Relevant bibliographies by topics / Clinical decision making

Academic literature on the topic 'Clinical decision making'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 August 2024

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Journal articles on the topic "Clinical decision making"

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Kostbade Hughes, Katherine, and Wendy B. Young. "Decision Making Stability of Clinical Decisions." Nurse Educator 17, no. 3 (May 1992): 12–16. http://dx.doi.org/10.1097/00006223-199205000-00010.

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Richards, Kathy, Katherine Carroll Britt, Norma Cuellar, Yanyan Wang, and Janet Morrison. "Clinical Decision-Making." Nursing Clinics of North America 56, no. 2 (June 2021): 265–74. http://dx.doi.org/10.1016/j.cnur.2021.02.005.

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Goodwin, C. Rory, Nancy Abu-Bonsrah, Mark H. Bilsky, Jeremy J. Reynolds, Laurence D. Rhines, Ilya Laufer, Alexander C. Disch, et al. "Clinical Decision Making." SPINE 41 (October 2016): S171—S177. http://dx.doi.org/10.1097/brs.0000000000001836.

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Records, Nancy L., and J. Bruce Tomblin. "Clinical Decision Making." Journal of Speech, Language, and Hearing Research 37, no. 1 (February 1994): 144–56. http://dx.doi.org/10.1044/jshr.3701.144.

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Ludwick, Ruth. "Clinical Decision Making." Orthopaedic Nursing 18, no. 1 (January 1999): 65???72. http://dx.doi.org/10.1097/00006416-199901000-00013.

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McCullough, Gary H., and Balaji Rangarathnam. "Clinical Decision Making." Seminars in Speech and Language 40, no. 03 (June 2019): 149–50. http://dx.doi.org/10.1055/s-0039-1688996.

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Croskerry, Pat, and Gordon Tait. "Clinical Decision Making." Academic Medicine 88, no. 2 (February 2013): 149–50. http://dx.doi.org/10.1097/acm.0b013e31827b258d.

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Sherbino, Jonathan, Geoffrey R. Norman, and Wolfgang Gaissmaier. "Clinical Decision Making." Academic Medicine 88, no. 2 (February 2013): 150–51. http://dx.doi.org/10.1097/acm.0b013e31827b2941.

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Restrepo, Daniel, Katrina A. Armstrong, and Joshua P. Metlay. "Annals Clinical Decision Making: Avoiding Cognitive Errors in Clinical Decision Making." Annals of Internal Medicine 172, no. 11 (June 2, 2020): 747–51. http://dx.doi.org/10.7326/m19-3692.

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Metlay, Joshua P., and Katrina A. Armstrong. "Annals Clinical Decision Making: Incorporating Perspective Into Clinical Decisions." Annals of Internal Medicine 172, no. 11 (June 2, 2020): 743–46. http://dx.doi.org/10.7326/m19-3469.

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Dissertations / Theses on the topic "Clinical decision making"

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Jensen, Jan L. "Paramedic Clinical Decision Making." BMC Emergency Medicine, 2009. http://hdl.handle.net/10222/12738.

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Paramedics are responsible for the care of patients requiring emergency assistance in the out of hospital setting. These health care providers need to make many decisions during the course of an emergency call. This thesis on paramedic clinical decision-making includes two studies, intended to determine which decisions paramedics make that are most important for patient safety and clinical outcome, and what thinking strategies paramedics rely on to make decisions. Forty-two decisions were found to be most important for outcome and safety. The highest decision density of an emergency call is during the on-scene treatment phase. Paramedics use a mix of thinking strategies, including rule out worst scenario, algorithmic, and exhaustive thinking. The results of these studies have implications for future research, paramedic practice and training.
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Wong, Thomas Kwok Shing. "Clinical decision making in nursing." Thesis, Glasgow Caledonian University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283692.

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Gurbutt, Russell. "Demonstrating nurses' clinical decision-making." Thesis, University of Central Lancashire, 2005. http://clok.uclan.ac.uk/21842/.

