031, Australia; Sydney Youth Cancer Service, Nelune Comprehensive Cancer Centre, Prince of Wales Hospital, High Street, Randwick, NSW, Australia.
| | - Kimberly Alexander
- Cancer and Palliative Care Outcomes Centre and Centre for Healthcare Transformation, Queensland University of Technology (QUT), 60 Musk Ave, Kelvin Grove, QLD, 4059, Australia; Faculty of Health, Queensland University of Technology (QUT), Victoria Park Rd, Kelvin Grove, QLD, 4059, Australia.
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18
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Rieul G, Rojat G, Reizine F, Beloeil H. Organizing and Sharing Medical Knowledge Among Anesthesiology and Intensive care Residents: Evaluating Existing Practices and the Feasibility of Implementing a Dedicated Multiplatform Application. J Med Syst 2023; 47:101. [PMID: 37749281 DOI: 10.1007/s10916-023-01996-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 09/15/2023] [Indexed: 09/27/2023]
Abstract
Treating patients with up-to-date medical knowledge is an ongoing goal for healthcare workers and implies efficient knowledge management at the point of care. Widely available mobile wireless technologies influence practices but a significant gap remains between technological possibilities and actual usage. The purpose of this study was to analyze residents' baseline practices in managing medical knowledge and to evaluate the use and impact of an innovative multiplatform application dedicated to anesthesiology and intensive care residents. This study took place in Rennes Teaching Hospital and comprised two distinct surveys. First, in April 2018, all residents received a ten-items online survey focusing on managing medical knowledge. Then, through a second online survey constituted of ten items, we sought to assess the use of a new multiplatform cloud-based application named "DansMaBlouse", dedicated to sharing and indexing medical knowledge, in anesthesiology and intensive care residents. Among 148 residents that answered the evaluation survey, the most sought out pieces of information in clinical setting were a phone or fax number (74%), drugs' characteristics (68%) and expert guidelines (57%). The main sources were senior staff (68%), medical databases (60%) and an Internet search engine (59%). Computers and smartphones were more frequently used than bound paper notebooks. After implementation of the multiplatform application DansMaBlouse, fifty-nine (82%) of the 72 residents that answered the evaluation survey reported using the application and 39% used it more than ten times. Among application users, 90% found it easy to use and 92% agreed that it improved point-of-care access to knowledge. Accessing appropriate medical knowledge at the point of care remains an issue for residents and can be improved by a multiplatform application combining personal and shared up-to-date resources.
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Affiliation(s)
- Guillaume Rieul
- Service de Réanimation Polyvalente, Centre Hospitalier Bretagne Atlantique, Vannes, F-56000, France
- Anaesthesia and Intensive Care Department, CHU Rennes, Rennes, F-35000, France
| | - Gabrielle Rojat
- Service de Radiologie, Centre Hospitalier Bretagne Atlantique, Vannes, F-56000, France
| | - Florian Reizine
- Service de Réanimation Polyvalente, Centre Hospitalier Bretagne Atlantique, Vannes, F-56000, France.
| | - Hélène Beloeil
- Anaesthesia and Intensive Care Department, CHU Rennes, Rennes, F-35000, France
- CIC-1414, COSS-1242, Univ Rennes, CHU Rennes, Inserm, Rennes, F-35000, France
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19
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Forouzannia SM, Najafimehr H, Oskooi RK, Faridaalaee G, Dizaji SR, Toloui A, Forouzannia SA, Alavi SNR, Alizadeh M, Safari S, Baratloo A, Yousefifard M, Hosseini M. Clinical decision rules in predicting computed tomography scan findings and need for neurosurgical intervention in mild traumatic brain injury: a prospective observational study. Eur J Trauma Emerg Surg 2023:10.1007/s00068-023-02373-y. [PMID: 37747501 DOI: 10.1007/s00068-023-02373-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 09/12/2023] [Indexed: 09/26/2023]
Abstract
PURPOSE In this study, we will compare the diagnostic values of head CT decision rules in predicting the findings of CT scans in a prospective multicenter study in university emergency departments in Iran. METHODS The primary outcome was any traumatic lesion findings in brain CT scans, and the secondary outcomes were death, the need for mechanical ventilation, and neurosurgical intervention. Decision rules including the Canadian CT Head Rule (CCHR), New Orleans Criteria (NOC), National Institute for Health and Clinical Excellence (NICE), National Emergency X-Radiography Utilization Study (NEXUS), and Neurotraumatology Committee of the World Federation of Neurosurgical Societies (NCWFNS) were compared for the main outcomes. RESULTS In total, 434 mild TBI patients were enrolled in the study. The NCWFNS had the highest sensitivity (91.14%) and the lowest specificity (39.42%) for predicting abnormal finding in CT scan compared to other models. While the NICE obtained the lowest sensitivity (79.75%), it was associated with the highest specificity (66.67%). All model performances were improved when administered to predict neurosurgical intervention among patients with GCS 13-15. NEXUS (AUC 0.862, 95% CI 0.799-0.924) and NCWFNS (AUC 0.813, 95% CI 0.723-0.903) had the best performance among all evaluated models. CONCLUSION The NCWFNS and the NEXUS decision rules performed better than the CCHR and NICE guidelines for predicting any lesion in the CT imaging and neurosurgical intervention among patients with mTBI with GCS 13-15. For a subset of mTBI patients with GCS 15, the NOC criteria have higher sensitivity for abnormal CT imaging, but lower specificity and more requested CTs.
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Affiliation(s)
| | - Hadis Najafimehr
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Gholamreza Faridaalaee
- Emergency Medicine and Trauma Research Center, Tabriz University of Medical Sciences, Tabriz, IR, Iran
- Department of Emergency Medicine, Maragheh University of Medical Sciences, Maragheh, IR, Iran
| | - Shayan Roshdi Dizaji
- Physiology Research Center, Iran University of Medical Sciences, Hemmat Highway, Tehran, Iran
| | - Amirmohammad Toloui
- Physiology Research Center, Iran University of Medical Sciences, Hemmat Highway, Tehran, Iran
| | - Seyed Ali Forouzannia
- Physiology Research Center, Iran University of Medical Sciences, Hemmat Highway, Tehran, Iran
| | | | - Mohammadreza Alizadeh
- Physiology Research Center, Iran University of Medical Sciences, Hemmat Highway, Tehran, Iran
| | - Saeed Safari
- Mens' Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Emergency Department, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Baratloo
- Prehospital and Hospital Emergency Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahmoud Yousefifard
- Physiology Research Center, Iran University of Medical Sciences, Hemmat Highway, Tehran, Iran.
| | - Mostafa Hosseini
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Poursina Ave. Enghelab Ave., Tehran, Iran.
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20
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Frame ME, Acker-Mills B, Maresca A, Patterson RE, Curtis E, Buccello-Stout R, Nelson J. Evaluation of a decision support system using Bayesian network modeling in an applied Multi-INT surveillance environment. Mil Psychol 2023:1-13. [PMID: 37699140 DOI: 10.1080/08995605.2023.2250243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/02/2023] [Indexed: 09/14/2023]
Abstract
Sensemaking and decision-making are fundamental components of applied Intelligence, Surveillance, and Reconnaissance (ISR). Analysts acquire information from multiple sources over a period of hours, days, or even over the scale of months or years, that must be interpreted and integrated to predict future adversarial events. Sensemaking is essential for developing an appropriate mental model that will lead to accurate predictions sooner. Decision Support Systems (DSS) are one proposed solution to improve analyst decision-making outcomes by leveraging computers to conduct calculations that may be difficult for human operators and provide recommendations. In this study, we tested two simulated DSS that were informed by a Bayesian Network Model as a potential prediction-assistive tool. Participants completed a simulated multi-day, multi-source intelligence task and were asked to make predictions regarding five potential outcomes on each day. Participants in both DSS conditions were able to converge on the correct solution significantly faster than the control group, and between 36-44% more of the sample was able to reach the correct conclusion. Furthermore, we found that a DSS representing projected outcome probabilities as numerical, rather than using verbal ordinal labels, were better able to differentiate which outcomes were extremely unlikely than the control group or verbal-probability DSS.
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Affiliation(s)
- Mary E Frame
- Research and Development Department, Parallax Advanced Research, Beavercreek, Ohio
| | - Barbara Acker-Mills
- Research and Development Department, Parallax Advanced Research, Beavercreek, Ohio
| | - Anna Maresca
- Research and Development Department, Parallax Advanced Research, Beavercreek, Ohio
| | | | - Erica Curtis
- Research and Development Department, Parallax Advanced Research, Beavercreek, Ohio
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21
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Harl MI, Saeed M, Saeed MH, Alharbi T, Alballa T. Bipolar picture fuzzy hypersoft set-based performance analysis of abrasive textiles for enhanced quality control. Heliyon 2023; 9:e19821. [PMID: 37810007 PMCID: PMC10559222 DOI: 10.1016/j.heliyon.2023.e19821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 08/21/2023] [Accepted: 09/01/2023] [Indexed: 10/10/2023] Open
Abstract
Abrasive textiles have widespread industrial applications in the fields of polishing, finishing, deburring, and cleaning of various surfaces. Effective decision making and performance analysis are crucial in the development and manufacturing of abrasive textiles, as it enables manufacturers to evaluate and optimize the performance of these materials for specific applications and to make informed decisions about their production processes. For that purpose, this work aims to introduce an innovative bipolar picture fuzzy hypersoft set (BPFHSS) which is composed of two picture fuzzy hyper soft sets; one of them gives us the positive information, and the other gives us the negative information, for each membership degree, neutral membership, and non-membership degree. The properties of the designed structure and discussed alongside a thorough discussion on the De-Morgan's laws. Also, the bipolar picture fuzzy hypersoft weighted geometric (BPFHSWG) operator is defined for the BPFHSS framework to aggregate bipolar picture fuzzy hypersoft numbers (BPFHSN) information. This research highlights the importance of considering inconsistent, bipolar, and multiple sub-attribute information in decision-making processes by using the defined operators to develop an algorithm for a multi-attribute analysis for quality control of manufacture of abrasive textiles.
