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"clinical decision support system" oncology

Seán Walsh, Erik Roelofs, Peter Kuess, Yvonka van Wijk, Ben Vanneste, Andre Dekker, Philippe Lambin, Bleddyn Jones, Dietmar Georg, Frank Verhaegen
We present a methodology which can be utilized to select proton or photon radiotherapy in prostate cancer patients. Four state-of-the-art competing treatment modalities were compared (by way of an in silico trial) for a cohort of 25 prostate cancer patients, with and without correction strategies for prostate displacements. Metrics measured from clinical image guidance systems were used. Three correction strategies were investigated; no-correction, extended-no-action-limit, and online-correction. Clinical efficacy was estimated via radiobiological models incorporating robustness (how probable a given treatment plan was delivered) and stability (the consistency between the probable best and worst delivered treatments at the 95% confidence limit)...
February 18, 2018: Cancers
Hesham A Salem, Giacomo Caddeo, Jon McFarlane, Kunjan Patel, Lynda Cochrane, Daniele Soria, Mike Henley, Jonathan Lund
OBJECTIVE: To test a computer-led follow-up service for prostate cancer in two UK hospitals; the testing aimed to validate the computer expert system in making clinical decisions according to the individual patient's clinical need with a valid model accurately identify patients with disease recurrence or treatment failure based on their blood test and clinical picture. PATIENTS AND METHODS: A clinical-decision support system (CDSS) was developed from European (European Association of Urology) and national (National Institute for Health and Care Excellence) guidelines along with knowledge acquired from Urologists...
February 2, 2018: BJU International
S P Somashekhar, M-J Sepúlveda, S Puglielli, A D Norden, E H Shortliffe, C Rohit Kumar, A Rauthan, N Arun Kumar, P Patil, K Rhee, Y Ramya
Background: Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer...
February 1, 2018: Annals of Oncology: Official Journal of the European Society for Medical Oncology
Luca Salmasi, Enrico Capobianco
Healthcare facilities (HF) may identify catchment areas (CA) by selecting criteria that depend on various factors. These refer to hospital activities, geographical definition, patient covariates, and more. The analyses that were traditionally pursued have a limiting factor in the consideration of only static conditions. Instead, some of the CA determinants involve influences occurring at both temporal and spatial scales. The study of CA in the cancer context means choosing between HF, usually divided into general hospitals versus oncological centers (OCs)...
2017: Frontiers in Public Health
Gilmer Valdes, Charles B Simone, Josephine Chen, Alexander Lin, Sue S Yom, Adam J Pattison, Colin M Carpenter, Timothy D Solberg
BACKGROUND AND PURPOSE: Clinical decision support systems are a growing class of tools with the potential to impact healthcare. This study investigates the construction of a decision support system through which clinicians can efficiently identify which previously approved historical treatment plans are achievable for a new patient to aid in selection of therapy. MATERIAL AND METHODS: Treatment data were collected for early-stage lung and postoperative oropharyngeal cancers treated using photon (lung and head and neck) and proton (head and neck) radiotherapy...
December 2017: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
Wendy J Haylett
An antileukemic agent prescribed for pediatric oncology patients during the maintenance phase of therapy for acute lymphoblastic leukemia, 6-mercaptopurine (6-MP), is highly influenced by genetic variations in the thiopurine S-methyltransferase enzyme. As such, 6-MP must be dosed so that patients with 1 or 2 inactive thiopurine S-methyltransferase alleles will not incur an increased risk for myelosuppression or other toxicities. Informatics tools such as clinical decision support systems are useful for the application of this and similar pharmacogenetics information to the realm of nursing and clinical practice for safe and effective patient care...
September 2017: Journal of Pediatric Oncology Nursing: Official Journal of the Association of Pediatric Oncology Nurses
Vagner José Lopes, Marcos Augusto Hochuli Shmeil
Objective: To compare computer-generated guidelines with and without the use of a Clinical Decision Support System - Oncology Care and Healthcare for Chemotherapy Patients, for the caregivers of children undergoing chemotherapy. Methods: This is a descriptive, evaluative, and quantitative study conducted at a paediatrics hospital in Curitiba, Paraná, Brazil, from December 2015 to January 2016. The sample consisted of 58 participants divided into two groups: Group 1, without the aid of software, and Group 2, with the aid of the software...
April 27, 2017: Revista Gaúcha de Enfermagem
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
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