Relevant bibliographies by topics / Clinical Decision Support System (CDSS)

Academic literature on the topic 'Clinical Decision Support System (CDSS)'

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Published: 10 March 2023

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Journal articles on the topic "Clinical Decision Support System (CDSS)"

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Wiharto, Wiharto. "CLINICAL DECISION SUPPORT SYSTEMS THEORY AND PRACTICE." Jurnal Teknosains 7, no. 2 (September 8, 2018): 148. http://dx.doi.org/10.22146/teknosains.38641.

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Clinical Decision Support Systems Theory and Practice, adalah buku teks dalam seri Health Informatics yang membahas tentang sistem pendukung keputusan klinis. Pada buku ini terbagi menjadi dua kelompok bahasan, Pertama membahas tentang dasar dalam mengembangkan sistem Clinical Decision Supprot System (CDSS) dan evaluasinya. Kedua, Aplikasi CDSS dalam praktik klinis. Bahasan tersebut menjadikan pembaca dapat memperoleh gambaran tentang dasar-dasar yang diperlukan dalam membangun dan mengaplikasikan CDSS dalam praktik klinis. Secara rinci buku tersebut terbagi menjadi 11 Bab, yang dapat dikelompokkan menjadi 7 Bab tentang konsep dalam pengembangan CDSS, dan 4 Bab tentang aplikasi CDSS dalam praktik klinis.
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Conway, Nicholas, Karen A. Adamson, Scott G. Cunningham, Alistair Emslie Smith, Peter Nyberg, Blair H. Smith, Ann Wales, and Deborah J. Wake. "Decision Support for Diabetes in Scotland: Implementation and Evaluation of a Clinical Decision Support System." Journal of Diabetes Science and Technology 12, no. 2 (September 14, 2017): 381–88. http://dx.doi.org/10.1177/1932296817729489.

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Background: Automated clinical decision support systems (CDSS) are associated with improvements in health care delivery to those with long-term conditions, including diabetes. A CDSS was introduced to two Scottish regions (combined diabetes population ~30 000) via a national diabetes electronic health record. This study aims to describe users’ reactions to the CDSS and to quantify impact on clinical processes and outcomes over two improvement cycles: December 2013 to February 2014 and August 2014 to November 2014. Methods: Feedback was sought via patient questionnaires, health care professional (HCP) focus groups, and questionnaires. Multivariable regression was used to analyze HCP SCI-Diabetes usage (with respect to CDSS message presence/absence) and case-control comparison of clinical processes/outcomes. Cases were patients whose HCP received a CDSS messages during the study period. Closely matched controls were selected from regions outside the study, following similar clinical practice (without CDSS). Clinical process measures were screening rates for diabetes-related complications. Clinical outcomes included HbA1c at 1 year. Results: The CDSS had no adverse impact on consultations. HCPs were generally positive toward CDSS and used it within normal clinical workflow. CDSS messages were generated for 5692 cases, matched to 10 667 controls. Following clinic, the probability of patients being appropriately screened for complications more than doubled for most measures. Mean HbA1c improved in cases and controls but more so in cases (–2.3 mmol/mol [–0.2%] versus –1.1 [–0.1%], P = .003). Discussion and Conclusions: The CDSS was well received; associated with improved efficiencies in working practices; and large improvements in guideline adherence. These evidence-based, early interventions can significantly reduce costly and devastating complications.
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Lu, Sheng-Chieh, Rebecca J. Brown, and Martin Michalowski. "A Clinical Decision Support System Design Framework for Nursing Practice." ACI Open 05, no. 02 (July 2021): e84-e93. http://dx.doi.org/10.1055/s-0041-1736470.

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Abstract Background As nurses increasingly engage in decision-making for patients, a unique opportunity exists to translate research into practice using clinical decision support systems (CDSSs). While research has shown that CDSS has led to improvements in patient outcomes and nursing workflow, the success rate of CDSS implementation in nursing is low. Further, the majority of CDSS for nursing are not designed to support the care of patients with comorbidity. Objectives The aim of the study is to conceptualize an evidence-based CDSS supporting complex patient care for nursing. Methods We conceptualized the CDSS through extracting scientific findings of CDSS design and development. To describe the CDSS, we developed a conceptual framework comprising the key components of the CDSS and the relationships between the components. We instantiated the framework in the context of a hypothetical clinical case. Results We present the conceptualized CDSS with a framework comprising six interrelated components and demonstrate how each component is implemented via a hypothetical clinical case. Conclusion The proposed framework provides a common architecture for CDSS development and bridges CDSS research findings and development. Next research steps include (1) working with clinical nurses to identify their knowledge resources for a particular disease to better articulate the knowledge base needed by a CDSS, (2) develop and deploy a CDSS in practice using the framework, and (3) evaluate the CDSS in the context of nursing care.
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Sharmi, Meenakshi, and Himanshu Aggarwal. "Methodologies of Legacy Clinical Decision Support System." International Journal of Computers in Clinical Practice 2, no. 2 (July 2017): 20–37. http://dx.doi.org/10.4018/ijccp.2017070102.

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Information technology playing a prominent role in the field of medical by incorporating the clinical decision support system (CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at right time. Nowadays, clinical decision support systems are a dynamic research area in the field of computers, but the lack of understanding, as well as functions of the system, make adoption slow by physicians and patients. The literature review of this article focuses on the overview of legacy CDSS, the kind of methodologies and classifiers employed to prepare such a decision support system using a non-technical approach to the physician and the strategy-makers. This article provides understanding of the clinical decision support along with the gateway to physician, and to policy-makers to develop and deploy decision support systems as a healthcare service to make the quick, agile and right decision. Future directions to handle the uncertainties along with the challenges of clinical decision support systems are also enlightened in this study.
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António Ferreira Rodrigues Nogueira dos Santos, Marco, Hans Tygesen, Henrik Eriksson, and Johan Herlitz. "Clinical decision support system (CDSS) – effects on care quality." International Journal of Health Care Quality Assurance 27, no. 8 (October 7, 2014): 707–18. http://dx.doi.org/10.1108/ijhcqa-01-2014-0010.

