Clinical decision making and risk management.

2016 ◽  
pp. 245-271
Author(s):  
Steven A. Sobelman ◽  
Jeffrey N. Younggren
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Clinical Decision Making ◽  
Clinical Decision

Making defensible decisions in the era of recovery and rights

2015 ◽  
Vol 26 (5) ◽  
pp. 474-477 ◽  
Author(s):  
Andrew Carroll ◽  
Bernadette McSherry
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Clinical Decision Making ◽  
Civil Liability ◽  
Clinical Decision ◽  
Liability Law ◽  
Essential Components ◽  
Information And Communication ◽  
Available Information

Objectives: Our aim was to develop a framework for clinical decision-making that can be used to take into account risk in an era of recovery and rights. Conclusion: We developed a framework influenced by civil liability law to develop a guide for clinical decision-making which emphasises collaboration, clarification of the available information and communication of decisions as essential components of recovery-oriented risk management.

A structural design of clinical decision support system for chronic diseases risk management

Open Medicine ◽  
2007 ◽  
Vol 2 (2) ◽  
pp. 129-139 ◽  
Author(s):  
Chi-Chang Chang ◽  
Chuen-Sheng Cheng
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Structural Design ◽  
Clinical Decision Support System ◽  
Clinical Decision Making ◽  
Clinical Decision

AbstractIn clinical decision making, the event of primary interest is recurrent, so that for a given unit the event could be observed more than once during the study. In general, the successive times between failures of human physiological systems are not necessarily identically distributed. However, if any critical deterioration is detected, then the decision of when to take thei ntervention, given the costs of diagnosis and therapeutics, is of fundamental importance This paper develops a possible structural design of clinical decision support system (CDSS) by considering the sensitivity analysis as well as the optimal prior and posterior decisions for chronic diseases risk management. Indeed, Bayesian inference of a nonhomogeneous Poisson process with three different failure models (linear, exponential, and power law) were considered, and the effects of the scale factor and the aging rate of these models were investigated. In addition, we illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease. The proposed structural design of CDSS facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert’s opinions and the sampling information which will furnish decision makers with valuable support for quality clinical decision making.

Risk management in the era of recovery and rights

2020 ◽  
pp. 1-11
Author(s):  
Andrew Carroll ◽  
Bernadette McSherry
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Clinical Decision Making ◽  
Civil Liability ◽  
Clinical Decision ◽  
Personal Recovery ◽  
Liability Law ◽  
Information And Communication ◽  
Available Information

SUMMARY Clinical decision-making in psychiatry is affected by many factors, including how best to reduce risks of harm while promoting autonomy and personal recovery. This article proposes guidance for clinical decision-making that is consistent with civil liability law. It emphasises collaboration, clarification of the available information and communication of decisions as a basis for recovery-oriented risk management.

scholarly journals Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19

2020 ◽  
Vol 14 (5) ◽  
pp. 652-657 ◽  
Author(s):  
Qiong-Na Zheng ◽  
Mei-Yan Xu ◽  
Yong-Le Zheng ◽  
Xiu-Ying Wang ◽  
Hui Zhao
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Risk Management ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Linear Regression Analysis ◽  
Significant Risk ◽  
Clinical Decision ◽  
Clinical Practices

ABSTRACTObjectives:More than 80% of coronavirus disease 2019 (COVID-19) cases are mild or moderate. In this study, a risk model was developed for predicting rehabilitation duration (the time from hospital admission to discharge) of the mild-moderate COVID-19 cases and was used to conduct refined risk management for different risk populations.Methods:A total of 90 consecutive patients with mild-moderate COVID-19 were enrolled. Large-scale datasets were extracted from clinical practices. Through the multivariable linear regression analysis, the model was based on significant risk factors and was developed for predicting the rehabilitation duration of mild-moderate cases of COVID-19. To assess the local epidemic situation, risk management was conducted by weighing the risk of populations at different risk.Results:Ten risk factors from 44 high-dimensional clinical datasets were significantly correlated to rehabilitation duration (P < 0.05). Among these factors, 5 risk predictors were incorporated into a risk model. Individual rehabilitation durations were effectively calculated. Weighing the local epidemic situation, threshold probability was classified for low risk, intermediate risk, and high risk. Using this classification, risk management was based on a treatment flowchart tailored for clinical decision-making.Conclusions:The proposed novel model is a useful tool for individualized risk management of mild-moderate COVID-19 cases, and it may readily facilitate dynamic clinical decision-making for different risk populations.

Debates in Dysphagia Management: How Do You Use Evidence-Based Practice in Your Dysphagia Patient Care?

2011 ◽  
Vol 20 (4) ◽  
pp. 121-123
Author(s):  
Jeri A. Logemann
Keyword(s):  
Decision Making ◽  
Patient Care ◽  
Clinical Outcomes ◽  
Clinical Judgment ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Method ◽  
Do So

Evidence-based practice requires astute clinicians to blend our best clinical judgment with the best available external evidence and the patient's own values and expectations. Sometimes, we value one more than another during clinical decision-making, though it is never wise to do so, and sometimes other factors that we are unaware of produce unanticipated clinical outcomes. Sometimes, we feel very strongly about one clinical method or another, and hopefully that belief is founded in evidence. Some beliefs, however, are not founded in evidence. The sound use of evidence is the best way to navigate the debates within our field of practice.

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