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The study answers the question: 'How can nurses' properly considered decisions relating to patient care be demonstrated?' Nurses in the United Kingdom have a professional requirement to demonstrate': the properly considered clinical decisions relating to patient care' (UKCC, 1994; NMC, 2002). However, their decisionmaking has been reported as complex and poorly understood, and apart from nursing records, little evidence exists to demonstrate their decisions. The development of the nurses' role as a decision-maker is traced from an origin in Nightingale's text (1860) through to the present day. This role is shaped by organisational, nursing and medical profession influences. Having established that nurses have a role as decision-makers, a conceptual framework is used to examine different explanations about the decision process, outcome, context and how decisions are made. Before undertaking fieldwork, a survey of nurses' decision-making in general medical and surgical wards was conducted. The findings were compared with the conceptual framework to generate questions and avenues for enquiry. An ethnographic study was undertaken in 1999 - 2000 in four general medical wards in two English provincial NHS Trusts with registered nurses (general). A model of decision-making was developed as a mid range theoretical explanation of how they made decisions. This involved a narrative based approach in which nurses generated an account (narrative) of knowing a patient and used this to identify needs. The patient was known in a narrative through three categories of information: nursing, management and medical. These categories were constructed through nurses' information seeking and processing using a tripartite conceptual lens. These facets correspond to different aspects of the nurse's role as a carer, care manager and medical assistant. The patient is known in three ways in a narrative, as a person to care for, an object to be managed, and as a medical case. An oral tradition surrounded its use, and nursing records were not central to decision-making. The narrative was used to make decisions and influence medical decisions. Once it was established how nurses made decisions, a method was developed to show how they could demonstrate their properly considered clinical decisions relating to patient care. This involved using the narrative based decision-making model as an analytical framework applied to nurse decision narratives. Narrative based decisionmaking offers a development of existing descriptive theoretical accounts and new explanations of some features of the decision process. This particularly includes the use of personal note sheets, the role of judgements and the cycle of communicating the narrative to nurses and its subsequent development as a process of developing an explanation of how the patient is known. Having addressed how nurses can demonstrate their properly considered clinical decisions relating to patient care, conclusions are drawn and implications explored in relation to practice, professional regulation, education and method. Recommendations include a challenge to the assumption about decision-making underpinning existing NMC guidance on recordkeeping, and the need to recognise diversity of decision-making practice across different nursing sub-groups. The narrative revealed nurses' ways of constructing knowing patients and rendering this visible. Nurses' not only have a duty, but also a need, to demonstrate decisions so that they can render visible what it is they are and do.
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Everitt, Sally. "Clinical decision making in veterinary practice." Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/12051/.

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Aim The aim of this study is to develop an understanding of the factors which influence veterinary surgeons’ clinical decision making during routine consultations. Methods The research takes a qualitative approach using video-cued interviews, in which one of the veterinary surgeon’s own consultations is used as the basis of a semi-structured interview exploring decision making in real cases. The research focuses primarily on small animal consultations in first opinion practice, however small numbers of consultations from different types of practice are included to highlight contextual influences on decision making. Findings The study reveals differences between the way clinical decision making is taught and the way that it is carried out in practice. In comparison to human medicine, decision making in veterinary practice appears to be more a negotiated activity, relying on social context, which takes account of the animals’ and owners’ circumstances, as well as biomedical information. Conclusions Veterinary practice especially that provided for companion animals has similarities with medical practice, however there are also differences caused by the status of the animal; the contrast between predominately fee for service veterinary care and state funded medical provision; and the acceptability of euthanasia as a “treatment” option. Clinical decision making in veterinary practice is affected by a range of factors including the resources of the owner, the value placed on the individual animal and the circumstances in which the decision making takes place. Veterinary surgeons in practice need teaching and evidence based resources to take account of these factors in order to provide the best care to their animal patients. Further sociologically informed research is required to provide a greater understanding of the contextual factors which influence clinical decision making.
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Winfield, Catherine V. "Clinical decision making in district nursing." Thesis, University of Surrey, 1998. http://epubs.surrey.ac.uk/2830/.