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Purpose – Despite their efficacy, some recommended therapies are underused. The purpose of this paper is to describe clinical decision support system (CDSS) development and its impact on clinical guideline adherence. Design/methodology/approach – A new CDSS was developed and introduced in a cardiac intensive care unit (CICU) in 2003, which provided physicians with patient-tailored reminders and permitted data export from electronic patient records into a national quality registry. To evaluate CDSS effects in the CICU, process indicators were compared to a control group using registry data. All CICUs were in the same region and only patients with acute coronary syndrome were included. Findings – CDSS introduction was associated with increases in guideline adherence, which ranged from 16 to 35 per cent, depending on the therapy. Statistically significant associations between guideline adherence and CDSS use remained over the five-year period after its introduction. During the same period, no relapses occurred in the intervention CICU. Practical implications – Guideline adherence and healthcare quality can be enhanced using CDSS. This study suggests that practitioners should turn to CDSS to improve healthcare quality. Originality/value – This paper describes and evaluates an intervention that successfully increased guideline adherence, which improved healthcare quality when the intervention CICU was compared to the control group.
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Kinney, William C. "Web-Based Clinical Decision Support System for Triage of Vestibular Patients." Otolaryngology–Head and Neck Surgery 128, no. 1 (January 2003): 48–53. http://dx.doi.org/10.1067/mhn.2003.33.

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OBJECTIVES: We sought to use a clinical decision support system (CDSS) to save costs and to improve scheduling of vestibular patients in an otolaryngology clinic. STUDY DESIGN AND SETTING: We conducted a concurrent review of 50 vestibular patients scheduled in the University of Missouri otolaryngology clinic with or without testing based on the outcome of a CDSS. The CDSS was implemented using Web-based technology. Charges incurred by the health care system through tests determined by the CDSS were compared with those incurred using the standard procedure of ordering hearing tests and electronystagmography for all patients. RESULTS: Thirty-nine tests were prescheduled using the CDSS. Twenty-five additional tests were ordered after the visit. The CDSS resulted in savings of $37,904.00 in charges to the health care system. The CDSS showed high specificity and variable sensitivity. CONCLUSION: A Web-based CDSS can be used to better manage and coordinate patient encounters. SIGNIFICANCE: One important reason to use a CDSS in health care management is to lower costs.
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Rahim, Nur Raidah, Sharifalillah Nordin, and Rosma Mohd Dom. "A Clinical Decision Support System based on Ontology and Causal Reasoning Models." Jurnal Intelek 14, no. 2 (November 29, 2019): 187–97. http://dx.doi.org/10.24191/ji.v14i2.234.

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Clinical decision support system (CDSS) is promising in assisting physicians for improving decision-making process and facilitates healthcare services. In medicine, causality has become the main concern throughout healthcare and decision-making. Causality is necessary for understanding all structures ofscientific reasoning and for providing a coherent and sufficient explanation for any event. However, thereare lack of existing CDSS that provide causal reasoning for the presented outcomes or decisions. Theseare necessary for showing reliability of the outcomes, and helping the physicians in making properdecisions. In this study, an ontology-based CDSS model is developed based on several key concepts andfeatures of causality and graphical modeling techniques. For the evaluation process, the Pellet reasoneris used to evaluate the consistency of the developed ontology model. In addition, an evaluation toolknown as Ontology Pitfall Scanner is used for validating the ontology model through pitfalls detection.The developed ontology-based CDSS model has potentials to be applied in clinical practice and helpingthe physicians in decision-making process. Keywords: clinical decision support system, ontology, causality, causal reasoning, graphical modeling
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Hak, Francini, Tiago Guimarães, and Manuel Santos. "Towards effective clinical decision support systems: A systematic review." PLOS ONE 17, no. 8 (August 15, 2022): e0272846. http://dx.doi.org/10.1371/journal.pone.0272846.

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Background Clinical Decision Support Systems (CDSS) are used to assist the decision-making process in the healthcare field. Developing an effective CDSS is an arduous task that can take advantage from prior assessment of the most promising theories, techniques and methods used at the present time. Objective To identify the features of Clinical Decision Support Systems and provide an analysis of their effectiveness. Thus, two research questions were formulated: RQ1—What are the most common trend characteristics in a CDSS? RQ2—What is the maturity level of the CDSS based on the decision-making theory proposed by Simon? Methods AIS e-library, Decision Support Systems journal, Nature, PlosOne and PubMed were selected as information sources to conduct this systematic literature review. Studies from 2000 to 2020 were chosen covering search terms in CDSS, selected according to defined eligibility criteria. The data were extracted and managed in a worksheet, based on the defined criteria. PRISMA statements were used to report the systematic review. Results The outcomes showed that rule-based module was the most used approach regarding knowledge management and representation. The most common technological feature adopted by the CDSS were the recommendations and suggestions. 19,23% of studies adopt the type of system as a web-based application, and 51,92% are standalone CDSS. Temporal evolution was also possible to visualize. This study contributed to the development of a Maturity Staging Model, where it was possible to verify that most CDSS do not exceed level 2 of maturity. Conclusion The trend characteristics addressed in the revised CDSS were identified, compared to the four predefined groups. A maturity stage model was developed based on Simon’s decision-making theory, allowing to assess the level of maturity of the most common features of the CDSS. With the application of the model, it was noticed that the phases of choice and implementation are underrepresented. This constitutes the main gap in the development of an effective CDSS.
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Farion, K., W. Michalowski, D. O’Sullivan, S. Rubin, D. Weiss, and S. Wilk. "Clinical Decision Support System for Point of Care Use." Methods of Information in Medicine 48, no. 04 (2009): 381–90. http://dx.doi.org/10.3414/me0574.