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Affiliation(s)
- Muhammad Imran Harl
- Department of Mathematics, University of Management and Technology, Lahore, 54000, Punjab, Pakistan
| | - Muhammad Saeed
- Department of Mathematics, University of Management and Technology, Lahore, 54000, Punjab, Pakistan
| | - Muhammad Haris Saeed
- Department of Chemistry, University of Management and Technology, Lahore, 54000, Punjab, Pakistan
| | - Talal Alharbi
- Department of Mathematics, College of Science and Arts in Uglat Asugour, Qassim University, Buraydah, 51411, Saudi Arabia
| | - Tmader Alballa
- Department of Mathematics, College of Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
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22
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Pan J, Huang X, Yang S, Ouyang F, Ouyang L, Wang L, Chen M, Zhou L, Du Y, Chen X, Deng L, Hu Q, Guo B. The added value of apparent diffusion coefficient and microcalcifications to the Kaiser score in the evaluation of BI-RADS 4 lesions. Eur J Radiol 2023; 165:110920. [PMID: 37320881 DOI: 10.1016/j.ejrad.2023.110920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/22/2023] [Accepted: 06/04/2023] [Indexed: 06/17/2023]
Abstract
PURPOSE To explore the added value of combining microcalcifications or apparent diffusion coefficient (ADC) with the Kaiser score (KS) for diagnosing BI-RADS 4 lesions. METHODS This retrospective study included 194 consecutive patients with 201 histologically verified BI-RADS 4 lesions. Two radiologists assigned the KS value to each lesion. Adding microcalcifications, ADC, or both these criteria to the KS yielded KS1, KS2, and KS3, respectively. The potential of all four scores to avoid unnecessary biopsies was assessed using the sensitivity and specificity. Diagnostic performance was evaluated by the area under the curve (AUC) and compared between KS and KS1. RESULTS The sensitivity of KS, KS1, KS2, and KS3 ranged from 77.1% to 100.0%.KS1 yielded significantly higher sensitivity than other methods (P < 0.05), except for KS3 (P > 0.05), most of all, when assessing NME lesions. For mass lesions, the sensitivity of these four scores was comparable (p > 0.05). The specificity of KS, KS1, KS2, and KS3 ranged from 56.0% to 69.4%, with no statistically significant differences(P > 0.05), except between KS1 and KS2 (p < 0.05).The AUC of KS1 (0.877) was significantly higher than that of KS (0.837; P = 0.0005), particularly for assessing NME (0.847 vs 0.713; P < 0.0001). CONCLUSION KS can stratify BI-RADS 4 lesions to avoid unnecessary biopsies. Adding microcalcifications, but not adding ADC, as an adjunct to KS improves diagnostic performance, particularly for NME lesions. ADC provides no additional diagnostic benefit to KS. Thus, only combining microcalcifications with KS is most conducive to clinical practice.
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Affiliation(s)
- Jialing Pan
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Xiyi Huang
- Department of Clinical Laboratory, Lecong Hospital of Shunde, Foshan, Guangdong, China
| | - Shaomin Yang
- Department of Radiology, Lecong Hospital of Shunde, Foshan, Guangdong, China
| | - Fusheng Ouyang
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Lizhu Ouyang
- Department of Ultrasound, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Liwen Wang
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Ming Chen
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Lanni Zhou
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Yongxing Du
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Xinjie Chen
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Lingda Deng
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China
| | - Qiugen Hu
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China.
| | - Baoliang Guo
- Department of Radiology, Shunde Hospital, Southern Medical University(The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, China.
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Allen CC, Swanson BL, Zhang X, Schnapp B, Ruhland SM, Bartlett HL. Optimizing efficiency of a custom clinical decision support tool improves adult congenital heart disease care. Am Heart J Plus 2023; 31:100303. [PMID: 38510558 PMCID: PMC10945959 DOI: 10.1016/j.ahjo.2023.100303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/13/2023] [Accepted: 05/19/2023] [Indexed: 03/22/2024]
Abstract
Study objective Improve the efficiency of an inpatient clinical decision support tool (CDS) for patients with adult congenital heart disease (ACHD). Design The efficiency of a CDS was evaluated across two time periods and compared. Setting An academic, tertiary care center. Participants ACHD patients roomed in an inpatient setting. Intervention Plan-Do-Study-Act (PDSA) methods were applied starting in 2021 and included refinement of diagnostic codes and the addition of department encounter codes. Main outcome measures True positive and false positive CDS alerts. Results Baseline data from 2017 had a median (IQR) of 38 (17) and 2019 baseline data had 65 (19) total alerts per month. Combining both baseline data years, the median true positive CDS alerts was 47.3 %. There were 71 (6) total alerts per month for the 2021-2022 time period and with ongoing PDSA cycles and optimization in the CDS the true positive alerts improved substantially resulting in a shifting of the median to 78.9 % within 9 months. Conclusion CDS can efficiently notify providers when an ACHD patient is encountered. The use of ICD 10 codes alone to identify ACHD patients has limited accuracy with a high proportion of false positives. Ongoing revision of the CDS system methods is important to improving efficiency and minimizing provider alert fatigue.
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Affiliation(s)
- Catherine C. Allen
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, United States of America
| | - Briana L. Swanson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, United States of America
| | - Xiao Zhang
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, United States of America
| | - Benjamin Schnapp
- Center for Clinical Knowledge Management, UW Hospitals and Clinics, Madison, WI, United States of America
| | - Sherri M. Ruhland
- Department of Cardiology, UW Hospitals and Clinics, Madison, WI, United States of America
| | - Heather L. Bartlett
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, United States of America
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, United States of America
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24
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Toffaha KM, Simsekler MCE, Omar MA. Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review. Artif Intell Med 2023; 141:102560. [PMID: 37295900 DOI: 10.1016/j.artmed.2023.102560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision support systems (DSS) can help to reduce HAPIs risks by proactively identifying patients at risk and preventing them before harming patients. OBJECTIVE This paper comprehensively reviews AI and DSS applications for HAPIs prediction using Electronic Health Records (EHR), including a systematic literature review and bibliometric analysis. METHODS A systematic literature review was conducted through PRISMA and bibliometric analysis. In February 2023, the search was performed using four electronic databases: SCOPIS, PubMed, EBSCO, and PMCID. Articles on using AI and DSS in the management of PIs were included. RESULTS The search approach yielded 319 articles, 39 of which have been included and classified into 27 AI-related and 12 DSS-related categories. The years of publication varied from 2006 to 2023, with 40% of the studies taking place in the US. Most studies focused on using AI algorithms or DSS for HAPIs prediction in inpatient units using various types of data such as electronic health records, PI assessment scales, and expert knowledge-based and environmental data to identify the risk factors associated with HAPIs development. CONCLUSIONS There is insufficient evidence in the existing literature concerning the real impact of AI or DSS on making decisions for HAPIs treatment or prevention. Most studies reviewed are solely hypothetical and retrospective prediction models, with no actual application in healthcare settings. The accuracy rates, prediction results, and intervention procedures suggested based on the prediction, on the other hand, should inspire researchers to combine both approaches with larger-scale data to bring a new venue for HAPIs prevention and to investigate and adopt the suggested solutions to the existing gaps in AI and DSS prediction methods.
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Affiliation(s)
- Khaled M Toffaha
- Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mecit Can Emre Simsekler
- Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
| | - Mohammed Atif Omar
- Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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25
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Ismail M, Al-Ansari T. Enhancing sustainability through resource efficiency in beef production systems using a sliding time window-based approach and frame scores. Heliyon 2023; 9:e17773. [PMID: 37496899 PMCID: PMC10366399 DOI: 10.1016/j.heliyon.2023.e17773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 06/06/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023] Open
Abstract
The food needs of the increasing global population, inefficiencies in supply chains, customer expectations and environmental concerns are the challenges to meeting resource-intensive protein needs sustainably. Collectively, this increases the need to enhance sustainability in the beef sector. This study proposes a sliding time-window-based multi-period livestock production model using mixed-integer linear programming (MILP) to simultaneously balance economic and environmental losses. It identifies the optimal finishing time using frame score (FS) and feed conversion ratio (FCR), targeting flexibility by allowing variable growth periods to reduce food/nutritional losses while meeting the variability in demands with minimum inventory levels. Furthermore, sequencing and assigning animals to facilities with optimum separation time is applied to avoid bad handling of animals and ensure quality meat with hygienic standards for longer shelf life. The system boundary of the proposed model includes beef farms and processing facilities. Compared to the recently proposed batch processing models over seven months with a herd size of 1980 animals, the findings reduce the average forage needed by ∼126.90 kips and methane emissions by ∼2560 kg, with a significant benefit in terms of the live animals' weight gain by ∼10,276 lbs.
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26
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Tatasciore M, Bowden V, Loft S. Do concurrent task demands impact the benefit of automation transparency? Appl Ergon 2023; 110:104022. [PMID: 37019048 DOI: 10.1016/j.apergo.2023.104022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 03/03/2023] [Accepted: 03/27/2023] [Indexed: 06/19/2023]
Abstract
Automated decision aids typically improve decision-making, but incorrect advice risks automation misuse or disuse. We examined the novel question of whether increased automation transparency improves the accuracy of automation use under conditions with/without concurrent (non-automated assisted) task demands. Participants completed an uninhabited vehicle (UV) management task whereby they assigned the best UV to complete missions. Automation advised the best UV but was not always correct. Concurrent non-automated task demands decreased the accuracy of automation use, and increased decision time and perceived workload. With no concurrent task demands, increased transparency which provided more information on how the automation made decisions, improved the accuracy of automation use. With concurrent task demands, increased transparency led to higher trust ratings, faster decisions, and a bias towards agreeing with automation. These outcomes indicate increased reliance on highly transparent automation under conditions with concurrent task demands and have potential implications for human-automation teaming design.
