Diagnosis and therapeutic decision-making for the neutropenic patient

Abstract Determining the cause of a low neutrophil count in a pediatric or adult patient is essential for the hematologist's clinical decision-making. Fundamental to this diagnostic process is establishing the presence or lack of a mature neutrophil storage pool, as absence places the patient at higher risk for infection and the need for supportive care measures. Many diagnostic tests, eg, a peripheral blood smear and bone marrow biopsy, remain important tools, but greater understanding of the diversity of neutropenic disorders has added new emphasis on evaluating for immune disorders and genetic testing. In this article, a structure is provided to assess patients based on the mechanism of neutropenia and to prioritize testing based on patient age and hypothesized pathophysiology. Common medical quandaries including fever management, need for growth factor support, risk of malignant transformation, and curative options in congenital neutropenia are reviewed to guide medical decision-making in neutropenic patients.

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Physician-Specific Maximum Acceptable Risk in Personalized Medicine: Implications for Medical Decision Making

Medical Decision Making ◽

10.1177/0272989x18758279 ◽

2018 ◽

Vol 38 (5) ◽

pp. 593-600

Author(s):

Marco Boeri ◽

Alan J. McMichael ◽

Joseph P. M. Kane ◽

Francis A. O’Neill ◽

Frank Kee

Keyword(s):

Decision Making ◽

Clinical Decision Making ◽

Clinical Decision ◽

Discrete Choice Experiments ◽

Medical Decision ◽

Random Parameters ◽

Acceptable Risk ◽

Syndrome Scale ◽

Unit Increase ◽

Negative Syndrome

Background. In discrete-choice experiments (DCEs), respondents are presented with a series of scenarios and asked to select their preferred choice. In clinical decision making, DCEs allow one to calculate the maximum acceptable risk (MAR) that a respondent is willing to accept for a one-unit increase in treatment efficacy. Most published studies report the average MAR for the whole sample, without conveying any information about heterogeneity. For a sample of psychiatrists prescribing drugs for a series of hypothetical patients with schizophrenia, this article demonstrates how heterogeneity accounted for in the DCE modeling can be incorporated in the derivation of the MAR. Methods. Psychiatrists were given information about a group of patients’ responses to treatment on the Positive and Negative Syndrome Scale (PANSS) and the weight gain associated with the treatment observed in a series of 26 vignettes. We estimated a random parameters logit (RPL) model with treatment choice as the dependent variable. Results. Results from the RPL were used to compute the MAR for the overall sample. This was found to be equal to 4%, implying that, overall, psychiatrists were willing to accept a 4% increase in the risk of an adverse event to obtain a one-unit improvement of symptoms – measured on the PANSS. Heterogeneity was then incorporated in the MAR calculation, finding that MARs ranged between 0.5 and 9.5 across the sample of psychiatrists. Limitations. We provided psychiatrists with hypothetical scenarios, and their MAR may change when making decisions for actual patients. Conclusions. This analysis aimed to show how it is possible to calculate physician-specific MARs and to discuss how MAR heterogeneity could have implications for medical practice.

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Clinical Decision Making by Emergency Room Physicians and Residents

Encyclopedia of Healthcare Information Systems ◽

10.4018/978-1-59904-889-5.ch030 ◽

2008 ◽

pp. 217-223

Author(s):

Ken J. Farion ◽

Michael J. Hine ◽

Wojtek Michalowski ◽

Szymon Wilk

Keyword(s):

Decision Making ◽

Information Technology ◽

Decision Support ◽

Clinical Decision Making ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Medical Community ◽

Medical Decision ◽

Best Interest ◽

Complex Process

Clinical decision-making is a complex process that is reliant on accurate and timely information. Clinicians are dependent (or should be dependent) on massive amounts of information and knowledge to make decisions that are in the best interest of the patient. Increasingly, information technology (IT) solutions are being used as a knowledge transfer mechanism to ensure that clinicians have access to appropriate knowledge sources to support and facilitate medical decision making. One particular class of IT that the medical community is showing increased interest in is clinical decision support systems (CDSSs).

