scholarly journals Categorical Medicine: a mathematical embedding of clinical medicine with applications to headache medicine.

2021 ◽  
Author(s):  
Pengfei Zhang
Keyword(s):  
Category Theory ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Clinical Decision ◽  
Disease Classification ◽  
Topological Spaces ◽  
Starting Point ◽  
Practice Of Medicine ◽  
Medication Intervention ◽  
Opening Up

The practice of clinical medicine, diagnosing and treating patients appear to be very different from proving mathematical theorems. Since the 1950s, category theory has been introduced as a potentially unifying theory for disparate disciplines in mathematics. Our project investigates whether it is possible to convert disease classification and iatrogenic interventions, and therefore clinical medicine, axiomatically through the application of category theory. Here we propose two ways of unifying these two seemingly disparate areas of research by interpreting clinical medicine as a mathematical category. Our models allow the practice of medicine to be interpreted from an algebraic topological fashion, thereby opening up the possibility of modeling disease phenotypes/classifications and clinical decision making as topological spaces. We also show that medication intervention and disease classification are inherently linked. We applied the above model to headache medicine as a practical example of the approach. We anticipate that our formulations can be applied to any classifiable arenas of medicine, serving as a theoretical starting point for more sophisticated modeling of clinical medicine mathematically and therefore computationally.

Introduction

2020 ◽  
pp. 1-14
Author(s):  
Pat Croskerry
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Medical Training ◽  
Clinical Decision ◽  
Current Medical ◽  
Dual Process ◽  
Cognitive Sciences ◽  
Clinical Detail ◽  
The Rich

Medical error is one of the leading causes of death, and most of these errors appear to occur in the ways that practitioners’ thoughts and feelings impact their decision making. Major gains have been made in the cognitive sciences in the past few decades that have provided a model for understanding how decisions are made—dual process theory. It is an excellent platform on which to examine the different ways decisions are made. Importantly, it allows for the examination of the pervasive influence of cognitive and affective biases on clinical decision making. Current medical training appears to fall short of what is needed to produce rational decision makers, due to what has been referred to as a mindware gap. Practitioners need to move from routine expertise to a higher level of expertise that will close this gap. A clear difficulty lies in finding ways of understanding and teaching the clinical decision-making process that do not violate the ecological characteristics of real-time clinical practice. By preserving as much as possible the rich clinical detail that makes up clinical medicine, this book attempts to offer important insights into the process.

Integrated Diagnostics for Rapid Quantitation of Biomarkers in Human Fluids

2010 ◽  
Author(s):  
Chenlu Hou ◽  
Kelly Karns ◽  
Amy E. Herr
Keyword(s):  
Differential Diagnosis ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Clinical Decision ◽  
Specific Protein ◽  
Clinical Diagnostics ◽  
Clinical Samples ◽  
Field Hospital ◽  
Multiple Biomarkers ◽  
Microfluidic Assays

A fast, accurate differential diagnosis is a tremendous challenge in clinical medicine. For example, in emergency settings differential diagnosis of infection from inflammation is needed, as a timely and actionable diagnosis is critical. Consequently, diagnostics capable of measuring multiple biomarkers would support clinical decision-making related to diagnosis, containment, and treatment. Microfluidic assays are exceptionally well-suited for near-patient clinical diagnostics (i.e., ambulance, emergency room, field hospital). We present spectrally multiplexed homogeneous electrophoretic immunoassays for specific protein disease biomarkers. Biomarkers are resolved quickly (< 60 s), in ultra-short separation lengths (< 1 mm), and at clinically relevant concentrations (nM). On-going work will be presented and centers on endogenous protein measurements in clinical samples in support of near-patient assessment.