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Affiliation(s)
| | | | - Shayne Loft
- The University of Western Australia, Australia.
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27
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Novikava N, Redjdal A, Bouaud J, Seroussi B. Clinical Decision Support Systems Applied to the Management of Breast Cancer Patients: A Scoping Review. Stud Health Technol Inform 2023; 305:353-356. [PMID: 37387037 DOI: 10.3233/shti230503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Breast cancer is the most commonly diagnosed cancer worldwide, and its burden has been rising over the past decades. A significant advance in healthcare is the integration of Clinical Decision Support Systems (CDSSs) into medical practice, which support healthcare professionals improving clinical decisions, leading to recommended patient-specific treatments and enhanced patient care. Breast cancer CDSSs are thus currently expanding, whether applied to screening, diagnostic, therapeutic or follow-up tasks. We conducted a scoping review to study their availability and use in practice. Except risk calculators, very few CDSSs are currently routinely used.
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Affiliation(s)
- Natallia Novikava
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
| | - Akram Redjdal
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
| | - Jacques Bouaud
- AP-HP, DRCI, Paris, France
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
| | - Brigitte Seroussi
- Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, F-75006 Paris, France
- AP-HP, Hôpital Tenon, Paris, France
- APREC, Paris, France
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28
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Dakshit S, Dakshit S, Khargonkar N, Prabhakaran B. Bias Analysis in Healthcare Time Series (BAHT) Decision Support Systems from Meta Data. J Healthc Inform Res 2023; 7:225-253. [PMID: 37377633 PMCID: PMC10290973 DOI: 10.1007/s41666-023-00133-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 04/19/2023] [Accepted: 05/12/2023] [Indexed: 06/29/2023]
Abstract
One of the hindrances in the widespread acceptance of deep learning-based decision support systems in healthcare is bias. Bias in its many forms occurs in the datasets used to train and test deep learning models and is amplified when deployed in the real world, leading to challenges such as model drift. Recent advancements in the field of deep learning have led to the deployment of deployable automated healthcare diagnosis decision support systems at hospitals as well as tele-medicine through IoT devices. Research has been focused primarily on the development and improvement of these systems leaving a gap in the analysis of the fairness. The domain of FAccT ML (fairness, accountability, and transparency) accounts for the analysis of these deployable machine learning systems. In this work, we present a framework for bias analysis in healthcare time series (BAHT) signals such as electrocardiogram (ECG) and electroencephalogram (EEG). BAHT provides a graphical interpretive analysis of bias in the training, testing datasets in terms of protected variables, and analysis of bias amplification by the trained supervised learning model for time series healthcare decision support systems. We thoroughly investigate three prominent time series ECG and EEG healthcare datasets used for model training and research. We show the extensive presence of bias in the datasets leads to potentially biased or unfair machine-learning models. Our experiments also demonstrate the amplification of identified bias with an observed maximum of 66.66%. We investigate the effect of model drift due to unanalyzed bias in datasets and algorithms. Bias mitigation though prudent is a nascent area of research. We present experiments and analyze the most prevalently accepted bias mitigation strategies of under-sampling, oversampling, and the use of synthetic data for balancing the dataset through augmentation. It is important that healthcare models, datasets, and bias mitigation strategies should be properly analyzed for a fair unbiased delivery of service.
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Affiliation(s)
- Sagnik Dakshit
- Computer Science, The University of Texas at Dallas, Dallas, USA
| | - Sristi Dakshit
- Computer Science, The University of Texas at Dallas, Dallas, USA
| | - Ninad Khargonkar
- Computer Science, The University of Texas at Dallas, Dallas, USA
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Hurley R, Jury F, van Staa TP, Palin V, Armitage CJ. Clinician acceptability of an antibiotic prescribing knowledge support system for primary care: a mixed-method evaluation of features and context. BMC Health Serv Res 2023; 23:367. [PMID: 37060063 PMCID: PMC10103677 DOI: 10.1186/s12913-023-09239-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 03/02/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Overprescribing of antibiotics is a major concern as it contributes to antimicrobial resistance. Research has found highly variable antibiotic prescribing in (UK) primary care, and to support more effective stewardship, the BRIT Project (Building Rapid Interventions to optimise prescribing) is implementing an eHealth Knowledge Support System. This will provide unique individualised analytics information to clinicians and patients at the point of care. The objective of the current study was to gauge the acceptability of the system to prescribing healthcare professionals and highlight factors to maximise intervention uptake. METHODS Two mixed-method co-design workshops were held online with primary care prescribing healthcare professionals (n = 16). Usefulness ratings of example features were collected using online polls and online whiteboards. Verbal discussion and textual comments were analysed thematically using inductive (participant-centred) and deductive perspectives (using the Theoretical Framework of Acceptability). RESULTS Hierarchical thematic coding generated three overarching themes relevant to intervention use and development. Clinician concerns (focal issues) were safe prescribing, accessible information, autonomy, avoiding duplication, technical issues and time. Requirements were ease and efficiency of use, integration of systems, patient-centeredness, personalisation, and training. Important features of the system included extraction of pertinent information from patient records (such as antibiotic prescribing history), recommended actions, personalised treatment, risk indicators and electronic patient communication leaflets. Anticipated acceptability and intention to use the knowledge support system was moderate to high. Time was identified as a focal cost/ burden, but this would be outweighed if the system improved patient outcomes and increased prescribing confidence. CONCLUSION Clinicians anticipate that an eHealth knowledge support system will be a useful and acceptable way to optimise antibiotic prescribing at the point of care. The mixed method workshop highlighted issues to assist person-centred eHealth intervention development, such as the value of communicating patient outcomes. Important features were identified including the ability to efficiently extract and summarise pertinent information from the patient records, provide explainable and transparent risk information, and personalised information to support patient communication. The Theoretical Framework of Acceptability enabled structured, theoretically sound feedback and creation of a profile to benchmark future evaluations. This may encourage a consistent user-focused approach to guide future eHealth intervention development.
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Affiliation(s)
- Ruth Hurley
- Manchester Centre for Health Psychology, Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
| | - Francine Jury
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Tjeerd P van Staa
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Victoria Palin
- Centre for Health Informatics, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Christopher J Armitage
- Manchester Centre for Health Psychology, Faculty of Biology, Medicine and Health, Division of Psychology and Mental Health, School of Health Sciences, The University of Manchester, Manchester, UK
- Academic Health Science Centre, Manchester University NHS Foundation Trust (MFT), NIHR Greater Manchester Patient Safety Translational Research Centre, The University of Manchester, Manchester, UK
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30
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Hjollund NHI, Larsen LP, de Thurah AL, Grove BE, Skuladottir H, Linnet H, Friis RB, Johnsen SP, May O, Jensen AL, Hansen TK, Taarnhøj GA, Tolstrup LK, Pappot H, Ivarsen P, Dørflinger L, Jessen A, Sørensen NT, Schougaard LMV, Team TA. Patient-reported outcome (PRO) measurements in chronic and malignant diseases: ten years' experience with PRO-algorithm-based patient-clinician interaction (telePRO) in AmbuFlex. Qual Life Res 2023; 32:1053-1067. [PMID: 36639598 PMCID: PMC10063508 DOI: 10.1007/s11136-022-03322-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/06/2022] [Indexed: 01/15/2023]
Abstract
BACKGROUND Patient-reported Outcome (PRO) measures may be used as the basis for out-patient follow-up instead of fixed appointments. The patients attend follow-up from home by filling in questionnaires developed for that specific aim and patient group (telePRO). The questionnaires are handled in real time by a specific algorithm, which assigns an outcome color reflecting clinical need. The specific questionnaires and algorithms (named solutions) are constructed in a consensus process with clinicians. We aimed to describe AmbuFlex' telePRO solutions and the algorithm outcomes and variation between patient groups, and to discuss possible applications and challenges. METHODS TelePRO solutions with more than 100 processed questionnaires were included in the analysis. Data were retrieved together with data from national registers. Characteristics of patients, questionnaires and outcomes were tabulated for each solution. Graphs were constructed depicting the overall and within-patient distribution of algorithm outcomes for each solution. RESULTS From 2011 to 2021, 29 specific telePRO solutions were implemented within 24 different ICD-10 groups. A total of 42,015 patients were referred and answered 171,268 questionnaires. An existing applicable instrument with cut-off values was available for four solutions, whereas items were selected or developed ad hoc for the other solutions. Mean age ranged from 10.7 (Pain in children) to 73.3 years (chronic kidney disease). Mortality among referred patients varied between 0 (obesity, asthma, endometriosis and pain in children) and 528 per 1000 patient years (Lung cancer). There was substantial variation in algorithm outcome across patient groups while different solutions within the same patient group varied little. DISCUSSION TelePRO can be applied in diseases where PRO can reflect clinical status and needs. Questionnaires and algorithms should be adapted for the specific patient groups and clinical aims. When PRO is used as replacement for clinical contact, special carefulness should be observed with respect to patient safety.
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Affiliation(s)
- Niels Henrik I Hjollund
- AmbuFlex - Center for Patient-Reported Outcomes, Central Denmark Region, Gødstrup Hospital, Herning, Denmark.