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Value-impregnated factual claims may undermine medical decision-making

Clinical Ethics ◽

10.1177/1477750918765283 ◽

2018 ◽

Vol 13 (3) ◽

pp. 151-158 ◽

Cited By ~ 2

Author(s):

Niels Lynøe ◽

Gert Helgesson ◽

Niklas Juth

Keyword(s):

Decision Making ◽

Medical Decision Making ◽

Clinical Decision Making ◽

Negative Impact ◽

Personal Values ◽

Healthcare Providers ◽

Clinical Decision ◽

Medical Decision ◽

Clinical Decisions ◽

Soft Laws

Clinical decisions are expected to be based on factual evidence and official values derived from healthcare law and soft laws such as regulations and guidelines. But sometimes personal values instead influence clinical decisions. One way in which personal values may influence medical decision-making is by their affecting factual claims or assumptions made by healthcare providers. Such influence, which we call ‘value-impregnation,’ may be concealed to all concerned stakeholders. We suggest as a hypothesis that healthcare providers’ decision making is sometimes affected by value-impregnated factual claims or assumptions. If such claims influence e.g. doctor–patient encounters, this will likely have a negative impact on the provision of correct information to patients and on patients’ influence on decision making regarding their own care. In this paper, we explore the idea that value-impregnated factual claims influence healthcare decisions through a series of medical examples. We suggest that more research is needed to further examine whether healthcare staff’s personal values influence clinical decision-making.

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Bias: a normal operating characteristic of the diagnosing brain

Diagnosis ◽

10.1515/dx-2013-0028 ◽

2014 ◽

Vol 1 (1) ◽

pp. 23-27 ◽

Cited By ~ 22

Author(s):

Pat Croskerry

Keyword(s):

Decision Making ◽

Process Model ◽

Clinical Decision Making ◽

Conventional Medicine ◽

Clinical Decision ◽

Blind Spot ◽

Research Area ◽

Dual Process ◽

Medical Decision ◽

The Impact

AbstractPeople diagnose themselves or receive advice about their illnesses from a variety of sources ranging from their family or friends, alternate medicine, or through conventional medicine. In all cases, the diagnosing mechanism is the human brain which normally operates under the influence of a variety of biases. Most, but not all biases, reside in intuitive decision making, and no individual or group is immune from them. Two biases in particular, bias blind spot and myside bias, have presented obstacles to accepting the impact of bias on medical decision making. Nevertheless, there is now a widespread appreciation of the important role of bias in the majority of medical disciplines. The dual process model of decision making now seems well accepted, although a polarization of opinions has arisen with some arguing the merits of intuitive approaches over analytical ones and vice versa. We should instead accept that it is not one mode or the other that enables well-calibrated thinking but the discriminating use of both. A pivotal role for analytical thinking lies in its ability to allow decision makers the means to detach from the intuitive mode to mitigate bias; it is the gatekeeper for the final diagnostic decision. Exploring and cultivating such debiasing initiatives should be seen as the next major research area in clinical decision making. Awareness of bias and strategies for debiasing are important aspects of the critical thinker’s armamentarium. Promoting critical thinking in undergraduate, postgraduate and continuing medical education will lead to better calibrated diagnosticians.

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Operationalization of the learned non-use phenomenon - A Delphi study

10.1101/2020.03.18.20037374 ◽

2020 ◽

Author(s):

Theresa Hirsch ◽

Maria Barthel ◽

Pauline Aarts ◽

Yi-An Chen ◽

Susanna Freivogel ◽

...