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Summary Objectives: The objective of this research was to design a clinical decision support system (CDSS) that supports heterogeneous clinical decision problems and runs on multiple computing platforms. Meeting this objective required a novel design to create an extendable and easy to maintain clinical CDSS for point of care support. The proposed solution was evaluated in a proof of concept implementation. Methods: Based on our earlier research with the design of a mobile CDSS for emergency triage we used ontology-driven design to represent essential components of a CDSS. Models of clinical decision problems were derived from the ontology and they were processed into executable applications during runtime. This allowed scaling applications’ functionality to the capabilities of computing platforms. A prototype of the system was implemented using the extended client-server architecture and Web services to distribute the functions of the system and to make it operational in limited connectivity conditions. Results: The proposed design provided a common framework that facilitated development of diversified clinical applications running seamlessly on a variety of computing platforms. It was prototyped for two clinical decision problems and settings (triage of acute pain in the emergency department and postoperative management of radical pros-tatectomy on the hospital ward) and implemented on two computing platforms – desktop and handheld computers. Conclusions: The requirement of the CDSS heterogeneity was satisfied with ontology-driven design. Processing of application models described with the help of ontological models allowed having a complex system running on multiple computing platforms with different capabilities. Finally, separation of models and runtime components contributed to improved extensibility and maintainability of the system.
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Stone, Erin G. "Unintended adverse consequences of a clinical decision support system: two cases." Journal of the American Medical Informatics Association 25, no. 5 (September 23, 2017): 564–67. http://dx.doi.org/10.1093/jamia/ocx096.

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Abstract Many institutions have implemented clinical decision support systems (CDSSs). While CDSS research papers have focused on benefits of these systems, there is a smaller body of literature showing that CDSSs may also produce unintended adverse consequences (UACs). Detailed here are 2 cases of UACs resulting from a CDSS. Both of these cases were related to external systems that fed data into the CDSS. In the first case, lack of knowledge of data categorization in an external pharmacy system produced a UAC; in the second case, the change of a clinical laboratory instrument produced the UAC. CDSSs rely on data from many external systems. These systems are dynamic and may have changes in hardware, software, vendors, or processes. Such changes can affect the accuracy of CDSSs. These cases point to the need for the CDSS team to be familiar with these external systems. This team (manager and alert builders) should include members in specific clinical specialties with deep knowledge of these external systems.
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Dissertations / Theses on the topic "Clinical Decision Support System (CDSS)"

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Eriksson, Falk Filiph, and Fredrik Frenning. "Intelligent Matching For Clinical Decision Support System For Cerebral Palsy Using Domain Knowledge." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-36231.

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Relevant information at the right time can be critically important for clinicians when treating patients with cerebral palsy (CP). Gathering this information could be done through the usage of a clinical decision support system with a matching algorithm that finds relevant patients. The relevancy of this information for clinicians is determined by the relevancy of the matched patients. The aim of this thesis was therefore to investigate how an algorithm that matches similar patients with CP could be improved in terms of relevancy. The goal was also to explore the possibilities of domain knowledge and temporal aspects and how they could be combined and utilized in order to improve the matching algorithm. In this bachelor's thesis, we have conducted a literature study about the domain and a domain knowledge survey. The domain knowledge survey included gathering domain knowledge through contact with an expert in the area of CP. We also implemented an algorithm using intelligent similarity measurements based on validation from experts that could accurately match similar patients according to the domain knowledge gathered. The resulting algorithm is presented through a prototype of a CDSS, which allows clinicians to select and match patients through a GUI, and including features such as adjusting weight values for different attributes. The algorithm uses patient data retrieved from the CPUP database, which is specfic to patients with CP, to match with. From the CPUP database many temporal aspects could be concluded to be relevant for similarity assessment. Due to the limited scope of the thesis however, only the most important aspect was utilized. By treating this aspect as an attribute like the other domain knowledge based attributes, but with respect to other variables that affected it, a combination of temporal aspects and domain knowledge was done when identifying similar patients with CP. Using the prototype of the CDSS with the implemented algorithm could help clinicians make better informed decisions, and this leads to improved health care for children and patients with CP, which is why this thesis was important.
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TÖCKSBERG, EMMA, and ERIK ÖHLÉN. "Clinical decision support systemsin the Swedish health care system : Mapping and analysing existing needs." Thesis, KTH, Hållbarhet och industriell dynamik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-147793.

