Clinical Decision Support Systems . Mohammed Saleem . Overview. Scope of Clinical Decision Support Systems Issues for success or failure Evaluation of Clinical Decision Support Systems Computing techniques used to create DSS Design Cycle for the development of DSS Slideshow 387683 by nam
Clinical Decision Support System Market Global Industry Analysis, Size, Share, Growth, Trends, and Forecast 2017-2025. Clinical Decision Support System Market : OverviewThis report on the clinical decision support system market analyzes the current and future scenario of the global market. Large number of chronic disease patients is increasing the
PhD ceremony: Mr. A.E. de Vries, 12.45 uur, Academiegebouw, Broerstraat 5, Groningen. Dissertation: The use of computer decision support systems and telemonitoring in heart failure Promotor(s): prof. H.L. Hillege, prof. T. Jaarsma, prof. R.J.J.M. Jorna. Faculty: Medical Sciences. This thesis focuses on two examples of innovative ICT developments in health care that can be used for patients with heart failure: telemonitoring (TM) and computer decision support systems (CDSS). This thesis shows that innovative information and communication technologies such as telemonitoring and computer decision support systems are promising tools that may help to support and improve care for heart failure patients. However, TM and CDSS are not one size fits all solutions and TM does not automatically reduce readmissions, mortality and improve the quality of life. ...
ACU and the Alliance of Chicago have partnered to create an electronic Asthma Clinical Decision Support tool intended to improve asthma care in primary care settings. The tool is is based on the National Heart Lung and Blood Institutes evidence-based asthma clinical guidelines.. Embedded in the electronic health record, the Asthma Clinical Decision Support tool prompts clinicians to educate and engage patients and their families about environmental asthma triggers. It also provides data for quality monitoring and supports population-level quality improvement efforts.. ...
One of the most important ways to achieve optimum warfarin control is the use of anticoagulation, electronic clinical decision support tools that incorporates a validated warfarin dosing nomogram. Managing patients on NOACs is also a critical component of anticoagulation electronic decision support.
The use of a clinical decision support tool significantly improved documented adherence to a national imaging quality measure, according to a study published in the March issue of Academic Radiology.
In some healthcare systems, it is common that patients address laboratory test centers directly without a physicians recommendation. This practice is widely spread in Russia with about 28% of patients who visiting laboratory test centers for diagnostics. This causes an issue when patients get no help from the physician in understanding the results. Computer decision support systems proved to efficiently solve a resource consuming task of interpretation of the test results. So, a decision support system can be implemented to rise motivation and empower the patients who visit a laboratory service without a doctors referral. We have developed a clinical decision support system for patients that solves a classification task and finds a set of diagnoses for the provided laboratory tests results. The Wilson and Lanktons assessment model was applied to measure patients acceptance of the solution. A first order predicates-based decision support system has been implemented to analyze laboratory test results
Clinical decision support with Philips can help you recognize subtle changes and enable you to take action early. Explore our clinical decision support systems.
Contributors AW was responsible for design and implementation of the presented clinical decision-support system and the outline of the study protocol, and has drafted the manuscript. TJ provided clinical expertise for the use case and the design of the underlying knowledge model, leaded the proof-of-concept study and co-drafted the manuscript. AK was primarily responsible for the design of the statistical analysis, the sample size calculation and the authoring of the corresponding sections. BS and SM helped in the conception of the general study approach but especially for definition of goals and outcome measures, timing and patient recruitment; SM is responsible for patient recruitment and monitors the study at the ward. PB and MM provided clinical expertise for study design, revised the manuscript critically, and gave subject-specific advices as well as the final approval of the manuscript version to be published. All authors read and approved the final manuscript. ...
Clinical decision support systems can be defined as any software designed to directly aid in clinical decision making in which characteristics of individual patients are matched to a computerized knowledge base for the purpose of generating patient-specific assessments or recommendations that are then presented to clinicians for consideration [1, 2]. They are important in the practice of medicine because they can improve practitioner performance [1, 3-5], clinical management [6, 7], drug dosing and medication error rates [8-10], and preventive care [1, 11-16].. Machine learning (ML) gives computers the ability to learn from, and make predictions on the data without being explicitly programmed regarding the characteristics of that data [17]. It should not be surprising, then, that ML pervades clinical decision support, for two reasons. First, clinical decision support systems are structured such that patients are represented as features which can be used to map them to categories [18]. Second, ...