Keyword(s):

Decision Making ◽

Clinical Practice ◽

Delphi Study ◽

Clinical Decision Making ◽

Negative Reinforcement ◽

Brain Lesion ◽

Clinical Decision ◽

Diagnostic Process ◽

Sources Of Information ◽

Wide Range

AbstractThe discrepancy between residual functional capacity and reduced use of the contralesional hand, frequently observed after a brain lesion, has been termed Learned Non-Use (LNU) and is thought to depend on the interaction of neuronal mechanisms during recovery and learning-dependent mechanisms such as negative reinforcement. Despite the generally accepted existence of the LNU phenomenon among clinicians and researchers, no unequivocal and transdisciplinary definition exists to date. Furthermore, although therapeutic approaches are implemented in clinical practice to explicitly target LNU, no standardized diagnostic routine is described in the current literature.Based on a structured group communication following the Delphi method among clinical and scientific experts in the field of LNU, knowledge from both, the work with patient populations and with animal models, was synthesized and integrated to reach consensus regarding a transdisciplinary definition of the LNU phenomenon. Furthermore, the mode and strategy of the diagnostic process, as well as the sources of information and outcome parameters relevant for the clinical decision making, were described with a wide range showing the current lack of a consistent universal diagnostic approach. Building on these results, the need for the development of a structured diagnostic procedure and its implementation into clinical practice is emphasized. Moreover, it exists a striking gap between the prevailing hypotheses regarding the mechanisms underlying the LNU phenomenon and the actual evidence. Therefore, basic research is needed to bridge between bedside and bench and eventually improve clinical decision making and further development of interventional strategies beyond the field of stroke rehabilitation.

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Desenvolvimento e Avaliação de uma Plataforma Colaborativa Digital para Educação e Tomada de Decisão Médica Baseada em Evidências

Revista Brasileira de Educação Médica ◽

10.1590/1981-5271v43suplemento1-20190083 ◽

2019 ◽

Vol 43 (1 suppl 1) ◽

pp. 513-524

Author(s):

Álisson Oliveira dos Santos ◽

Alexandre Sztajnberg ◽

Tales Mota Machado ◽

Daniel Magalhães Nobre ◽

Adriano Neves de Paula e Souza ◽

...

Keyword(s):

Decision Making ◽

Clinical Practice ◽

Clinical Decision Making ◽

Real Life ◽

Evaluation Process ◽

Clinical Decision ◽

Medical Decision ◽

Alternative Form ◽

Evidence Based ◽

Life Problems

ABSTRACT The medical education for clinical decision-making has undergone changes in recent years. Previously supported by printed material, problem solving in clinical practice has recently been aided by digital tools known as summaries platforms. Doctors and medical students have been using such tools from questions found in practice scenarios. These platforms have the advantage of high-quality, evidence-based and always up-to-date content. Its popularization was mainly due to the rise of the internet use and, more recently, of mobile devices such as tablets and smartphones, facilitating their use in clinical practice. Despite this platform is widely available, the most of them actually present several access barriers as costs, foreign language and not be able to Brazilian epidemiology. A free national platform of evidence-based medical summaries was proposed, using the crowdsourcing concept to resolve those barriers. Furthermore, concepts of gamification and content evaluation were implemented. Also, there is the possibility of evaluation by the users, who assigns note for each content created. The platform was built with modern technological tools and made available for web and mobile application. After development, an evaluation process was conducted by researchers to attest to the valid of content, usability, and user satisfying. Consolidated questionnaires and evaluation tools by the literature were applied. The process of developing the digital platform fostered interdisciplinarity, from the involvement of medical and information technology professionals. The work also allowed the reflection on the innovative educational processes, in which the learning from real life problems and the construction of knowledge in a collaborative way are integrated. The assessment results suggest that platform can be real alternative form the evidence-based medical decision-making.

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Supporting Knowledge-Based Decision Making in the Medical Context

International Journal of Knowledge-Based Organizations ◽

10.4018/ijkbo.2011010103 ◽

2011 ◽

Vol 1 (1) ◽

pp. 42-60 ◽

Cited By ~ 13

Author(s):

Luca Anselma ◽

Alessio Bottrighi ◽

Gianpaolo Molino ◽

Stefania Montani ◽

Paolo Terenziani ◽

...