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The two studies described here address the question of how District Nurses determine patients' nursing problems and plan care. The theoretical framework for the investigation is derived from Information Processing Theory. A process tracing methodology was used to capture the content of District Nurses' thinking during an assessment visit to a newly referred patient. Data was collected in the natural setting to ensure ecological validity. The assessment visits were tape recorded and immediately following the visit a stimulated recall session was conducted in which the nurse was asked to describe her thinking during the assessment, prompted by the tape recording. This session was itself tape-recorded. Thus two verbal protocols were elicited for each assessment: a visit protocol and a recall protocol. Data were analysed by content analysis. The verbal protocols were assessed to ensure that they met the criteria for validity and reliability of the coding schedules was established using two measures or interrater reliability. The first study sought evidence of hypothetico-deductive reasoning by nurses and describes the type of decisions made by nurses. Although evidence of hypothesis generation and testing was found, nurses' knowledge was found to determine how they interpreted data initially and what data they sought. It was therefore concluded that a model of diagnostic reasoning that focused on cognitive processes alone was insufficient to explain the dynamics of clinical problem solving. The second study, therefore, sought to establish the structure and content of District Nurses knowledge and the cognitive processes they used during an assessment. The results suggest that nurses attend to both clinical and personal phenomena in order to make a judgement about the state of the patient and that their knowledge is organised internally as schema. This provides an explanation of how nurses recognise salient information and determine what further data is required. Four key cognitive activities were identified: search, inference, action and plan. The study concludes by drawing a line of reasoning to show how nurses integrate knowledge and reasoning processes to accomplish clinical problem solving.
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Gil-Herrera, Eleazar. "Classification Models in Clinical Decision Making." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4895.

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In this dissertation, we present a collection of manuscripts describing the development of prognostic models designed to assist clinical decision making. This work is motivated by limitations of commonly used techniques to produce accessible prognostic models with easily interpretable and clinically credible results. Such limitations hinder prognostic model widespread utilization in medical practice. Our methodology is based on Rough Set Theory (RST) as a mathematical tool for clinical data anal- ysis. We focus on developing rule-based prognostic models for end-of life care decision making in an effort to improve the hospice referral process. The development of the prognostic models is demonstrated using a retrospective data set of 9,103 terminally ill patients containing physiological characteristics, diagnostic information and neurological function values. We develop four RST-based prognostic models and compare them with commonly used classification techniques including logistic regression, support vector machines, random forest and decision trees in terms of characteristics related to clinical credibility such as accessibility and accuracy. RST based models show comparable accuracy with other methodologies while providing accessible models with a structure that facilitates clinical interpretation. They offer both more insight into the model process and more opportunity for the model to incorporate personal information of those making and being affected by the decision.
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Wang, Shicai. "Big tranSMART for clinical decision making." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/33348.

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Molecular profiling data based patient stratification plays a key role in clinical decision making, such as identification of disease subgroups and prediction of treatment responses of individual subjects. Many existing knowledge management systems like tranSMART enable scientists to do such analysis. But in the big data era, molecular profiling data size increases sharply due to new biological techniques, such as next generation sequencing. None of the existing storage systems work well while considering the three 'V' features of big data (Volume, Variety, and Velocity). New Key Value data stores like Apache HBase and Google Bigtable can provide high speed queries by the Key. These databases can be modeled as Distributed Ordered Table (DOT), which horizontally partitions a table into regions and distributes regions to region servers by the Key. However, none of existing data models work well for DOT. A Collaborative Genomic Data Model (CGDM) has been designed to solve all these is- sues. CGDM creates three Collaborative Global Clustering Index Tables to improve the data query velocity. Microarray implementation of CGDM on HBase performed up to 246, 7 and 20 times faster than the relational data model on HBase, MySQL Cluster and MongoDB. Single nucleotide polymorphism implementation of CGDM on HBase outperformed the relational model on HBase and MySQL Cluster by up to 351 and 9 times. Raw sequence implementation of CGDM on HBase gains up to 440-fold and 22-fold speedup, compared to the sequence alignment map format implemented in HBase and a binary alignment map server. The integration into tranSMART shows up to 7-fold speedup in the data export function. In addition, a popular hierarchical clustering algorithm in tranSMART has been used as an application to indicate how CGDM can influence the velocity of the algorithm. The optimized method using CGDM performs more than 7 times faster than the same method using the relational model implemented in MySQL Cluster.
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Ogunsanya, Oluwole Victor. "Decision support using Bayesian networks for clinical decision making." Thesis, Queen Mary, University of London, 2012. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8688.