Clinical decision support systems (CDSS) are active knowledge systems using two or more items of patient data to produce encounter specific advice1 and have existed since the 1950s. Designing a bias-free RCT of CDSS is challenging1,2 but over 160 RCTS have been carried out. In some settings they are effective3, even when their advice is printed on paper.4 Many different methods can be used to represent the knowledge in CDSS including rules, frames and causal probabilistic networks5, but clinicians are rightly sceptical about neural networks6 whose knowledge base cannot be inspected, as this opens them up to legal liability for damages.7 While CDSS are an attractive way to disseminate evidence based guideline recommendations or help implement tested clinical prediction rules, some key questions are:. 1. Against which implementation barriers are CDSS an effective tool?8. 2. How to build the CDSS knowledge base for easy maintenance?8. 3. How to ensure that the CDSS can access complete patient ...
With chronic wound prevalence growing at around 12% annually1, and increasing pressure on resources, there is a need for consistent, intuitive clinical practice that expands beyond the wound and provides structured, measurable outcomes.. Read: The T.I.M.E. clinical decision support tool provided a structured wound management approach and encouraged consistency of care by non-specialists. Read a EWMA survey on the inconsistent use of assessment tools and variations in clinical practice ,. Download the T.I.M.E clinical decision support tool ,. Through consultation with leading wound care experts, weve built on the globally-recognised T.I.M.E.2-3 concept to develop a comprehensive clinical decision support framework that now encompasses a full holistic patient review.. Designed to evolve with future clinical developments, our T.I.M.E. clinical decision support tool seeks to build clearer, more comprehensive information to facilitate consistent, efficient and cost-effective wound bed preparation ...
Application of the best research evidence in clinical practice can improve the quality and safety of health care. Successfully translating evidence into practice requires that clinicians are aware of the evidence, agree with it, are confident about delivering the intervention and adhere to it in appropriate situations [1]. Furthermore, patients should agree and adhere to the treatment [1]. When there is more than one reasonable healthcare option, decision-making involves weighing the benefits and harms of the options, often with scientific uncertainty, and the preferences of the patients. Unfortunately, there is often a gap between the recommended care and the care that patients receive, and patient adherence to appropriate care can be poor [1]. Furthermore, some healthcare interventions may not be needed or may even be harmful. Finally, expenses can decrease if care options are chosen according to their comparative cost-effectiveness [2].. A computerised clinical decision support system (CCDSS) ...
Commenting on the release of the new report, Mr Abdul Wahid, Lead Analyst, BIS Research, said, The clinical decision support systems market is, to a large extent, going to be driven by the urgent need to reduce healthcare costs and the rising number of deaths due to preventable medical errors. While the sector is still at a nascent stage in India, most large hospitals in Tier I cities already have modular information systems, while some have fully integrated systems. In the future, we expect the full integration of systems, more shareable information platforms, and the standardization that could lead to user-friendliness and greater usability. We also believe that in the near future, these systems will use tablets and other mobile devices to enhance the uptake of advanced tools like telemedicine and virtual meeting systems for knowledge sharing ...
Free Online Library: Clinical Decision Support Systems for Comorbidity: Architecture, Algorithms, and Applications.(Research Article, Report) by International Journal of Telemedicine and Applications; Health, general Technology application Practice guidelines (Medicine)
Press release - The Insight Partners - Clinical Decision Support System Market Business Opportunities, Technological Advancement and Future Analysis 2025 - Medical Information Technology, Koninklijke Philips, Wolters Kluwer, Hearst Communications, Elsevier, Allscripts Healthcare Solutions - published on openPR.com
On the one hand, the extreme heterogeneity indicates non-random variation in effect sizes, such that a minority of interventions might have achieved significantly larger effects than the 95% confidence intervals around the meta-analytic average. Indeed, 25% of studies reported absolute improvements greater than 10%, with one as high as 62%.67 Yet, even with the identification of two significant predictors of larger effects-namely, paediatric studies and those with low baseline adherence-the meta-regression model still showed extreme heterogeneity. Thus even when these characteristics were taken into account, a wide, non-random variation remained in the improvements seen with clinical decision support systems. The reason for this remains largely unknown.. Other systematic reviews have similarly reported extreme heterogeneity, with I2 as high as 97% in one instance.18 Moja and colleagues reported more moderate heterogeneity (I2=41-64%),21 but their review focused specifically on measures of ...
