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 facialeFacial 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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Williams, C. Lesley. "A computer-based decision support system for orthodontic diagnosis and treatment planning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq21223.pdf.
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Bennasar, Mohamed. "Clinical decision support system for early detection and diagnosis of dementia." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/73073/.
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Dementia is a syndrome caused by a chronic or progressive disease of the brain, which affects memory, orientation, thinking, calculation, learning ability and language. Until recently, early diagnosis of dementia was not a high priority, since the related diseases were considered untreatable and irreversible. However, more effective treatments are becoming available, which can slow the progress of dementia if they are used in the early stages of the disease. Therefore, early diagnosis is becoming more important. The Clock Drawing Test (CDT) and Mini Mental State Examination (MMSE) are well-known cognitive assessment tests. A known obstacle to the wider usage of the CDT assessments is the scoring and interpretation of the results. This thesis introduces a novel diagnostic Clinical Decision Support System (CDSS) based on CDT which can help in the diagnosis of three stages of dementia. It also introduces the advanced methods developed for the interpretation and analysis of CDTs. The data used in this research consist of 604 clock drawings produced by dementia patients and healthy individuals. A comprehensive catalogue of 47 visual features within CDT drawings is proposed to enhance the sensitivity of the CDT in diagnosing the early stages of dementia. These features are selected following a comprehensive analysis of the available data and the most common CDT scoring systems reported in the medical literature. These features are used to build a new digitised dataset necessary for training and validating the proposed CDSS. In this thesis, a novel feature selection method is proposed for the study of CDT feature significance and to define the most important features in diagnosing dementia. iii A new framework is also introduced to analyse the temporal changes in the CDT features corresponding to the progress of dementia over time, and to define the first onset symptoms. The proposed CDSS is designed to differentiate between four cognitive function statuses: (i) normal; (ii) mild cognitive impairment or mild dementia; (iii) moderate or severe dementia; and (vi) functional. This represents a new application of the CDT, as it was previously used only to detect the positive dementia cases. Diagnosing mild cognitive impairment or early stage dementia using CDT as a standalone tool is a very challenging task. To address this, a novel cascade classifier is proposed, which benefits from combining CDT and MMSE to enhance the overall performance of the system. The proposed CDSS diagnoses the CDT drawings and places them into one of three cognitive statuses (normal or functional, mild cognitive impairment or mild dementia, and moderate or severe dementia) with an accuracy of 78.34 %. Moreover, the proposed CDSS can distinguish between the normal and the abnormal cases with accuracy of 89.54 %. The achieved results are good and outperform most of CDT scoring systems in discriminating between normal and abnormal cases as reported in existing literature. Moreover, the system shows a good performance in diagnosing the CDT drawings into one of the three cognitive statuses, even comparing well with the performance of dementia specialists. The research has been granted ethical approval from the South East Wales Research Ethics Committee to employ anonymised copies of clock drawings and copies of Mini Mental State Examination made by patients during their examination by the memory team in Llandough hospital, Cardiff.
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De, Sousa Barroca José Duarte. "Verification and validation of knowledge-based clinical decision support systems - a practical approach : A descriptive case study at Cambio CDS." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104935.
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The use of clinical decision support (CDS) systems has grown progressively during the past decades. CDS systems are associated with improved patient safety and outcomes, better prescription and diagnosing practices by clinicians and lower healthcare costs. Quality assurance of these systems is critical, given the potentially severe consequences of any errors. Yet, after several decades of research, there is still no consensual or standardized approach to their verification and validation (V&V). This project is a descriptive and exploratory case study aiming to provide a practical description of how Cambio CDS, a market-leading developer of CDS services, conducts its V&V process. Qualitative methods including semi-structured interviews and coding-based textual data analysis were used to elicit the description of the V&V approaches used by the company. The results showed that the company’s V&V methodology is strongly influenced by the company’s model-driven development approach, a strong focus and leveraging of domain knowledge and good testing practices with a focus on automation and test-driven development. A few suggestions for future directions were discussed.
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Zimit, Sani Ibrahim. "Hybrid approach to interpretable multiple classifier system for intelligent clinical decision support." Thesis, University of Reading, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.631699.
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Data-driven decision support approaches have been increasingly employed in recent years in order to unveil useful diagnostic and prognostic patterns from data accumulated in clinical repositories. Given the diverse amount of evidence generated through everyday clinical practice and the exponential growth in the number of parameters accumulated in the data, the capability of finding purposeful task-oriented patterns from patient records is crucial for providing effective healthcare delivery. The application of classification decision support tool in clinical settings has brought about formidable challenges that require a robust system. Knowledge Discovery in Database (KDD) provides a viable solution to decipher implicit knowledge in a given context. KDD classification techniques create models of the accumulated data according to induction algorithms. Despite the availability of numerous classification techniques, the accuracy and interpretability of the decision model are fundamental in the decision processes. Multiple Classifier Systems (MCS) based on the aggregation of individual classifiers usually achieve better decision accuracy. The down size of such models is due to their black box nature. Description of the clinical concepts that influence each decision outcome is fundamental in clinical settings. To overcome this deficiency, the use of artificial data is one technique advocated by researchers to extract an interpretable classifier that mimics the MCS. In the clinical context, practical utilisation of the mimetic procedure depends on the appropriateness of the data generation method to reflect the complexities of the evidence domain. A well-defined intelligent data generation method is required to unveil associations and dependency relationships between various entities the evidence domain. This thesis has devised an Interpretable Multiple classifier system (IMC) using the KDD process as the underlying platform. The approach integrates the flexibility of MCS, the robustness of Bayesian network (BN) and the concept of mimetic classifier to build an interpretable classification system. The BN provides a robust and a clinically accepted formalism to generate synthetic data based on encoded joint relationships of the evidence space. The practical applicability of the IMC was evaluated against the conventional approach for inducing an interpretable classifier on nine clinical domain problems. Results of statistical tests substantiated that the IMC model outperforms the direct approach in terms of decision accuracy.
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Vernier, Stanley J. "Clinical Evaluation and Enhancement of a Medical Case-Based Decision Support System." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1258132492.
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Anya, Obinna. "Practice-centred e-health system design for cross-boundary clinical decision support." Thesis, University of Liverpool, 2012. http://livrepository.liverpool.ac.uk/9053/.
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The idea of cross-boundary clinical decision support has the potential to transform the design of future work environments for e-health through a connected healthcare system that allows for harnessing of information and peer opinion across geographical boundaries for better decision-making. The trouble, however, is that the use of healthcare information in decision-making usually occurs within the context of a complex structure of clinical work practices that is often shaped by a wide range of factors, including organisational culture, local work contexts, socially constructed traditions of actions, experiences and patients’ circumstances. They vary across geographical boundaries, and have remained largely unaccounted for in the design of current e-health systems. As a result, achieving the visions of e-health, particularly in relation to cross-boundary clinical decision support, requires a rethinking of key clinical and organisational processes in a manner that accommodates work practice as a fundamental part of how clinicians work and make decisions in the real-world. This thesis investigates the concept of work practice as a design requirement for cross-boundary clinical decision support systems in e-health. It is argued that the task of enabling informed decision support across geographical boundaries in e-health can be enhanced through an understanding, and a formal characterisation, of work practices in various healthcare work contexts, and a specification of how practice can be used, managed and transformed to suit various clinical problem situations and patients’ needs. This research takes a clinical practice-centred approach to inform e-health system design, and draws on the concept of work practice and cultural-historical theory in social science as well as situation awareness in order to describe the local traditions of actions that guide clinicians’ work in the real world. It contributes a coherent conceptual architecture comprising a practice-centred awareness model for cross-boundary awareness, a frame-based technique, named PracticeFrame, for formalising and representing work practice for system design, and ContextMorph, for adaptively transforming a suggestion across work boundaries to suit a user’s local work context and practices. An in-depth user-informed requirements capture was used to gain an understanding of clinical work practices for designing e-health system for cross-boundary decision support. A proof of concept prototype, named CaDHealth, which is based on the Brahms work practice modelling tool and includes a work practice visualisation model, named the practice display, was developed and used to conduct user-based evaluation. The evaluation revealed that incorporating practice-centred awareness enhances usefulness, acceptance and user adoption of e-health systems for cross-boundary clinical decision support.
