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Relevant bibliographies by topics / Clinical support

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Academic literature on the topic 'Clinical support'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Clinical support"

1

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 (February 7, 2021): 520–28. http://dx.doi.org/10.1111/jep.13541.

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NOTTE, CHRISTOPHER, and NEIL SKOLNIK. "Clinical decision support." Family Practice News 43, no. 2 (February 2013): 51. http://dx.doi.org/10.1016/s0300-7073(13)70075-6.

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NOTTE, CHRISTOPHER, and NEIL SKOLNIK. "Clinical decision support." Internal Medicine News 46, no. 2 (February 2013): 52. http://dx.doi.org/10.1016/s1097-8690(13)70081-7.

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Payne-James, J., and D. Silk. "Clinical nutrition support." BMJ 301, no. 6742 (July 7, 1990): 1–2. http://dx.doi.org/10.1136/bmj.301.6742.1.

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Sensmeier, Joyce. "Clinical decision support." Nursing Management (Springhouse) 49, no. 11 (November 2018): 8–11. http://dx.doi.org/10.1097/01.numa.0000547253.84591.e2.

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Brailer, David J. "Clinical Decision Support." Quality Management in Health Care 4, no. 2 (1996): 24–33. http://dx.doi.org/10.1097/00019514-199600420-00004.

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Lazaro, Juan. "Clinical Decision Support Systems in Critical Care during Covid-19." Clinical Medical Reviews and Reports 3, no. 2 (March 17, 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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Oelschlegel, Sandy. "5MinuteConsult: Clinical Decision Support." Journal of Electronic Resources in Medical Libraries 8, no. 3 (July 2011): 272–79. http://dx.doi.org/10.1080/15424065.2011.602310.

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Lykins, Terri Clark. "Nutrition Support Clinical Pathways." Nutrition in Clinical Practice 11, no. 1 (February 1996): 16–20. http://dx.doi.org/10.1177/011542659601100116.

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Crouch, Robert. "Clinical interactive support system." Emergency Nurse 5, no. 6 (October 1, 1997): 7. http://dx.doi.org/10.7748/en.5.6.7.s13.

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Dissertations / Theses on the topic "Clinical support"

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Cumming, Jonathan. "Clinical decision support." Thesis, Durham University, 2006. http://etheses.dur.ac.uk/1814/.

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Muller, Johann Heinrich. "A clinical engineering decision support system." Master's thesis, University of Cape Town, 1988. http://hdl.handle.net/11427/26533.

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The use of technology in health-care today is increasing dramatically with a corresponding increase in cost and complexity to provide and support it. The degree to which a hospital manages this technology affects its ability to treat patients, to perform research, to teach and to attract competent staff. This thesis project has identified the role that clinical engineering could play in health-care technology provision and support in South Africa. A system synthesis technique was employed to develop an idealized clinical engineering model (ICE) that would satisfy South African technological requirements. An extensive literature survey of the current status of clinical engineering in both developed and developing countries was undertaken to provide input to the synthesis process. Surveys were then conducted to determine the actual current status of clinical engineering and its environment in the RSA. To enable such an idealised department to function as defined, it must be supported by appropriate and timeous information. The information needs of the idealised clinical engineering model were analysed and a corresponding decision support system (DSS) defined. Further surveys were conducted to test the applicability and acceptability of the idealised clinical engineering model. The feasibility of implementing the idealised clinical engineering model in South Africa was investigated and recommendations were made based on the research results of this thesis to bring the actual status of clinical engineering closer to the idealised model. ii

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Patrick, Louise. "Expressed support, perceived support and physical ability in chronic pain patients." Thesis, University of Ottawa (Canada), 1992. http://hdl.handle.net/10393/7783.