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Purpose:The thesis will shed light on the overall need of CDSSs in the Swedish health care system,  and  it  will  also  present  a  specific  efficiency  problem  that  could  be  solved  by implementing a CDSS. The need for a CDSS is where an implementation would improve patient outcome, by delivering the right care at the right time, and where the CDSS could reduce the cost of the delivered care. A better understanding of the current need could help eliminate the existing empirical gap and ultimately lead to better and more efficient health care in Sweden. The research question was formulated as: Where within Swedish health care can a need for increased efficiency be met through the implementation of a realistic CDSS system? Design and methodology: The  thesis  is  a  case  study  where qualitative data, collected through a literature review and interviews, was used to answer the research question. The methodology used was tailored to the unique setting of the research and in accordance to the purpose of the study. The method was divided into five phases. (1) Finding an area of focus, such as a specific diagnosis, within the health care system where the need for a CDSS system is deemed high. (2) Mapping the care chain of the identified area of interest. (3) Developing hypotheses concerning where in the care chain challenges could be solved using a clinical decision support system. (4) Confirming or rejecting the proposed hypotheses through interviews with relevant experts. (5) Presenting the specific efficiency problem that could be solved using a CDSS and a presentation of the design of said CDSS. Findings: The efficiency problem that could be solved using a CDSS was identified to be within the area of heart failure treatment. There were a multitude of areas of improvement found along the care chain and a number of them could be solved by developing and using specific CDSSs. A CDSS that could help physicians, within the primary care system, to identify patients that  could benefit from  being  assessed  by  cardiology specialist was  proposed  as  the  most beneficial  CDSS  system.  The  proposed  CDSS  would  be  both  beneficial  and  realistically implementable.
Syftet med uppsatsen är att belysa det övergripande behovet av kliniska beslutsstödssystem inom den svenska vården och slutligen finna det mest trängande behovet. En bättre förståelse för detta behov kan hjälpa att minska det existerande empiriska gapet och slutligen leda till en bättre och mer effektiv vård i Sverige. Forskarfrågan formulerades som uppdraget att finna ett behov för ökad effektivitet inom svensk sjukvård, som kan lösas genom implementering av ett realistiskt kliniskt beslutsstöd. Design och metodologi: Uppsatsen är en casestudie där kvalitativ data, samlad genom en litteraturstudie samt intervjuer, användes för att besvara forskningsfrågan. Metodologin som brukades var anpassad efter den unika naturen för forskningen, samt i enighet med syftet av studien. Metoden delades in i fem faser. (1) Finna ett fokusområde, exempelvis en specifik diagnos, där behovet av ett kliniskt beslutsstöd bedömdes högt. (2) Kartlägga vårdkedjan för den identifierade diagnosen. (3) Utveckla hypoteser angående var inom vårdkedjan som  utmaningar skulle kunna lösas med ett kliniskt beslutsstöd. (4) Bekräfta eller förkasta ypoteserna genom intervjuer med relevanta experter. (5) Presentera problemet med det mest trängande behovet efter ett kliniskt beslutsstöd och hur ett sådans skulle utformas. Fynd: Effektivitetsproblemet som kunde lösas bäst via ett kliniskt beslutsstöd identifierades att vara inom området hjärtsviktsbehandling. Det fanns flertalet områden med utvecklingspotential som urskiljdes ur vårdkedjan för hjärtsviktspatienter, och vissa av dessa utmaningar kunde lösas genom utveckling och implementering av specifika kliniska beslutsstöd. Det kliniska beslutsstöd som skulle lösa det mest trängande behovet inom vården idag föreslås vara ett system som hjälper läkare inom vårdcentralerna att identifiera patienter som skulle gagnas av en remiss till en kardiolog. Det föreslagna kliniska beslutsstödet skulle vara både fördelaktigt för vårdpersonal samt patienter samt är realistiskt implementerbart.
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Khambhammettu, Prashanth. "A Comprehensive Decision Support System(CDSS) for Optimal Pipe Renewal using Trenchless Technologies." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/35450.

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Water distribution system pipes span thousands of miles and form a significant part of the total infrastructure of the country. Rehabilitation of this underground infrastructure is one of the biggest challenges currently facing the water industry. Water main deterioration is twofold: the main itself loses strength over time and breaks; also, there is degradation of water quality and hydraulic capacity due to build of material within a main. The increasing repair and damage costs and degrading services demand that a deteriorating water main be replaced at an optimal time instead of continuing to repair it. In addition, expanding business districts, indirect costs, and interruptions including protected areas, waterways and roadways require examination of trenchless technologies for pipe installation. In this thesis a new threshold break rate criterion for the optimal replacement of pipes is provided. As opposed to the traditional present worth cost (PWC) criterion, the derived method uses the equivalent uniform annualized cost (EUAC). It is shown the EUAC based threshold break rate subsumes the PWC based threshold break rate. In addition, practicing engineers need a user-friendly decision support system to aid in the optimal pipeline replacement process. They also need a task-by-task cost evaluation in a project. As a part of this thesis a comprehensive decision support system that includes both technology selection knowledge base and cost evaluation spreadsheet program within a graphical user interface framework is developed. Numerical examples illustrating the theoretical derivations are also included.
Master of Science
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Muller, Johann Heinrich. "A clinical engineering decision support system." Master's thesis, University of Cape Town, 1988. http://hdl.handle.net/11427/26533.

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The use of technology in health-care today is increasing dramatically with a corresponding increase in cost and complexity to provide and support it. The degree to which a hospital manages this technology affects its ability to treat patients, to perform research, to teach and to attract competent staff. This thesis project has identified the role that clinical engineering could play in health-care technology provision and support in South Africa. A system synthesis technique was employed to develop an idealized clinical engineering model (ICE) that would satisfy South African technological requirements. An extensive literature survey of the current status of clinical engineering in both developed and developing countries was undertaken to provide input to the synthesis process. Surveys were then conducted to determine the actual current status of clinical engineering and its environment in the RSA. To enable such an idealised department to function as defined, it must be supported by appropriate and timeous information. The information needs of the idealised clinical engineering model were analysed and a corresponding decision support system (DSS) defined. Further surveys were conducted to test the applicability and acceptability of the idealised clinical engineering model. The feasibility of implementing the idealised clinical engineering model in South Africa was investigated and recommendations were made based on the research results of this thesis to bring the actual status of clinical engineering closer to the idealised model. ii
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Nguyen, Tan-Nhu. "Clinical decision support system for facial mimic rehabilitation." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2590.