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Osop, Hamzah Bin. "A practice-based evidence approach for clinical decision support." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/123320/2/Hamzah%20Bin%20Osop%20Thesis.pdf.
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This thesis studies the conceptualisation and evaluation of a Practice-Based Evidence approach to decision making in healthcare. It examines the existing ICT architecture of a public hospital in Singapore to design a decision support system that leverages practical clinical evidence meaningfully captured in electronic health records. In doing so, healthcare professionals are supported in decision making through findings from past similar patients that can be generalised to the current patient population.
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Saguilig, Lauren G. "A Clinical Decision Support System for the Prevention of Genetic-Related Heart Disease." Thesis, California State University, Long Beach, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10264716.
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Drug-induced long QT syndrome (diLQTS) is a common adverse drug reaction characterized by rapid and erratic heart beats that may instigate fainting or seizures. The onset of diLQTS can lead to torsades de points (TdP), a specific form of abnormal heart rhythm that often leads to sudden cardiac arrest and death. This study aims to understand the genetic similarities between diLQTS and TdP to develop a clinical decision support system (CDSS) to aide physicians in the prevention of TdP. Highly accurate classification algorithms, including random forests, shrunken centroid, and diagonal linear discriminant analysis are considered to build a prediction model for TdP. With a feasible set of markers, we accurately predict TdP classifications with an accuracy above 90%. The methodology used in this study can be extended to dealing with other biomedical high-dimensional data.
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Ramnarayan, Padmanabhan. "Clinical decision-making in acute paediatrics : evaluation of the impact of an internet-delivered paediatric decision support system." Thesis, Imperial College London, 2009. http://hdl.handle.net/10044/1/7373.
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A newer generation diagnostic aid (Isabel), capable of rapid advice provision using a simplified user interface, was developed at St Mary's Hospital in 2000. This thesis describes a series of evaluations conducted to explore Isabel's effects on diagnostic decision-making in acute paediatric practice. Preliminary assessment of system performance by the developers using a wide variety of hypothetical and real cases indicated that Isabel demonstrated significant accuracy with minimal usage time. In the next stage of evaluation, clinicians used the system to make decisions on a balanced set of cases in a simulated field study. Changes in the quality of diagnostic assessment by various grades of subjects were measured before and after Isabel consultation. Since no suitable metric was available to perform this measurement, a new reliable and valid score was developed as part of this investigation. All grades of subjects benefited from the use of the diagnostic aid in easy and difficult cases. Despite variability in the clinical features input into the Isabel system, diagnostic suggestions did not vary significantly across subjects. In the next step of evaluation, an assessment of Isabel's impact on junior doctors' decision making was performed in a multi-centre clinical trial at four paediatric sites. Subjects improved their diagnostic performance by including a number of important diagnoses in their workup after system consultation. Numerous technical, cultural and systematic barriers presented the routine use of decision support. Findings from this project provide valuable insights into how newer generation diagnostic aids can be designed and used to achieve a reduction in diagnostic error.
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wang, Jingyi. "A Service-Oriented Architecture for Integrating Clinical Decision Support in a National E-Health System." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-92137.
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With the help of appropriate IT support, health care services can be executed in a more effective and secure way. In Sweden, the NPÖ (National Patients’ Översikt) stands for National Patients’ Overview. It is a platform where authorized health care providers can access comprehensive and continuous information about health care and patients’ situation, based on which care providers can offer safe and qualified services. The NPÖ project is focusing on the information sharing phase. In order to improve the efficiency and correctness of care services, the next step is that health care systems can offer clinical suggestions and warnings with the existing patients’ data and medication information. Clinical Decision Support Systems (CDSSs) are aimed to offer such assistance and are necessary to be integrated. But by now, there is no explicit architecture to guide Swedish government to implement the integration. Although some architectures have been proposed for integrating CDSSs in health information systems, those architectures are developed for certain use cases and cannot be adopted directly in NPÖ. An integration architecture which takes full consideration of NPÖ-adopting data types, message structures and interface types is needed. This thesis adopts constructive research method, which contains three main phases. First, related backgrounds about national electronic health care system, clinical decision supports system and integration techniques are introduced. Second, the integration architecture is constructed following service-oriented principles. Third, theoretical valuation work is finished by assessing system features and making interviews. This thesis takes advantage of service-oriented architecture to design an architecture with Clinical Decision Support (CDS) middleware for health care information system integration. With this structure, national electronic health care systems, such as NPÖ, can have interaction with various types of CDSSs to provide more efficient and secure health care. It offers united interfaces which enable different CDSSs with different developing platforms to communicate without obstacles. Unlike the existing CDSS integration architectures, the new one with CDS Middleware can provide maximized scalability. Evaluation work has been done from two aspects. Feature criteria and interviews with national health care system developers indicate that the architecture can contribute to the development of NPÖ, and future works such as involving security agents can be continued to optimize the results.
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Qi, Xuguang. "AUTOMATED MIDLINE SHIFT DETECTION ON BRAIN CT IMAGES FOR COMPUTER-AIDED CLINICAL DECISION SUPPORT." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/504.
Full textAbstract:
Midline shift (MLS), the amount of displacement of the brain’s midline from its normal symmetric position due to illness or injury, is an important index for clinicians to assess the severity of traumatic brain injury (TBI). In this dissertation, an automated computer-aided midline shift estimation system is proposed. First, a CT slice selection algorithm (SSA) is designed to automatically select a subset of appropriate CT slices from a large number of raw images for MLS detection. Next, ideal midline detection is implemented based on skull bone anatomical features and global rotation assumptions. For the actual midline detection algorithm, a window selection algorithm (WSA) is applied first to confine the region of interest, then the variational level set method is used to segment the image and extract the ventricle contours. With a ventricle identification algorithm (VIA), the position of actual midline is detected based on the identified right and left lateral ventricle contours. Finally, the brain midline shift is calculated using the positions of detected ideal midline and actual midline. One of the important applications of midline shift in clinical medical decision making is to estimate the intracranial pressure (ICP). ICP monitoring is a standard procedure in the care of severe traumatic brain injury (TBI) patients. An automated ICP level prediction model based on machine learning method is proposed in this work. Multiple features, including midline shift, intracranial air cavities, ventricle size, texture patterns, and blood amount, are used in the ICP level prediction. Finally, the results are evaluated to assess the effectiveness of the proposed method in ICP level prediction.
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Laleci, Gokce Banu. "Intelligent Healthcare Monitoring System Based On Semantically Enriched Clinical Guidelines." Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609657/index.pdf.
Full textAbstract:
Clinical guidelines are developed to assist healthcare practitioners to make
decisions on a patient's medical problems and as such they communicate with external applications to retrieve patient data, to initiate medical actions through clinical workflows and to transmit information to alert/reminder systems. The interoperability problems in the healthcare IT domain for interacting with heterogeneous clinical workflow systems and Electronic Healthcare Record (EHR) Systems prevent wider deployment of clinical guidelines because each deployment requires a tedious custom adaptation phase. In this thesis, we provide machine processable mechanisms that express the semantics of clinical guideline interfaces so that automated processes can be used to access the clinical resources for guideline deployment and execution. For this purpose, we propose a semantically enriched clinical guideline representation formalism by extending one of the computer interpretable guideline representation languages, GuideLine Interchange Format (GLIF). To be able to deploy the semantically extended guidelines to healthcare settings semi-automatically, the underlying application'
s semantics must also be available. We describe how this can be achieved based on two prominent implementation technologies in use in the eHealth domain: Integrating Healthcare Enterprise (IHE) Cross Enterprise Document Sharing Integration Profile (XDS) for discovering and exchanging EHRs and Web service technology for interacting with the clinical workflows and wireless medical sensor devices. Since the deployment and execution architecture should be dynamic, and address the heterogeneity of underlying clinical environment, the deployment and execution is coordinated by a multi-agent system. The system described in this thesis is realized within the scope of the SAPHIRE Project.