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
| | - Louise Pape Larsen
- AmbuFlex - Center for Patient-Reported Outcomes, Central Denmark Region, Gødstrup Hospital, Herning, Denmark
| | | | - Birgith Engelst Grove
- AmbuFlex - Center for Patient-Reported Outcomes, Central Denmark Region, Gødstrup Hospital, Herning, Denmark
| | | | - Hanne Linnet
- Department of Oncology, Gødstrup Hospital, Herning, Denmark
| | | | - Søren Paaske Johnsen
- Danish Center for Clinical Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Ole May
- Department of Medicine, Gødstrup Hospital, Herning, Denmark
| | | | | | - Gry Assam Taarnhøj
- Department of Oncology, University of Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Lærke Kjær Tolstrup
- Department of Oncology, University of Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Helle Pappot
- Department of Oncology, University of Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Per Ivarsen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Renal Medicine, Aarhus University Hospital, Aarhus, Denmark
| | | | - Anne Jessen
- AmbuFlex - Center for Patient-Reported Outcomes, Central Denmark Region, Gødstrup Hospital, Herning, Denmark
| | - Nanna Toxvig Sørensen
- AmbuFlex - Center for Patient-Reported Outcomes, Central Denmark Region, Gødstrup Hospital, Herning, Denmark
| | - Liv Marit Valen Schougaard
- AmbuFlex - Center for Patient-Reported Outcomes, Central Denmark Region, Gødstrup Hospital, Herning, Denmark
| | - The AmbuFlex Team
- AmbuFlex - Center for Patient-Reported Outcomes, Central Denmark Region, Gødstrup Hospital, Herning, Denmark
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31
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Tokgöz P, Hafner J, Dockweiler C. Factors influencing the implementation of decision support systems for antibiotic prescription in hospitals: a systematic review. BMC Med Inform Decis Mak 2023; 23:27. [PMID: 36747193 PMCID: PMC9903563 DOI: 10.1186/s12911-023-02124-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 01/30/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Antibiotic resistance is a major health threat. Inappropriate antibiotic use has been shown to be an important determinant of the emergence of antibiotic resistance. Decision support systems for antimicrobial management can support clinicians to optimize antibiotic prescription. OBJECTIVE The aim of this systematic review is to identify factors influencing the implementation of decision support systems for antibiotic prescription in hospitals. METHODS A systematic search of factors impeding or facilitating successful implementation of decision support systems for antibiotic prescription was performed in January 2022 in the databases PubMed, Web of Science and The Cochrane Library. Only studies were included which comprised decision support systems in hospitals for prescribing antibiotic therapy, published in English with a qualitative, quantitative or mixed-methods study design and between 2011 and 2021. Factors influencing the implementation were identified through text analysis by two reviewers. RESULTS A total of 14 publications were identified matching the inclusion criteria. The majority of factors relate to technological and organizational aspects of decision support system implementation. Some factors include the integration of the decision support systems into existing systems, system design, consideration of potential end-users as well as training and support for end-users. In addition, user-related factors, like user attitude towards the system, computer literacy and prior experience with the system seem to be important for successful implementation of decision support systems for antibiotic prescription in hospitals. CONCLUSION The results indicate a broad spectrum of factors of decision support system implementation for antibiotic prescription and contributes to the literature by identifying important organizational as well as user-related factors. Wider organizational dimensions as well as the interaction between user and technology appear important for supporting implementation.
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Affiliation(s)
- Pinar Tokgöz
- School of Life Sciences, Department Digital Health Sciences and Biomedicine, Professorship of Digital Public Health, University of Siegen, 57068, Siegen, Germany.
| | - Jessica Hafner
- grid.5836.80000 0001 2242 8751School of Life Sciences, Department Digital Health Sciences and Biomedicine, Professorship of Digital Public Health, University of Siegen, 57068 Siegen, Germany
| | - Christoph Dockweiler
- grid.5836.80000 0001 2242 8751School of Life Sciences, Department Digital Health Sciences and Biomedicine, Professorship of Digital Public Health, University of Siegen, 57068 Siegen, Germany
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de Andrade EC, Pinheiro LICC, Pinheiro PR, Nunes LC, Pinheiro MCD, Pereira MLD, de Abreu WC, Filho RH, Simão Filho M, Pinheiro PGCD, Nunes REC. Hybrid model for early identification post-Covid-19 sequelae. J Ambient Intell Humaniz Comput 2023; 14:1-14. [PMID: 36779007 PMCID: PMC9902243 DOI: 10.1007/s12652-023-04555-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 01/30/2023] [Indexed: 06/18/2023]
Abstract
Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.
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Affiliation(s)
- Evandro Carvalho de Andrade
- Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza, Ceará Brazil
- Ceara State University, Fortaleza, Ceara Brazil
| | | | - Plácido Rogério Pinheiro
- Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza, Ceará Brazil
- Ceara State University, Fortaleza, Ceara Brazil
| | - Luciano Comin Nunes
- Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza, Ceará Brazil
- University Center September 7, Fortaleza, Ceara Brazil
| | | | | | | | | | - Marum Simão Filho
- Graduate Program in Applied Informatics, University of Fortaleza, Fortaleza, Ceará Brazil
- University Center September 7, Fortaleza, Ceara Brazil
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Reeder S, Foster E, Vishwanath S, Kwan P. Experience of waiting for seizure freedom and perception of machine learning technologies to support treatment decision: A qualitative study in adults with recent onset epilepsy. Epilepsy Res 2023; 190:107096. [PMID: 36738538 DOI: 10.1016/j.eplepsyres.2023.107096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 01/16/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023]
Abstract
PURPOSE With no reliable surrogate biomarkers for treatment response, people with epilepsy currently await the passage of time to determine whether prescribed treatments are effective. Few studies have examined the issues faced by people with epilepsy during this waiting period. We aim to explore the experiences of people with recently diagnosed epilepsy as they wait to achieve seizure freedom. METHODS We purposively sampled adults of working age who had been diagnosed and treated for epilepsy for less than four years. Semi-structured interviews were undertaken between July and September 2021. A thematic analysis using a framework approach was performed. RESULTS We recruited 15 patients. Results revealed four main themes: 1) Impact on mental health, as people with newly diagnosed epilepsy described waiting for seizure freedom as a time of vulnerability, uncertainty, and confusion. 2) Participants described their life as "on hold", prior to achieving effective seizure control 3) Difficulty navigating health systems to find and understand information about epilepsy, tests, and medications, and to find the 'right' health professional to address their needs. 4) Technology systems that support clinician decision making with selecting effective medications early after diagnosis were cautiously welcomed by participants. CONCLUSION Interventions are needed to reduce the negative impacts experienced by people who are newly diagnosed with epilepsy while waiting for effective seizure control. Technology systems that support clinician decision making were acceptable, as people with epilepsy sought accessible and effective solutions to restore a sense of control in their lives.
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Affiliation(s)
- Sandra Reeder
- School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne 3004, Australia; Department of Neurosciences, Monash University, Central Clinical School, 99 Commercial Road, Melbourne 3004, Australia.
| | - Emma Foster
- Department of Neurosciences, Monash University, Central Clinical School, 99 Commercial Road, Melbourne 3004, Australia; Department of Neurology, The Alfred, 55 Commercial Road, Melbourne 3004, Australia.
| | - Swarna Vishwanath
- School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne 3004, Australia; Department of Neurosciences, Monash University, Central Clinical School, 99 Commercial Road, Melbourne 3004, Australia.
| | - Patrick Kwan
- Department of Neurosciences, Monash University, Central Clinical School, 99 Commercial Road, Melbourne 3004, Australia; Department of Neurology, The Alfred, 55 Commercial Road, Melbourne 3004, Australia.
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Lochner M, Smilek D. The uncertain advisor: trust, accuracy, and self-correction in an automated decision support system. Cogn Process 2023; 24:95-106. [PMID: 36344855 DOI: 10.1007/s10339-022-01113-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 10/05/2022] [Indexed: 11/09/2022]
Abstract
The increasing use of automated systems to support human decision-making is a development that has practical implications across multiple domains, and the dynamics of trust formation in an autonomous system is a critical element in the success of the human-automation team. Here, we employ existing models of human-automation trust to narrow our scope to address, specifically, the concept of dynamically learned trust. In the present experiments we explored how trust in an autonomous system is influenced by variations in system speed, system accuracy, and a novel operationalization of system uncertainty, in which the automated system corrects itself mid-response. Participants monitored the performance of an automated 'Captcha'-like decision support system, and were tasked with indicating whether the system was correct or incorrect on each trial. Dependent variables included subjective trust ratings, response times, hit rates, and false alarm rates. In addition to validating our methodology for quantifying the impact of low-level system design features, we further demonstrate that participants are more likely to miss system errors when they have high trust in a system, and that the speed and level of self-correction with which an automated system produces responses has an impact on human trust in that system.
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Affiliation(s)
- Martin Lochner
- Department of Psychology, University of Waterloo, Waterloo, Canada.
| | - Daniel Smilek
- Department of Psychology, University of Waterloo, Waterloo, Canada
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Gioia DG, Fior J, Cagliero L. Early portfolio pruning: a scalable approach to hybrid portfolio selection. Knowl Inf Syst 2023; 65:2485-2508. [PMID: 36743270 PMCID: PMC9888753 DOI: 10.1007/s10115-023-01832-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 12/14/2022] [Accepted: 01/07/2023] [Indexed: 02/04/2023]
Abstract
Driving the decisions of stock market investors is among the most challenging financial research problems. Markowitz's approach to portfolio selection models stock profitability and risk level through a mean-variance model, which involves estimating a very large number of parameters. In addition to requiring considerable computational effort, this raises serious concerns about the reliability of the model in real-world scenarios. This paper presents a hybrid approach that combines itemset extraction with portfolio selection. We propose to adapt Markowitz's model logic to deal with sets of candidate portfolios rather than with single stocks. We overcome some of the known issues of the Markovitz model as follows: (i) Complexity: we reduce the model complexity, in terms of parameter estimation, by studying the interactions among stocks within a shortlist of candidate stock portfolios previously selected by an itemset mining algorithm. (ii) Portfolio-level constraints: we not only perform stock-level selection, but also support the enforcement of arbitrary constraints at the portfolio level, including the properties of diversification and the fundamental indicators. (iii) Usability: we simplify the decision-maker's work by proposing a decision support system that enables flexible use of domain knowledge and human-in-the-loop feedback. The experimental results, achieved on the US stock market, confirm the proposed approach's flexibility, effectiveness, and scalability.