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This study investigated the relationship between social support within the marital context, and physical ability in chronic pain patients. Fifty patients diagnosed with chronic pain and their spouses participated in the study. Each patient was asked to exercise on a stationary bicycle, with his/her spouse present. The interactions between patient and spouse were videorecorded and the spouse's behaviour was rated for the amount of task-related and emotional support expressed. The relationships among the spouse's behaviour, the patient's perception of that behaviour and the patient's physical ability were examined. Marital adjustment, depressive symptomatology and the spouse's perception of the patient's physical limitations were investigated as predictors of expressed and perceived support. Zero-order correlations replicated the previously demonstrated positive relationships among the patient's report of spouse support, pain intensity and marital adjustment. Using hierarchical regression to control for the patient's depressive symptomatology and marital adjustment, it was found that observed spouse support was positively related to the patient's physical ability, accounting for 13% of the variance. When pain severity was also entered into the equation, results indicated that pain intensity was the only significant predictor and was negatively related to the patient's physical ability, accounting for 43% of the variance. No significant predictor of the spouse's expressed support was identified, while the patient's marital adjustment was positively related to his/her perception of support. Descriptive reports by patients of their perception of support during the physical ability task indicated that patients experienced task and emotional support differently. The majority of patients reported that emotional support was experienced as supportive and helpful, but task-related support was not.

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Denney, Kimberly B. "Assessing Clinical Software User Needs for Improved Clinical Decision Support Tools." ScholarWorks, 2015. https://scholarworks.waldenu.edu/dissertations/1563.

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Consolidating patient and clinical data to support better-informed clinical decisions remains a primary function of electronic health records (EHRs). In the United States, nearly 6 million patients receive care from an accountable care organization (ACO). Knowledge of clinical decision support (CDS) tool design for use by physicians participating in ACOs remains limited. The purpose of this quantitative study was to examine whether a significant correlation exists between characteristics of alert content and alert timing (the independent variables) and physician perceptions of improved ACO quality measure adherence during electronic ordering (the dependent variable). Sociotechnical theory supported the theoretical framework for this research. Sixty-nine physician executives using either a Cerner Incorporated or Epic Systems EHR in a hospital or health system affiliated ACO participated in the online survey. The results of the regression analysis were statistically significant, R2 = .108, F(2,66) = 3.99, p = .023, indicating that characteristics of alert content and timing affect physician perceptions for improving their adherence to ACO quality measures. However, analysis of each independent variable showed alert content highly correlated with the dependent variable (p = .007) with no significant correlation found between workflow timing and the dependent variable (p = .724). Understanding the factors that support physician acceptance of alerts is essential to third-party software developers and health care organizations designing CDS tools. Providing physicians with improved EHR-integrated CDS tools supports the population health goal of ACOs in delivering better patient care.

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Sánchez, Pinsach David. "Handling Missing Data in Clinical Decision Support." Doctoral thesis, Universitat Autònoma de Barcelona, 2020. http://hdl.handle.net/10803/671318.