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Hagiwara, Magnus. "Development and Evaluation of a Computerised Decision Support System for use in pre-hospital care." Doctoral thesis, Hälsohögskolan, Högskolan i Jönköping, HHJ. Kvalitetsförbättring och ledarskap inom hälsa och välfärd, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-23781.
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The aim of the thesis was to develop and evaluate a Computerised Decision Support System (CDSS) for use in pre-hospital care. The thesis was guided by a theoretical framework for developing and evaluating a complex intervention. The four studies used different designs and methods. The first study was a systematic review of randomised controlled trials. The second and the last studies had experimental and quasi-experimental designs, where the CDSS was evaluated in a simulation setting and in a clinical setting. The third study included in the thesis had a qualitative case study design. The main findings from the studies in the thesis were that there is a weak evidence base for the use of CDSS in pre-hospital care. No studies have previously evaluated the effect of CDSS in pre-hospital care. Due to the context, pre-hospital care is dependent on protocol-based care to be able to deliver safe, high-quality care. The physical format of the current paper based guidelines and protocols are the main obstacle to their use. There is a request for guidelines and protocols in an electronic format among both clinicians and leaders of the ambulance organisations. The use of CDSS in the pre-hospital setting has a positive effect on compliance with pre-hospital guidelines. The largest effect is in the primary survey and in the anamnesis of the patient. The CDSS also increases the amount of information collected in the basic pre-hospital assessment process. The evaluated CDSS had a limited effect on on-the-scene time. The developed and evaluated CDSS has the ability to increase pre-hospital patient safety by reducing the risks of cognitive bias. Standardising the assessment process, enabling explicit decision support in the form of checklists, assessment rules, differential diagnosis lists and rule out worst-case scenario strategies, reduces the risk of premature closure in the assessment of the pre-hospital patient.
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Islam, Mohammed Ashrafull. "Enhancing the interactivity of a clinical decision support system by using knowledge engineering and natural language processing." Thesis, Aston University, 2018. http://publications.aston.ac.uk/37540/.
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Mental illness is a serious health problem and it affects many people. Increasingly,Clinical Decision Support Systems (CDSS) are being used for diagnosis and it is important to improve the reliability and performance of these systems. Missing a potential clue or a wrong diagnosis can have a detrimental effect on the patient's quality of life and could lead to a fatal outcome. The context of this research is the Galatean Risk and Safety Tool (GRiST), a mental-health-risk assessment system. Previous research has shown that success of a CDSS depends on its ease of use, reliability and interactivity. This research addresses these concerns for the GRiST by deploying data mining techniques. Clinical narratives and numerical data have both been analysed for this purpose. Clinical narratives have been processed by natural language processing (NLP)technology to extract knowledge from them. SNOMED-CT was used as a reference ontology and the performance of the different extraction algorithms have been compared. A new Ensemble Concept Mining (ECM) method has been proposed, which may eliminate the need for domain specific phrase annotation requirements. Word embedding has been used to filter phrases semantically and to build a semantic representation of each of the GRiST ontology nodes. The Chi-square and FP-growth methods have been used to find relationships between GRiST ontology nodes. Interesting patterns have been found that could be used to provide real-time feedback to clinicians. Information gain has been used efficaciously to explain the differences between the clinicians and the consensus risk. A new risk management strategy has been explored by analysing repeat assessments. A few novel methods have been proposed to perform automatic background analysis of the patient data and improve the interactivity and reliability of GRiST and similar systems.
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Chaudry, Qaiser Mahmood. "Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47745.
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This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment.
This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.
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Erdogan, Onur. "Predicting The Disease Of Alzheimer (ad) With Snp Biomarkers And Clinical Data Based Decision Support System Using Data Mining Classification Approaches." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614832/index.pdf.
Full textAbstract:
Single Nucleotide Polymorphisms (SNPs) are the most common DNA sequence variations where only a single nucleotide (A, T, C, G) in the human genome differs between individuals. Besides being the main genetic reason behind individual phenotypic differences, SNP variations have the potential to exploit the molecular basis of many complex diseases. Association of SNPs subset with diseases and analysis of the genotyping data with clinical findings will provide practical and affordable methodologies for the prediction of diseases in clinical settings. So, there is a need to determine the SNP subsets and patients&rsquoclinical data which is informative for the prediction or the diagnosis of the particular diseases. So far, there is no established approach for selecting the representative SNP subset and patients&rsquo
clinical data, and data mining methodology that is based on finding hidden and key patterns over huge databases. This approach have the highest potential for extracting the knowledge from genomic datasets and to select the number of SNPs and most effective clinical features for diseases that are informative and relevant for clinical diagnosis. In this study we have applied one of the widely used data mining classification methodology: &ldquo
decision tree&rdquo
for associating the SNP Biomarkers and clinical data with the Alzheimer&rsquo
s disease (AD), which is the most common form of &ldquo
dementia&rdquo
. Different tree construction parameters have been compared for the optimization, and the most efficient and accurate tree for predicting the AD is presented.
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Persson, Mats. "Bring hypertension guidelines into play : guideline-based decision support system for drug treatment of hypertension and epidemiological aspects of hypertension guidelines." Doctoral thesis, Umeå universitet, Allmänmedicin, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-94105.
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Viklund, Herman, and Hanna Karlsson. "Clinical Decision Support Rules in an Archetype-Based Health Record System : Combining Archetype Query Language (AQL) and Semantic Web Rule Language (SWRL)." Thesis, Linköping University, Department of Biomedical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51851.
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By using archetypes, it is possible to define how data are stored in the EHR,which facilitates querying for data.
The objective of this thesis is to investigate the possibility of connecting a decisionsupport system to archetype-based medical records by using the ArchetypeQuery Language (AQL) and the Semantic Web Rule Language (SWRL).
The result shows that, since SWRL is a logic language rather than a programminglanguage, built-ins are necessary to allow SWRL rules to function as programmingrules. Built-ins are SWRL modules that can be written in e.g. Java,which allows complex functions to be created.
The conclusion is that built-ins can be used to connect archetypes and SWRLrules by querying the archetype path with AQL. There are however several ruledesign factors to consider when using SWRL e.g. data location problems.
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Wen, Hongyang. "The development of ontological model for clinical decision support system: A case study of triage of pediatric hip pain in the emergency department." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27427.
Full textAbstract:
Clinical Decision Support Systems (CDSS), for providing patient specific advice, can only be accepted in clinical practice if they can fit in a clinician workflow. This would require such a CDSS to have diversified support capabilities, to be mobile, and to have flexible functionality. Such a system can be designed and developed only in a modular fashion where the high level abstractions describe the logic among different system components. Ontology, which is a formal specification of shared conceptualization, can be used to create a high level abstraction. Such decoupling of abstract CDSS logic from low level implementation facilitates developing and adding new applications and increases the reusability of different system components. In this research it is argued that a developed CDSS, according to ontology driven design with the ontological model of a problem domain expanded by a clinical decision support requirements, allows the creation of a system that is aligned with clinical workflow. In this research the proposed approach is illustrated with the CDSS for triaging pediatric hip pain (HP) in the Emergency Department. This application (called MET-HP) is created within the MET (Mobile Emergency Triage) environment that implements the ontology driven design principles. MET-HP is a mobile CDSS that includes a decision model derived from the analysis of retrospective chart data and it facilitates early triage of a child using incomplete data.
Keywords. Ontology; ontological model; ontology driven design; ontological engineering; clinical decision support system; data mining; knowledge model; knowledge based system.
