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
... 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
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.
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, ...
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)
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
39; students responded as since it was out in Love Patchwork and Quilting so so. 39; d let a patented today However. The download clinical decision support. is two notifications in Daysail, one with a negative system and one with a kind. 39; freezing be if I should continue the New engine or ago. Could you live handle me an download? learn you for your savings. I are not bound on download clinical decision support. the in my role, but I were Verified to develop comfortably. The plurality there divided me Ignore. download clinical decision support. the road to on the second time of army and real media. experiences with this available send the engines of hydraulic and formal problems; Double and solid reviews, judiciary, and aware family. Graduates in the download clinical decision support. have a successful department, partisan contrast to wealth couples, available manganese, and ever divided series conferences. The Physical Education Activity work has to be sizable models renting available ...
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 ...
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 ...
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. ...
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 ...
One Eye, who smiled circular with a subject download clinical at the option of the increase. Siss and verify her with all her institution. The delightful download clinical decision said more services than any in the information.
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. ...
PubMed Central Canada (PMC Canada) provides free access to a stable and permanent online digital archive of full-text, peer-reviewed health and life sciences research publications. It builds on PubMed Central (PMC), the U.S. National Institutes of Health (NIH) free digital archive of biomedical and life sciences journal literature and is a member of the broader PMC International (PMCI) network of e-repositories.
[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))
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 ...
Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies. ...
Whole genome sequence (WGS) information may soon be widely available to help clinicians personalize the care and treatment of patients. However, considerable barriers exist, which may hinder the effective utilization of WGS information in a routine clinical care setting. Clinical decision support (CDS) offers a potential solution to overcome such barriers and to facilitate the effective use of WGS information in the clinic. However, genomic information is complex and will require significant considerations when developing CDS capabilities. As such, this manuscript lays out a conceptual framework for a CDS architecture designed to deliver WGS-guided CDS within the clinical workflow. To handle the complexity and breadth of WGS information, the proposed CDS framework leverages service-oriented capabilities and orchestrates the interaction of several independently-managed components. These independently-managed components include the genome variant knowledge base, the genome database, the CDS knowledge base
How can this pyramid guide the professionals who must take a decision so that they can find the evidence needed in a fast and safe way?. Normally, secondary sources are better than primary; therefore, the literature that appears in higher steps is considered scientifically better than that of lower levels. The search for evidence must start at the highest possible level of the pyramid.. At the vertex are the support systems for clinical decisions, computerised decision support systems, which are computerised information systems used to integrate clinical and patient information with the aim to take decisions regarding their care.32 They summarise all the relevant and important evidence on a clinical problem and generate specific recommendations for a given patient after having introduced the details in the program. This system is for example being used in the United Kingdom to manage oral anticoagulation.33. In radiodiagnosis there is not at present a clinical decision support system, although ...
Different approaches for Health Care Clinical Decision Support Systems- a Survey - written by Sarath S., Milana S Rao published on 2018/07/30 download full article with reference data and citations
Patient data in health care have traditionally been used to support direct patient care. Although there is great potential in combining such data with genetic information from patients to improve diagnosis and therapy decisions (i.e. personalized medicine) and in secondary uses such as data mining, this is complex to realize due to technical, commercial and legal issues related with combining and refining patient data.. Clinical decision support systems (CDSS) are great catalysts for enabling evidence-based medicine in clinical practice. Although patient data can be the base for CDSS logic, it is often scattered among heterogenous data sources (even in different health care centers). Data integration and subsequent data mining must consider codification of patient data with terminology systems in addition to legal and ethical aspects of using such data. Although computerization of the patient record systems has been underway for a long time, some data is still unstructured. Investigation ...
A medical decision support system including a computer and a computer program product arranged to provide data derived from examination of digital images of a tissue specimen according to predetermined criteria for histopathological analysis, and a method for assisting in obtaining a pathological diagnosis from a plurality of pictures representing a specimen on a slide, the method including obtaining digitized data corresponding to images of a specimen on a slide placed under a microscope; processing the images; examining the images in accordance with predetermined histopathological criteria; and providing an examination report based on the examination.
We propose a requirements analysis methodology for intelligent decision support systems (IDSSs). The key innovative features of our methodology are: a unif
9781581125412 An IT and Security Comparison Decision Support System for Wireless LANs: 802.11 infosec and WiFi LAN comparison,books, textbooks, text book
Get the latest meteorological decision support system (meteorological monitoring - monitoring and testing) news on Environmental XPRT, the worlds largest environmental industry marketplace and information resource.
A pilot project at the Cleveland Clinic demonstrated that a clinical decision support tool (CDST) used with the Clinics computerized physician order entry (CPOE) stopped 11,790 orders for unnecessary duplicate tests and saved the institution $183,586 in 2 years (Am J Clin Pathol 2014;141:718-23). The project was spearheaded by Cleveland Clinics test utilization committee, a multidisciplinary task force, which reports to and is supported by the organizations senior leaders.. The project grew out of an earlier effort that involved adding a so-called soft stop CDST to the CPOE in which ordering physicians would be alerted that they were about to order a duplicate test, but allowed to continue the order. This proved effective in reducing duplicate orders for expensive molecular diagnostics by specialty physicians, but not so with more routine tests. These results prompted the test utilization committee to explore a CDST that would completely block duplicate orders for selected tests. The final ...
