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"clinical decision support system" oncology

Antonella Casiraghi, Silvia Franzè, Paolo Rocco, Paola Minghetti
PURPOSE: The different stages of antineoplastic agent management build up a complex process, from supply to prescription, preparation, and administration. All steps in this process must be carefully monitored in order to control/reduce the risk of errors that can impact on patient safety. This work overviews the prevention of medication errors in oncology, including regulatory and legislative frameworks with specific reference to the Raccomandazione 14 (Recommendation 14) issued by the Italian Ministry of Health...
August 29, 2016: Tumori
Jan Gaebel, Mario A Cypko, Heinz U Lemke
Clinical decision support systems (CDSS) are developed to facilitate physicians' decision making, particularly for complex, oncological diseases. Access to relevant patient specific information from electronic health records (EHR) is limited to the structure and transmission formats in the respective hospital information system. We propose a system-architecture for a standardized access to patient specific information for a CDSS for laryngeal cancer. Following the idea of a CDSS using Bayesian Networks, we developed an architecture concept applying clinical standards...
2016: Studies in Health Technology and Informatics
Philippe Lambin, Jaap Zindler, Ben G L Vanneste, Lien Van De Voorde, Daniëlle Eekers, Inge Compter, Kranthi Marella Panth, Jurgen Peerlings, Ruben T H M Larue, Timo M Deist, Arthur Jochems, Tim Lustberg, Johan van Soest, Evelyn E C de Jong, Aniek J G Even, Bart Reymen, Nicolle Rekers, Marike van Gisbergen, Erik Roelofs, Sara Carvalho, Ralph T H Leijenaar, Catharina M L Zegers, Maria Jacobs, Janita van Timmeren, Patricia Brouwers, Jonathan A Lal, Ludwig Dubois, Ala Yaromina, Evert Jan Van Limbergen, Maaike Berbee, Wouter van Elmpt, Cary Oberije, Bram Ramaekers, Andre Dekker, Liesbeth J Boersma, Frank Hoebers, Kim M Smits, Adriana J Berlanga, Sean Walsh
A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc...
January 15, 2017: Advanced Drug Delivery Reviews
Jeroen S de Bruin, Christian Schuh, Walter Seeling, Eva Luger, Michaela Gall, Elisabeth Hütterer, Gabriela Kornek, Bernhard Ludvik, Friedrich Hoppichler, Karin Schindler
BACKGROUND: Nutritional screening procedures followed by regular nutrition monitoring for oncological outpatients are no standard practice in many European hospital wards and outpatient settings. As a result, early signs of malnutrition are missed and nutritional treatment is initiated when patients have already experienced severe weight loss. OBJECTIVE: We report on a novel clinical decision support system (CDSS) for the global assessment and nutritional triage of the nutritional condition of oncology outpatients...
October 22, 2015: Artificial Intelligence in Medicine
Laura Vera Righi
A clinical decision support system is able to provide oncologists with suitable treatment options at the moment of decision making regarding which chemotherapy protocol is the best to apply to a particular oncological case. The National Cancer Institute has created a Guidelines Committee that establishes therapeutical options for each clinical case. The Health Informatics Department has developed Oncotherapy, a knowledge database that incorporates information provided by the Guidelines Committee. Oncotherapy includes a tailored information repository to provide oncologists in the public health system with the chemotherapy protocols available given three types of data: clinical diagnosis, clinical stage and therapy criteria...
2015: Studies in Health Technology and Informatics
Arzu Akman Yılmaz, Leyla Ozdemir
PURPOSE: The purpose of this study was to develop and implement the clinical decision support system (CDSS) for oncology nurses in the care of patients with cancer and to explore the nurses' experiences about the system. METHODS: The study was conducted using a mixed-methods research design with 14 nurses working at a gynecological oncology clinic at a university hospital in Turkey. FINDINGS: The nurses stated that they did not experience any problems during the implementation of the CDSS, and its usage facilitated the assessment of patients' needs and care management...
January 2017: International Journal of Nursing Knowledge
Peter Paul Yu
One of the most important benefits of health information technology is to assist the cognitive process of the human mind in the face of vast amounts of health data, limited time for decision making, and the complexity of the patient with cancer. Clinical decision support tools are frequently cited as a technologic solution to this problem, but to date useful clinical decision support systems (CDSS) have been limited in utility and implementation. This article describes three unique sources of health data that underlie fundamentally different types of knowledge bases which feed into CDSS...
