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Journal articles on the topic 'Decision Support Systems, Clinical'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

McCartney, Patricia R. "Clinical Decision Support Systems." MCN, The American Journal of Maternal/Child Nursing 32, no. 1 (2007): 58. http://dx.doi.org/10.1097/00005721-200701000-00014.

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Zusman, Edie E., and Yuriy Vinokur. "Clinical Decision Support Systems." Neurosurgery 71, no. 4 (2012): N12—N14. http://dx.doi.org/10.1227/01.neu.0000419707.79663.e7.

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3

Hornberger, John, and Mary Kane Goldstein. "Clinical Decision-support Systems:." Medical Decision Making 20, no. 1 (2000): 130–31. http://dx.doi.org/10.1177/0272989x0002000116.

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4

Lazaro, Juan. "Clinical Decision Support Systems in Critical Care during Covid-19." Clinical Medical Reviews and Reports 3, no. 2 (2021): 01. http://dx.doi.org/10.31579/2690-8794/064.

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We congratulate Alexander Supady and colleagues for their recent informative work on rationing decisions for COVID-19 patients when resources are scarce. We appreciate the proposal of involving triage committees in the application of rationing. However, we suggest that the clinical needs are somewhat broader than those discussed in this work.

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5

Ramnarayan, P. "Paediatric clinical decision support systems." Archives of Disease in Childhood 87, no. 5 (2002): 361–62. http://dx.doi.org/10.1136/adc.87.5.361.

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6

Schulz, Erich. "Computerised Clinical Decision Support Systems." Health Information Management 27, no. 2 (1997): 95–96. http://dx.doi.org/10.1177/183335839702700214.

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7

O’Sullivan, Dympna, Paolo Fraccaro, Ewart Carson, and Peter Weller. "Decision time for clinical decision support systems." Clinical Medicine 14, no. 4 (2014): 338–41. http://dx.doi.org/10.7861/clinmedicine.14-4-338.

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8

Baalen, Sophie, Mieke Boon, and Petra Verhoef. "From clinical decision support to clinical reasoning support systems." Journal of Evaluation in Clinical Practice 27, no. 3 (2021): 520–28. http://dx.doi.org/10.1111/jep.13541.

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9

Bright, Tiffani J., Anthony Wong, Ravi Dhurjati, et al. "Effect of Clinical Decision-Support Systems." Annals of Internal Medicine 157, no. 1 (2012): 29. http://dx.doi.org/10.7326/0003-4819-157-1-201207030-00450.

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10

Litvin, A. A., and V. A. Litvin. "Clinical decision support systems for surgery." Novosti Khirurgii 22, no. 1 (2014): 96–100. http://dx.doi.org/10.18484/2305-0047.2014.1.96.

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11

Njie, Gibril J., Krista K. Proia, Anilkrishna B. Thota, et al. "Clinical Decision Support Systems and Prevention." American Journal of Preventive Medicine 49, no. 5 (2015): 784–95. http://dx.doi.org/10.1016/j.amepre.2015.04.006.

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ANDERSON, JANE A., and PAMELA WILLSON. "Clinical Decision Support Systems in Nursing." CIN: Computers, Informatics, Nursing 26, no. 3 (2008): 151–58. http://dx.doi.org/10.1097/01.ncn.0000304783.72811.8e.

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13

Harini, Cherukuri. "Clinical Decision Support Systems Using Sequential Pattern Mining Algorithms for Cardio Vascular Diseases." Revista Gestão Inovação e Tecnologias 11, no. 3 (2021): 756–70. http://dx.doi.org/10.47059/revistageintec.v11i3.1973.

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14

Kabachinski, Jeff. "A Look at Clinical Decision Support Systems." Biomedical Instrumentation & Technology 47, no. 5 (2013): 432–34. http://dx.doi.org/10.2345/0899-8205-47.5.432.

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15

Wiharto, Wiharto. "CLINICAL DECISION SUPPORT SYSTEMS THEORY AND PRACTICE." Jurnal Teknosains 7, no. 2 (2018): 148. http://dx.doi.org/10.22146/teknosains.38641.

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Clinical Decision Support Systems Theory and Practice, adalah buku teks dalam seri Health Informatics yang membahas tentang sistem pendukung keputusan klinis. Pada buku ini terbagi menjadi dua kelompok bahasan, Pertama membahas tentang dasar dalam mengembangkan sistem Clinical Decision Supprot System (CDSS) dan evaluasinya. Kedua, Aplikasi CDSS dalam praktik klinis. Bahasan tersebut menjadikan pembaca dapat memperoleh gambaran tentang dasar-dasar yang diperlukan dalam membangun dan mengaplikasikan CDSS dalam praktik klinis. Secara rinci buku tersebut terbagi menjadi 11 Bab, yang dapat dikelomp

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Moore, Mary, and Kimberly A. Loper. "An Introduction to Clinical Decision Support Systems." Journal of Electronic Resources in Medical Libraries 8, no. 4 (2011): 348–66. http://dx.doi.org/10.1080/15424065.2011.626345.