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Affiliation(s)
- Daniele G. Gioia
- Department of Mathematical Sciences, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Jacopo Fior
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
| | - Luca Cagliero
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
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Devkumar S C. A decision support system for environmentally-sustainable strategies for the Mauritian Textile and apparel industry using system dynamics: The materials and land perspectives. Heliyon 2023; 9:e12939. [PMID: 36711319 DOI: 10.1016/j.heliyon.2023.e12939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 01/04/2023] [Accepted: 01/09/2023] [Indexed: 01/18/2023] Open
Abstract
As anywhere in the world, Mauritius has seen the depletion of its natural resources, mainly as a result of the effects of global warming. Industries need to recognize this and rethink on the way products are manufactured so as to minimize the negative impact of their businesses on the environment, the workforce and the surroundings. The main objective of this research is to investigate what support systems the Mauritian Textile & Apparel industry requires to embark on a sustainable manufacturing journey. The primary focus of the research is from the perspectives of the use of materials and land availability. This present work is not about "Greenwash", it is about analyzing the full range of economic, environmental and social benefits to support the transitions to sustainable business models over time. Development of methods that can support academics, researchers and industry practitioners within the textile & apparel industry to integrate sustainable manufacturing practices into their day-to-day practices. In a nutshell, this research provides a cut-and-dried and standardized approach for the stakeholders of the textile and apparel industry to shift to more sustainable practices by reshaping their resource flows. This study confirms the dynamic behavior of the textile industry and reveals that if the appropriate strategies and decisions are made, there is still hope for the survival of the industry in the future.
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Anda U, Andreea-Sorina M, Laurentiu PC, Dan R, Rodica N, Ruxandra S, Catalin S, Gabriel ID. Learning deep architectures for the interpretation of first-trimester fetal echocardiography (LIFE) - a study protocol for developing an automated intelligent decision support system for early fetal echocardiography. BMC Pregnancy Childbirth 2023; 23:20. [PMID: 36631859 DOI: 10.1186/s12884-022-05204-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 11/09/2022] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Congenital Heart Disease represents the most frequent fetal malformation. The lack of prenatal identification of congenital heart defects can have adverse consequences for the neonate, while a correct prenatal diagnosis of specific cardiac anomalies improves neonatal care neurologic and surgery outcomes. Sonographers perform prenatal diagnosis manually during the first or second-trimester scan, but the reported detection rates are low. This project's primary objective is to develop an Intelligent Decision Support System that uses two-dimensional video files of cardiac sweeps obtained during the standard first-trimester fetal echocardiography (FE) to signal the presence/absence of previously learned key features. METHODS The cross-sectional study will be divided into a training part of the machine learning approaches and the testing phase on previously unseen frames and eventually on actual video scans. Pregnant women in their 12-13 + 6 weeks of gestation admitted for routine first-trimester anomaly scan will be consecutively included in a two-year study, depending on the availability of the experienced sonographers in early fetal cardiac imaging involved in this research. The Data Science / IT department (DSIT) will process the key planes identified by the sonographers in the two- dimensional heart cine loop sweeps: four-chamber view, left and right ventricular outflow tracts, three vessels, and trachea view. The frames will be grouped into the classes representing the plane views, and then different state-of-the- art deep-learning (DL) pre-trained algorithms will be tested on the data set. The sonographers will validate all the intermediary findings at the frame level and the meaningfulness of the video labeling. DISCUSSION FE is feasible and efficient during the first trimester. Still, the continuous training process is impaired by the lack of specialists or their limited availability. Therefore, in our study design, the sonographer benefits from a second opinion provided by the developed software, which may be very helpful, especially if a more experienced colleague is unavailable. In addition, the software may be implemented on the ultrasound device so that the process could take place during the live examination. TRIAL REGISTRATION The study is registered under the name "Learning deep architectures for the Interpretation of Fetal Echocardiography (LIFE)", project number 408PED/2020, project code PN-III-P2-2.1-PED-2019. TRIAL REGISTRATION ClinicalTrials.gov , unique identifying number NCT05090306, date of registration 30.10.2020.
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Hammond EB, Coulon F, Hallett SH, Thomas R, Hardy D, Beriro DJ. Digital tools for brownfield redevelopment: Stakeholder perspectives and opportunities. J Environ Manage 2023; 325:116393. [PMID: 36270126 DOI: 10.1016/j.jenvman.2022.116393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 09/15/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Brownfield redevelopment is a complex process often involving a wide range of stakeholders holding differing priorities and opinions. The use of digital systems and products for decision making, modelling, and supporting discussion has been recognised throughout literature and industry. The inclusion of stakeholder preferences is an important consideration in the design and development of impactful digital tools and decision support systems. In this study, we present findings from stakeholder consultation with professionals from the UK brownfield sector with the aim of informing the design of future digital tools and systems. Our research investigates two broad themes; digitalisation and the use of digital tools across the sector; and perceptions of key brownfield challenge areas where digital tools could help better inform decision-makers. The methodology employed for this study comprises the collection of data and information using a combination of interviews and an online questionnaire. The results from these methods were evaluated both qualitatively and quantitatively. Findings reveal a disparity in levels of digital capability between stakeholder groups including between technical stakeholder types, and that cross-discipline communication of important issues may be aided by the development of carefully designed digital tools. To this end, we present seven core principles to guide the design and implementation of future digital tools for the brownfield sector. These principles are that future digital tools should be: (1) Stakeholder driven, (2) Problem centred, (3) Visual, (4) Intuitive, (5) Interactive, (6) Interoperable, and (7) Geospatial data driven.
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Affiliation(s)
- Ellis B Hammond
- British Geological Survey, Keyworth, Nottingham, NG12 5GG, UK; School of Water, Energy and Environment, Cranfield University, Cranfield, MK43 0AL, UK
| | - Frederic Coulon
- School of Water, Energy and Environment, Cranfield University, Cranfield, MK43 0AL, UK
| | - Stephen H Hallett
- School of Water, Energy and Environment, Cranfield University, Cranfield, MK43 0AL, UK
| | | | - Drew Hardy
- Groundsure, Sovereign House, Church Street, Brighton BN1 1UJ, UK
| | - Darren J Beriro
- British Geological Survey, Keyworth, Nottingham, NG12 5GG, UK.
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Schroé H, Carlier S, Van Dyck D, De Backere F, Crombez G. Towards more personalized digital health interventions: a clustering method of action and coping plans to promote physical activity. BMC Public Health 2022; 22:2325. [PMID: 36510181 PMCID: PMC9746174 DOI: 10.1186/s12889-022-14455-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 10/12/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Despite effectiveness of action and coping planning in digital health interventions to promote physical activity (PA), attrition rates remain high. Indeed, support to make plans is often abstract and similar for each individual. Nevertheless, people are different, and context varies. Tailored support at the content level, involving suggestions of specific plans that are personalized to the individual, may reduce attrition and improve outcomes in digital health interventions. The aim of this study was to investigate whether user information relates toward specific action and coping plans using a clustering method. In doing so, we demonstrate how knowledge can be acquired in order to develop a knowledge-base, which might provide personalized suggestions in a later phase. METHODS To establish proof-of-concept for this approach, data of 65 healthy adults, including 222 action plans and 204 coping plans, were used and were collected as part of the digital health intervention MyPlan 2.0 to promote PA. As a first step, clusters of action plans, clusters of coping plans and clusters of combinations of action plans and barriers of coping plans were identified using hierarchical clustering. As a second step, relations with user information (i.e. gender, motivational stage, ...) were examined using anova's and chi2-tests. RESULTS First, three clusters of action plans, eight clusters of coping plans and eight clusters of the combination of action and coping plans were identified. Second, relating these clusters to user information was possible for action plans: 1) Users with a higher BMI related more to outdoor leisure activities (F = 13.40, P < .001), 2) Women, users that didn't perform PA regularly yet, or users with a job related more to household activities (X2 = 16.92, P < .001; X2 = 20.34, P < .001; X2 = 10.79, P = .004; respectively), 3) Younger users related more to active transport and different sports activities (F = 14.40, P < .001). However, relating clusters to user information proved difficult for the coping plans and combination of action and coping plans. CONCLUSIONS The approach used in this study might be a feasible approach to acquire input for a knowledge-base, however more data (i.e. contextual and dynamic user information) from possible end users should be acquired in future research. This might result in a first type of context-aware personalized suggestions on the content level. TRIAL REGISTRATION The digital health intervention MyPlan 2.0 was preregistered as a clinical trial (ID:NCT03274271). Release date: 6-September-2017.