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Mahabee-Gittens, E. Melinda, Judith W. Dexheimer, Jane C. Khoury, Julie A. Miller, and Judith S. Gordon. "Development and Testing of a Computerized Decision Support System to Facilitate Brief Tobacco Cessation Treatment in the Pediatric Emergency Department: Proposal and Protocol." JMIR PUBLICATIONS, INC, 2016. http://hdl.handle.net/10150/621711.
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Background: Tobacco smoke exposure (TSE) is unequivocally harmful to children's health, yet up to 48% of children who visit the pediatric emergency department (PED) and urgent care setting are exposed to tobacco smoke. The incorporation of clinical decision support systems (CDSS) into the electronic health records (EHR) of PED patients may improve the rates of screening and brief TSE intervention of caregivers and result in decreased TSE in children. Objective: We propose a study that will be the first to develop and evaluate the integration of a CDSS for Registered Nurses (RNs) into the EHR of pediatric patients to facilitate the identification of caregivers who smoke and the delivery of TSE interventions to caregivers in the urgent care setting. Methods: We will conduct a two-phase project to develop, refine, and integrate an evidence-based CDSS into the pediatric urgent care setting. RNs will provide input on program content, function, and design. In Phase I, we will develop a CDSS with prompts to: (1) ASK about child TSE and caregiver smoking, (2) use a software program, Research Electronic Data Capture (REDCap), to ADVISE caregivers to reduce their child's TSE via total smoking home and car bans and quitting smoking, and (3) ASSESS their interest in quitting and ASSIST caregivers to quit by directly connecting them to their choice of free cessation resources (eg, Quitline, SmokefreeTXT, or SmokefreeGOV) during the urgent care visit. We will create reports to provide feedback to RNs on their TSE counseling behaviors. In Phase II, we will conduct a 3-month feasibility trial to test the results of implementing our CDSS on changes in RNs' TSE-related behaviors, and child and caregiver outcomes. Results: This trial is currently underway with funding support from the National Institutes of Health/National Cancer Institute. We have completed Phase I. The CDSS has been developed with input from our advisory panel and RNs, and pilot tested. We are nearing completion of Phase II, in which we are conducting the feasibility trial, analyzing data, and disseminating results. Conclusions: This project will develop, iteratively refine, integrate, and pilot test the use of an innovative CDSS to prompt RNs to provide TSE reduction and smoking cessation counseling to caregivers who smoke. If successful, this approach will create a sustainable and disseminable model for prompting pediatric practitioners to apply tobacco-related guideline recommendations. This systems-based approach has the potential to reach at least 12 million smokers a year and significantly reduce TSE-related pediatric illnesses and related costs.
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Arant, Bandy Neel. "Development, Implementation and Utilisation of a Mobile Technology Enhanced, Electronic Medical Record/Clinical Decision Support System for the Co-management of HIV and Pregnancy." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/66660.
Full textAbstract:
Perinatal antiretroviral therapy can virtually eliminate perinatal transmission of HIV though care provision in resource-limited settings remains a challenge. A mobile technology enhanced, cloud-based, combined electronic medical record (EMR) and passive/active clinical decision support system (CDSS) focused on the co-management of HIV and pregnancy for point of care use by the clinician was developed, piloted, and evaluated for challenges and successes in implementation with recommendations for future projects.
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Arthur, Gerald L. Gong Yang. "Implementation of a fuzzy rule-based decision support system for the immunohistochemical diagnosis of small B-cell lymphomas." Diss., Columbia, Mo. : University of Missouri-Columbia, 2009. http://hdl.handle.net/10355/6569.
Full textAbstract:
Thesis (M.S.)--University of Missouri-Columbia, 2009.The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Thesis advisor: Yang Gong. "May 2009" Includes bibliographical references.
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Juhlin, Madeleine. "Elektroniskt expertstöd ur kundens perspektiv : En enkätstudie om kundens kännedom och inställning till EES." Thesis, Linnéuniversitetet, Institutionen för kemi och biomedicin (KOB), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-74488.
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Abstract Medicines are substances used to prevent, alleviate or cure diseases which is an important part of health care. The role of the pharmacist in the healthcare chain is important because they are the patients last contact with healthcare professionals. After this contact they must assume responsibility over their own treatment. The pharmacist's responsibility is to make sure that the patient has received essential information about their drugs and to check that the prescribed dose is correct. The increased digitalization of society brings an opportunity for the patients to increase participation and understanding of their drug treatment. Decision support systems are different methods used to obtain a basis for decision making. Pharmacists use these systems to check if prescriptions are correct, for example by ensuring that the right medication, in the correct dose, has been prescribed to the right patient at the right time. In Sweden there is a decision support system called elektroniskt expertstöd (EES). This system offers pharmacy customers further drug control in addition to other controls that are available through the pharmacy's other systems. The benefit of this system is that the pharmacist has better support in detecting incorrect doses, interactions, duplicate medications and if the drug is inappropriate for the patient's gender or age. When the system alerts, the pharmacist makes an assessment which may be discussed with the patient or the physician if needed. Purpose The purpose of this survey study was to investigate what the pharmacy customers knows about EES and the pharmacists use of the system. Method Before the study started, approval from the southeast ethic committee was obtained which said that there were no ethical barriers for the performance of the study. The surveys were handed out to anyone who would receive prescriptions at different pharmacies in Luleå, Grängesberg, Värnamo, Kalmar and Torsås. The common goal was to spend 20 hours of handing out surveys per student. The results were put together and analyzed in IBM SPSS Statistics with descriptive statistics. Results and discussion The results show that most of the respondents did not know about EES and did not know if the pharmacists are using this support system. The results also show that most of the respondents had not given their consent to EES and did not know if EES could support the pharmacists work. More than half of the survey population answered “do not know” on a question asking if they wanted the pharmacist to use EES when dispensing drugs. Almost a quarter of the total population wanted the pharmacist to use EES and close to three quarters of the total population had no knowledge of the system. This could mean that the customers who have knowledge wants the system to be used. A reason for wanting the system to be used without having knowledge of it could be that customers trust it is beneficial for themselves and for the pharmacists. Building trust and showing care in each customer meeting is important to make the customer susceptible to information. Sufficient information can lead to better compliance in drug treatments. Before the pharmacist can use EES for the first time the customer must consent. Although it was a few years since the introduction of EES into pharmacies, some pharmacists find it difficult to connect customers to the system. It is a relatively new way of working with drug analysis at the pharmacies in Sweden. More practice in the system could provide increased use of it and help the pharmacists decision making. Conclusion Only a small proportion of the pharmacy customers have knowledge of EES. But even so, one fourth of them wanted the system to be used to analyze their medication which is considered positive.
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Dronamraj, Saritha. "Electronic Prescribing Management System for Rural Settings of Developing Countries : A Patient Centric System." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-80986.
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During the last decade, electronic prescribing has been a point of focus in healthcare industry and is rapidly becoming a standard of practice. It has proven as an important element in improving the quality of patient care, mitigating or eliminating the phone calls back and forth from pharmacies to point of care/health centers. Many e-prescribing systems were developed and marketed but these usually were unsuccessful because of the lack of direct electronic connectivity to local pharmacies and the lack of up-to-date formulary information, clinical guidelines, health plans & services among other reasons. Despite their benefits, the adoption and usage of electronic prescribing systems has been low. In some of the developing countries like Uganda, the problem is even worst. Due to lack of essential resources and manpower, healthcare services have significantly impacted on the productivity and quality of patient care.In an effort to improve, promote and maintain the quality of health services in rural settings of developing countries like Uganda, a high level design for e-prescribing system has been proposed. Design specifications for Electronic Prescribing Management System (EPMS) along with functional prototype are built based on ICT4MPOWER project requirements and previous research and publications in this area.Initially research began with Drug and Stock Management System and EPMS emerged as one of its essential components. In order to strengthen and establish connection between ongoing electronic health record system and drug and stock management development, EPMS component came into lime light. Mare prescription management is not enough to serve patient centric needs. Hence, clinical decision support has been introduced into e- prescribing system to improve the quality of prescribing decisions. In order to develop a patient-centric e-prescribing system that is self-evolving and self sustaining, it is important to update the clinical decision-support system, formularies & guidelines on regular basis. In order to make it usable, it is required to formulate effective health plans and increase associations between pharmacies and other health organizational units. The principal benefit of introducing E-prescribing system into Electronic Health Record (EHR) System is to connect open ended systems to form a strong knowledge base for future.ICT4MPOWER
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Hammar, Tora. "eMedication – improving medication management using information technology." Doctoral thesis, Linnéuniversitetet, Institutionen för medicin och optometri (MEO), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-37167.