The budding European market for clinical decision support systems could be propelled into growth with the development of more robust technologies, according to a new report from Frost & Sullivan, which says this is being driven by concerns over patient safety. In fact, it estimates that 2005s revenue of $239 million could almost double by 2012 to reach $431 million. - News - PharmaTimes
Clinical Decision Support System To Prevent Toxicity In Patients Treated With Digoxin: 10.4018/978-1-61520-977-4.ch001: In this chapter, authors develop a system for prevention and detection of congestive heart failure and fibrillation. Due to its narrow therapeutic range more
TY - JOUR. T1 - Improving hospital venous thromboembolism prophylaxis with electronic decision support. AU - Bhalla, Rohit. AU - Berger, Matthew A.. AU - Reissman, Stan H.. AU - Yongue, Brandon G.. AU - Adelman, Jason S.. AU - Jacobs, Laurie G.. AU - Billett, Henny. AU - Sinnett, Mark J.. AU - Kalkut, Gary. PY - 2013/3/1. Y1 - 2013/3/1. N2 - BACKGROUND: Venous thromboembolism (VTE) disease prophylaxis rates among medical inpatients have been noted to be ,50%. OBJECTIVE: Our objective was to evaluate the effectiveness and safety of a computerized decision support application to improve VTE prophylaxis. DESIGN: Observational cohort study. SETTING: Academic medical center. PATIENTS: Adult inpatients on hospital medicine and nonmedicine services. INTERVENTION: A decision support application designed by a quality improvement team was implemented on medicine services in September 2009. MEASUREMENTS: Effectiveness and safety parameters were compared on medicine services and nonmedicine ...
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Methods and apparatus for providing a comprehensive decision support system to include predictions, recommendations with consequences and optimal follow-up actions in specific situations are described. Data is obtained from multiple disparate data sources, depending on the information deemed necessary for the situation being modeled. Some embodiments perform complex systems modeling including performing massive correlative analyses of the data obtained from the multiple disparate data sources with current situational data obtained regarding the situation for which the decision support process is being utilized. The decision support system provides a prediction or predictions and a recommendation or a choice of recommendations based on the correlative analysis and/or other analyses. In some embodiments the decision support system provides possible consequences that could result from a recommendation. In other embodiments the decision support system provides a list of tasks for acting upon a
Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate. Manual delineation of lung nodules by radiologists is a tedious task. We developed a novel computer-aided decision support system for lung nodule detection based on a 3D Deep Convolutional Neural Network (3DDCNN) for assisting the radiologists. Our decision support system provides a second opinion to the radiologists in lung cancer diagnostic decision making. In order to leverage 3-dimensional information from Computed Tomography (CT) scans, we applied median intensity projection and multi-Region Proposal Network (mRPN) for automatic selection of potential region-of-interests. Our Computer Aided Diagnosis (CAD) system has been trained and validated using LUNA16, ANODE09, and LIDC-IDR datasets; the experiments demonstrate the superior performance of our system, attaining sensitivity, specificity, AUROC, accuracy, of 98.4, 92, 96 and 98.51 with 2.1 FPs ...
Breast cancer is the most common female cancer. In the United States, the second most common cause of cancer death in women, and the main cause of death in women ages 45 to 55 years old. The U.S. Preventive Services Task Force recommends screening mammography, with or without clinical breast examination, every one to two years among women aged 50 to 69 years old.. Recent research has shown that health care delivered in industrialized nations often falls short of optimal, evidence based care. US adults receive only about half of recommended care. To address these deficiencies in care, health-care organizations are increasingly turning to clinical decision support systems. A clinical decision-support system is any computer program designed to help health-care professionals to make clinical decisions. In a sense, any computer system that deals with clinical data or knowledge is intended to provide decision support.. Examples include manual or computer based systems that attach care reminders to the ...
INTRODUCTION: Handheld computers (PDAs) uploaded with clinical decision support software (CDSS) have the potential to facilitate the adoption of evidence-based medicine (EBM) at the point-of-care among undergraduate medical students. Further evaluation of the usefulness and acceptability of these tools is required. METHODS: All 169 Year 4 undergraduate medical students at the University of Hong Kong completed a post-randomised controlled trial survey. Primary outcome measures were CDSS/PDA usefulness, satisfaction, functionality and utilisation. Focus groups were also conducted to derive complementary qualitative data on the students attitudes towards using such new technology. RESULTS: Overall, the students found the CDSS/PDA useful (mean score = 3.90 out of 6, 95% confidence interval (CI) = 3.78, 4.03). They were less satisfied with the functional features of the CDSS (mean score = 3.45, 95% CI = 3.32, 3.59) and the PDA (mean score = 3.51 95% CI = 3.40, 3.62). Utilisation was low, with the ...