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Affiliation(s)
- Helene Schroé
- grid.5342.00000 0001 2069 7798Department of Experimental-Clinical and Health Psychology, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium ,grid.5342.00000 0001 2069 7798Department of Movement and Sports Sciences, Faculty of Medicine and Health, Research Group Physical Activity and Health, Ghent University, Watersportlaan 2, 9000 Ghent, Belgium
| | - Stéphanie Carlier
- grid.5342.00000 0001 2069 7798IDLab, Department of Information Technology, Ghent University—imec, Technologiepark-Zwijnaarde 126, 9052 Ghent, Belgium
| | - Delfien Van Dyck
- grid.5342.00000 0001 2069 7798Department of Movement and Sports Sciences, Faculty of Medicine and Health, Research Group Physical Activity and Health, Ghent University, Watersportlaan 2, 9000 Ghent, Belgium
| | - Femke De Backere
- grid.5342.00000 0001 2069 7798IDLab, Department of Information Technology, Ghent University—imec, Technologiepark-Zwijnaarde 126, 9052 Ghent, Belgium
| | - Geert Crombez
- grid.5342.00000 0001 2069 7798Department of Experimental-Clinical and Health Psychology, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
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Tennant R, Tetui M, Grindrod K, Burns CM. Multi-Disciplinary Design and Implementation of a Mass Vaccination Clinic Mobile Application to Support Decision-Making. IEEE J Transl Eng Health Med 2022; 11:60-69. [PMID: 36654771 PMCID: PMC9842226 DOI: 10.1109/jtehm.2022.3224740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 10/26/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022]
Abstract
Mass vaccination clinics are complex systems that combine professionals who do not typically work together. Coordinating vaccine preparation and patient intake is critically important to maintain patient flow equilibrium, requiring continuous communication and shared decision-making to reduce vaccine waste. OBJECTIVES (1) To develop a mobile application (app) that can address the information needs of vaccination clinic stakeholders for end-of-day doses decision-making in mass immunization settings; and (2) to understand usability and clinical implementation among multi-disciplinary users. METHODS Contextual inquiry guided 71.5 hours of observations to inform design characteristics. Rapid iterative testing and evaluation were performed to validate and improve the design. Usability and integration were evaluated through observations, interviews, and the system usability scale. RESULTS Designing the app required consolidating contextual factors to support information and workload needs. Twenty-four participants used the app at four clinics who reported its effectiveness in reducing stress and improving communication efficiency and satisfaction. They also discussed positive workflow changes and design recommendations to improve its usefulness. The average system usability score was 87 (n = 22). DISCUSSION There is significant potential for mobile apps to improve workflow efficiencies for information sharing and decision-making in vaccination clinics when designed for established cultures and usability, thereby providing frontline workers with greater time to focus on patient care and immunization needs. However, designing and implementing digital systems for dynamic settings is challenging when healthcare teams constantly adapt to evolving complexities. System-level barriers to adoption require further investigation. Future research should explore the implementation of the app within global contexts.
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Affiliation(s)
- Ryan Tennant
- Department of Systems Design EngineeringUniversity of Waterloo Waterloo ON N2L 3G1 Canada
| | - Moses Tetui
- Department of Epidemiology and Global HealthUmeå University 901 87 Umeå Sweden
- School of PharmacyUniversity of Waterloo Waterloo ON N2G 1C5 Canada
| | - Kelly Grindrod
- School of PharmacyUniversity of Waterloo Waterloo ON N2G 1C5 Canada
| | - Catherine M Burns
- Department of Systems Design EngineeringUniversity of Waterloo Waterloo ON N2L 3G1 Canada
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Rinaldi L, Krücken J, Martinez-Valladares M, Pepe P, Maurelli MP, de Queiroz C, Castilla Gómez de Agüero V, Wang T, Cringoli G, Charlier J, Gilleard JS, von Samson-Himmelstjerna G. Advances in diagnosis of gastrointestinal nematodes in livestock and companion animals. Adv Parasitol 2022; 118:85-176. [PMID: 36088084 DOI: 10.1016/bs.apar.2022.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Diagnosis of gastrointestinal nematodes in livestock and companion animals has been neglected for years and there has been an historical underinvestment in the development and improvement of diagnostic tools, undermining the undoubted utility of surveillance and control programmes. However, a new impetus by the scientific community and the quickening pace of technological innovations, are promoting a renaissance of interest in developing diagnostic capacity for nematode infections in veterinary parasitology. A cross-cutting priority for diagnostic tools is the development of pen-side tests and associated decision support tools that rapidly inform on the levels of infection and morbidity. This includes development of scalable, parasite detection using artificial intelligence for automated counting of parasitic elements and research towards establishing biomarkers using innovative molecular and proteomic methods. The aim of this review is to assess the state-of-the-art in the diagnosis of helminth infections in livestock and companion animals and presents the current advances of diagnostic methods for intestinal parasites harnessing (i) automated methods for copromicroscopy based on artificial intelligence, (ii) immunodiagnosis, and (iii) molecular- and proteome-based approaches. Regardless of the method used, multiple factors need to be considered before diagnostics test results can be interpreted in terms of control decisions. Guidelines on how to apply diagnostics and how to interpret test results in different animal species are increasingly requested and some were recently made available in veterinary parasitology for the different domestic species.
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Affiliation(s)
- Laura Rinaldi
- Department of Veterinary Medicine and Animal Production, University of Naples "Federico II", Naples, Italy.
| | - J Krücken
- Institute for Parasitology and Tropical Veterinary Medicine, Veterinary Centre for Resistance Research, Freie Universität Berlin, Berlin, Germany
| | - M Martinez-Valladares
- Instituto de Ganadería de Montaña (CSIC-Universidad de León), Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad de León, León, Spain
| | - P Pepe
- Department of Veterinary Medicine and Animal Production, University of Naples "Federico II", Naples, Italy
| | - M P Maurelli
- Department of Veterinary Medicine and Animal Production, University of Naples "Federico II", Naples, Italy
| | - C de Queiroz
- Faculty of Veterinary Medicine, 3331 Hospital Drive, Host-Parasite Interactions (HPI) Program University of Calgary, Calgary, Alberta, Canada; Faculty of Veterinary Medicine, St Georges University, Grenada
| | - V Castilla Gómez de Agüero
- Instituto de Ganadería de Montaña (CSIC-Universidad de León), Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad de León, León, Spain
| | - T Wang
- Kreavet, Kruibeke, Belgium
| | - Giuseppe Cringoli
- Department of Veterinary Medicine and Animal Production, University of Naples "Federico II", Naples, Italy
| | | | - J S Gilleard
- Faculty of Veterinary Medicine, 3331 Hospital Drive, Host-Parasite Interactions (HPI) Program University of Calgary, Calgary, Alberta, Canada
| | - G von Samson-Himmelstjerna
- Institute for Parasitology and Tropical Veterinary Medicine, Veterinary Centre for Resistance Research, Freie Universität Berlin, Berlin, Germany
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Borges D, Nascimento MCV. COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach. Appl Soft Comput 2022; 125:109181. [PMID: 35755299 PMCID: PMC9212961 DOI: 10.1016/j.asoc.2022.109181] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/04/2022] [Accepted: 06/08/2022] [Indexed: 11/05/2022]
Abstract
Recent literature has revealed a growing interest in methods for anticipating the demand for medical items and personnel at hospital, especially during turbulent scenarios such as the COVID-19 pandemic. In times like those, new variables appear and affect the once known demand behavior. This paper investigates the hypothesis that the combined Prophet-LSTM method results in more accurate forecastings for COVID-19 hospital Intensive Care Units (ICUs) demand than both standalone models, Prophet and LSTM (Long Short-Term Memory Neural Network). We also compare the model to well-established demand forecasting benchmarks. The model is tested to a representative Brazilian municipality that serves as a medical reference to other cities within its region. In addition to traditional time series components, such as trend and seasonality, other variables such as the current number of daily COVID-19 cases, vaccination rates, non-pharmaceutical interventions, social isolation index, and regional hospital beds occupation are also used to explain the variations in COVID-19 hospital ICU demand. Results indicate that the proposed method produced Mean Average Errors (MAE) from 13% to 45% lower than well established statistical and machine learning forecasting models, including the standalone models.
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Affiliation(s)
- Dalton Borges
- Instituto de Ciência e Tecnologia, Universidade Federal Fluminense (UFF), Rio das Ostras, RJ, 28.890-000, Brazil.,Divisão de Ciências da Computação, Instituto Tecnológico de Aeronáutica (ITA), São José dos Campos, SP, 12.228-900, Brazil.,Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo (UNIFESP), São José dos Campos, SP, 12.247-014, Brazil
| | - Mariá C V Nascimento
- Divisão de Ciências da Computação, Instituto Tecnológico de Aeronáutica (ITA), São José dos Campos, SP, 12.228-900, Brazil.,Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo (UNIFESP), São José dos Campos, SP, 12.247-014, Brazil
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Sansone M, Holmstrom P, Hallberg S, Nordén R, Andersson LM, Westin J. System dynamic modelling of healthcare associated influenza -a tool for infection control. BMC Health Serv Res 2022; 22:709. [PMID: 35624510 PMCID: PMC9136787 DOI: 10.1186/s12913-022-07959-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 04/12/2022] [Indexed: 12/02/2022] Open
Abstract
Background The transmission dynamics of influenza virus within healthcare settings are not fully understood. Capturing the interplay between host, viral and environmental factors is difficult using conventional research methods. Instead, system dynamic modelling may be used to illustrate the complex scenarios including non-linear relationships and multiple interactions which occur within hospitals during a seasonal influenza epidemic. We developed such a model intended as a support for health-care providers in identifying potentially effective control strategies to prevent influenza transmission. Methods By using computer simulation software, we constructed a system dynamic model to illustrate transmission dynamics within a large acute-care hospital. We used local real-world clinical and epidemiological data collected during the season 2016/17, as well as data from the national surveillance programs and relevant publications to form the basic structure of the model. Multiple stepwise simulations were performed to identify the relative effectiveness of various control strategies and to produce estimates of the accumulated number of healthcare-associated influenza cases per season. Results Scenarios regarding the number of patients exposed for influenza virus by shared room and the extent of antiviral prophylaxis and treatment were investigated in relation to estimations of influenza vaccine coverage, vaccine effectiveness and inflow of patients with influenza. In total, 680 simulations were performed, of which each one resulted in an estimated number per season. The most effective preventive measure identified by our model was administration of antiviral prophylaxis to exposed patients followed by reducing the number of patients receiving care in shared rooms. Conclusions This study presents an system dynamic model that can be used to capture the complex dynamics of in-hospital transmission of viral infections and identify potentially effective interventions to prevent healthcare-associated influenza infections. Our simulations identified antiviral prophylaxis as the most effective way to control in-hospital influenza transmission. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-022-07959-7.