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Decidir quins són els millors tractaments és una tasca complexa quan els pacients pateixen múltiples problemes i quan un equip multidisciplinari està involucrat en la intervenció. Sempre hi ha més d’una opció de tractament i els resultats a vegades es poden veure en un període curt o un cop finalitzat el tractament. En aquest context, el disseny de sistemes eficaços de suport a la decisió clínica (CDSS) per ajudar als metges a seleccionar les intervencions més apropiades segueix sent avui dia un desafiament. La quantitat de dades disponibles no sempre és la mateixa per a tots els pacients, especialment en les fases primerenques del tractament, dificultant la inferència en els CDSS. Per millorar les capacitats dels CDSS, es proposen diferents components per als tractaments de llarga durada. Un primer component se centra a millorar la qualitat de les inferències en les dades desconegudes. L’algoritme d’imputació múltiple dinàmica (DMI) es presenta com una metodologia eficaç per a la millora de les dades. DMI és capaç d’adaptar-se a diferents escenaris amb un percentatge alt o baix de dades desconegudes. Els experiments realitzats revelen que DMI és especialment competitiu en problemes de regressió. Un segon component està dedicat a compensar les mesures de confiança, donada la incertesa associada a la informació desconeguda, incorporant mesures d’Informació Mútua en les confiances existents. El tercer component, basat en un algorisme de detecció de comunitats està orientat a trobar relacions entre decisions clíniques que no són explícites. Finalment, per il·lustrar l’aplicabilitat dels diferents components proposats, es presenten dos casos d’ús clínics reals. Un en el context hospitalari i un altre en el context del domicili.
Decidir cuáles son los mejores tratamientos es una tarea compleja cuando los pacientes sufren múltiples problemas y cuando un equipo multidisciplinario está involucrado en la intervención. Siempre hay más de una opción de tratamiento y los resultados a veces se pueden ver en un período corto o al final, una vez finalizado el tratamiento.En este contexto, el diseño de sistemas eficaces de soporte a la decisión clínica (CDSS) para ayudar a los médicos a seleccionar las intervenciones más apropiadas sigue siendo hoy en día un desafío. La cantidad de datos disponibles no siempre es la misma para todos los pacientes, especialmente en las fases tempranas del tratamiento, lo que dificulta la inferencia en los CDSS. Para mejorar las capacidades de los CDSS, se proponen diferentes componentes para tratamientos a largo plazo. Un primer componente se centra en mejorar la calidad de las inferencias en los datos desconocidos. El algoritmo de imputación múltiple dinámica (DMI) se presenta como un metodología eficaz para la mejora de los datos. DMI es capaz de adaptarse a diferentes escenarios con un porcentaje alto o bajo de datos desconocidos. Los experimentos realizados revelan que DMI es especialmente competitivo en problemas de regresión. Un segundo componente está dedicado a compensar las medidas de confianza, dada la incertidumbre asociada a la información desconocida, incorporando medidas de Información Mutua en las confianzas existentes. El tercer componente basado en un algoritmo de detección de comunidades esta orientado a encontrar relaciones entre decisiones clínicas que no son explícitas. Finalmente, para ilustrar la aplicabilidad de los diferentes componentes propuestos, se presentan dos casos de uso clínico reales. Uno en el contexto hospitalario y otro en el contexto del domicilio.
Deciding which are the best treatments is a complex task when patients suffer multiple impairments and when a multidisciplinary team is involved in the intervention. There is always more than a unique treatment option and the results sometimes can be viewed in a short period or only be capable to be measured when the treatment is finished. In this context, the design of effective Clinical Decision Support Systems (CDSS) to help clinicians to select most appropriate interventions is still a challenge. The amount of available data is not always the same for all patients, especially in early treatment stages, hindering the inference in CDSS. To improve the capabilities of CDSS, different components are proposed within a CDSS framework for long-term treatments. A first component is focused on improving the quality of the inferences in missing data scenarios. The Dynamic Multiple Imputation (DMI) algorithm is presented as an effective methodology for data enhancement in CDSS. DMI is capable to adapt to different scenarios with a low or high percentage of missing data. Several experiments conducted reveal that DMI is competitive with regression problems. A second component is devoted to weigh confidence measures, given the uncertainty associated to missing information, by incorporating Mutual Information measures in confidence existing estimators. A third component, based on a community detection algorithm, is proposed to find relationships between clinical decisions that are not explicit. Finally, to illustrate the applicability of different proposed components, two real clinical use cases with chronic patients are presented. The first in the hospital context and the other in the home context.

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Merode, Godefridus Gerardus van. "Decision support for clinical laboratory capacity planning." [Maastricht : Maastricht : Rijksuniversiteit Limburg] ; University Library, Maastricht University [Host], 1994. http://arno.unimaas.nl/show.cgi?fid=6591.

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Zhuang, Wenjie. "Query Expansion Study for Clinical Decision Support." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/82068.