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17

Schulz, Erich. "Computerised Clinical Decision Support Systems — Part II." Health Information Management 27, no. 3 (1997): 143–44. http://dx.doi.org/10.1177/183335839702700316.

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18

CHOI, MONA, RAN CHOI, YOUNG-RAN BAE, and SUN-MI LEE. "Clinical Decision Support Systems for Patient Safety." CIN: Computers, Informatics, Nursing 20, no. 11 (2011): 671–78. http://dx.doi.org/10.1097/ncn.0b013e3182148c18.

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19

Miller, Randolph A., Reed M. Gardner, Kevin B. Johnson, and George Hripcsak. "Clinical Decision Support and Electronic Prescribing Systems." Journal of the American Medical Informatics Association 12, no. 4 (2005): 403–9. http://dx.doi.org/10.1197/jamia.m1830.

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20

van der Wouden, Johannes C., and Marijke M. Kuyvenhoven. "Clinical Decision Support Systems and Antibiotic Prescribing." JAMA 295, no. 15 (2006): 1771. http://dx.doi.org/10.1001/jama.295.15.1771-a.

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21

Mendonça, Eneida A. "Clinical Decision Support Systems: Perspectives in Dentistry." Journal of Dental Education 68, no. 6 (2004): 589–97. http://dx.doi.org/10.1002/j.0022-0337.2004.68.6.tb03777.x.

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22

Buican, Brett, William D. Spaulding, Bruce Gordon, and Toni Hindman. "Clinical decision support systems in state hospitals." New Directions for Mental Health Services 1999, no. 84 (1999): 99–112. http://dx.doi.org/10.1002/yd.23319998410.

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23

Shebl, Nada Atef, Bryony Dean Franklin, and Nick Barber. "Clinical decision support systems and antibiotic use." Pharmacy World & Science 29, no. 4 (2007): 342–49. http://dx.doi.org/10.1007/s11096-007-9113-3.

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24

Eberhardt, John, Anton Bilchik, and Alexander Stojadinovic. "Clinical decision support systems: Potential with pitfalls." Journal of Surgical Oncology 105, no. 5 (2012): 502–10. http://dx.doi.org/10.1002/jso.23053.

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25

Aktürk, Cemal. "Bibliometric Analysis of Clinical Decision Support Systems." Acta Informatica Pragensia 10, no. 1 (2021): 61–74. http://dx.doi.org/10.18267/j.aip.146.

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26

Gad El-Rab, Wessam, Osmar R. Zaïane, and Mohammad El-Hajj. "Formalizing clinical practice guideline for clinical decision support systems." Health Informatics Journal 23, no. 2 (2016): 146–56. http://dx.doi.org/10.1177/1460458216632272.

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Clinical practice guidelines are valuable sources of clinical knowledge for healthcare professionals. However, the passive dissemination of clinical practice guidelines like publishing in medical journals is ineffective in changing clinical practice behaviour. In this work, we proposed a framework to help adopting an active clinical practice guideline dissemination approach by automatically extracting clinical knowledge from clinical practice guidelines into a clinical decision support system–friendly format. The proposed framework is intended to help human modellers by automating some of the

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27

Wright, A., and R. N. Shiffman. "Evidence-Based Clinical Decision Support." Yearbook of Medical Informatics 22, no. 01 (2013): 120–27. http://dx.doi.org/10.1055/s-0038-1638843.

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Summary Background: Clinical decision support (CDS) is a key tool for enabling evidence-based medicine and improving the quality of healthcare. However, effective CDS faces a variety of challenges, including those relating to knowledge synthesis, capture, transformation, localization and maintenance. If not properly addressed, these challenges can limit the effectiveness of CDS, and potentially risk inaccurate or inappropriate interventions to clinicians. Objectives: (1) To describe an approach to CDS development using evidence as a basis for clinical decision support systems that promote effe

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28

Walsh, Seán, Evelyn E. C. de Jong, Janna E. van Timmeren, et al. "Decision Support Systems in Oncology." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–9. http://dx.doi.org/10.1200/cci.18.00001.

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Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708 . As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restrictio

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29

Cho, Insook, and David Bates. "Behavioral Economics Interventions in Clinical Decision Support Systems." Yearbook of Medical Informatics 27, no. 01 (2018): 114–21. http://dx.doi.org/10.1055/s-0038-1641221.