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Affiliation(s)
- Martina Sansone
- Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10B, 413 46, Gothenburg, Sweden. .,Department of Infectious Diseases, Region Vastra Gotaland, Sahlgrenska University Hospital, Journalvagen 10, 416 50, Gothenburg, Sweden.
| | - Paul Holmstrom
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University Medicinaregatan 3, 413 45, Gothenburg, Sweden
| | - Stefan Hallberg
- Regional Cancer Centre West, Western Sweden Healthcare Region, 413 45, Gothenburg, Sweden
| | - Rickard Nordén
- Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10B, 413 46, Gothenburg, Sweden.,Department of Clinical Microbiology, Region Vastra Gotaland, Sahlgrenska University Hospital, Guldhedsgatan 10A, 402 34, Gothenburg, Sweden
| | - Lars-Magnus Andersson
- Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10B, 413 46, Gothenburg, Sweden.,Department of Infectious Diseases, Region Vastra Gotaland, Sahlgrenska University Hospital, Journalvagen 10, 416 50, Gothenburg, Sweden
| | - Johan Westin
- Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10B, 413 46, Gothenburg, Sweden.,Department of Infectious Diseases, Region Vastra Gotaland, Sahlgrenska University Hospital, Journalvagen 10, 416 50, Gothenburg, Sweden.,Regional Cancer Centre West, Western Sweden Healthcare Region, 413 45, Gothenburg, Sweden
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Hajek C, Hutchinson AM, Galbraith LN, Green RC, Murray MF, Petry N, Preys CL, Zawatsky CLB, Zoltick ES, Christensen KD. Improved provider preparedness through an 8-part genetics and genomic education program. Genet Med 2022; 24:214-224. [PMID: 34906462 PMCID: PMC9121992 DOI: 10.1016/j.gim.2021.08.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/25/2021] [Accepted: 08/13/2021] [Indexed: 01/03/2023] Open
Abstract
PURPOSE Large-scale genetics education appropriate for general practice providers is a growing priority. We describe the content and impact of a mandatory system-wide program implemented at Sanford Health. METHODS The Imagenetics Initiative at Sanford Health developed a 2-year genetics education program with quarterly web-based modules that were mandatory for all physicians and advanced practice providers. Scores of 0 to 5 were calculated for each module on the basis of the number of objectives that the participants reported as fulfilled. In addition, the participants completed surveys before starting and after finishing the education program, which included a 7-item measure scored 7 to 28 on the perceived preparedness to practice genetics. RESULTS Between 2252 and 2822 Sanford Health employees completed each of the 8 quarterly education modules. The ratings were highest for the module about using genomics to improve patient management (mean score = 4.3) and lowest for the module about different types of genetic tests and specialists. The mean perceived preparedness scores increased from 15.7 at pre-education to 19.1 at post-education (P < .001). CONCLUSION Web-based genetics education was highly effective in increasing health care providers' confidence about using genetics. Both comfort with personal knowledge and confidence regarding access to the system's genomic medicine experts increased significantly. The results demonstrate how scalable approaches can improve provider preparedness.
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Affiliation(s)
- Catherine Hajek
- Sanford Health Imagenetics, Sioux Falls, SD; Sanford School of Medicine, University of South Dakota, Sioux Falls, SD.
| | | | - Lauren N Galbraith
- Department of Population Medicine, Center for Healthcare Research in Pediatrics (CHERP), Harvard Pilgrim Health Care Institute, Boston, MA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA; Department of Medicine, Harvard Medical School, Boston, MA; Ariadne Labs, Boston, MA
| | | | - Natasha Petry
- Sanford Health Imagenetics, Fargo, ND; Department of Pharmacy Practice, School of Pharmacy, North Dakota State University, Fargo, ND
| | - Charlene L Preys
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA; MGH Institute of Health Professions, Boston, MA
| | - Carrie L B Zawatsky
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA; Ariadne Labs, Boston, MA
| | - Emilie S Zoltick
- Department of Population Medicine, Center for Healthcare Research in Pediatrics (CHERP), Harvard Pilgrim Health Care Institute, Boston, MA
| | - Kurt D Christensen
- Department of Population Medicine, Center for Healthcare Research in Pediatrics (CHERP), Harvard Pilgrim Health Care Institute, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA; Department of Population Medicine, Harvard Medical School, Boston, MA
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45
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Pascual A, Giardina CP, Povak NA, Hessburg PF, Heider C, Salminen E, Asner GP. Optimizing invasive species management using mathematical programming to support stewardship of water and carbon-based ecosystem services. J Environ Manage 2022; 301:113803. [PMID: 34626944 DOI: 10.1016/j.jenvman.2021.113803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/25/2021] [Accepted: 09/19/2021] [Indexed: 06/13/2023]
Abstract
Invasive species alter hydrologic processes at watershed scales, with impacts to biodiversity and the supporting ecosystem services. This effect is aggravated by climate change. Here, we integrated modelled hydrologic data, remote sensing products, climate data, and linear mixed integer optimization (MIP) to identify stewardship actions across space and time that can reduce the impact of invasive species. The study area is the windward coast of Hawai'i Island (USA) across which non-native strawberry guava occurrence varies from extremely dense stands in lower watershed reaches, to low densities in upper watershed forests. We focused on the removal of strawberry guava, an invader that exerts significant impacts on watershed condition. MIP analyses spatially optimized the assignment of effective management actions to increase water yield, generate revenue from enhanced freshwater services, and income from removed biomass. The hydrological benefit of removing guava, often marginal when considered in isolation, was financially quantified, and single- and multiobjective MIP formulations were then developed over a 10-year planning horizon. Optimization resulted in $2.27 million USD benefit over the planning horizon using a payment-for-ecosystem-services scheme. That value jumped to $4.67 million when allowing work schedules with overnight camping to reduce costs. Pareto frontiers of weighted pairs of management goals showed the benefit of clustering treatments over space and time to improve financial efficiency. Values of improved land-water natural capital using payment-for-ecosystem-services schemes are provided for several combinations of spatial, temporal, economical, and ecosystem services flows.
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Affiliation(s)
- Adrián Pascual
- Center for Global Discovery and Conservation Science, Arizona State University, Hilo, HI, USA.
| | - Christian P Giardina
- USDA Forest Service, Institute of Pacific Islands Forestry, 60 Nowelo Street, Hilo, HI, USA
| | - Nicholas A Povak
- USDA-FS, Pacific Northwest Research Station, 1133 N. Western Ave., Wenatchee, WA, 98801, USA
| | - Paul F Hessburg
- USDA-FS, Pacific Northwest Research Station, 1133 N. Western Ave., Wenatchee, WA, 98801, USA
| | - Chris Heider
- Watershed Professionals Network (WPN), PO Box 8, Mount Hood-Parkdale, OR, 970441, United States
| | - Ed Salminen
- Watershed Professional Network, PO Box 8, Mt. Hood-Parkdale, OR, 97041, United States
| | - Gregory P Asner
- Center for Global Discovery and Conservation Science, Arizona State University, Hilo, HI, USA
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Oluoch T, Cornet R, Muthusi J, Katana A, Kimanga D, Kwaro D, Okeyo N, Abu-Hanna A, de Keizer N. A clinical decision support system is associated with reduced loss to follow-up among patients receiving HIV treatment in Kenya: a cluster randomized trial. BMC Med Inform Decis Mak 2021; 21:357. [PMID: 34930228 PMCID: PMC8686234 DOI: 10.1186/s12911-021-01718-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 12/12/2021] [Indexed: 11/13/2022] Open
Abstract
Background Loss to follow-up (LFTU) among HIV patients remains a major obstacle to achieving treatment goals with the risk of failure to achieve viral suppression and thereby increased HIV transmission. Although use of clinical decision support systems (CDSS) has been shown to improve adherence to HIV clinical guidance, to our knowledge, this is among the first studies conducted to show its effect on LTFU in low-resource settings. Methods We analyzed data from a cluster randomized controlled trial in adults and children (aged ≥ 18 months) who were receiving antiretroviral therapy at 20 HIV clinics in western Kenya between Sept 1, 2012 and Jan 31, 2014. Participating clinics were randomly assigned, via block randomization. Clinics in the control arm had electronic health records (EHR) only while the intervention arm had an EHR with CDSS. The study objectives were to assess the effects of a CDSS, implemented as alerts on an EHR system, on: (1) the proportion of patients that were LTFU, (2) LTFU patients traced and successfully linked back to treatment, and (3) time from enrollment on the study to documentation of LTFU. Results Among 5901 eligible patients receiving ART, 40.6% (n = 2396) were LTFU during the study period. CDSS was associated with lower LTFU among the patients (Adjusted Odds Ratio—aOR 0.70 (95% CI 0.65–0.77)). The proportions of patients linked back to treatment were 25.8% (95% CI 21.5–25.0) and 30.6% (95% CI 27.9–33.4)) in EHR only and EHR with CDSS sites respectively. CDSS was marginally associated with reduced time from enrollment on the study to first documentation of LTFU (adjusted Hazard Ratio—aHR 0.85 (95% CI 0.78–0.92)). Conclusion A CDSS can potentially improve quality of care through reduction and early detection of defaulting and LTFU among HIV patients and their re-engagement in care in a resource-limited country. Future research is needed on how CDSS can best be combined with other interventions to reduce LTFU. Trial registration NCT01634802. Registered at www.clinicaltrials.gov on 12-Jul-2012. Registered prospectively.