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This thesis investigates the use of Bayesian Networks (BNs), augmented by the Dynamic Discretization Algorithm, to model a variety of clinical problems. In particular, the thesis demonstrates four novel applications of BN and dynamic discretization to clinical problems. Firstly, it demonstrates the flexibility of the Dynamic Discretization Algorithm in modeling existing medical knowledge using appropriate statistical distributions. Many practical applications of BNs use the relative frequency approach while translating existing medical knowledge to a prior distribution in a BN model. This approach does not capture the full uncertainty surrounding the prior knowledge. Secondly, it demonstrates a novel use of the multinomial BN formulation in learning parameters of categorical variables. The traditional approach requires fixed number of parameters during the learning process but this framework allows an analyst to generate a multinomial BN model based on the number of parameters required. Thirdly, it presents a novel application of the multinomial BN formulation and dynamic discretization to learning causal relations between variables. The idea is to consider competing causal relations between variables as hypotheses and use data to identify the best hypothesis. The result shows that BN models can provide an alternative to the conventional causal learning techniques. The fourth novel application is the use of Hierarchical Bayesian Network (HBN) models, augmented by dynamic discretization technique, to meta-analysis of clinical data. The result shows that BN models can provide an alternative to classical meta analysis techniques. The thesis presents two clinical case studies to demonstrate these novel applications of BN models. The first case study uses data from a multi-disciplinary team at the Royal London hospital to demonstrate the flexibility of the multinomial BN framework in learning parameters of a clinical model. The second case study demonstrates the use of BN and dynamic discretization to solving decision problem. In summary, the combination of the Junction Tree Algorithm and Dynamic Discretization Algorithm provide a unified modeling framework for solving interesting clinical problems.
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Miller, Jaclyn Nieman. "Dreaming and decision-making." Case Western Reserve University School of Graduate Studies / OhioLINK, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=case1055519665.

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Burnett, Thomas. "Bayesian decision making in adaptive clinical trials." Thesis, University of Bath, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760912.

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The key original contribution of this work is the use of a Bayes optimisation framework for the decision made at the interim analysis of Adaptive Enrichment trials. Adaptive Enrichment designs make efficient use of pre-identified patient sub-populations. They begin by recruiting from all eligible patients, then at a pre-planned interim analysis select which sub-populations will be recruited from for the remainder of the sample. We ensure strong control of the Familywise Error Rate whichever sub-populations are selected by constructing an overall hypothesis testing structure using both closed testing procedures and combination tests. This allows us to make interim decision by any method we choose. We find the Bayes optimal decision, recruiting the remainder of the trial to optimise the Bayes expected gain of the trial. We compare the Bayes optimal Adaptive Enrichment trials with fixed sampling designs to understand the overall advantage of using adaptive trials. This optimisation framework is very flexible, we evaluate the performance of Bayes optimal Adaptive Enrichment designs for different forms of data: delayed responses, longitudinal analysis and discuss the extension of these methods to survival data. Through this we see that although the information at the interim analysis is reduced the adaptive trials still offer some benefit. Additionally we investigate what may happen when we alter the pattern of recruitment of the Adaptive Enrichment trials, showing that adaptation may be useful in a broad range of scenarios.
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Books on the topic "Clinical decision making"

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D, Cebul Randall, Beck Laurence H, and University of Pennsylvania. Section of General Medicine., eds. Teaching clinical decision making. New York: Praeger, 1985.

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D, Cebul Randall, and Beck Laurence H, eds. Teaching clinical decision making. New York: Praeger, 1985.

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C, Aron David, and Sowers Maryfran, eds. Epidemiology and clinical decision making. Philadelphia: Saunders, 1997.

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Richter, Ettinger Ellen, and Rouse Michael W, eds. Clinical decision making in optometry. Boston: Butterworth-Heinemann, 1997.

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G, Sheldon Michael, Brooke John 1951-, and Rector Alan, eds. Decision-making ingeneral practice. London: Stockton, 1985.

