A review of clinical decision-making: Models and current research

Aims and objectives: The aim of this paper was to review the current literature with respect to clinical decision-making models and the educational application of models to clinical practice. This was achieved by exploring the function and related research of the three available models of clinical decision making: information processing model, the intuitive-humanist model and the clinical decision making model. Background: Clinical decision-making is a unique process that involves the interplay between knowledge of pre-existing pathological conditions, explicit patient information, nursing care and experiential learning. Historically, two models of clinical decision making are recognised from the literature; the information processing model and the intuitive-humanist model. The usefulness and application of both models has been examined in relation the provision of nursing care and care related outcomes. More recently a third model of clinical decision making has been proposed. This new multidimensional model contains elements of the information processing model but also examines patient specific elements that are necessary for cue and pattern recognition. Design: Literature review Methods: Evaluation of the literature generated from MEDLINE, CINAHL, OVID, PUBMED and EBESCO systems and the Internet from 1980 – November 2005

Clinical Decision Making

The preconference, or postgraduate course, sessions of the 2018 meeting of the Dysphagia Research Society in Baltimore, MD, brought together experts from a variety of disciplines, including psychology, otolaryngology, gastroenterology, nutrition, speech pathology, and others, to address and discuss the science and clinical application of reasoning and decision making with respect to the care of patients with dysphagia. Presentations started by breaking down the cognitive processes we, as clinicians, employ in making decisions on a daily basis and then proceeded to case study presentations and panel discussions regarding the impact of scientific advancements and big data on shaping our current and future clinical care. This issue of Seminars in Speech and Language was inspired by those sessions and introduces the reader to approaches to clinical decision making with respect to assessment and management of dysphagia in various clinical settings with various types of patients. As Doeltgen, Murray, and Attrill describe in our first article, “Clinical reasoning … is a cognitively complex process, as it requires synthesis of multiple sources of information that are generated during a thorough, evidence-based assessment process and which are moderated by the patient's individual situation, including their social and demographic circumstances, comorbidities or other health concerns.

The Impact of Simulation Sequencing on Perceived Clinical Decision Making

An emerging nursing education trend is to utilize simulated learning experiences as a means to optimize competency and decision making skills. The purpose of this study was to examine differences in students\u27 perception of clinical decision making and clinical decision making-related self-confidence and anxiety based on the sequence (order) in which they participated in a block of simulated versus hospital-based learning experiences. A quasi-experimental crossover design was used. Between and within group differences were found relative to self-confidence with the decision making process. When comparing groups, at baseline the simulation followed by hospital group had significantly higher self-confidence scores, however, at 14-weeks both groups were not significantly different. Significant within group differences were found in the simulation followed by hospital group only, demonstrating a significant decrease in clinical decision making related anxiety across the semester. Finally, there were no significant difference in; perceived clinical decision making within or between the groups at the two measurement points. Preliminary findings suggest that simulated learning experiences can be offered with alternating sequences without impacting the process, anxiety or confidence with clinical decision making. This study provides beginning evidence to guide curriculum development and allow flexibility based on student needs and available resources

Clinical decision-making theories

Clinical decision-making is a cornerstone of healthcare, influencing patient diagnosis, treatment, and ongoing care. This article explores the multifaceted nature of clinical decision-making, emphasizing its significance, challenges, and implications for modern healthcare. It delves into three primary decision-making theories: the rationalist approach, which prioritizes evidence-based decision-making; the phenomenological approach, focusing on intuition and experience; and the hypothetic-deductive approach, which seeks a balance between the previous two. These theories, while offering valuable perspectives, must be applied with consideration of the complex factors that influence decision-making, including competence, confidence, organizational support, and the clinical environment. Ultimately, clinical decision-making is both an art and a science, demanding a nuanced understanding to ensure patient-centered care and improved healthcare outcomes