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La paralysie faciale affecte négativement la vie professionnelle, sociale et personnelle des patients concernés. La récupération des mimiques faciales dans des conditions normales et symétriques permet à ces patients d'améliorer leurs qualités de vie. La rééducation fonctionnelle est une étape clinique importante pour améliorer les qualités des interventions chirurgicales. Cependant, la rééducation faciale reste actuellement un défi scientifique, technologique et clinique majeur. En particulier, l'approche conventionnelle manque des retours quantitatifs et objectifs pour optimiser les gestes et les exercices associées. L'objectif de cette thèse est de développer et d'évaluer un système d'aide à la rééducation fonctionnelle de la mimique faciale. La thèse a six contributions principales : (1) un nouveau processus de génération de la tête patient-spécifique avec la texture à partir d'un capteur sans contact de type Kinect ; (2) un nouveau processus de prédiction du crâne à partir de la surface de la tête en utilisant la modélisation statistique de la forme ; (3) une nouvelle méthode d'évaluation des mouvements de la mimique faciale en se basant sur les propriétés musculaires ; (4) un nouveau système de jeu sérieux pour la rééducation fonctionnelle de la mimique faciale (5) un nouveau système d'aide à la décision clinique pour le visage et (6) un guide de référence pour le développement de systèmes de simulation médicale en considérant la déformation des tissus mous en temps réel. Cette thèse ouvre de nouvelles perspectives liées aux différents domaines de recherche allant de la vision par ordinateur (génération automatique des modèles patient-spécifique à partir d'un capteur visuel), la modélisation biomécanique, et l'ingénierie des systèmes pour la rééducation fonctionnelle de la mimique faciale
Facial disorders negatively affect professional, social, and personal lives of involved patients.Thus, recovery of facial mimics into normal and symmetrical conditions allows these patients to improve their life qualities. Functional rehabilitation of facial disorders is an important clinical step to improve qualities of surgical interventions and drug therapies. However, facialmimic rehabilitation currently remains a major scientific, technological, and clinical challenge.Especially, conventional rehabilitation processes lack of quantitative and objective biofeedbacks. Moreover, rehabilitation exercises just included long-term and repetitive actions. This makes patients less ambitious for completing their training programs. Besides, numerous modeling methods, interaction devices, and system architectures have been successfully employed in clinical applications, but they have not been successfully applied for facial mimic rehabilitation. Consequently, this thesis was conducted to complement these drawbacks by designing a clinical decision-support system for facial mimic rehabilitation. Especially, patientspecific models and serious games were integrated with the system for providing quantitative and objective bio-feedbacks and training motivations. The thesis has six main contributions: (1) a novel real-time subject-specific head generation & animation systems, (2) a novel head-to-skull prediction process, (3) a muscle-oriented patientspecific facial paralysis grading system, (4) a novel serious game system for facial mimic rehabilitation, (5) a novel clinical decision-support system for facial mimic rehabilitation, and (6) a reference guide for developing real-time soft-tissues simulation systems. This thesis opens new avenues for new research areas relating to automatic generation of patient specific head from visual sensor and internal structures using statistical shape modeling and real-time modeling and simulation for facial mimic rehabilitation
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Spencer, Malik. "CHRISTINE: A Flexible Web-Based Clinical Decision Support System." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282052336.

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Pedersen, Kim Ohme. "Explanation Methods in Clinical Decision Support : A Hybrid System Approach." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11833.

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The use of computer-based decision support systems within the field of health science has over the last decades been extensively researched and tested, both in controlled environments and in clinical practice. Despite the obvious benefits of utilizing such systems in the day-to-day activities, many of the designed systems fail to make the impact one could hope to achieve. We have designed and implemented a prototype of a decision support system which use both Case-Based Reasoning and probabilistic inference through a Bayesian Network as a basis for the solution. To achieve user acceptance an explanation module has been implemented which gives the user full access to the data which has been used in the reasoning process, both from the Case-Based Reasoning and the Bayesian Network. The system has shown promising results within the domain of wine recommendation, with a very high accuracy despite uncertain accuracy of the knowledge within the system. Furthermore the explanations presented to an expert conformed to the causal way of reasoning used by said expert, and was accepted as a very useful tool to get pointed in the right direction for evaluation of the solution.
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Kong, Guilan. "An online belief rule-based group clinical decision support system." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/an-online-belief-rulebased-group-clinical-decision-support-system(c31a65c7-60c3-4e7a-b18e-44fee95f7da1).html.

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Around ten percent of patients admitted to National Health Service (NHS) hospitals have experienced a patient safety incident, and an important reason for the high rate of patient safety incidents is medical errors. Research shows that appropriate increase in the use of clinical decision support systems (CDSSs) could help to reduce medical errors and result in substantial improvement in patient safety. However several barriers continue to impede the effective implementation of CDSSs in clinical settings, among which representation of and reasoning about medical knowledge particularly under uncertainty are areas that require refined methodologies and techniques. Particularly, the knowledge base in a CDSS needs to be updated automatically based on accumulated clinical cases to provide evidence-based clinical decision support. In the research, we employed the recently developed belief Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER) for design and development of an online belief rule-based group CDSS prototype. In the system, belief rule base (BRB) was used to model uncertain clinical domain knowledge, the evidential reasoning (ER) approach was employed to build inference engine, a BRB training module was developed for learning the BRB through accumulated clinical cases, and an online discussion forum together with an ER-based group preferences aggregation tool were developed for providing online clinical group decision support.We used a set of simulated patients in cardiac chest pain provided by our research collaborators in Manchester Royal Infirmary to validate the developed online belief rule-based CDSS prototype. The results show that the prototype can provide reliable diagnosis recommendations and the diagnostic performance of the system can be improved significantly after training BRB using accumulated clinical cases.
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Kanwal, Summrina. "Towards a novel medical diagnosis system for clinical decision support system applications." Thesis, University of Stirling, 2016. http://hdl.handle.net/1893/25397.