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Sullivan, Patricia E. Clinical decision making in therapeutic exercise. Norwalk, Conn: Appleton & Lange, 1995.

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Jamison, Jennifer R. Diagnostic decision making in clinical practice. Baltimore: Williams & Wilkins, 1991.

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Liu, Rong, ed. Clinical Decision Making for Improving Prognosis. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2952-6.

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Steele, Scott R., Justin A. Maykel, and Steven D. Wexner, eds. Clinical Decision Making in Colorectal Surgery. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-65942-8.

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Bush, Shane S. Ethical decision-making in clinical neuropsychology. New York: Oxford University Press, 2008.

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Book chapters on the topic "Clinical decision making"

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Ridderikhoff, J. "Clinical decision-making." In Methods in Medicine, 83–117. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-1097-3_4.

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Croskerry, Pat. "Clinical Decision Making." In Pediatric and Congenital Cardiac Care, 397–409. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6566-8_33.

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Swallow, Veronica, Joanna Smith, and Trish Smith. "Clinical Decision Making." In Clinical Leadership in Nursing and Healthcare, 149–65. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119253785.ch8.

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Zia, Jasmine K., and John M. Inadomi. "Clinical Decision Making." In Yamada' s Textbook of Gastroenterology, 639–50. Oxford, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118512074.ch34.

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Turner, J. Rick. "Clinical Decision-Making." In Encyclopedia of Behavioral Medicine, 424–25. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_996.

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Downs, Stephen M., and Lydia K. Johns. "Clinical Decision-Making." In Clinical Informatics Study Guide, 69–99. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-22753-5_4.

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Blessing, Nathan W. "Clinical Decision-Making." In Anophthalmia, 17–24. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29753-4_2.

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Cooke, Mary. "Clinical Decision Making." In Foundations of Adult Nursing, 179–204. 1 Oliver’s Yard, 55 City Road London EC1Y 1SP: SAGE Publications Ltd, 2015. http://dx.doi.org/10.4135/9781529715071.n8.

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Turner, J. Rick. "Clinical Decision-Making." In Encyclopedia of Behavioral Medicine, 468–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_996.

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Marley, Daniel S., and Mark B. Mengel. "Clinical Decision Making." In Principles of Clinical Practice, 99–123. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-1657-0_5.

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Conference papers on the topic "Clinical decision making"

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Mandrekar, Jay. "Data driven decision making for survey reduction: case study from neurology research." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19104.

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The goal of this research was to develop an abbreviated and statistically robust instrument to assess autonomic symptoms that provides clinically relevant scores of autonomic symptom severity based on the well-established questionnaires. Data from 405 healthy control subjects seen at the Mayo Clinic Autonomic Disorders Center were collected. The length of the questionnaire was reduced from a total of 169 to 31 questions using exploratory factor analysis. Our new simplified scoring algorithm resulted in higher Cronbach alpha values in all domains. This reduced instrument allowed researchers to focus on clinically meaningful variables. Also, a shorter survey instrument was less time consuming and less burdensome for critically ill patients, allowing for capturing accurate responses and limiting missing data. The application of exploratory factor analysis in reduction of dimension reduction in this area of neurology research is novel. This reduced survey instrument is now being used to capture data from various clinical studies around the world.
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Silsand, Line, and Gunnar Ellingsen. "Complex Decision-Making in Clinical Practice." In CSCW '16: Computer Supported Cooperative Work and Social Computing. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2818048.2819952.

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Giani, Umberto. "Measurement, complexity and clinical decision-making." In 2011 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2011. http://dx.doi.org/10.1109/memea.2011.5966783.

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Kidanemariam, Martha, Matthijs Graner, Willem Jan W Bos, Marielle A. Schroijen, Eelco JPDe Koning, Anne M. Stiggelbout, Victor M. Montori, Arwen H. Pieterse, and Marleen Kunneman. "129 Patient-clinician collaboration in making care fit: a qualitative analysis of clinical consultations in diabetes care." In 12th International Shared Decision Making Conference. BMJ Publishing Group Ltd, 2024. http://dx.doi.org/10.1136/bmjebm-2024-sdc.128.