Managing patients on oral anticoagulation treatment is time consuming for the primary care provider. The frequent blood testing is disruptive to patients daily lives. Despite considerable time and effort, studies confirm that patients are often outside their prescribed INR range.1, 2 Because of the difficulties of the treatment, many patients who need anticoagulation are not treated.. The search for better methods for managing anticoagulation includes the use of computer decision support by physicians, nurses, or patients to reduce time and costs. The use of such software provides an algorithm, reduces disparities among providers in decision making, and increases adherence with care standards.3 The potential also exists for enhancing pattern recognition in individual patients, which could assist in the detection of interfering drugs or foods.. The study by Fitzmaurice et al is 1 of several that have attempted to verify reduced costs and increased effectiveness of oral anticoagulation treatment ...
TY - GEN. T1 - Improving the performance of clinical decision support for early detection of sepsis. T2 - a retrospective observational cohort study. AU - Li, Ling. AU - Rathnayake, Kasun. AU - Green, Malcolm. AU - Fullick, Mary. AU - Shetty, Amith. AU - Walter, Scott. AU - Braithwaite, Jeffrey. AU - Lander, Harvey. AU - Westbrook, Johanna I.. N1 - Copyright International Medical Informatics Association (IMIA) and IOS Press 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.. PY - 2019/8/21. Y1 - 2019/8/21. N2 - Sepsis remains a significant global health problem. It is a life-threatening, but poorly defined and recognized condition. Early recognition and intervention are essential to optimize patient outcomes. Automated clinical decision support systems (CDS) may be particularly beneficial for early detection of sepsis. The aim of this study was to use retrospective ...
Clinicians and pharmacists continue to turn to Wolters Kluwers evidence-based support resources Wolters Kluwer, Health has continued to build its customer base in the UK, gaining six new customers for its clinical decision support tool UpToDate and drug information tool Lexicomp. This follows significant growth in 2020 and highlights increased demand for evidence-based decision support tools, especially during the pandemic. Wolters Kluwers renowned clinical content from Lexicomp and UpToDate gives clinicians access to peer-reviewed treatment recommendations and the latest best-practice guidance on over 12,000 clinical topics and more than 9,500 graded recommendations. UpToDate and Lexicomp have both been shown to deliver improved care and outcomes by supporting safe, evidence-based medication and therapeutic decisions.. According to research of UpToDate use in 2020, 1 in 4 clinicians (21%) in the UK and Ireland changed their course of action to a more appropriate treatment or diagnosis after ...
Our study contributes new findings to the understanding of how a CDSS is used in the initial management of a common symptom. Despite use of the OMA CDSS in 86 % of consultations for which it was available, there was little evidence of impact on medication prescribing or on investigation choice. Through qualitative data analysis we gained an in-depth understanding of the reasons for this discrepancy. We identified problems in entering patient data into the CDSS. These included difficulties in classification of symptoms and risk factors, and in the incorporation of all available clinical information emerging during the consultation. In the majority of observed cases, the CDSS was used after the patient had left the room. Structural and practical barriers to the use of the CDSS included the availability of investigations and the prescribing competencies of the clinicians. Analysis of observational data revealed how clinicians privileged their clinical expertise over CDSS advice, responding to CDSS ...
A clinical decision support system (CDSS) is a health information technology system that is designed to provide physicians and other health professionals with clinical decision support (CDS), that is, assistance with clinical decision-making tasks.
An increase in naturally-occurring porphyrins has been described in the blood of subjects bearing different kinds of tumors, including colorectal, and this is probably related to a systemic alteration of heme metabolism induced by tumor cells. The aim of our study was to develop an artificial neural network (ANN) classifier for early detection of colorectal adenocarcinoma based on plasma porphyrin accumulation and risk factors. We measured the endogenous fluorescence of blood plasma in 100 colorectal adenocarcinoma patients and 112 controls using a conventional spectrofluorometer. Height, weight, personal and family medical history, use of alcohol, red meat, vegetables and tobacco were all recorded. An ANN model was built up from demographic data and from the integral of the fluorescence emission peak in the range 610-650 nm. We used the Receiver Operating Characteristic (ROC) curve to assess performance in distinguishing colorectal adenocarcinoma patients and controls. A liquid chromatography-high
Design: Cluster randomized controlled trial. Allocation: Unclear allocation concealment.* Blinding: Unblinded.* Follow-up period: 21 months. Setting: Rural communities in Utah and Idaho, United States. Participants: 67 910 persons (50% women, 70% adults) from 12 rural communities. 6 nonrandomized communities (n = 19 310) served as a reference group. Intervention: A CDSS plus a community intervention (6 communities, n = 32 490) or a community intervention alone (6 communities, n = 35 420). The CDSS intervention included 3 parallel decision support tools (2 paper-based versions including flow charts or self-completed medical histories and 1 personal digital assistant version), each providing the diagnostic and therapeutic guidelines on several acute RTIs (e.g., sinusitis, pharyngitis, and otitis media). The CDSS was introduced to primary care clinicians by educational lectures, small group meetings, and 1-on-1 interactions between primary care clinicians and physician members of the study team. ...