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Information retrieval is widely used for retrieving relevant information among a variety of data, such as text documents, images, audio and videos. Since the first medical batch retrieval system was developed in mid 1960s, significant research efforts have focused on applying information retrieval to medical data. However, despite the vast developments in medical information retrieval and accompanying technologies, the actual promise of this area remains unfulfilled due to properties of medical data and the huge volume of medical literature. Specifically, the recall and precision of the selected dataset from the TREC clinical decision support track are low. The overriding objective of this thesis is to improve the performance of information retrieval techniques applied to biomedical text documents. We have focused on improving recall and precision among the top retrieved results. To that end, we have removed redundant words, and then expanded queries by adding MeSH terms in TREC CDS topics. We have also used other external data sources and domain knowledge to implement the expansion. In addition, we have also considered using the doc2vec model to optimize retrieval. Finally, we have applied learning to rank which sorts documents based on relevance and put relevant documents in front of irrelevant documents, so as to return the relevant retrieved data on the top. We have discovered that queries, expanded with external data sources and domain knowledge, perform better than applying the TREC topic information directly.
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Brandt, Joachim. "Clinical decision support in breast cancer using neurocomputing modelling of clinical trial data." Thesis, Coventry University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251860.

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Asare, Adam L. "Improving clinical hematopathology quality using decision support methods." Free to MU Campus, others may purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052142.

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Yet, Barbaros. "Bayesian networks for evidence based clinical decision support." Thesis, Queen Mary, University of London, 2013. http://qmro.qmul.ac.uk/xmlui/handle/123456789/9096.

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Evidence based medicine (EBM) is defined as the use of best available evidence for decision making, and it has been the predominant paradigm in clinical decision making for the last 20 years. EBM requires evidence from multiple sources to be combined, as published results may not be directly applicable to individual patients. For example, randomised controlled trials (RCT) often exclude patients with comorbidities, so a clinician has to combine the results of the RCT with evidence about comorbidities using his clinical knowledge of how disease, treatment and comorbidities interact with each other. Bayesian networks (BN) are well suited for assisting clinicians making evidence-based decisions as they can combine knowledge, data and other sources of evidence. The graphical structure of BN is suitable for representing knowledge about the mechanisms linking diseases, treatments and comorbidities and the strength of relations in this structure can be learned from data and published results. However, there is still a lack of techniques that systematically use knowledge, data and published results together to build BNs. This thesis advances techniques for using knowledge, data and published results to develop and refine BNs for assisting clinical decision-making. In particular, the thesis presents four novel contributions. First, it proposes a method of combining knowledge and data to build BNs that reason in a way that is consistent with knowledge and data by allowing the BN model to include variables that cannot be measured directly. Second, it proposes techniques to build BNs that provide decision support by combining the evidence from meta-analysis of published studies with clinical knowledge and data. Third, it presents an evidence framework that supplements clinical BNs by representing the description and source of medical evidence supporting each element of a BN. Fourth, it proposes a knowledge engineering method for abstracting a BN structure by showing how each abstraction operation changes knowledge encoded in the structure. These novel techniques are illustrated by a clinical case-study in trauma-care. The aim of the case-study is to provide decision support in treatment of mangled extremities by using clinical expertise, data and published evidence about the subject. The case study is done in collaboration with the trauma unit of the Royal London Hospital.

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Books on the topic "Clinical support"

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Berner, Eta S., ed. Clinical Decision Support Systems. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-38319-4.

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Berner, Eta S., ed. Clinical Decision Support Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31913-1.

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Berner, Eta S., ed. Clinical Decision Support Systems. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4757-3903-9.

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Life support: A family clinical guide. Jefferson, N.C: McFarland & Co., 1996.

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Mechanical circulatory support: Principles and clinical applications. New York: McGraw-Hill Professional, 2012.

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Syeda-Mahmood, Tanveer, Klaus Drechsler, Hayit Greenspan, Anant Madabhushi, Alexandros Karargyris, Marius George Linguraru, Cristina Oyarzun Laura, et al., eds. Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60946-7.

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Novalis, Peter N. Clinical manual of supportive psychotherapy. Washington, DC: American Psychiatric Press, 1993.

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Caputo, Barbara, Henning Müller, Tanveer Syeda-Mahmood, James S. Duncan, Fei Wang, and Jayashree Kalpathy-Cramer, eds. Medical Content-Based Retrieval for Clinical Decision Support. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11769-5.

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Greenspan, Hayit, Henning Müller, and Tanveer Syeda-Mahmood, eds. Medical Content-Based Retrieval for Clinical Decision Support. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36678-9.