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Background: Clinical decision support (CDS) systems can improve safety and facilitate evidence-based practice. However, clinical decisions are often affected by the cognitive biases and heuristics of clinicians, which is increasing the interest in behavioral and cognitive science approaches in the medical field. Objectives: This review aimed to identify decision biases that lead clinicians to exhibit irrational behaviors or responses, and to show how behavioral economics can be applied to interventions in order to promote and reveal the contributions of CDS to improving health care quality. Me

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30

Barach, P., V. Levashenko, and E. Zaitseva. "Fuzzy Decision Trees in Medical Decision Making Support Systems." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 8, no. 1 (2019): 37–42. http://dx.doi.org/10.1177/2327857919081009.

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Fuzzy decision trees represent classification knowledge more naturally to the way of human thinking and are more robust in tolerating imprecise, conflict, and missing information. Decision Making Support Systems are used widely in clinical medicine because decisions play an important role in diagnostic processes. Decision trees are a very suitable candidate for induction of simple decision-making models with the possibility of automatic learning. The goal of this paper is to demonstrate a new approach for predictive data mining models in clinical medicine. This approach is based on induction o

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31

Mazo, Claudia, Cathriona Kearns, Catherine Mooney, and William M. Gallagher. "Clinical Decision Support Systems in Breast Cancer: A Systematic Review." Cancers 12, no. 2 (2020): 369. http://dx.doi.org/10.3390/cancers12020369.

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Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be diff

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32

Thomas, Karl W., Charles S. Dayton, and Michael W. Peterson. "Evaluation of Internet-Based Clinical Decision Support Systems." Journal of Medical Internet Research 1, no. 2 (1999): e6. http://dx.doi.org/10.2196/jmir.1.2.e6.

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33

Raghupathi, Wullianallur. "Designing Clinical Decision Support Systems in Health Care." International Journal of Healthcare Information Systems and Informatics 2, no. 1 (2007): 44–53. http://dx.doi.org/10.4018/jhisi.2007010104.

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34

Balashova, E. A., and L. I. Mazur. "Clinical Decision Support Systems in outpatient pediatric practice." Practical medicine 17, no. 5 (2019): 185–90. http://dx.doi.org/10.32000/2072-1757-2019-5-185-190.

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35

MD, Lorenzo Quinzio. "CLINICAL DECISION SUPPORT IN MONITORING AND INFORMATION SYSTEMS." IFAC Proceedings Volumes 39, no. 18 (2006): 7–10. http://dx.doi.org/10.3182/20060920-3-fr-2912.00006.

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36

Samore, Matthew H., Gregory J. Stoddard, Stephen C. Alder, Bassam Haddadin, Michael A. Rubin, and Kim Bateman. "Clinical Decision Support Systems and Antibiotic Prescribing—Reply." JAMA 295, no. 15 (2006): 1771. http://dx.doi.org/10.1001/jama.295.15.1771-b.

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37

Cook, Timothy Wayne, and Luciana Tricai Cavalini. "Clinical Decision Support Systems and Advanced Diagnostic Imaging." JAMA 314, no. 14 (2015): 1518. http://dx.doi.org/10.1001/jama.2015.10919.

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38

Levy, Sharon, and Barbara Heyes. "Information systems that support effective clinical decision making." Nursing Management 19, no. 7 (2012): 20–22. http://dx.doi.org/10.7748/nm2012.11.19.7.20.c9371.

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39

Wanderer, Jonathan P., and Jesse M. Ehrenfeld. "Clinical Decision Support for Perioperative Information Management Systems." Seminars in Cardiothoracic and Vascular Anesthesia 17, no. 4 (2013): 288–93. http://dx.doi.org/10.1177/1089253213490078.

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40

Williams, M., F. Wu, P. Kazanzides, K. Brady, and J. Fackler. "A modular framework for clinical decision support systems." ACM SIGBED Review 6, no. 2 (2009): 1–11. http://dx.doi.org/10.1145/1859823.1859831.

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41

Garcia, Ernest V., J. Larry Klein, and Andrew T. Taylor. "Clinical decision support systems in myocardial perfusion imaging." Journal of Nuclear Cardiology 21, no. 3 (2014): 427–39. http://dx.doi.org/10.1007/s12350-014-9857-9.

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42

Panigutti, Cecilia, Alan Perotti, André Panisson, Paolo Bajardi, and Dino Pedreschi. "FairLens: Auditing black-box clinical decision support systems." Information Processing & Management 58, no. 5 (2021): 102657. http://dx.doi.org/10.1016/j.ipm.2021.102657.