Ideally, there would be a seamless link between the clinical decision support system and the patients electronic health record, so that suggestions regarding the patients diagnosis and treatment would be made in real time as data is entered into the chart. This system would be automatically updated with the latest best evidence as it became available. Obviously, these systems are not in general use, but the soundness of this approach has been shown, particularly in the area of prescribing multiple medications for hospitalized or geriatric patients. To date, the applications with the most immediate usefulness have been those that promote guideline adherence and alert providers to drug interactions or treatment omissions. Examples include computer provider order entry systems combined with clinical decision support systems, which have been shown to improve patient safety through reducing the incidence of adverse drug interactions. The third step of the classical EBHC process is critical ...
Guidelines exist for chronic kidney disease (CKD) but are not well implemented in clinical practice. We evaluated the impact of a guideline-based clinical decision support system (CDSS) on laboratory monitoring and achievement of laboratory targets in stage 3-4 CKD patients. We performed a matched cohort study of 12,353 stage 3-4 CKD patients whose physicians opted to receive an automated guideline-based CDSS with CKD-related lab results, and 42,996 matched controls whose physicians did not receive the CDSS. Physicians were from US community-based physician practices utilizing a large, commercial laboratory (LabCorp®). We compared the percentage of laboratory tests obtained within guideline-recommended intervals and the percentage of results within guideline target ranges between CDSS and non-CDSS patients. Laboratory tests analyzed included estimated glomerular filtration rate, plasma parathyroid hormone, serum calcium, phosphorus, 25-hydroxy vitamin D (25-D), total carbon dioxide, transferrin
Acknowledgment: The authors thank the patients and staff of the Massachusetts General Hospital HIV Clinic.. Grant Support: By the National Institute of Allergy and Infectious Diseases (K01AI062435, K24AI062476, P30AI42851, K24DK080140, and R37AI42006) and the Massachusetts General Hospital Clinical Research Program.. Potential Conflicts of Interest: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M12-0054.. Reproducible Research Statement: Study protocol, statistical code, and data set: Available from Dr. Robbins (e-mail, [email protected]).. Requests for Single Reprints: Gregory K. Robbins, MD, MPH, Massachusetts General Hospital, Division of Infectious Diseases, 55 Fruit Street, Cox 5, Boston, MA 02114; e-mail, [email protected] Current Author Addresses: Dr. Robbins: Massachusetts General Hospital, Division of Infectious Diseases, 55 Fruit Street, Cox 5, Boston, MA 02114.. Drs. Lester and Chueh, Mr. Estey, and Mr. Surrao: Massachusetts ...
AHRQs new Community-Acquired Pneumonia Clinical Decision Support Implementation Toolkit helps clinicians in emergency departments, primary care and other ambulatory settings implement and adopt a clinical decision support (CDS) alert for identifying and managing patients with community-acquired pneumonia.
Infectious diseases are a threat to human health around the world. In recent years, there have been a growing number of outbreaks of rare infectious diseases. These include outbreaks of Ebola, Zika and influenza.1 2 These infections pose special challenges to learners and educators alike in medical education. Under normal conditions they are rare and so it is difficult to dedicate too much time or resources to them in undergraduate or postgraduate curricula. However, the situation can quickly change to that of an epidemic. In these circumstances, doctors and other healthcare professionals need instant education and support. It is in these circumstances that online clinical decision support could play a major role in controlling outbreaks of infectious diseases.. Online clinical decision support provided must be aligned to the needs of the healthcare professional learners.3 In this regard, it is clear that, under normal circumstances, healthcare professionals need certain features in clinical ...