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Clinical diagnosis of chronic disease is a vital and challenging research problem which requires intensive clinical practice guidelines in order to ensure consistent and efficient patient care. Conventional medical diagnosis systems inculcate certain limitations, like complex diagnosis processes, lack of expertise, lack of well described procedures for conducting diagnoses, low computing skills, and so on. Automated clinical decision support system (CDSS) can help physicians and radiologists to overcome these challenges by combining the competency of radiologists and physicians with the capabilities of computers. CDSS depend on many techniques from the fields of image acquisition, image processing, pattern recognition, machine learning as well as optimization for medical data analysis to produce efficient diagnoses. In this dissertation, we discuss the current challenges in designing an efficient CDSS as well as a number of the latest techniques (while identifying best practices for each stage of the framework) to meet these challenges by finding informative patterns in the medical dataset, analysing them and building a descriptive model of the object of interest and thus aiding in medical diagnosis. To meet these challenges, we propose an extension of conventional clinical decision support system framework, by incorporating artificial immune network (AIN) based hyper-parameter optimization as integral part of it. We applied the conventional as well as optimized CDSS on four case studies (most of them comprise medical images) for efficient medical diagnosis and compared the results. The first key contribution is the novel application of a local energy-based shape histogram (LESH) as the feature set for the recognition of abnormalities in mammograms. We investigated the implication of this technique for the mammogram datasets of the Mammographic Image Analysis Society and INbreast. In the evaluation, regions of interest were extracted from the mammograms, their LESH features were calculated, and they were fed to support vector machine (SVM) and echo state network (ESN) classifiers. In addition, the impact of selecting a subset of LESH features based on the classification performance was also observed and benchmarked against a state-of-the-art wavelet based feature extraction method. The second key contribution is to apply the LESH technique to detect lung cancer. The JSRT Digital Image Database of chest radiographs was selected for research experimentation. Prior to LESH feature extraction, we enhanced the radiograph images using a contrast limited adaptive histogram equalization (CLAHE) approach. Selected state-of-the-art cognitive machine learning classifiers, namely the extreme learning machine (ELM), SVM and ESN, were then applied using the LESH extracted features to enable the efficient diagnosis of a correct medical state (the existence of benign or malignant cancer) in the x-ray images. Comparative simulation results, evaluated using the classification accuracy performance measure, were further benchmarked against state-of-the-art wavelet based features, and authenticated the distinct capability of our proposed framework for enhancing the diagnosis outcome. As the third contribution, this thesis presents a novel technique for detecting breast cancer in volumetric medical images based on a three-dimensional (3D) LESH model. It is a hybrid approach, and combines the 3D LESH feature extraction technique with machine learning classifiers to detect breast cancer from MRI images. The proposed system applies CLAHE to the MRI images before extracting the 3D LESH features. Furthermore, a selected subset of features is fed to a machine learning classifier, namely the SVM, ELM or ESN, to detect abnormalities and to distinguish between different stages of abnormality. The results indicate the high performance of the proposed system. When compared with the wavelet-based feature extraction technique, statistical analysis testifies to the significance of our proposed algorithm. The fourth contribution is a novel application of the (AIN) for optimizing machine learning classification algorithms as part of CDSS. We employed our proposed technique in conjunction with selected machine learning classifiers, namely the ELM, SVM and ESN, and validated it using the benchmark medical datasets of PIMA India diabetes and BUPA liver disorders, two-dimensional (2D) medical images, namely MIAS and INbreast and JSRT chest radiographs, as well as on the three-dimensional TCGA-BRCA breast MRI dataset. The results were investigated using the classification accuracy measure and the learning time. We also compared our methodology with the benchmarked multi-objective genetic algorithm (ES)-based optimization technique. The results authenticate the potential of the AIN optimised CDSS.
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Horner, Vincent Zion. "Developing a consumer health informatics decision support system using formal concept analysis." Diss., Pretoria : [s.n.], 2007. http://upetd.up.ac.za/thesis/available/etd-05052008-112403/.

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Books on the topic "Clinical Decision Support System (CDSS)"

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J, Savino Peter, and Trobe Jonathan D. 1943-, eds. Clinical decisions in neuro-ophthalmology. St. Louis: Mosby, 1985.

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J, Savino Peter, and Trobe Jonathan D. 1943-, eds. Clinical decisions in neuro-ophthalmology. 2nd ed. St. Louis: Mosby Year Book, 1992.

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International Conference on Systems Research, Informatics, and Cybernetics (19th 2007 Baden-Baden, Germany). Advances in environmental systems research: Sustainability, environmental sciences, support systems : effects of electromagnetic exposition on honeybees, principles of neuro-empirism and dynamic models, application of stochastic networks, sustainability of fuzzy theory, object oriented analysis, integrated logistic support principles, business information management system, sustainable decision support systems, health service delivery. Tecumseh, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2007.

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International Conference on Systems Research, Informatics, and Cybernetics (19th 2007 Baden-Baden, Germany). Advances in environmental systems research: Sustainability, environmental sciences, support systems : effects of electromagnetic exposition on honeybees, principles of neuro-empirism and dynamic models, application of stochastic networks, sustainability of fuzzy theory, object oriented analysis, integrated logistic support principles, business information management system, sustainable decision support systems, health service delivery. Tecumseh, Ont: International Institute for Advanced Studies in Systems Research and Cybernetics, 2007.

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Cody, Weisbach P., ed. Clinical prediction rules: A physical therapy reference manual. Sudbury, Mass: Jones and Bartlett Publishers, 2010.

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Hayit, Greenspan, Syeda-Mahmood Tanveer, and SpringerLink (Online service), eds. Medical Content-Based Retrieval for Clinical Decision Support: Second MICCAI International Workshop, MCBR-CDS 2011, Toronto, ON, Canada, September 22, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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MCBR-CDS 2009 (2009 London, England). Medical content-based retrieval for clinical decision support: First MICCAI international workshop, MCBR-CDS 2009, London, UK, September 20, 2009 : revised selected papers. Berlin: Springer, 2010.

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Halamka, John D., and Paul Cerrato. Reinventing Clinical Decision Support. Taylor & Francis Group, 2021.

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Müller, Henning, Hayit Greenspan, and Tanveer Syeda-Mahmood. Medical Content-Based Retrieval for Clinical Decision Support: Third MICCAI International Workshop, MCBR-CDS 2012, Nice, France, October 1st, 2012, ... Springer, 2013.

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Cerrato, Paul, and John Halamka. Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning. Taylor & Francis Group, 2020.

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Book chapters on the topic "Clinical Decision Support System (CDSS)"

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Wasylewicz, A. T. M., and A. M. J. W. Scheepers-Hoeks. "Clinical Decision Support Systems." In Fundamentals of Clinical Data Science, 153–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99713-1_11.