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Yen, Renata W., Marie-Anne Durand, Joanna Leyenaar, A. James O’malley, Edward Rego, Rachel C. Forcino, Catherine H. Saunders, Talia Isaacs, and Glyn Elwyn. "303 Preliminary development of the clinician spoken plain language measure using a secondary analysis of clinical encounters." In 12th International Shared Decision Making Conference. BMJ Publishing Group Ltd, 2024. http://dx.doi.org/10.1136/bmjebm-2024-sdc.302.

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Ovabor, Kelvin, Adeyinka Adams-Momoh, and Travis Atkison. "Cybersecurity-Enhanced Game-Based Learning for Clinical Decision-Making: A Comparative Study." In 3rd International Conference on Cryptography and Blockchain. Academy & Industry Research Collaboration Center, 2023. http://dx.doi.org/10.5121/csit.2023.132105.

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The purpose of this research is to determine whether medical professionals may benefit from gamebased learning to enhance their decision-making abilities. The research included 200 medical professionals from renowned institutions such as St. Peter's Hospital Albany, MedStar Union Memorial Hospital, Baltimore, St. Vincent Charity Medical Center, Cleveland, and Kindred Hospital, Los Angeles, who had prior experience with game-based learning systems. Both a pre-and post-test assessing the participants' ability to make difficult clinical decisions were done. Participants were exposed to an online game-based learning intervention available on MediSim Clinic, which offers a virtual simulation platform, and they demonstrated a considerable increase in their ability to make sound decisions. The results of the research provide preliminary evidence that game-based learning may be an efficient method for enhancing clinical decision-making abilities, including those related to cybersecurity challenges. Limitations and ideas for further research are presented, along with the study's consequences and recommendations for practice. This research contributes to the expanding literature on game-based learning and its potential for enhancing clinical decision-making abilities in the medical profession.
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Albuquerque, Aline. "008 Interfaces between clinical empathy and patient-centered care." In 12th International Shared Decision Making Conference. BMJ Publishing Group Ltd, 2024. http://dx.doi.org/10.1136/bmjebm-2024-sdc.8.

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Haselen, Robbert van, Lefteris Tapakis, and Theodoros Lilas. "Improving Decision-Making in Homeopathic Clinical Practice." In HRI London 2019—Cutting Edge Research in Homeopathy: Presentation Abstracts. The Faculty of Homeopathy, 2020. http://dx.doi.org/10.1055/s-0040-1702111.

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Farooq, Kamran, Peipei Yang, Amir Hussain, Kaizhu Huang, Calum MacRae, Chris Eckl, and Warner Slack. "Efficient clinical decision making by learning from missing clinical data." In 2013 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE). IEEE, 2013. http://dx.doi.org/10.1109/cicare.2013.6583064.

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Osop, Hamzah, and Tony Sahama. "Effective clinical decision-making from Practice-Based Evidence." In 2015 Fifteenth International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 2015. http://dx.doi.org/10.1109/icter.2015.7377707.

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Reports on the topic "Clinical decision making"

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Gathu, Michael. What are the effects of interventions to encourage the use of systematic reviews in clinical decision making? SUPPORT, 2017. http://dx.doi.org/10.30846/170111.

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Clinical decision making is often not based on the best available evidence. Reasons for this vary, and may be related to factors within the healthcare setting, patients, or health practitioners. Interventions have been designed to encourage the use of systematic reviews in making clinical decisions as one way of improving clinical decision making.
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Anderson, Richard. The effect of administrative mandate on social workers' clinical decision making. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2742.

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Taylor, Robin. Clinical decision-making and treatment escalation planning in the last year of life. BJUI Knowledge, May 2019. http://dx.doi.org/10.18591/bjuik.0361.

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O'Flynn, Keiran. Making the right decision - bias in clinical practice and how to manage it. BJUI Knowledge, January 2020. http://dx.doi.org/10.18591/bjuik.0695.

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Manski, Charles, John Mullahy, and Atheendar Venkataramani. Using Measures of Race to Make Clinical Predictions: Decision Making, Patient Health, and Fairness. Cambridge, MA: National Bureau of Economic Research, December 2022. http://dx.doi.org/10.3386/w30700.