Keyword(s):

Decision Making ◽

Clinical Decision Making ◽

Clinical Decision ◽

Medical Decision ◽

Theory Analysis ◽

Knowledge Based ◽

Therapeutic Decisions ◽

Computer Based ◽

Based Medicine ◽

Domain Independent

Knowledge-based clinical decision making is one of the most challenging activities of physicians. Clinical Practice Guidelines are commonly recognized as a useful tool to help physicians in such activities by encoding the indications provided by evidence-based medicine. Computer-based approaches can provide useful facilities to put guidelines into practice and to support physicians in decision-making. Specifically, GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent prototypical tool providing advanced Artificial Intelligence techniques to support medical decision making, including what-if analysis, temporal reasoning, and decision theory analysis. The paper describes such facilities considering a real-world running example and focusing on the treatment of therapeutic decisions.

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Medical Informatics and Clinical Decision Making: The Science and the Pragmatics

Medical Decision Making ◽

10.1177/0272989x9101104s02 ◽

1991 ◽

Vol 11 (4_suppl) ◽

pp. S2-S14 ◽

Cited By ~ 16

Author(s):

Edward H. Shortliffe

Keyword(s):

Decision Making ◽

Medical Informatics ◽

Ad Hoc ◽

Clinical Decision Making ◽

Clinical Decision ◽

Decision Makers ◽

Medical Decision ◽

Normative Models ◽

Outcomes Management ◽

Collaborative Efforts

There are important scientific and pragmatic synergies between the medical decision making field and the emerging discipline of medical informatics. In the 1970s, the field of medicine forced clinically oriented artificial intelligence (AI) researchers to develop ways to manage explicit statements of uncertainty in expert systems. Classic probability theory was considered and discussed, but it tended to be abandoned because of complexities that limited its use. In medical AI systems, uncertainty was handled by a variety of ad hoc models that simulated probabilistic considerations. To illustrate the scientific interactions between the fields, the author describes recent work in his laboratory that has attempted to show that formal normative models based on probability and decision theory can be practically melded with AI methods to deliver effective advisory tools. In addition, the practical needs of decision makers and health policy planners are increasingly necessitating collaborative efforts to develop a computing and communications infrastructure for the decision making and informatics communities. This point is illustrated with an example drawn from outcomes management research.

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TSH Measurement and Its Implications for Personalised Clinical Decision-Making

Journal of Thyroid Research ◽

10.1155/2012/438037 ◽

2012 ◽

Vol 2012 ◽

pp. 1-9 ◽

Cited By ~ 13

Author(s):

Rudolf Hoermann ◽

John E. M. Midgley

Keyword(s):

Decision Making ◽

Thyroid Function ◽

Clinical Decision Making ◽

Reference Range ◽

Clinical Decision ◽

Population Based ◽

Medical Decision ◽

Basic Question ◽

Screening Parameter ◽

Log Linear

Advances in assay technology have promoted thyrotropin (TSH) measurements from participation in a multi-analyte assessment of thyroid function to a statistically defined screening parameter in its own right. While this approach has been successful in many ways, it has some grave limitations. This includes the basic question of what constitutes an agreed reference range and the fact that the population-based reference range by far exceeds the variation of the intraindividual set point. Both problems result in a potential misdiagnosis of normal and pathological thyroid function in a substantial proportion of patients. From a physiological perspective, TSH plays an integrated role in thyroid homeostasis. Few attempts have been made to adopt physiological insights into thyroid homeostasis for medical decision-making. Some emerging novel findings question the widely assumed log-linear TSH-FT4relationship over the entire thyroid function spectrum. This data favours more complex hierarchically structured models. With a better understanding of its role in thyroid homeostasis in thyroid health and disease, TSH can be revisited in the context of thyroid regulation. This, in turn, could help overcome some of the limitations arising from its isolated statistical use and offer new prospects towards a more personalised interpretation of thyroid test results.

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