March 2015: Journal of Oncology Practice
Claudio Eccher, Andreas Seyfang, Antonella Ferro
The domain of cancer treatment is a promising field for the implementation and evaluation of a protocol-based clinical decision support system, because of the algorithmic nature of treatment recommendations. However, many factors can limit such systems' potential to support the decision of clinicians: technical challenges related to the interoperability with existing electronic patient records and clinical challenges related to the inherent complexity of the decisions, often collectively taken by panels of different specialists...
November 2014: Computer Methods and Programs in Biomedicine
Scott R Steele, Anton Bilchik, Eric K Johnson, Aviram Nissan, George E Peoples, John S Eberhardt, Philip Kalina, Benjamin Petersen, Björn Brücher, Mladjan Protic, Itzhak Avital, Alexander Stojadinovic
Unanswered questions remain in determining which high-risk node-negative colon cancer (CC) cohorts benefit from adjuvant therapy and how it may differ in an equal access population. Machine-learned Bayesian Belief Networks (ml-BBNs) accurately estimate outcomes in CC, providing clinicians with Clinical Decision Support System (CDSS) tools to facilitate treatment planning. We evaluated ml-BBNs ability to estimate survival and recurrence in CC. We performed a retrospective analysis of registry data of patients with CC to train-test-crossvalidate ml-BBNs using the Department of Defense Automated Central Tumor Registry (January 1993 to December 2004)...
May 2014: American Surgeon
Peter Yu, David Artz, Jeremy Warner
ASCO's vision for cancer care in 2030 is built on the expanding importance of panomics and big data, and envisions enabling better health for patients with cancer by the rapid transformation of systems biology knowledge into cancer care advances. This vision will be heavily dependent on the use of health information technology for computational biology and clinical decision support systems (CDSS). Computational biology will allow us to construct models of cancer biology that encompass the complexity of cancer panomics data and provide us with better understanding of the mechanisms governing cancer behavior...
2014: American Society of Clinical Oncology Educational Book
Mary E Cooley, David F Lobach, Ellis Johns, Barbara Halpenny, Toni-Ann Saunders, Guilherme Del Fiol, Michael S Rabin, Pamela Calarese, Isidore L Berenbaum, Ken Zaner, Kathleen Finn, Donna L Berry, Janet L Abrahm
CONTEXT: Adequate symptom management is essential to ensure quality cancer care, but symptom management is not always evidence based. Adapting and automating national guidelines for use at the point of care may enhance use by clinicians. OBJECTIVES: This article reports on a process of adapting research evidence for use in a clinical decision support system that provided individualized symptom management recommendations to clinicians at the point of care. METHODS: Using a modified ADAPTE process, panels of local experts adapted national guidelines and integrated research evidence to create computable algorithms with explicit recommendations for management of the most common symptoms (pain, fatigue, dyspnea, depression, and anxiety) associated with lung cancer...
December 2013: Journal of Pain and Symptom Management
Zeev Waks, Esther Goldbraich, Ariel Farkash, Michele Torresani, Rossella Bertulli, Nicola Restifo, Paolo Locatelli, Paolo Casali, Boaz Carmeli
Clinical decision support systems (CDSSs) are gaining popularity as tools that assist physicians in optimizing medical care. These systems typically comply with evidence-based medicine and are designed with input from domain experts. Nonetheless, deviations from CDSS recommendations are abundant across a broad spectrum of disorders, raising the question as to why this phenomenon exists. Here, we analyze this gap in adherence to a clinical guidelines-based CDSS by examining the physician treatment decisions for 1329 adult soft tissue sarcoma patients in northern Italy using patient-specific parameters...
2013: Studies in Health Technology and Informatics
Andreas Wicht, Thomas Wetter, Ulrike Klein
We introduce a web-based clinical decision support system (CDSS) and knowledge maintenance based on rules and a set covering method focusing on the problem of detecting serious comorbidities in hemato-oncological patients who are at high risk of developing serious infections and life threatening complications. We experienced that diagnostic problems which are characterized by fuzzy, uncertain knowledge and overlapping signs, still reveal some kind of patterns that can be transferred into a computer-based decision model...