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Medication is an essential part of health care and enables the prevention andtreatment of many conditions. However, medication errors and drug-relatedproblems (DRP) are frequent and cause suffering for patients and substantial costsfor society. eMedication, defined as information technology (IT) in themedication management process, has the potential to increase quality, efficiencyand safety but can also cause new problems and risks.In this thesis, we have studied the employment of IT in different steps of themedication management process with a focus on the user's perspective. Sweden isone of the leading countries when it comes to ePrescribing, i.e. prescriptionstransferred and stored electronically. We found that ePrescribing is well acceptedand appreciated by pharmacists (Study I) and patients (Study II), but that therewas a need for improvement in several aspects. When the pharmacy market inSweden was re-regulated, four new dispensing systems were developed andimplemented. Soon after the implementation, we found weaknesses related toreliability, functionality, and usability, which could affect patient safety (StudyIII). In the last decade, several county councils in Sweden have implementedshared medication lists within the respective region. We found that physiciansperceived that a regionally shared medication list generally was more complete butoften not accurate (Study IV). Electronic expert support (EES) is a decisionsupport system which analyses patients´ electronically-stored prescriptions in orderto detect potential DRP, i.e. drug-drug interactions, therapy duplication, highdose, and inappropriate drugs for geriatric or pediatric patients. We found thatEES detected potential DRP in most patients with multi-dose drug dispensing inSweden (Study V), and that the majority of alerts were regarded as clinicallyrelevant (Study VI).For an improved eMedication, we need a holistic approach that combinestechnology, users, and organization in implementation and evaluation. The thesissuggests a need for improved sharing of information and support for decisionmaking, coordination, and education, as well as clarification of responsibilitiesamong involved actors in order to employ appropriate IT. We suggestcollaborative strategic work and that the relevant authorities establish guidelinesand requirements for IT in the medication management process.Läkemedel förbättrar och förlänger livet för många och utgör en väsentlig del av dagens hälso- och sjukvård men om läkemedel tas i fel dos eller kombineras felaktigt med varandra kan behandlingen leda till en försämrad livskvalitet, sjukhusinläggningar och dödsfall. En del av dessa problem skulle kunna förebyggas med rätt information till rätt person vid rätt tidpunkt och i rätt form. Informationsteknik i läkemedelsprocessen har potentialen att öka kvalitet, effektivitet och säkerhet genom att göra information tillgänglig och användbar men kan också innebära problem och risker. Det är dock en stor utmaning att i läkemedelsprocessen föra in effektiva och användbara IT-system som stödjer och inte stör personalen inom sjukvård och på apotek, skyddar den känsliga informationen för obehöriga och dessutom fungerar tillsammans med andra system. Dagens IT-stöd i läkemedelsprocessen är otillräckliga. Till exempel saknar läkare, farmaceuter och patienter ofta tillgång på fullständig och korrekt information om en patients aktuella läkemedel; det händer att fel läkemedel blir utskrivet eller expedierat på apotek; och bristande eller långsamma system skapar frustration hos användarna. Dessutom är det flera delar av läkemedelsprocessen som fortfarande är pappersbaserade. Därför är det viktigt att utvärdera IT-system i läkemedelsprocessen. Vi har studerat IT i olika delar av läkemedelsprocessen, före eller efter införandet, framför allt utifrån användarnas perspektiv. Sverige har lång erfarenhet och tillhör de ledande länderna i världen när det gäller eRecept, det vill säga recept som skickas och lagras elektroniskt. I två studier fann vi att eRecept är väl accepterat och uppskattat av farmaceuter (Studie I) och patienter (Studie II), men att det finns behov av förbättringar. När apoteksmarknaden omreglerades 2009 infördes fyra nya receptexpeditionssystem på apoteken. Vi fann att det efter införandet uppstod problem med användbarhet, tillförlitlighet och funktionalitet som kan ha inneburit en risk för patientsäkerheten (Studie III). I Sverige har man inom flera sjukvårdsregioner infört gemensamma elektroniska läkemedelslistor. I en av studierna kunde vi visa att detta har inneburit en ökad tillgänglighet av information, men att en gemensam lista inte alltid blir mer korrekt och kan innebära en ökad risk att känslig information nås av obehöriga (Studie IV). I två av studierna undersöktes beslutsstödssystemet elektroniskt expertstöd (EES):s potential som stöd för läkare att upptäcka läkemedelsrelaterade problem till exempel om en patient har två olika läkemedel som inte passar ihop, eller ett läkemedel som kanske är olämpligt för en äldre person. Studierna visade att EES gav signaler för potentiella problem hos de flesta patienter med dosdispenserade läkemedel i Sverige (Studie V), och läkarna ansåg att majoriteten av signalerna är kliniskt relevanta och att några av signalerna kan leda till förändringar i läkemedelsbehandlingen (Studie VI). Sammantaget visar avhandlingen att IT-stöd har blivit en naturlig och nödvändig del i läkemedelsprocessen i Sverige men att flera problem är olösta. Vi fann svagheter med användbarhet, tillförlitlighet och funktionalitet i de använda IT-systemen. Patienterna är inte tillräckligt informerade och delaktiga i sin läkemedelsbehandling. Läkare och farmaceuter saknar fullständig och korrekt information om patienters läkemedel, och de har i dagsläget inte tillräckliga beslutsstöd för att förebygga läkemedelsrelaterade problem. Eftersom läkemedelsprocessen är komplex med många aspekter som påverkar utfall behöver vi ett helhetstänkande när vi planerar, utvecklar, implementerar och utvärderar IT-lösningar där vi väger in både tekniska, sociala och organisatoriska aspekter. Avhandlingens resultat visar på ett behov av ökad koordination och utbildning samt förtydligande av ansvaret för inblandade aktörer. Vi föreslår gemensamt strategiskt arbete och att inblandade myndigheter tar fram vägledning och krav för IT i läkemedelsprocessen.
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Laka, Mah. "The Role of Computer Computer-based Clinical Decision Support Systems (CDSS) in Improving Antibiotic Management." Thesis, 2021. https://hdl.handle.net/2440/135248.