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Evans, Jon, Ian Porter, Emma Cockcroft, Al-Amin Kassam, and Jose Valderas. Collecting linked patient reported and technology reported outcome measures for informing clinical decision making: a scoping review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2021. http://dx.doi.org/10.37766/inplasy2021.10.0038.

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Review question / Objective: We aim to map out the existing research where concomitant use of patient reported and technology reported outcome measures is used for patients with musculoskeletal conditions. Condition being studied: Musculoskeletal disorders (MSD) covering injuries or disorders of the muscles, nerves, tendons, joints, cartilage, and spinal discs. Musculoskeletal manifestations of joint pathology. Eligibility criteria: 1) Peer-reviewed primary studies and literature reviews. Grey literature not included. 2) Studies which include co-administration of Patient-Reported Outcomes (PROMs) AND wearable electronic devices (e.g. fitness trackers, accelerometers, gyroscopes, pedometers smartphones, smartwatches) in musculoskeletal manifestations of joint pathology. Studies are EXCLUDED which feature wearable electronic devices but not concomitant/real time capturing of PROMs (e.g. they are recorded retrospectively/ at different timepoints). 3) Studies in languages other than English will be excluded unless a translation is available.
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Han, Nana, Yang Fang, Guozhen Zhao, and Bo Ji. The comparative efficacy and safety of acupuncture for mild and moderate Alzheimer's disease: A systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2021. http://dx.doi.org/10.37766/inplasy2021.11.0014.

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Review question / Objective: According to the current randomized clinical trials (RCT) of acupuncture therapy for Alzheimer's disease (AD), to evaluate their methodology, the quality of evidence and the report are evaluated and summarize evidence of important outcomes of randomized clinical trials. We aim to provide accurate clinical decision-making for acupuncture treatment of Alzheimer's disease. Condition being studied: According to the current randomized clinical trials (RCT) of acupuncture therapy for Alzheimer's disease (AD), to evaluate their methodology, the quality of evidence and the report are evaluated and summarize evidence of important outcomes of randomized clinical trials. We aim to provide accurate clinical decision-making for acupuncture treatment of Alzheimer's disease.
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Waldfogel, Julie M., Michael Rosen, Ritu Sharma, Allen Zhang, Eric B. Bass, and Sydney M. Dy. Making Healthcare Safer IV: Opioid Stewardship. Agency for Healthcare Research and Quality (AHRQ), December 2023. http://dx.doi.org/10.23970/ahrqepc_mhs4opioid.

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Objectives. Opioid stewardship interventions promote the appropriate use of prescribed and ordered opioids to reduce the risk of opioid adverse events. Our main objectives were to determine the effectiveness of these interventions in healthcare settings on opioid prescribing and clinical outcomes (e.g., number of opioid prescriptions, opioid dosage, overdose, emergency department visits, and hospitalizations) including unintended consequences (e.g., changes in patient-reported pain intensity), and ways these interventions can be effectively implemented. Methods. We followed rapid review processes of the Agency for Healthcare Research and Quality Evidence-based Practice Center Program. We searched PubMed and the Cochrane Library to identify eligible systematic reviews from January 2019 to April 2023 and primary studies published from January 2016 to April 2023, supplemented by targeted gray literature searches. We included systematic reviews and studies that addressed opioid stewardship interventions implemented in healthcare settings in the United States and that reported on opioid prescribing and clinical outcomes. Findings. Our search retrieved 6,431 citations, of which 34 articles were eligible (including 1 overview of systematic reviews, 13 additional systematic reviews, 13 randomized controlled trials (RCTs) [reported in 14 articles] and 6 nonrandomized studies). Systematic reviews, mostly summarizing pre-post studies, included a wide variety of opioid stewardship practices that focused on patient and family engagement, healthcare organization policy, or clinician knowledge and behavior interventions, in inpatient, perioperative, emergency department, and ambulatory settings. RCTs addressed multicomponent interventions (typically a combination of prescriber education, care management and facilitated access to resources), and patient education and engagement, mainly in ambulatory chronic pain. Opioid stewardship practices involving clinical decision support or electronic health records, or multicomponent interventions (including for chronic pain) were associated with decreases in opioid prescribing or reduced doses and no increases in pain, emergency department visits, or hospitalizations (low strength of evidence for all outcomes). Patient engagement and education interventions had mixed results for opioid prescribing outcomes (insufficient strength of evidence) and no increases in pain, emergency department visits, or hospitalizations (low strength of evidence). The evidence was insufficient on other types of interventions and on outcomes of opioid refill requests and refills, patient satisfaction, or overdose. Barriers included lack of training, workload, gaps in communication, and inadequate access to nonpharmacological resources. Facilitators included clinician and patient acceptance of intervention components. Conclusions. Selected opioid stewardship interventions may be effective for reducing opioid prescribing and dosing without adversely affecting clinical outcomes overall, although strength of evidence was low. Unintended consequences were often not measured or not measured rigorously. Interventions to reduce opioid use should monitor unintended consequences and include access to nonpharmacological pain management resources with appropriate patient education and engagement.
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Syrowatka, Ania, Aneesa Motala, Emily Lawson, and Paul Shekelle. Computerized Clinical Decision Support To Prevent Medication Errors and Adverse Drug Events. Agency for Healthcare Research and Quality (AHRQ), February 2024. http://dx.doi.org/10.23970/ahrqepc_mhs4mederror.