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Müller, Henning, Hayit Greenspan, and Tanveer Syeda-Mahmood, eds. Medical Content-Based Retrieval for Clinical Decision Support. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28460-1.

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Book chapters on the topic "Clinical support"

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van den Hogen, Esther, Marian AE van Bokhorst-de van der Schueren, and Cora F. Jonkers-Schuitema. "Nutritional Support." In Clinical Nutrition, 140–60. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781119211945.ch9.

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Wright, Adam. "Clinical Decision Support." In Encyclopedia of Database Systems, 449–52. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_382.

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Wright, Adam. "Clinical Decision Support." In Encyclopedia of Database Systems, 350–53. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_382.

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Herasevich, Vitaly, Mikhail Dziadzko, and Brian W. Pickering. "Clinical Decision Support." In Neurocritical Care Informatics, 149–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-59307-3_8.

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Wright, Adam. "Clinical Decision Support." In Encyclopedia of Database Systems, 1–4. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_382-2.

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Tarczy-Hornoch, Peter, and Thomas H. Payne. "Clinical Decision Support." In Informatics in Primary Care, 89–102. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0069-4_7.

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Singh, Karandeep, and Adam Wright. "Clinical Decision Support." In Clinical Informatics Study Guide, 111–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-22753-5_6.

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Molewijk, Bert, Anne Slowther, and Mark Aulisio. "Clinical Ethics: Support." In Encyclopedia of Global Bioethics, 1–8. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-05544-2_87-1.

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Molewijk, Bert, Anne Slowther, and Mark Aulisio. "Clinical Ethics: Support." In Encyclopedia of Global Bioethics, 562–70. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-09483-0_87.

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ten Have, Henk, and Maria do Céu Patrão Neves. "Clinical Ethics, Support." In Dictionary of Global Bioethics, 275. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54161-3_133.

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Conference papers on the topic "Clinical support"

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Cohan, Arman, Luca Soldaini, Andrew Yates, Nazli Goharian, and Ophir Frieder. "On clinical decision support." In BCB '14: ACM-BCB '14. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2649387.2660820.

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Oleary, Padraig, Lucia Brunetti, John Noll, and Ita Richardson. "Clinical Pathway Support System." In 2014 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2014. http://dx.doi.org/10.1109/ichi.2014.34.

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Schwartz, Jessica, and Kenrick Cato. "Machine Learning Based Clinical Decision Support and Clinician Trust." In 2020 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2020. http://dx.doi.org/10.1109/ichi48887.2020.9374365.

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Katzenberg, Barbara, Fred Pickard, and John McDermott. "Computer support for clinical practice." In the 1996 ACM conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/240080.240346.

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Motorin, Sergey S., Nikolay V. Golishev, and Anna A. Afanasyeva. "Clinical decisions support information system." In 2008 9th International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE). IEEE, 2008. http://dx.doi.org/10.1109/apeie.2008.4897066.

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"INTELLIGENT CLINICAL DECISION SUPPORT SYSTEMS." In International Conference on Health Informatics. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002740802820287.

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Calamanti, Chiara, Annalisa Cenci, Michele Bernardini, Emanuele Frontoni, and Primo Zingaretti. "A Clinical Decision Support System for Chronic Venous Insufficiency." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68016.

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Earlier diagnosis plays a pivotal role in clinical applications, since it can strongly reduce the incidence and impact of many diseases and, consequently, the reduction of health care costs. This last aspect depends strongly from right therapy prescriptions, especially when there are various opportunities. Within this context, Clinical Decision Support Systems (CDSS) could bring several benefits. In this paper, we propose a CDSS with the aim of improving the clinician practice based on recommendations, assessment of the patient and screening of patients with risk factors to prevent chronic venous insufficiency (CVI) complications. The proposed CDSS is implemented in the Nu.Sa. cloud system, which involves thousands of italian General Practitioners (GPs) collecting data (EHR data, personal data, patient’s medical history) from millions of patients. The proposed architecture is designed to collect data from a distributed scenario where GPs are collecting clinical history and pharmacy or second level hospitals gather data from medical devices connected to the cloud over a standard data architecture. We show that exploiting the integration of the medical device VenoScreen Plus with the patient EHR, this CDSS is capable to improve preventive care, to enhance clinical performance, to influence clinical decision making and to significantly improve the decision quality levering on data driven approach.