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43

Abu-Hanna, A., and B. Nannings. "Characterizing Decision Support Telemedicine Systems." Methods of Information in Medicine 45, no. 05 (2006): 523–27. http://dx.doi.org/10.1055/s-0038-1634113.

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Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consi

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44

Kiyani, Shamim, Sanaz Abasi, Zahra Koohjani, and Azam Aslani. "Technical Requirement of Clinical Decision Support system for Diabetic Patients." Frontiers in Health Informatics 9, no. 1 (2020): 31. http://dx.doi.org/10.30699/fhi.v9i1.217.

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Introduction: Diabetes is a public health problem which is originating an increment in the demand for health services. There is an obvious gap exists between actual clinical practice and optimal patient care, Clinical decision support systems (CDSSs) have been promoted as a promising approach that targets safe and effective diabetes management. The purpose of this article is reviewing diabetes decision support systems based on system design metrics, type and purpose of decision support systems. Materials and Methods: The literature search was performed in peer reviewed journals indexed in PubM

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45

Wright, A., D. F. Sittig, J. S. Ash, S. Sharma, J. E. Pang, and B. Middleton. "Clinical Decision Support Capabilities of Commercially-available Clinical Information Systems." Journal of the American Medical Informatics Association 16, no. 5 (2009): 637–44. http://dx.doi.org/10.1197/jamia.m3111.

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46

Bouaud, J., and V. Koutkias. "Computerized Clinical Decision Support: Contributions from 2015." Yearbook of Medical Informatics 25, no. 01 (2016): 170–77. http://dx.doi.org/10.15265/iy-2016-055.

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Summary Objective: To summarize recent research and select the best papers published in 2015 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook. Method: A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry (CPOE) systems. The aim was to identify a list of candidate best papers from the retrieved papers that were then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the IM

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47

Kawamoto, Kensaku, Guilherme Del Fiol, Charles Orton, and David F. Lobach. "System-Agnostic Clinical Decision Support Services: Benefits and Challenges for Scalable Decision Support." Open Medical Informatics Journal 4, no. 1 (2010): 245–54. http://dx.doi.org/10.2174/1874431101004010245.

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System-agnostic clinical decision support (CDS) services provide patient evaluation capabilities that are independent of specific CDS systems and system implementation contexts. While such system-agnostic CDS services hold great potential for facilitating the widespread implementation of CDS systems, little has been described regarding the benefits and challenges of their use. In this manuscript, the authors address this need by describing potential benefits and challenges of using a system-agnostic CDS service. This analysis is based on the authors’ formal assessments of, and practical experi

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48

Sharmi, Meenakshi, and Himanshu Aggarwal. "Methodologies of Legacy Clinical Decision Support System." International Journal of Computers in Clinical Practice 2, no. 2 (2017): 20–37. http://dx.doi.org/10.4018/ijccp.2017070102.

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Information technology playing a prominent role in the field of medical by incorporating the clinical decision support system (CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at right time. Nowadays, clinical decision support systems are a dynamic research area in the field of computers, but the lack of understanding, as well as functions of the system, make adoption slow by physicians and patients. The literature review of this article focuses on the overview of legacy CDSS, the kind of methodologies and classif

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49

Zhang, Danchen, and Daqing He. "Enhancing Clinical Decision Support Systems with Public Knowledge Bases." Data and Information Management 1, no. 1 (2017): 49–60. http://dx.doi.org/10.1515/dim-2017-0005.

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Abstract With vast amount of biomedical literature available online, doctors have the benefits of consulting the literature before making clinical decisions, but they are facing the daunting task of finding needles in haystacks. In this situation, it would be of great use to the doctors if an effective clinical decision support system is available to generate accurate queries and return a manageable size of highly useful articles. Existing studies showed the usefulness of patients’ diagnosis information in supporting effective retrieval of relevant literature, but such diagnosis information is

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50

Maloney, F. L., J. Feblowitz, L. Samal, L. Sato, A. Wright, and G. Zuccotti. "Reducing Risk with Clinical Decision Support." Applied Clinical Informatics 05, no. 03 (2014): 746–56. http://dx.doi.org/10.4338/aci-2014-02-ra-0018.

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SummaryObjective: Identify clinical opportunities to intervene to prevent a malpractice event and determine the proportion of malpractice claims potentially preventable by clinical decision support (CDS).Materials and Methods: Cross-sectional review of closed malpractice claims over seven years from one malpractice insurance company and seven hospitals in the Boston area. For each event, clinical opportunities to intervene to avert the malpractice event and the presence or absence of CDS that might have a role in preventing the event, were assigned by a panel of expert raters. Compensation pai

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