Moral Intuition in Clinical Decision-Making

The United Kingdom’s General Medical Council investigation of Dr. Michael Munro raises concerns about the ability of normative ethics to satisfactorily ‘solve’ ethical dilemmas in isolation within the real world. In this particular case it seems vague utilitarian principles were used to justify actions by a doctor that many people find morally unacceptable. This raises questions of what we might do when we find our normative ethical theories conflicting with our moral intuitions. Is there more to our ethical deliberations than merely implementing specific normative theories? Is there in fact a role for considering other elements in the decision-making process, such as one’s moral intuition? I suggest that despite being criticized as overly subjective or unreliable, there may still be a persuasive social and moral justification for paying attention to the unease of moral intuition when we find it conflicting with our normative judgments, especially in complex real-life clinical situations

Big tranSMART for clinical decision making

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.Open Acces

Clinical decision making: managing postprandial hyperglycemia

The primary objective of treating all patients with diabetes is to establish and maintain near-normal blood glucose levels to prevent microvascular and macrovascular complications. The glycated hemoglobin (HbA1c) is the accepted standard for monitoring overall glycemic control with treatments and management strategies traditionally targeting fasting and preprandial glucose levels. However, postprandial glucose levels also contribute to HbA1c, and optimization of glycemic control may also require targeting these values. Exaggerated postmeal glucose excursions are common in patients with diabetes, and postprandial hyperglycemia (PPHG) is an independent risk factor for cardiovascular disease. Regular self-monitoring of blood glucose concentrations (SMBG) at appropriate times can detect PPHG, provide patient feedback regarding meals and lifestyle, and monitor response to therapy. SMBG can also help detect fluctuations in blood glucose levels, which may be an additional risk factor for complications, independent of HbA1c. New therapeutic options that specifically target postprandial glucose levels may improve overall glycemic control and reduce the risk of microvascular and macrovascular complications

Professional autonomy in 21st century healthcare: nurses’ accounts of clinical decision-making

Autonomy in decision-making has traditionally been described as a feature of professional work, however the work of healthcare professionals has been seen as steadily encroached upon by State and managerialist forces. Nursing has faced particular problems in establishing itself as a credible profession for reasons including history, gender and a traditional subservience to medicine. This paper reports on a focus group study of UK nurses participating in post-qualifying professional development in a London university in 2008. Three groups of nurses in different specialist areas comprised a total of 26 participants. The study uses accounts of decision-making to gain insight into contemporary professional nursing. The study also aims to explore the usefulness of a theory of professional work set out by Jamous and Peloille in 1970. The analysis draws on notions of interpretive repertoires and elements of narrative analysis. We identified two interpretive repertoires: ‘clinical judgement’ which was used to describe the different grounds for making judgements; and ‘decision-making’ which was used to describe organisational circumstances influencing decision-making. Jamous and Peloille’s theory proved useful for interpreting instances where the nurses collectively withdrew from the potential dangers of too extreme claims for technicality or indeterminacy in their work. However, their theory did not explain the full range of accounts of decision-making that were given. Taken at face value, the accounts from the participants depict nurses as sometimes practising in indirect ways in order to have influence in the clinical and bureaucratic setting. However, a focus on language use and in particular, interpretive repertoires, has enabled us to suggest that despite an overall picture of severely limited autonomy, nurses in the groups reproduced stories of the successful accomplishment of moral and influential action

Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes

Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice. Empirical research diagnosed poor contextual fit as the cause. This paper describes the design and field evaluation of a radically new form of DST. It automatically generates slides for clinicians' decision meetings with subtly embedded machine prognostics. This design took inspiration from the notion of "Unremarkable Computing", that by augmenting the users' routines technology/AI can have significant importance for the users yet remain unobtrusive. Our field evaluation suggests clinicians are more likely to encounter and embrace such a DST. Drawing on their responses, we discuss the importance and intricacies of finding the right level of unremarkableness in DST design, and share lessons learned in prototyping critical AI systems as a situated experience