July 2013: Computer Methods and Programs in Biomedicine
Philippe Lambin, Ruud G P M van Stiphout, Maud H W Starmans, Emmanuel Rios-Velazquez, Georgi Nalbantov, Hugo J W L Aerts, Erik Roelofs, Wouter van Elmpt, Paul C Boutros, Pierluigi Granone, Vincenzo Valentini, Adrian C Begg, Dirk De Ruysscher, Andre Dekker
With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process...
January 2013: Nature Reviews. Clinical Oncology
Jacques Bouaud, Nizar Messai, Cédric Laouénan, France Mentré, Brigitte Séroussi
Because they provide patient-specific guideline-based recommendations, clinical decision support systems (CDSSs) are expected to promote the implementation of clinical practice guidelines (CPGs). OncoDoc2 is a CDSS applied to the management of breast cancer. However, despite it was routinely used during weekly multidisciplinary staff meetings (MSMs) at the Tenon Hospital (Paris, France), the compliance rate of MSMs' decisions with CPGs did not reach 100%. Formal Concept Analysis (FCA) has been applied to elicit formal concepts related to non-compliance...
2012: Studies in Health Technology and Informatics
I M Collins, O Breathnach, P Felle
BACKGROUND: There is little evidence regarding attitudes to clinical decision support systems (CDSS) in oncology. AIMS: We examined the current usage, awareness, and concerns of Irish medical oncologists and oncology pharmacists in this area. METHODS: A questionnaire was sent to 27 medical oncologists and 34 oncology pharmacists, identified through professional interest groups. Respondents ranked concerns regarding their use of a CDSS on a scale from 1 to 4, with 4 being most important...
December 2012: Irish Journal of Medical Science
Tibor van Rooij, Sharon Marsh
Typically, chemotherapy selection takes into account patient demographic data, including disease symptoms, family history, environmental factors and concurrent medications. Although validated and approved genomics tests are available for targeted therapeutics, a major challenge facing healthcare is the ability to process the genomic data in the patient's context and to return clinically interpretable dosing guidance to the physician in a realistic time frame. Delivery of these targeted therapeutics, made possible by clinical decision support systems connected to an electronic health record may help drive both the acceptance and adaptation of an electronic health record system, as well as provide personalized information at point-of-care, as part of the routine workflow...
May 2011: Future Oncology
Benjamin Djulbegovic
Patients with cancer are at substantial risk for morbidity and mortality from venous thromboembolism (VTE). However, the benefits of anticoagulant prophylaxis in preventing VTE morbidity and mortality have to be weighed against the risks of morbidity and mortality associated with major bleeding events. Risk-benefit analyses should take into account that the risk of VTE and risk of bleeding vary among cancer patients based on a number of factors, including patient characteristics and the causes of VTE. Current guidelines for VTE prophylaxis recommend that anticoagulant prophylaxis be considered for all hospitalized patients with cancer and do not recommend routine prophylaxis in ambulatory patients, except in particular high-risk settings...
March 2010: Journal of Supportive Oncology
Thilo Bertsche, Vasileios Askoxylakis, Gregor Habl, Friederike Laidig, Jens Kaltschmidt, Simon P W Schmitt, Hamid Ghaderi, Angelika Zabel-du Bois, Stefanie Milker-Zabel, Jürgen Debus, Hubert J Bardenheuer, Walter E Haefeli
A prospective controlled intervention cohort study in cancer pain patients (n=50 per group) admitted to radiation oncology wards (62 beds, 3 wards) was conducted in a 1621-bed university hospital. We investigated the effect of an intervention consisting of daily pain assessment using the numeric visual analog scale (NVAS) and pain therapy counseling to clinicians based on a computerized clinical decision support system (CDSS) to correct deviations from pain therapy guidelines. Effects on guideline adherence (primary outcome), pain relief (NVAS) at rest and during physical activity (both groups: cross-sectional assessment on day 5; intervention group: every day assessment), co-analgesic prescription, and acceptance rates of recommendations (secondary outcomes) were assessed...
December 15, 2009: Pain
J Wiemer, F Schubert, M Granzow, T Ragg, J Fieres, J Mattes, R Eils
OBJECTIVES: Medical informatics, neuroinformatics and bioinformatics provide a wide spectrum of research. Here, we show the great potential of synergies between these research areas on the basis of four exemplary studies where techniques are transferred from one of the disciplines to the other. METHODS: Reviewing and analyzing exemplary and specific projects at the intersection of medical informatics, neuroinformatics, and bioinformatics from our experience in an interdisciplinary research group...
2003: Methods of Information in Medicine
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