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Background Inappropriate antibiotic prescribing is a key contributor to increasing antibiotic resistance. Despite the standard practices promoted through clinical practice guidelines (CPGs), treatment regimens are not always in accordance with these guidelines. In Australia, a significant proportion of inappropriate antibiotic prescriptions in hospitals and primary care is due to noncompliance with CPGs. This is further exacerbated by the difficulty faced by clinicians in integrating and managing multiple information streams at the point of care to inform evidence-based decision- making. There is increasing recognition that digital health interventions such as clinical decision support systems (CDSS) may assist in optimising antimicrobial management. CDSS provide treatment recommendations based on patient-specific risk factors and research evidence, allowing clinicians to provide personalised care. Many studies provide evidence of the potential of CDSS for promoting optimal antibiotic management; however, adoption of these systems in clinical practice remains low. In addition to this lack of effective system adoption, there is a high rate of clinicians’ ignoring or overriding the systems’ recommendations or only engaging with partial use of the systems’ features. These factors limit the efficacy of CDSS in improving antibiotic prescribing. Objective The objective of this thesis was to evaluate individual, organisational, and system-level factors that impact CDSS implementation for evidence-based antibiotic management. An understanding of the different aspects of CDSS implementation in Australia has been sought by bringing together the perceptions and experiences of different stakeholders. The project aimed to achieve this objective by i) synthesising the evidence on the efficacy of CDSS for antibiotic management; ii) understanding clinicians’ perceptions regarding CDSS use for optimal antibiotic prescribing; and iii) evaluating the challenges of integrating CDSS into the healthcare system. Methods To achieve the objectives outlined above, the thesis was divided into four studies: In study I, a systematic review and meta-analyses were conducted to evaluate the impact of CDSS implementation on various clinical and economic outcomes associated with antibiotic management. The study protocol was developed using the PRISMA-P checklist. Studies were selected using specific pre-defined study eligibility criteria. Studies providing sufficient data on the outcomes were included in the meta-analyses to calculate pooled effect estimates of the impact of CDSS implementation on antibiotic management. In studies II & III, a cross-sectional online survey was conducted in Australia. Clinicians directly involved in prescribing, administering, and managing antibiotics in hospital and primary care settings were invited to participate. We adopted the Unified Theory of Acceptance and Use of Technology (UTAUT) model to understand factors contributing to clinicians’ inappropriate antibiotic prescribing behaviour and their behavioural intent to adopt CDSS. Using this framework, we also examined the role of moderating factors such as gender, age, clinical experience, and care settings in shaping users’ behaviour in adopting CDSS. We used multivariate logistic regression models to investigate the association between these moderating factors and users’ perceptions regarding CDSS adoption. Finally, in study IV, we used a qualitative approach to conduct in-depth interviews with policymakers involved in the implementation and evaluation of CDSS in Australia. The focus of this study was to understand what is required to effectively scale-up CDSS implementation from pilot studies to a system-wide innovation. Participants shared their experiences and perceptions concerning the gaps and challenges in the Australian healthcare system for integration of CDSS into healthcare processes. The interview transcripts were thematically analysed to establish a contextual understanding of the system-wide challenges for CDSS implementation. Results Results from this research highlight that CDSS can help reduce the risk of inappropriate antibiotic prescribing by increasing compliance with prescribing guidelines. The findings further indicate that CDSS can improve antibiotic prescribing by reducing the volume of overall antibiotic use, duration of therapy, length of hospital stay and thereby decreasing the overall cost of therapy. However, most of the evidence included in our systematic review was from studies having moderate to low methodological quality. Non-randomised studies tended to overestimate the effect of CDSS on appropriate antibiotic management, compared to randomised studies. However, the direction of the effect was largely consistent across both study types and favoured the positive impact of CDSS for antibiotic management. There was also substantial statistical heterogeneity in the results across the included studies which can be explained by the large variability in CDSS adoption across studies. Findings from the survey with clinicians indicated that different individual and setting specific characteristics are important factors that influence clinicians’ perceptions regarding CDSS adoption and lead to variability in uptake across different clinicians. Experienced clinicians were more sceptical of using CDSS for clinical decision-making, potentially due to limited digital health literacy, mistrust in the information provided by CDSS and fear of compromising their professional autonomy. Similarly, in comparison to users, CDSS non-users were more likely to lack trust in CDSS recommendations and fear compromising their professional autonomy due to CDSS adoption. A lack of transparency and explainability in CDSS design, in which end-users are not aware of how systems have computed recommendations can reduce their trust in CDSS. Consistent with the context of primary care, primary care clinicians believed that time constraints and patient expectations were important drivers of CDSS adoption. These findings highlight that the efficacy of CDSS implementation may be limited by a lack of consideration of contextual factors such as clinical experience, setting of use, and users’ skills which impact the users’ behaviour to adopt CDSS. Targeted clinician engagement, digital health literacy and better communication of the reliability of information provided may assist with more successful implementation of CDSS at point of care. Interviews with Australian policymakers further explored system-level challenges and gaps that may impede successful CDSS implementation. The results show that the lack of shared vision between different stakeholders, and the fragmented infrastructure within the healthcare system are major barriers to the integration of CDSS within existing processes in the healthcare system. CDSS implementation needs to be supported by an effective governance structure that can establish clear roles, prioritise investment in health system capacity building and incorporate cross-discipline and inter-organisational collaboration for quality data sharing. The ability of CDSS to ensure coordinated and interoperable care by exchanging information across organisations requires mutually agreed data standards at a national level. There is a need to establish standards not only for generating data in a standardised format, but for semantic interoperability that allows data communication and interpretation across different systems. Notwithstanding the significance of standardisation to ensure interoperability in CDSS, our findings also highlight that this standardisation must be balanced with adequate flexibility in the CDSS design and implementation process, so that user and setting specific requirements can be incorporated to improve adoption. Conclusion In conclusion, our findings illustrate that CDSS reflects best practice for antibiotic management through evidence-based clinical decision making, integrating the knowledge base, and flagging medication errors. The integration of these systems in healthcare settings is, however, challenging due to the complex interaction between the system, organisational and human factors. The findings from our research suggests that individual and setting characteristics such as clinical experience, use of CDSS and the type of setting, influence the clinicians’ perception of CDSS role in antibiotic management. These characteristics provide a better understanding of why CDSS adoption varies across different clinicians and care settings. We also found that the lack of synergy evident between multiple stakeholders and organisations - who seem to have varying interests and objectives regarding CDSS implementation - is limiting the ability to develop a shared vision and collaborative action. These findings provide evidence firm foundation for policymakers for developing a holistic CDSS implementation framework that considers the interaction of the system within the context of organisational and human behavioural characteristics. Implementation processes need to be tailored to specific user and setting requirements for improved adoption and use of CDSS by clinicians. A better understanding of the clinical culture would support successful CDSS implementation, along with effective strategies to develop broader digital literacy, methods for sustaining clinicians’ engagement with the technology, and approaches to facilitating cross-discipline collaboration.Thesis (Ph.D.) -- University of Adelaide, School of Public Health, 2022
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Wenk, Aniko-Gabriela [Verfasser]. "Modellierung der Suchregeln zur Informationserschlieung in einem Combined Clinical Decision Support System (C-CDSS) für die gastroenterologische Endoskopie / Aniko-Gabriela Wenk." 2010. http://d-nb.info/1005079528/34.
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PATARA, FULVIO. "Multi-level meta-modeling architectures applied to eHealth." Doctoral thesis, 2016. http://hdl.handle.net/2158/1041924.
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Over the last decade, a growing digital universe of unstructured or semi- structured human-sourced information, structured process-mediated data, and well-structured machine-generated data, encourages the adoption of innovative forms of data modeling and information processing to enable enhanced insight, decision making, and process automation applied to a variety of different contexts. Healthcare comprises a notable domain of interest, where the availability of a large amount of information can be exploited to take relevant and tangible benefits in terms of efficiency of the care process, improved out- comes and reduced health system costs. However, due to the complex nature of clinical data, a number of challenges needs to be faced, mainly related on how data characterized by volume, variety, variability, velocity, and veracity can be effectively and efficiently modeled, and how these data can be exploited for increasing the domain knowledge and supporting decision-making processes.
The aim of this dissertation is to describe the crucial role played by soft- ware architectures in order to overcome challenges posed by the healthcare context. Specifically, this dissertation addresses the development and applicability of multi-level meta-modeling architectures to various scenarios of eHealth, where flexibility and changeability represent primary requirements. Meta-modeling principles are concretely exploited in the implementation of an adaptable patient-centric Electronic Health Record (EHR) system to face a number of challenging requirements, including: adaptability to different specialities and organizational contexts; run-time configurability by domain experts; interoperability of heterogeneous data produced by various sources and accessed by various actors; applicability of guideline recommendations for evaluating clinical practice compliance; applicability of Activity Recognition techniques for monitoring and classifying human activities in pervasive intelligent environments.