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Wu, Fran, Mitch Williams, Peter Kazanzides, Ken Brady, and Jim Fackler. "A modular Clinical Decision Support System Clinical prototype extensible into multiple clinical settings." In 3d International ICST Conference on Pervasive Computing Technologies for Healthcare. ICST, 2009. http://dx.doi.org/10.4108/icst.pervasivehealth2009.6078.

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Nazarenko, G. I., D. A. Sychev, E. B. Kleymenova, S. A. Payushik, A. I. Akhmetova, and L. P. Yashina. "Multifunctional clinical decision support system based on clinical practice guidelines." In 2015 International Conference on Biomedical Engineering and Computational Technologies (SIBIRCON). IEEE, 2015. http://dx.doi.org/10.1109/sibircon.2015.7361854.

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Jegelevicius, D., A. Krisciukaitis, A. Lukosevicius, V. Marozas, A. Paunksnis, V. Barzdziukas, M. Patasius, D. Buteikiene, A. Vainoras, and L. Gargasas. "Network based clinical decision support system." In 2009 9th International Conference on Information Technology and Applications in Biomedicine (ITAB 2009). IEEE, 2009. http://dx.doi.org/10.1109/itab.2009.5394348.

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Reports on the topic "Clinical support"

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McGovern, Greg, A. Infrastructure support for Clinical Information Systems. Office of Scientific and Technical Information (OSTI), June 2007. http://dx.doi.org/10.2172/908749.

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Flori, Heidi. Advanced Clinical Decision Support for Transport of the Critically Ill Patient. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada621284.

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Douglas, Bettina, and Ann Bonner. Nephrology-specific Clinical Performance Indicators for Nurse Practitioner Education in Australia: A Resource for Students and Clinical Support Team Members. Queensland, Australia: Queensland University of Technology, June 2017. http://dx.doi.org/10.5204/rep.eprints.106890.

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Michel, Jeremy, Emilia Flores, Nikhil Mull, and Amy Y. Tsou. Translation of a C. difficile Treatment Clinical Pathway Into Machine-Readable Clinical Decision Support Artifacts Prototyped for Electronic Health Record Integration. Agency for Healthcare Research and Quality (AHRQ), November 2091. http://dx.doi.org/10.23970/ahrqepcmethqualimprcdiff.

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Green, Eric, Caricia Catalani, Aggrey Keny, Lameck Diero, Charity Ndwiga, Dennis Israelski, and Paul Biondich. Do clinical decision-support reminders for medical providers improve isoniazid prescription rates among HIV-positive adults? Population Council, 2015. http://dx.doi.org/10.31899/hiv8.1001.

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Ohno-Machado, Lucila. FACTS (Find Appropriate Clinical Trials) for You: A Computer-Based Decision Support System for Breast Cancer Patients. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada408466.

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Ohno-Machado, Lucila. FACTS (Find the Appropriate Clinical Trials) for You: A Computer-Based Decision Support System for Breast Cancer Patients. Fort Belvoir, VA: Defense Technical Information Center, May 1999. http://dx.doi.org/10.21236/ada381179.

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Datena, Stephen J., and Christopher M. Strear. Prototype Application of Mobile, Cloud-based, Watson-Like Technologies for TBI/PTSD Clinical Decision Support and Predictive Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada601921.

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Ohno-Machado, Lucila. FACTS (Find the Appropriate Clinical Trials) for You: A Computer-Based Decision Support System for Breast Cancer Patients. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada391925.

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Evuarherhe, Obaro, William Gattrell, Richard White, and Christopher Winchester. Association between professional medical writing support and the quality, ethics and timeliness of clinical trials reporting: a systematic review. Oxford PharmaGenesis, January 2018. http://dx.doi.org/10.21305/ismppeu2018.004.

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