Remote Monitoring of Patients with Implanted Cardiac Devices – A Review

2012 ◽  
Vol 8 (2) ◽  
pp. 88 ◽  
Author(s):  
Bosede A Afolabi ◽  
Fred M Kusumoto ◽  
◽  
Keyword(s):  
Remote Monitoring ◽  
Clinical Decision Making ◽  
Clinical Information ◽  
Drug Efficacy ◽  
Clinical Decision ◽  
Developed Countries ◽  
Cardiac Devices ◽  
Number Of Patients ◽  
Practice Of Medicine ◽  
The Impact

There has been a rapid growth in the number of patients with cardiovascular implantable electronic devices (CIEDs), due to the consistent good results from large randomised trials and changing worldwide demographics with progressive ageing in all developed countries. Early generations of CIEDs provided only basic operations and stored only rudimentary data, but the evolution of all types of CIEDs (pacemakers, defibrillators, cardiac resynchronisation devices, implantable monitors) has led to their increased complexity and the development of a myriad of specialised features. As an outgrowth of this increased sophistication, once implanted, CIEDs can provide significant amounts of important clinical information, allowing to identify the presence of significant arrhythmias, assess drug efficacy, evaluate heart failure status and continuously monitor device function. With the advent of new methods of remote monitoring, the information recorded by these devices can be accessible in real time and thus lead to more timely clinical decision-making. This article summarises the impact of remote monitoring on clinical practice today and how the use of remote monitoring may evolve to affect the practice of medicine in the future.

scholarly journals Nurses' barriers when using research information in clinical decision making

2018 ◽  
Vol 7 (1) ◽  
pp. 27
Author(s):  
Safaa Mohamed ◽  
Mona Thabet
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Research Utilization ◽  
Clinical Decision ◽  
Complex Nature ◽  
University Hospitals ◽  
Research Information ◽  
Starting Point ◽  
Apply Research ◽  
Cooperative Process

Recently, there has been more emphasis on clinical decision making to be a cooperative process, which encompasses shared and parallel decision making with patients and health care teams. For this, nurses’ clinical decision-making is a complicated process with a possibility to affect the provided quality of care and. Therefore, affect patient condition progress. Thus, it is the critical point to study the nurses' barriers to research to identify the starting point of how do nurses currently view, and apply research based information in their decision. The aim of the study is to evaluate the nurses' barriers when using research information in clinical decision making. A descriptive correlation research design was utilized. The sample was consisted of of 140 nurse participants at Minia University hospitals. One tool was used as Barriers to using research information in clinical decision making. This study revealed that the nurse participants agree on the research barriers such as lack of time, lack of organization support to use and implement insufficient nurse skills to use research, and complex nature of research. Thus it was concluded that nurses appraise the value of research utilization, but there were many factors hinder and become the barrier to them to implement research.

scholarly journals Accessing Artificial Intelligence for Clinical Decision-Making

2021 ◽  
Vol 3 ◽  
Author(s):  
Chris Giordano ◽  
Meghan Brennan ◽  
Basma Mohamed ◽  
Parisa Rashidi ◽  
François Modave ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Patient Care ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Clinical Decision ◽  
Disruptive Innovation ◽  
Decision Makers ◽  
Data Sets ◽  
Sequential Decision

Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.

scholarly journals Clinical Integration of Genome Diagnostics for Congenital Anomalies of the Kidney and Urinary Tract

2020 ◽  
Vol 16 (1) ◽  
pp. 128-137 ◽  
Author(s):  
Rik Westland ◽  
Kirsten Y. Renkema ◽  
Nine V.A.M. Knoers
Keyword(s):  
Urinary Tract ◽  
Congenital Anomalies ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Clinical Care ◽  
Clinical Decision ◽  
Environment Interaction ◽  
Clinical Integration ◽  
Urinary Tract Malformations

Revolutions in genetics, epigenetics, and bioinformatics are currently changing the outline of diagnostics and clinical medicine. From a nephrologist’s perspective, individuals with congenital anomalies of the kidney and urinary tract (CAKUT) are an important patient category: not only is CAKUT the predominant cause of kidney failure in children and young adults, but the strong phenotypic and genotypic heterogeneity of kidney and urinary tract malformations has hampered standardization of clinical decision making until now. However, patients with CAKUT may benefit from precision medicine, including an integrated diagnostics trajectory, genetic counseling, and personalized management to improve clinical outcomes of developmental kidney and urinary tract defects. In this review, we discuss the present understanding of the molecular etiology of CAKUT and the currently available genome diagnostic modalities in the clinical care of patients with CAKUT. Finally, we discuss how clinical integration of findings from large-scale genetic, epigenetic, and gene-environment interaction studies may improve the prognosis of all individuals with CAKUT.