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Affiliation(s)
- Tom Oluoch
- Division of Global HIV and TB, US Centers for Disease Control and Prevention, 1600 Clifton Road NE, GA, 30329, Atlanta, USA.
| | - Ronald Cornet
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jacques Muthusi
- Division of Global HIV and TB, US Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Abraham Katana
- Division of Global HIV and TB, US Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Davies Kimanga
- Division of Global HIV and TB, US Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Daniel Kwaro
- Kenya Medical Research Institute - CDC Collaborative Program, Kisumu, Kenya
| | - Nicky Okeyo
- Kenya Medical Research Institute - CDC Collaborative Program, Kisumu, Kenya
| | - Ameen Abu-Hanna
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Nicolette de Keizer
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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Retico A, Avanzo M, Boccali T, Bonacorsi D, Botta F, Cuttone G, Martelli B, Salomoni D, Spiga D, Trianni A, Stasi M, Iori M, Talamonti C. Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure. Phys Med 2021; 91:140-150. [PMID: 34801873 DOI: 10.1016/j.ejmp.2021.10.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 12/23/2022] Open
Abstract
Artificial Intelligence (AI) techniques have been implemented in the field of Medical Imaging for more than forty years. Medical Physicists, Clinicians and Computer Scientists have been collaborating since the beginning to realize software solutions to enhance the informative content of medical images, including AI-based support systems for image interpretation. Despite the recent massive progress in this field due to the current emphasis on Radiomics, Machine Learning and Deep Learning, there are still some barriers to overcome before these tools are fully integrated into the clinical workflows to finally enable a precision medicine approach to patients' care. Nowadays, as Medical Imaging has entered the Big Data era, innovative solutions to efficiently deal with huge amounts of data and to exploit large and distributed computing resources are urgently needed. In the framework of a collaboration agreement between the Italian Association of Medical Physicists (AIFM) and the National Institute for Nuclear Physics (INFN), we propose a model of an intensive computing infrastructure, especially suited for training AI models, equipped with secure storage systems, compliant with data protection regulation, which will accelerate the development and extensive validation of AI-based solutions in the Medical Imaging field of research. This solution can be developed and made operational by Physicists and Computer Scientists working on complementary fields of research in Physics, such as High Energy Physics and Medical Physics, who have all the necessary skills to tailor the AI-technology to the needs of the Medical Imaging community and to shorten the pathway towards the clinical applicability of AI-based decision support systems.
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Affiliation(s)
- Alessandra Retico
- National Institute for Nuclear Physics (INFN), Pisa Division, 56127 Pisa, Italy
| | - Michele Avanzo
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy
| | - Tommaso Boccali
- National Institute for Nuclear Physics (INFN), Pisa Division, 56127 Pisa, Italy
| | - Daniele Bonacorsi
- University of Bologna, 40126 Bologna, Italy; INFN, Bologna Division, 40126 Bologna, Italy
| | - Francesca Botta
- Medical Physics Unit, Istituto Europeo di oncologia IRCCS, 20141 Milan, Italy
| | - Giacomo Cuttone
- INFN, Southern National Laboratory (LNS), 95123 Catania, Italy
| | | | | | | | - Annalisa Trianni
- Medical Physics Unit, Ospedale Santa Chiara APSS, 38122 Trento, Italy
| | - Michele Stasi
- Medical Physics Unit, A.O. Ordine Mauriziano di Torino, 10128 Torino, Italy
| | - Mauro Iori
- Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, 42122 Reggio Emilia, Italy.
| | - Cinzia Talamonti
- Department Biomedical Experimental and Clinical Science "Mario Serio", University of Florence, 50134 Florence, Italy; INFN, Florence Division, 50134 Florence, Italy
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Büyüközkan G, Tüfekçi G. A multi-stage fuzzy decision-making framework to evaluate the appropriate wastewater treatment system: a case study. Environ Sci Pollut Res Int 2021; 28:53507-53519. [PMID: 34031840 DOI: 10.1007/s11356-021-14116-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 04/21/2021] [Indexed: 06/12/2023]
Abstract
Selection of appropriate treatment processes for wastewater treatment (WWT) plants at the design stage involves a careful examination of different economic, environmental, and social parameters. Designers and decision-makers seek a compromise among such conflicting elements, which can be facilitated by decision support tools that are adapted for the ambiguity of individual opinions and decision parameters. This study aims to improve the qualification and efficiency of decision-making in WWT processes. A multi-stage framework is proposed to help select investments, technology, appropriate technology-specific system, and companies that apply such systems. The framework combines the Analytic Hierarchy Process (AHP), Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), cash flow analysis, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) within fuzzy logic. The main contribution is the description and formation of an integrated framework to guide businesses and researchers for the evaluation of several WWT decision processes. To the best of the authors' knowledge, no study in the literature fuses multiple stages of this WWT process with the proposed approaches.
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Affiliation(s)
- Gülçin Büyüközkan
- Industrial Engineering Department, Galatasaray University, 34349 Ortakoy, Istanbul, Turkey.
| | - Gizem Tüfekçi
- Industrial Engineering Department, Galatasaray University, 34349 Ortakoy, Istanbul, Turkey
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Van Woensel W, Abidi SSR, Abidi SR. Decision support for comorbid conditions via execution-time integration of clinical guidelines using transaction-based semantics and temporal planning. Artif Intell Med 2021; 118:102127. [PMID: 34412844 DOI: 10.1016/j.artmed.2021.102127] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 05/04/2021] [Accepted: 06/22/2021] [Indexed: 11/25/2022]
Abstract
In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG). However, treatments from multiple comorbid CPG often interact adversely (e.g., drug-drug interactions) or introduce operational inefficiencies (e.g., redundant scans). A common solution is the a-priori integration of computerized CPG, which involves integration decisions such as discarding, replacing or delaying clinical tasks (e.g., treatments) to avoid adverse interactions or inefficiencies. We argue this insufficiently deals with execution-time events: as the patient's health profile evolves, acute conditions occur, and real-time delays take place, new CPG integration decisions will often be needed, and prior ones may need to be reverted or undone. Any realistic CPG integration effort needs to further consider temporal aspects of clinical tasks-these are not only restricted by temporal constraints from CPGs (e.g., sequential relations, task durations) but also by CPG integration efforts (e.g., avoid treatment overlap). This poses a complex execution-time challenge and makes it difficult to determine an up-to-date, optimal comorbid care plan. We present a solution for dynamic integration of CPG in response to evolving health profiles and execution-time events. CPG integration policies are formulated by clinical experts for coping with comorbidity at execution-time, with clearly defined integration semantics that build on Description and Transaction Logics. A dynamic planning approach reconciles temporal constraints of CPG tasks at execution-time based on their importance, and continuously updates an optimal task schedule.
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Affiliation(s)
- William Van Woensel
- NICHE Research Group, Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS B3H 1W5, Canada.
| | - Syed Sibte Raza Abidi
- NICHE Research Group, Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS B3H 1W5, Canada.
| | - Samina Raza Abidi
- Faculty of Medicine, Dalhousie University, 1459 Oxford Street, Halifax, NS B3H 4R2, Canada.
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Villena F, Pérez J, Lagos R, Dunstan J. Supporting the classification of patients in public hospitals in Chile by designing, deploying and validating a system based on natural language processing. BMC Med Inform Decis Mak 2021; 21:208. [PMID: 34210317 PMCID: PMC8252255 DOI: 10.1186/s12911-021-01565-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 06/23/2021] [Indexed: 11/22/2022] Open
Abstract
Background In Chile, a patient needing a specialty consultation or surgery has to first be referred by a general practitioner, then placed on a waiting list. The Explicit Health Guarantees (GES in Spanish) ensures, by law, the maximum time to solve 85 health problems. Usually, a health professional manually verifies if each referral, written in natural language, corresponds or not to a GES-covered disease. An error in this classification is catastrophic for patients, as it puts them on a non-prioritized waiting list, characterized by prolonged waiting times. Methods To support the manual process, we developed and deployed a system that automatically classifies referrals as GES-covered or not using historical data. Our system is based on word embeddings specially trained for clinical text produced in Chile. We used a vector representation of the reason for referral and patient's age as features for training machine learning models using human-labeled historical data. We constructed a ground truth dataset combining classifications made by three healthcare experts, which was used to validate our results. Results The best performing model over ground truth reached an AUC score of 0.94, with a weighted F1-score of 0.85 (0.87 in precision and 0.86 in recall). During seven months of continuous and voluntary use, the system has amended 87 patient misclassifications. Conclusion This system is a result of a collaboration between technical and clinical experts, and the design of the classifier was custom-tailored for a hospital's clinical workflow, which encouraged the voluntary use of the platform. Our solution can be easily expanded across other hospitals since the registry is uniform in Chile.
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Affiliation(s)
- Fabián Villena
- Center for Mathematical Modeling - CNRS UMI2807, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago, Chile.,Center for Medical Informatics and Telemedicine, ICBM, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Jorge Pérez
- Computer Science Department, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago, Chile.,Millennium Institute for Foundational Research on Data, Santiago, Chile
| | - René Lagos
- Digital Health Unit, South East Metropolitan Health Service, Santiago, Chile
| | - Jocelyn Dunstan
- Center for Mathematical Modeling - CNRS UMI2807, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago, Chile. .,Center for Medical Informatics and Telemedicine, ICBM, Faculty of Medicine, University of Chile, Santiago, Chile.
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