Supporting Knowledge-Based Decision Making in the Medical Context

2011 ◽  
Vol 1 (1) ◽  
pp. 42-60 ◽  
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.

Supporting Knowledge-Based Decision Making in the Medical Context

2011 ◽  
pp. 1721-1737
Author(s):  
Luca Anselma ◽  
Alessio Bottrighi ◽  
Gianpaolo Molino ◽  
Stefania Montani ◽  
Paolo Terenziani ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Medical Decision ◽  
Evidence Based ◽  
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.

Supporting Knowledge-Based Decision Making in the Medical Context

2012 ◽  
pp. 24-42
Author(s):  
Luca Anselma ◽  
Alessio Bottrighi ◽  
Gianpaolo Molino ◽  
Stefania Montani ◽  
Paolo Terenziani ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Medical Decision ◽  
Evidence Based ◽  
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.

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

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.

Clinical Decision Making by Emergency Room Physicians and Residents

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).

Clinical outcome assessment in mental health

2018 ◽  
pp. 191-200
Author(s):  
Skye P. Barbic ◽  
Stefan J. Cano
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Clinical Outcome ◽  
Outcome Assessment ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Evidence Based ◽  
Measurement Issues ◽  
Health Disorders ◽  
Based Medicine

Clinical outcome assessment (COA) in mental health is essential to inform patient-centred care and clinical decision-making. In this chapter, the reader is introduced to COA as it is evolving in the field of mental health. Multiple approaches to COA are presented, but emphasis is placed on approaches that generate clinically meaningful data. Understanding COA can position clinicians and stakeholders to better evaluate their own practice and to contribute to the ongoing evolution of COA research and evidence-based medicine. This chapter begins with the definitions of assessment and measurement. Conceptual frameworks and models of COA development and testing are then presented. These are followed by a discussion of measurement in practice that reviews measurement issues related to clinical decision-making, programme evaluation, and clinical trials. Finally, this chapter highlights the contribution of metrology to improving health outcomes of individuals who experience mental health disorders.

scholarly journals Value-impregnated factual claims may undermine medical decision-making

2018 ◽  
Vol 13 (3) ◽  
pp. 151-158 ◽  
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.

Computer-based ICU data acquisition as an aid to clinical decision-making

1982 ◽  
Vol 10 (12) ◽  
pp. 823-830 ◽  
Author(s):  
REED M. GARDNER ◽  
BLAIR J. WEST ◽  
T. ALLAN PRYOR ◽  
KEITH G. LARSEN ◽  
HOMER R. WARNER ◽  
...  
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Computer Based

Clinical Decision-Making for a Person Centered Medicine: the necessity of casuistic reasoning

2014 ◽  
Vol 2 (1) ◽  
pp. 71
Author(s):  
Mark Tonelli
Keyword(s):  
Decision Making ◽  
Clinical Judgment ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Patient Choice ◽  
Clinical Expertise ◽  
Patient Centered ◽  
Patient Centered Medicine ◽  
The Individual ◽  
Based Medicine

In their call to develop a consistent, coherent and comprehensive notion of person-centered medicine, Miles and Mezzich have elucidated several specific challenges that need to be urgently addressed. One of these foundational tasks is the development of a more complete understanding of person-centered clinical decision-making. Miles and Mezzich note that while the emphasis on clinical research in evidence-based medicine has served to de-emphasize the importance of the individual patient, the alternative of patient-centered medicine has the potential to de-emphasize the judgment of the clinician by making unfettered patient choice paramount. A practice of medicine that reduces professional healers to an informational role only, one where they lay out potential interventions devoid of context and allow patients to choose from amongst them, undervalues clinical expertise and will clearly not lead to better outcomes. Person-centered medicine (PCM), then, must be able to develop and defend a model of clinical judgment and practice that strikes the correct balance between the science of medicine and the personal experience of the individual in search of care.

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