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João, Daniela José Antão. "Qvida+: Development of a Clinical Decision Support System." Master's thesis, 2020. https://hdl.handle.net/10216/133588.
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In these last few decades, there has been a significant increase in the average life expectancy due to improved general life conditions as well as to several advances in the medicine field. Contrary to what happened then, people with chronic conditions live more now, and so it's essential to ensure their quality of life. Furthermore, in addition to prolonging their life, another goal of medical treatment is to maintain or increase the quality of life of patients.
Health Related Quality of Life (HRQOL) can be defined as the individuals' perception of their own health status (physical, functional, emotional and social) and the impact of their condition or treatment in their daily life (job, family, friends).
The QVida+ project, based on recent scientific and technological advances in the HRQOL fields and mobile devices, intends to create an innovative paradigm when it comes to the assessment and application of HRQOL.
The following step to this project, and the aim of the current dissertation would be the development of a clinical support system that would gather all the data collected in previous steps of this project such as biometric data (e.g., sleep, heart rate variability) and physical activity( e.g., number of daily steps) collected through smartbands, responses to self-report questionnaires and clinical data from cancer patients and provide health care professionals with more and better information about their patients. This system, with the help of machine learning (ML) techniques, would focus particularly on patients' evolution regarding their health status and HRQOL, and consequently assist health care professionals on future decisions with greater quantity and quality of information.
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João, Daniela José Antão. "Qvida+: Development of a Clinical Decision Support System." Dissertação, 2020. https://hdl.handle.net/10216/133588.
Full textAbstract:
In these last few decades, there has been a significant increase in the average life expectancy due to improved general life conditions as well as to several advances in the medicine field. Contrary to what happened then, people with chronic conditions live more now, and so it's essential to ensure their quality of life. Furthermore, in addition to prolonging their life, another goal of medical treatment is to maintain or increase the quality of life of patients.
Health Related Quality of Life (HRQOL) can be defined as the individuals' perception of their own health status (physical, functional, emotional and social) and the impact of their condition or treatment in their daily life (job, family, friends).
The QVida+ project, based on recent scientific and technological advances in the HRQOL fields and mobile devices, intends to create an innovative paradigm when it comes to the assessment and application of HRQOL.
The following step to this project, and the aim of the current dissertation would be the development of a clinical support system that would gather all the data collected in previous steps of this project such as biometric data (e.g., sleep, heart rate variability) and physical activity( e.g., number of daily steps) collected through smartbands, responses to self-report questionnaires and clinical data from cancer patients and provide health care professionals with more and better information about their patients. This system, with the help of machine learning (ML) techniques, would focus particularly on patients' evolution regarding their health status and HRQOL, and consequently assist health care professionals on future decisions with greater quantity and quality of information.
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Kuo, Kuan-Liang, and 郭冠良. "A Health Examination System Integrated with Clinical Decision Support System." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/89511716862854329243.
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博士國立臺灣大學
資訊工程學研究所
98
Health examinations are important for the personal and public health management. Besides they play a key role in preventive medicine. We propose a health examination system named Health Examination Automatic Logic System (HEALS) to efficiently assist clinical workers in improving the total quality of health examinations. In order to customize the clinical decision support system intuitively and flexibly, we also propose a novel rule syntax to implement computer-interpretable logic for health examinations. Quality of automated inference is confirmed by the zero inference error where during 6 months and 14,773 cases. Automated inference time is less than one second per case in contrast to two to five minutes for physicians. The most significant result of efficiency evaluation is that 3,494 of 4,356 (80.2%) cases take less than three minutes per case for making a report summary. In the evaluation of effectiveness, novice physicians got 18 percent improvement in making decisions with the assistance of our system. A survey on user satisfaction revealed high satisfaction with our system. We conclude that a health examination system integrated with a clinical decision support system can markedly reduce the mundane burden on clinical workers and improve the quality and efficiency of health examination tasks.
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Bilykh, Iryna. "An interoperable framework for a clinical decision support system." 2004. http://hdl.handle.net/1828/592.
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The healthcare sector is facing a significant challenge: delivering quality clinical care in a costly and intricate environment. There is a general consensus that a solution for many aspects of this problem lies in establishing a framework for effective and efficient clinical decision support. The key to good decision support is offering clinicians just-in-time accessibility to relevant patient specific knowledge. However, at the present time, management of clinical knowledge and patient records is significantly inadequate resulting in sometimes uninformed, erroneous, and costly clinical decisions. One of the contributing factors is that the field of healthcare is characterized by large volumes of highly complex medical knowledge and patient information that must be captured, processed, interpreted, stored, analyzed, and exchanged. Moreover, different clinical information systems are typically not interoperable. This thesis introduces an approach for realizing a clinical decision support framework that manages complex clinical knowledge in a form of evidence-based clinical practice guidelines. The focus of presented work is directed on the interoperability of knowledge, information, and processes in a heterogeneous distributed environment. The main contributions of this thesis include definition of requirements, conceptual architecture, and approach for an interoperable clinical decision support system that is stand-alone, independent, and based on open source standards.
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Shiu, Shih-Jung, and 徐世融. "A Clinical Decision Support System for MRI Vessel Detection." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/41521285434488491038.
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碩士中國醫藥大學
醫務管理學系碩士班
100
In this study we have proposed an automated method to detect boundaries of the carotid artery using based on the elliptic model to developed dynamic programming on MR image sequences. We use α weighting parameters to control the impact of the ellipse on the dynamic programming. The images are form Trans-Europe race (TEFR09) in 2009 (April 19 to June 21).Using a mobile MRI to collect images. It will automated selection as ROI (region of interest) on the first MR image then the system can detect the boundaries of all images in the same sequence. On the phantom study with added noises (SNR ranges from 20 to 10 dB), the relative unsigned error of this algorithm is under 3.5%. When SNR = 10, the maximum error rate is 3.31%. When SNR = 20, the minimum error rate is 2.84%. The average error rate is 3.03%. The highest and lowest error rate is only a difference of 1.01%. On the real image study, α weight set to 0.6, relative the lowest unsigned error is around 3.87% comparing to the manual depicting.
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Jhu, Yi-cheng, and 朱逸誠. "Knowledge Construction Methodology of Stroke Clinical Decision Support System." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/28727751481726601884.
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碩士國立中山大學
資訊管理學系研究所
99
Clinical decision support systems (CDSS) and the Picture Archiving and Communication System (PACS) have been adopted by large healthcare organization to support stroke diagnosis to reduce the level of misdiagnosis occurrence. This research presents a methodology for constructing a stroke decision support system (Stroke DSS) which integrates basic information, physical and image stroke assessment criterions, constructs ischemic, hemorrhage and subarachnoid hemorrhage of stroke diagnosis flow. A prototype embedded methodology was built to support stroke diagnosis in healthcare organization. Using a design science approach, we embed the constructs of our methodology in a prototype and perform a usability evaluation to demonstrate the utility of our approach. The usability evaluation demonstrates the effectiveness of our approach in terms of efficiency, effectiveness and satisfaction. The resulting system allowed flexible knowledge model and representation that are useful for stroke diagnosis.
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Wu, Sheng-Han, and 吳昇翰. "Construction of a Clinical Decision Support System Using Ensemble Classification." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/21786473498664866252.
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碩士中臺科技大學
醫學工程暨材料研究所
101
Clinical Decision Support System (CDSS) assists clinical staff in the diagnoses of diseases, provision of information for care support, and improvement of efficiency to enhance the quality of care. Accurate data analysis is the crucial element during the diagnosis process. Nowadays novel techniques have been developed to provide a wide range of data analysis and applied in various fields to obtain valuable information. However, single classifier does not perform consistently for all data sets. Integration of recommendations and proposed measures for improving classification effectiveness were suggested to avoid the inconsistency. In this research, Genetic Algorithm (GA) was combined with Support Vector Machine (SVM) to provide a foundation for CDSS design. The proposed EnsCV method was compared with BAIS based on the data in the UCI machine learning database containing 11 datasets. It was shown that EnsCV is more effective, especially for Sonar and Glass, in the classification of categorical datasets. For instance, the accuracy using the proposed EnsCV method is 6.9% and 13.3% higher than the BAIS in classifying the datasets Sonar (EnsCV: 93.3%; BAIS: 86.4%) and Glass (EnsCV: 86.9%; BAIS: 73.6%), respectively. It is concluded that the ensemble classifiers present greater classification performance than the traditional method.