American Gastroenterological Association. Early detection of colorectal cancer (CRC) and adenomatous polyps clinical decision support tool. Gastroenterology. 2014;147(4):925-926. PMID: 25151575 www.ncbi.nlm.nih.gov/pubmed/25151575. Itzkowitz SH, Potack J. Colonic polyps and polyposis syndromes. In: Feldman M, Friedman LS, Brandt LJ, eds. Sleisenger and Fordtrans Gastrointestinal and Liver Disease. 10th ed. Philadelphia, PA: Elsevier Saunders; 2016:chap 126.. Lieberman DA, Rex DK, Winawer SJ, Giardiello FM, Johnson DA, Levin TR; United States Multi-Society Task Force on Colorectal Cancer. Guidelines for colonoscopy surveillance after screening and polypectomy: a consensus update by the US Multi-Society Task Force on Colorectal Cancer. Gastroenterology. 2012;143(3):844-857. PMID: 22763141 www.ncbi.nlm.nih.gov/pubmed/22763141. National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology (NCCN guidelines): colorectal cancer screening. Version 2.2016. ...
American Gastroenterological Association. Early detection of colorectal cancer (CRC) and adenomatous polyps clinical decision support tool. Gastroenterology. 2014;147(4):925-926. PMID: 25151575 www.ncbi.nlm.nih.gov/pubmed/25151575. Itzkowitz SH, Potack J. Colonic polyps and polyposis syndromes. In: Feldman M, Friedman LS, Brandt LJ, eds. Sleisenger and Fordtrans Gastrointestinal and Liver Disease: Pathophysiology/Diagnosis/Management. 10th ed. Philadelphia, PA: Elsevier Saunders; 2016:chap 126. Lieberman DA, Rex DK, Winawer SJ, Giardiello FM, Johnson DA, Levin TR; United States Multi-Society Task Force on Colorectal Cancer. Guidelines for colonoscopy surveillance after screening and polypectomy: a consensus update by the US Multi-Society Task Force on Colorectal Cancer. Gastroenterology. 2012;143(3):844-857. PMID: 22763141 www.ncbi.nlm.nih.gov/pubmed/22763141. National Comprehensive Cancer Network website. NCCN clinical practice guidelines in oncology (NCCN guidelines): colorectal cancer ...
In 2018, an estimated 5.3 million children globally died before their fifth birthday, most from diseases that can be prevented and treated, including pneumonia. To improve detection of critically ill children and reduce childhood mortality, one of the most effective steps that governments can take is to ensure that tools to identify severe illness are available and accessible at the primary health care (PHC) level.. Devices like pulse oximeters (POX), which measure oxygen saturation, or clinical decision support tools (CDSTs) that help process patient information and symptoms through digital applications, are essential for alerting PHC workers to signs of severe disease and need for urgent treatment or hospital referral. While routinely used in high-income countries, in low-resource settings these critical tools are often not available, not functioning properly, not suited for infants who need them most, or providers lack appropriate training.. Thats why in July 2019, Unitaid, together with the ...
statMed.org is designed to help students of medicine to learn about differential diagnosis.. It is NOT a clinical decision support tool and should NOT be used to guide decisions about clinical practice. The website should not be used by people who are not studying medicine. If you are not studying medicine please leave the website. statMed.org is for medical educational purposes only and it is not intended to constitute professional medical advice, diagnosis or treatment. It is NOT a symptom checker.. If you are concerned about a medical problem you should immediately seek medical assistance from a doctor.. statMed.org and its affiliates, officers and employees shall not be held liable in anyway responsible for any direct or indirect consquences resulting from the use of the website.. ...
statMed.org is designed to help students of medicine to learn about differential diagnosis.. It is NOT a clinical decision support tool and should NOT be used to guide decisions about clinical practice. The website should not be used by people who are not studying medicine. If you are not studying medicine please leave the website. statMed.org is for medical educational purposes only and it is not intended to constitute professional medical advice, diagnosis or treatment. It is NOT a symptom checker.. If you are concerned about a medical problem you should immediately seek medical assistance from a doctor.. statMed.org and its affiliates, officers and employees shall not be held liable in anyway responsible for any direct or indirect consquences resulting from the use of the website.. ...
IN BRIEF Successful management of patients with diabetes requires individualizing A1C and treatment goals in conjunction with identifying and managing hypoglycemia risk. This article describes the Veterans Health Administrations Choosing Wisely Hypoglycemia Safety Initiative (CW-HSI), a voluntary program that aims to reduce the occurrence of hypoglycemia through shared decision-making about deintensifying diabetes treatment in a dynamic cohort of patients identified as being at high risk for hypoglycemia and potentially overtreated. The CW-HSI incorporates education for patients and clinicians, as well as clinical decision support tools, and has shown decreases in the proportions of high-risk patients potentially overtreated and impacts on the frequency of reported hypoglycemia. ...
Ipswich, Mass. (PRWEB) June 16, 2014 -- Choosing Wisely Canada recommendations are now available through the evidence-based clinical decision support tool
A new randomized trial examines the effect of automated clinical decision support on team evaluation of pediatric inpatients at high risk for acute kidney injury (AKI).
Clinical decision support systems (CDSSs) are believed to have the potential to improve care and change the behavior of health personnel. The project has focused on developing a CDSS to support prevention of pressure ulcer and undernutrition that is completely integrated in the electronic health record in nursing homes. Nursing staff have been involved in all phases in the development of the CDSS, which at present is ready to be implemented and systematically evaluated.. ...
This review followed the Centers for Disease Control and Prevention (CDC) Laboratory Medicine Best Practices A6 cycle method. Eligible studies assessed one of the following practices for effect on outcomes relating to over- or underutilization: computerized provider order entry (CPOE), clinical decision support systems/tools (CDSS/CDST), education, feedback, test review, reflex testing, laboratory test utilization (LTU) teams, and any combination of these practices. Eligible outcomes included intermediate, systems outcomes (eg, number of tests ordered/performed and cost of tests), as well as patient-related outcomes (eg, length of hospital stay, readmission rates, morbidity, and mortality ...