scholarly journals Vagueness in Medicine: On Disciplinary Indistinctness, Fuzzy Phenomena, Vague Concepts, Uncertain Knowledge, and Fact-Value-Interaction

Axiomathes ◽  
2021 ◽  
Author(s):  
Bjørn Hofmann
Keyword(s):  
Decision Making ◽  
Health Policy ◽  
Policy Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Modern Medicine ◽  
Borderline Cases ◽  
Wide Range ◽  
Practice Of Medicine ◽  
Uncertain Knowledge

AbstractThis article investigates five kinds of vagueness in medicine: disciplinary, ontological, conceptual, epistemic, and vagueness with respect to descriptive-prescriptive connections. First, medicine is a discipline with unclear borders, as it builds on a wide range of other disciplines and subjects. Second, medicine deals with many indistinct phenomena resulting in borderline cases. Third, medicine uses a variety of vague concepts, making it unclear which situations, conditions, and processes that fall under them. Fourth, medicine is based on and produces uncertain knowledge and evidence. Fifth, vagueness emerges in medicine as a result of a wide range of fact-value-interactions. The various kinds of vagueness in medicine can explain many of the basic challenges of modern medicine, such as overdiagnosis, underdiagnosis, and medicalization. Even more, it illustrates how complex and challenging the field of medicine is, but also how important contributions from the philosophy can be for the practice of medicine. By clarifying and, where possible, reducing or limiting vagueness, philosophy can help improving care. Reducing the various types of vagueness can improve clinical decision-making, informing individuals, and health policy making.

Case-based reasoning

2020 ◽  
Vol 8 (2) ◽  
pp. 207
Author(s):  
Mark Tonelli
Keyword(s):  
Clinical Decision Making ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Case Based Reasoning ◽  
Evidence Based ◽  
Therapeutic Decisions ◽  
Practice Of Medicine ◽  
Centered Care ◽  
Based Medicine ◽  
Case Based

The clinical case has been central to the practice of medicine since its inception, but the perceived value of the case, both a source of knowledge and as the basis for clinical decision making, has declined in the era of evidence-based medicine. Thinking in cases, however, is necessary for the practice of person-centered healthcare, ensuring that the individuality of the case-at-hand is recognized and incorporated into diagnostic and therapeutic decisions. The case-at-hand will be compared to other cases, derived from clinical research, pathophysiologic understanding, and clinical experience, as these kinds of cases serve as the repository of medical knowledge. Utilizing analogy and argument, clinicians derive and negotiate warrants relevant to particular patients, in order to make diagnoses, recommendations, and decisions. Case-based reasoning provides a rigorous and explicit framework for delivering person-centered care to individuals seeking healing.

scholarly journals From The Editor: Clinical Decision Science: Experience can be shared; can it be taught?

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
James Peter Meza
Keyword(s):  
Decision Making ◽  
Biopsychosocial Model ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Clinical Decision ◽  
Decision Science ◽  
Science Experience

The editor reflects on the biopsychosocial model of clinical decision making and whether it can be taught in clinical medicine.

scholarly journals Carrying out systematic literature reviews: an introduction

2019 ◽  
Vol 28 (15) ◽  
pp. 1008-1014 ◽  
Author(s):  
Alan Davies
Keyword(s):  
Systematic Review ◽  
Review Process ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Starting Point ◽  
Literature Reviews ◽  
Good Starting Point ◽  
The Common ◽  
Systematic Review Process

Systematic reviews provide a synthesis of evidence for a specific topic of interest, summarising the results of multiple studies to aid in clinical decisions and resource allocation. They remain among the best forms of evidence, and reduce the bias inherent in other methods. A solid understanding of the systematic review process can be of benefit to nurses that carry out such reviews, and for those who make decisions based on them. An overview of the main steps involved in carrying out a systematic review is presented, including some of the common tools and frameworks utilised in this area. This should provide a good starting point for those that are considering embarking on such work, and to aid readers of such reviews in their understanding of the main review components, in order to appraise the quality of a review that may be used to inform subsequent clinical decision making.

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