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AbstractClinical decision support (CDS) includes a variety of tools and interventions computerized as well as non- computerized. High-quality clinical decision support systems (CDSS), computerized CDS, are essential to achieve the full benefits of electronic health records and computerized physician order entry. A CDSS can take into account all data available in the EHR making it possible to notice changes outside the scope of the professional and notice changes specific for a certain patient, within normal limits. However, to use of CDSS in practice, it is important to understand the basic requirements of these systems.This chapter shows in what way CDSS can support the use of clinical data science in daily clinical practice. Moreover, it explains what types of CDSS are available and how such systems can be used. However, to achieve high-quality CDSS which is effective in use requires thoughtful design, implementation and critical evaluation. Therefore, challenges surrounding implementation of a CDSS are discussed, as well as a strategies to develop and validate CDSS.
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Gupta, Praveen Kumar, Abijith Trichur Ramachandran, Anusha Mysore Keerthi, Preshita Sanjay Dave, Swathi Giridhar, Shweta Sudam Kallapur, and Achisha Saikia. "An Overview of Clinical Decision Support System (CDSS) as a Computational Tool and Its Applications in Public Health." In EAI/Springer Innovations in Communication and Computing, 81–117. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35280-6_5.

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Mehta, Parag. "Clinical Decision Support System." In Health Informatics, 95–102. New York: Productivity Press, 2022. http://dx.doi.org/10.4324/9780429423109-5.

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Oyenuga, Solomon Olalekan, Lalit Garg, Amit Kumar Bhardwaj, and Divya Prakash Shrivastava. "Cloud-Based Clinical Decision Support System." In Conference Proceedings of ICDLAIR2019, 220–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67187-7_24.

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Kim, Jeong Ah, Min Hee Choi, and InSook Cho. "Implementation of Clinical Decision Support System Architecture." In Future Generation Information Technology, 371–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27142-7_43.

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Cypko, Mario A. "Development of a Clinical Decision Support System." In Development of Clinical Decision Support Systems using Bayesian Networks, 15–26. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-32594-7_3.

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Chu, Hua, Yijing Yang, Qingshan Li, Yongfei Xu, and Hongpeng Wei. "A Scalable Clinical Intelligent Decision Support System." In Inclusive Smart Cities and Digital Health, 159–65. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39601-9_14.

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Obukhova, Natalia, and Alexandr Motyko. "Image Analysis in Clinical Decision Support System." In Intelligent Systems Reference Library, 261–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67994-5_10.

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Aggarwal, Pushkar. "Dermatological Machine Learning Clinical Decision Support System." In Machine Learning in Medicine, 9–17. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781315101323-2.

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Managathayaru, N., B. Mathura Bai, G. Sunil, G. Hanisha Durga, C. Anjani Varma, V. Sai Sarath, and J. Sai Sandeep. "Diagnosis of Diabetes Using Clinical Decision Support System." In Proceedings of the Third International Conference on Computational Intelligence and Informatics, 337–48. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1480-7_29.

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Conference papers on the topic "Clinical Decision Support System (CDSS)"

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Calamanti, Chiara, Annalisa Cenci, Michele Bernardini, Emanuele Frontoni, and Primo Zingaretti. "A Clinical Decision Support System for Chronic Venous Insufficiency." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68016.

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Earlier diagnosis plays a pivotal role in clinical applications, since it can strongly reduce the incidence and impact of many diseases and, consequently, the reduction of health care costs. This last aspect depends strongly from right therapy prescriptions, especially when there are various opportunities. Within this context, Clinical Decision Support Systems (CDSS) could bring several benefits. In this paper, we propose a CDSS with the aim of improving the clinician practice based on recommendations, assessment of the patient and screening of patients with risk factors to prevent chronic venous insufficiency (CVI) complications. The proposed CDSS is implemented in the Nu.Sa. cloud system, which involves thousands of italian General Practitioners (GPs) collecting data (EHR data, personal data, patient’s medical history) from millions of patients. The proposed architecture is designed to collect data from a distributed scenario where GPs are collecting clinical history and pharmacy or second level hospitals gather data from medical devices connected to the cloud over a standard data architecture. We show that exploiting the integration of the medical device VenoScreen Plus with the patient EHR, this CDSS is capable to improve preventive care, to enhance clinical performance, to influence clinical decision making and to significantly improve the decision quality levering on data driven approach.
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Kim, Jeong Ah, InSook Cho, and Yoon Kim. "CDSS (Clinical Decision Support System) Architecture in Korea." In 2008 International Conference on Convergence and Hybrid Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/ichit.2008.223.

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Ulapane, Nalika. "A Rubric to Guide the Design, Development and Assessment of Mobile Clinical Decision Support Systems." In Digital Restructuring and Human (Re)action. University of Maribor Press, 2022. http://dx.doi.org/10.18690/um.fov.4.2022.50.

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Clinical decision making is vital for healthcare provision. Sound clinical decision support systems (CDSS) have therefore become crucial for healthcare delivery. This research aims to develop a rubric to guide the design, development and assessment of mobile (i.e., Smartphone or Tab-based) CDSS, combining socio-technological factors and decision-making principles.
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Maldonado, H., L. Leija, and A. Vera. "Selecting a computational classifier to develop a clinical decision support system (CDSS)." In 2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). IEEE, 2015. http://dx.doi.org/10.1109/iceee.2015.7357960.

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Frize, Monique, Erika Bariciak, and Sabine Weyand. "Suggested criteria for successful deployment of a Clinical Decision Support System (CDSS)." In 2010 IEEE International Workshop on Medical Measurements and Applications (MeMeA). IEEE, 2010. http://dx.doi.org/10.1109/memea.2010.5480227.

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Menekse, Gonca Gokce, Nergiz Ercil Cagiltay, and Gul Tokdemir. "Patient safety & clinical decision support systems (CDSS): A case study in Turkey." In 2015 E-Health and Bioengineering Conference (EHB). IEEE, 2015. http://dx.doi.org/10.1109/ehb.2015.7391580.

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Mahmud, Farahidayah Bt, Maryati Mohd Yusof, and Azman Naoh Shahrul. "Ontological based clinical decision support system (CDSS) for weaning ventilator in Intensive Care Unit (ICU)." In 2011 International Conference on Electrical Engineering and Informatics (ICEEI). IEEE, 2011. http://dx.doi.org/10.1109/iceei.2011.6021506.