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articles, news, reports and publications on quality of healthcare, quality assurance, quality improvement, quality indicators, quality measures, health services research, patient safety, medical errors, hospital performance, health information technology and more from The New England Journal of Medicine, The Lancet, JAMA, BMJ, CMAJ, MJA, Medical Care, Health Affairs and other leading medical journals and from AHRQ, CMWF, CMS, RAND, NHS and other international health Agency. ...
The Medical Decision Support Systems for Sepsis market report offers a detailed explanation of different Leading level industries that are functioning in global regions. The Medical Decision Support Systems for Sepsis market was developed with a primary focus on the competitive sphere, retail, geographical expansion, and market dynamics, including drivers, constraints, and opportunities. In this report, various chapters deliver a logical understanding of the market scenarios with relevant examples. This report also analyzes the global market competition landscape, market drivers and trends, opportunities and challenges, sales channels, distributors, and Porters Five Forces Analysis.. To Know More Details About This Report, Get Sample @ http://www.acquiremarketresearch.com/sample-request/386911. The objective of this examine is to determine market sizes for various sectors and countries in recent years and predict values for the next eight years. The report is created to integrate the ...
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1. Rheingold: Interactive Interactions. Who among us has not had a client or a friend that was currently on a bouillabaisse of 3-4 pharmaceuticals and perhaps 5-6 (or more) dietary supplements? Ever wonder what exactly was going in there? Perhaps a nifty little app I recently coded might be just what the doctor orders.. Rheingold can help identify possible drug/nutrient/food interactions by virtue of its ability to crawl a dataset of their known effects on cytochrome function. It then displays the results in network fashion.. Originally built for my genomic tool Opus23, where the cytochrome status of the particular loaded client is also figured into the outcomes. This open-source version still affords the attentive clinician the opportunity to provide some degree of due diligence, either when attempting to sort out a confusing plethora of ingested agents on initial presentation, or to help tailor a de novo intervention with the full confidence that ones protocol is not inherently ...
[65 Pages Report] Medical Decision Support Systems for Sepsis Market Revolutionary Primer for Clinical Decision Support (Market Dynamics, Case Studies, Regional Analysis (North America, Europe, Asia Pacific, Rest of the World))
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Other articles where Decision-support system is discussed: information system: Decision support systems and business intelligence: …decision making, however indirectly, but decision support systems are expressly designed for this purpose. As these systems are increasingly being developed to analyze massive collections of data (known as big data), they are becoming known as business intelligence, or business analytics, applications. The two principal varieties of decision support systems…
Objective: To determine the extent to which computerised decision support can improve concordance of multidisciplinary teams with therapeutic decisions recommended by guidelines.. Design: Multicentre cluster randomised trial.. Participants: Multidisciplinary cardiac rehabilitation teams in Dutch centres and their cardiac rehabilitation patients.. Interventions: Teams received an electronic patient record system with or without additional guideline based decision support.. Main outcome measures: Concordance with guideline recommendations assessed for two standard rehabilitation treatments-exercise and education therapy-and for two new but evidence based rehabilitation treatments-relaxation and lifestyle change therapy; generalised estimating equations were used to account for intra-cluster correlation and were adjusted for patients age, sex, and indication for cardiac rehabilitation and for type and volume of centre.. Results: Data from 21 centres, including 2787 patients, were analysed. ...
TY - JOUR. T1 - Inform. T2 - Integrated decision support in intensive care. AU - Hunter, James. AU - Chambrin, Marie-Christine. AU - Collinson, Paul. AU - Hedlund, Anders. AU - Groth, Torgny. AU - Kalli, Seppo. AU - Kari, Aarno. AU - Lenoudias, George. AU - Ravaux, Pierre. AU - Ross, Donnie. PY - 1990. Y1 - 1990. N2 - Many medical decision support systems that have been developed in the past have failed to enter routine clinical practice. Often this is because the developers have failed to analyse in sufficient detail the precise user requirements, because they have produced a system which takes too narrow a view of the patient, or because the decision support facilities have not been sufficiently well integrated into the routine clinical data handling activities. In this paper we discuss how the AIM-INFORM project is setting out to deal with these issues, in the context of the provision of decision support in the intensive care unit.. AB - Many medical decision support systems that have been ...
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The ontology-based intelligent system provides accurate annotations of ultrasound images and suggests that it may benefit non-expert operators. The precision rate is appropriate for accurate input of a computer-based clinical decision support and could be used to support medical imaging diagnosis of …
Electronic Health Records (EHR) and Clinical Decision Support - Etiology, pathophysiology, symptoms, signs, diagnosis & prognosis from the Merck Manuals - Medical Professional Version.