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Chen, Hung-Chieh, and 陳宏杰. "A Decision Support System Based on Clinical Symptoms of Diabetes." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/28357236600908311974.
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碩士國立屏東科技大學
資訊管理系所
99
In recent years, among the top ten causes of death in Taiwan, Diabetes Mellitus is the fastest rise in mortality and also a typical metabolic abnormality of chronic disease. According to the International Diabetes Federation(IDF), each year it is about three hundred and eighty million people die with diabetes-related diseases. On an average, every ten seconds there is one patient die of diabetes-related diseases and 2 new patients who get diabetes-related diseases. In this research, by using Data Mining technology to analyze the clinical records, the system not only provides physicians diverse and comprehensive information about clinical diagnosis and treatment, but also allows to reduce the incidence of complications and improve the quality of care. In this research, based on clinical symptoms of the diabetes, it has designed and implemented a Decision Support System which includes(1) Long Term Analysis Module－to analyze and integrate previous and current personal clinical data to obtain the trend graph. The module improves the integrity and accessibility of the data；(2) Risk Factor Analysis Module－by using the association rule to analyze and compare risk factors to find out the potential complications of the disease and the factor inspects that need to be tracked. This will reduce possibility of the risk factors exceeded the standard range；(3) Drug Control Analysis Module－by using the association rule to figure out the efficient drug combinations and provide further analyses of clinical treatment records that helps physicians effectively control patient’s blood sugar through the drug treatment；(4) Complications Analysis Module－by using Self-Organizing Map and United Kingdom Prospective Diabetes Study (UKPDS) prediction model to calculate future incidence of other complications, which helps physicians focus on the relate inspects in order to reduce the possibility of other derived complications.
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Chen, Yan-Ming, and 陳彥銘. "A Study on Precision Medicine Oriented Clinical Decision Support System." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/jt5bw8.
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碩士國立臺灣大學
資訊管理學研究所
106
Precision Medicine is an emerging data-oriented medical field that uses personal genotypes or gene expression and collects clinical information to choose the most suitable drug treatment or prevention method for patients, in order to achieve maximum efficacy with minimum toxicity for our patients. It opens up new opportunities in the healthcare field. The ultimate goal of precision medicine is to be realized in the clinical process. The clinical decision support system is a system to provide medical decision support to medical personnel. Therefore, we start our research with the objective of achieving precision medicine, design the clinical process in line with its spirit, and proposes a framework of clinical support system. We use the interview method as our main research method. After research and analysis, our research found what doctors think about precision medicine. At present, it is still in the early stage and it is necessary to collect more patient data, do whole genome sequencing and integrate all kinds of different medical data. Clinical procedures must be formulated according to each department and we should design in doctors’ place. The proposed system architecture should emphasize the presentation of patient data to be clear. The reasoning and predictive mechanism should be able to conform with traditional medical concepts to further enhance the willingness and trust of physicians. Currently, there is little research on precision medicine in the academic and medical industry. We hope our system architecture will enable medical organizations to have a reference of process and architecture that conforms to precision medicine when designing clinical decision support systems.
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Chu, Chia-Chen, and 朱家成. "Development of a Clinical Decision Support System for Respiratory Therapy." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/5zh565.
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博士中原大學
生物醫學工程研究所
105
Background: This study is based on electronic medical records (paperless) and patient safety (cross-disciplinary team care plan information exchange) considerations, the clinical use of high-risk instruments, ventilator, its’ settings and monitoring parameters to be connected to information system, so that the paper for medical records have indeed disappeared, and clinical treatment data have reached the goal of cross-disciplinary team communication and accessibility, ensuring consistency of clinical work quality, and accumulating data into clinical big data. After this information platform was set, we can use the material in clinical education and research. Methods: The procedure is divided into three stages. The first stage is the basic software and hardware construction. The parameters of the ventilator‘s setting and monitoring are read by the RM04 Wi-Fi embedded wireless module system, and then wirelessly transmitted to the N300RB-Plus wireless network base station, every minute to pass a message to the mini-commercial computer storage, hospital information system and then every five minutes to catch a piece of information for the respiratory therapist selected. The second stage is to create the various record forms in the hospital information system screen. The first of third stage is to set up three quickly query functions; include the daily screening, arterial blood gas analysis and pulmonary function test. Second, set the platform of inter-professional practice and Hand-over function (in ISBAR mode). Result: After the establishment of the clinical decision support system for respiratory therapy, the average daily savings per area was about 9.7 hours in terms of saving time. In terms of paper saving, the average monthly savings of paper is about 377 to 476. In addition, the three quick functional queries of the daily screening, arterial blood gas analysis, and pulmonary function test, inter-professional practice platform and the establishment of hand-over functions make communication with each other easier and faster. Conclusion: The above software and hardware setup has indeed achieved the goal, include clinical paperless, reduced chance of contact infection, more convenient cross-disciplinary team communication, reduced respiratory therapist paperwork time and to obtain more time for patient treatment, easier to grasp the ventilator setting and monitoring information in real time, the invariability of the respiratory therapist work quality, and the machine performance of the ventilator can be more easily observed so as to achieve the objective of maintaining patient safety and to strive to achieve the goal of a zero-predictable death event in 2020.
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Chen, Chong-Yi, and 陳崇毅. "Development of a Clinical Decision Support System for Diabetes Patients." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/x3777g.
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碩士元智大學
資訊工程學系
107
In Taiwan, diabetes has become one of the most common chronic diseases. In addition to causing serious harm to people's health, diabetes has also caused problems such as wasting many medical resources. Therefore, how to save the medical expenses while further improving the health of diabetic patients is the main problem to be solved in this thesis. The Clinical Decision Support System (CDSS) proposed by the thesis provides the Glycated hemoglobin (HbA1c) in diabetic patients predicted by our model to the physician to assist them to prescribe. The network architecture used is a deep neural network architecture based on Bidirectional Long Short-Term Memory (Bi-LSTM) model. The error between the HbA1c predicted by our architecture and the actual HbA1c measurement of the patient is generally smaller than that predicted by the Support Vector Regression (SVR). The future HbA1c of the patient can be predicted more accurately as a basis for assisting the physician to prescribe.
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Hsu, Ya-Han, and 許雅涵. "A Strategic Planning of the Cloud based Clinical Decision Support System." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/63472368427293108065.
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碩士國立交通大學
科技管理研究所
101
This thesis reports on an analysis of operating strategy of the Taiwan’s Clinical Decision Support System (CDSS) industry based on the model of innovation intensive services (IIS). Through the use of CDSS, a number of activities can be streamlined through the use of these tools, improving care and reducing healthcare spending. These tools enable clinicians to access relevant information to provide safe and effective care. The model respectively dissects four influential factors of industrial environments and technological systems at the industry-level analysis to verify the requirements of industrial innovation system. IIS Matrix will help deduce critical elements of industrial environment and technological systems at the industry level by strategic positioning and KSFs in the firm level. The requirements of industrial environment and technological systems will be consolidated into the industrial innovation systems by using the IIS approach. Results showed that in the Taiwan's CDSS industry, the future trend needs to be moved to “Unique Service”, “Restricted Service” and “Process Innovation” with the support of core competence in “validation”, “marketing”, “delivery”, “after service”, and “supporting activities” and the externalities of complementarities, production and market. The industry is still in its infancy stage; product innovation is the vendors’ first step to become a member of CDSS industry.