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Eduardo, Anderson A., Rafael M. Loureiro, Adriano Tachibana, Pedro V. Netto, Tatiana F. de Almeida, and André Pires. "A pipeline for tabular dataset formation from unstructured data provided by ACR Appropriateness Criteria guidelines." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbcas.2022.222497.

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Among the current data-centric technologies, clinical decision support systems (CDSS) figure out as one of the most promising for healthcare. Despite the technological advances facilitating its implementation, the maintenance of knowledge base for CDSS remains open to improvements. Here, we argue that the Appropriateness Criteria provided by ACR guidelines can be used as an open data-source that, combined with appropriate algorithms, can push forward basic research and technological developments regarding knowledge bases for CDSS. Therefore, we developed a pipeline capable of forming tabular datasets from ACR guidelines, stored in a web site in textual PDF files. We also experimentally demonstrate that the proposed pipeline successfully recovers the interested contents, and the best composition, in terms of its component algorithms, is discussed. Future research focused on algorithms flexibility in the face of PDF template updates could improve our work.
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Pyo, Kyoung-Ho, Beung-Chul Ahn, Chun-Feng Xin, Dongmin Jung, Chang Gon Kim, Min Hee Hong, Byoung Chul Cho, and Hye Ryun Kim. "Abstract 683: A machine learning based clinical decision support system (CDSS) for anti-PD-1 therapy using non-invasive blood marker and clinical information for lung cancer patients." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-683.

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Pyo, Kyoung-Ho, Beung-Chul Ahn, Chun-Feng Xin, Dongmin Jung, Chang Gon Kim, Min Hee Hong, Byoung Chul Cho, and Hye Ryun Kim. "Abstract 683: A machine learning based clinical decision support system (CDSS) for anti-PD-1 therapy using non-invasive blood marker and clinical information for lung cancer patients." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.am2019-683.

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Reports on the topic "Clinical Decision Support System (CDSS)"

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Djulbegovic, Benjamin. Proposal for Development of EBM-CDSS (Evidence-based Clinical Decision Support System) to Aid Prognostication in Terminally Ill Patients. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada610718.

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Djulbegovic, Benjamin. Proposal for Development of EBM-CDSS (Evidence-Based Clinical Decision Support System) to Aid Prognostication in Terminally Ill Patients. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada601835.

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Djulbegovic, Benjamin. Proposal for Development of EBM-CDSS (Evidence-based Clinical Decision Support System) to Aid Prognostication in Terminally Ill Patients. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada601841.

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Djulbegovic, Benjamin. Proposal for Development of EBM-CDSS (Evidence-Based Clinical Decision Support System) to Aid Prognostication in Terminally Ill Patients. Fort Belvoir, VA: Defense Technical Information Center, October 2013. http://dx.doi.org/10.21236/ada601844.

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Ohno-Machado, Lucila. FACTS (Find Appropriate Clinical Trials) for You: A Computer-Based Decision Support System for Breast Cancer Patients. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada408466.

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Ohno-Machado, Lucila. FACTS (Find the Appropriate Clinical Trials) for You: A Computer-Based Decision Support System for Breast Cancer Patients. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada391925.

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Ohno-Machado, Lucila. FACTS (Find the Appropriate Clinical Trials) for You: A Computer-Based Decision Support System for Breast Cancer Patients. Fort Belvoir, VA: Defense Technical Information Center, May 1999. http://dx.doi.org/10.21236/ada381179.

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Paez, Kathryn, Rachel Shapiro, Lee Thompson, Erica Shelton, Lucy Savitz, Sarah Mossburg, Susan Baseman, and Amy Lin. Health System Panel To Inform and Encourage Use of Evidence Reports: Findings From the Implementation and Evaluation of Two Evidence-Based Tools. Agency for Healthcare Research and Quality (AHRQ), August 2022. http://dx.doi.org/10.23970/ahrqepchealthsystempanel.

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Objectives. The Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) Program wants learning health systems (LHSs) to use the evidence from its reports to improve patient care. In 2018, to improve uptake of EPC Program findings, the EPC Program developed a project to enhance LHSs’ adoption of evidence to improve the quality and effectiveness of patient care. AHRQ contracted with the American Institutes for Research (AIR) and its partners to convene a panel of senior leaders from 11 LHSs to guide the development of tools to help health systems use findings from EPC evidence reports. The panel’s contributions led to developing, implementing, and evaluating two electronic tools to make the EPC report findings more accessible. AIR evaluated the LHSs’ use of the tools to understand (1) LHSs’ experiences with and impressions of the tools, (2) how well the tools helped them access evidence, and (3) how well the tools addressed barriers to LHS use of the EPC reports and barriers to applying the evidence from the reports. Data sources. (1) Implementation meetings with 6 LHSs; (2) interviews with 27 health system leaders and clinical staff who used the tools; and (3) website utilization metrics. Results. The tools were efficient and useful sources of summarized evidence to (1) inform systems change, (2) educate trainees and clinicians, (3) inform research, and (4) support shared decision making with patients and families. Clinical leaders appreciated the thoroughness and quality of the evidence reviews and view AHRQ as a trusted source of information. Participants found both tools to be valuable and complementary. Participants suggested optimizing the content for mobile device use to facilitate health system uptake of the tools. In addition, they felt it would be helpful to have training resources about tool navigation and interpreting the statistical content in the tools. Conclusions. The evaluation shows that LHSs find the tools to be useful resources for making the EPC Program reports more accessible to health system leaders. The tools have the potential to meet some, but not all, LHS evidence needs, while exposing health system leaders to AHRQ as a resource to help meet their information needs. The ability of the EPC reports to support LHSs in improving the quality of care is limited by the strength and robustness of the evidence, as well as the relevance of the report topics to patient care challenges faced by LHSs.
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