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Objectives. To assess the evidence on the effects of computerized clinical decision support systems (CDSSs) on the prevention of medication errors and adverse drug events, related implementation outcomes such as rates of medication alert overrides, and unintended consequences of use. We also summarized the literature around the effective implementation of a CDSS. Methods. We followed the rapid review processes of the Agency for Healthcare Research and Quality Evidence-based Practice Center Program. We queried PubMed and the Cochrane Library to locate relevant systematic reviews and primary studies published from 2015 to April 2023, supplemented by a targeted review of the grey literature. We narratively synthesized the evidence and assessed the overall strength of evidence for the outcomes of interest. The protocol for the review has been registered in PROSPERO (CRD42023449710). Findings. Our search yielded 1,335 unique abstracts, of which 33 articles met the target criteria and were included in the review (27 systematic reviews, one overview of reviews, and five primary studies). Twenty reviews (out of 22) reporting on effectiveness were rated “good” or “fair” quality. One primary study included in the narrative synthesis was rated as having a “low” risk of bias. The evidence covered the effects of CDSSs across various healthcare settings and specialties. The type of decision support provided by the CDSSs and outcomes were heterogeneous between studies. Overall, computerized provider order entry with medication-related CDSSs were associated with reduced medication errors (moderate strength of evidence) and prevention of adverse drug events (low strength of evidence). Improved or targeted medication-related CDSSs were associated with reductions of medication errors and adverse drug events (moderate strength of evidence). However, alert override rates were high and varied between studies, and the appropriateness of the overrides was largely influenced by the type of alert. Other unintended consequences included CDSS-related errors, overdependence on alerts, alert fatigue, inappropriate alert overrides, and provider burnout. An additional 48 articles focused on barriers and facilitators of CDSS implementation. 2 Making Healthcare Safer IV – Computerized Clinical Decision Support Conclusions. Overall, CDSSs reduce medication errors and adverse drug events, with moderate- and low-certainty evidence, respectively. However, there were several unintended consequences of CDSS implementation and use. The evidence of benefits and harms was generally reported in different studies with varying contexts, making the net benefit difficult to estimate. Future research should focus on measuring these outcomes and unintended consequences in the same study to generate evidence on both the benefits and harms associated with using a CDSS in the same context.
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Dudley, Lilian D., and Charles Shey Wiysonge. Does giving women their own case notes to carry in pregnancy improve maternal care? SUPPORT, 2016. http://dx.doi.org/10.30846/160804.

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Improvements in antenatal care have included changes to traditional practices in order to improve womens’ experiences of antenatal care and the clinical outcomes of maternity care. One such change has been giving women their own clinical case notes to carry throughout their pregnancy in order to enable women to participate in the decision making regarding their healthcare, and to improve the availability of the records when needed.
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