scholarly journals Health improvement framework for planning actionable treatment process using surrogate Bayesian model

2020 ◽  
Author(s):  
Kazuki Nakamura ◽  
Ryosuke Kojima ◽  
Eiichiro Uchino ◽  
Koichi Murashita ◽  
Ken Itoh ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Decision Making ◽  
Bayesian Model ◽  
High Performance ◽  
Clinical Decision Making ◽  
Personal Characteristics ◽  
Clinical Decision ◽  
Health Improvement ◽  
Primary Concern ◽  
Treatment Processes

Abstract Clinical decision making regarding treatments based on personal characteristics leads to effective health improvements. Machine learning (ML) has been the primary concern of diagnosis support according to comprehensive patient information. However, the remaining prominent issue is the development of objective treatment processes in clinical situations. This study proposes a novel framework to plan treatment processes in a data-driven manner. A key point of the framework is the evaluation of the "actionability" for personal health improvements by using a surrogate Bayesian model in addition to a high-performance nonlinear ML model. We first evaluated the framework from the viewpoint of its methodology using a synthetic dataset. Subsequently, the framework was applied to an actual health checkup dataset comprising data from 3,132 participants, to improve systolic blood pressure values at the individual level. We confirmed that the computed treatment processes are actionable and consistent with clinical knowledge for lowering blood pressure. These results demonstrate that our framework could contribute toward decision making in the medical field, providing clinicians with deeper insights.

scholarly journals Health improvement framework for actionable treatment planning using a surrogate Bayesian model

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kazuki Nakamura ◽  
Ryosuke Kojima ◽  
Eiichiro Uchino ◽  
Koh Ono ◽  
Motoko Yanagita ◽  
...  
Keyword(s):  
Decision Making ◽  
Bayesian Model ◽  
High Performance ◽  
Clinical Decision Making ◽  
Personal Characteristics ◽  
Clinical Decision ◽  
Health Improvement ◽  
Primary Concern ◽  
Lower Systolic Blood Pressure ◽  
Treatment Processes

AbstractClinical decision-making regarding treatments based on personal characteristics leads to effective health improvements. Machine learning (ML) has been the primary concern of diagnosis support according to comprehensive patient information. A prominent issue is the development of objective treatment processes in clinical situations. This study proposes a framework to plan treatment processes in a data-driven manner. A key point of the framework is the evaluation of the actionability for personal health improvements by using a surrogate Bayesian model in addition to a high-performance nonlinear ML model. We first evaluate the framework from the viewpoint of its methodology using a synthetic dataset. Subsequently, the framework is applied to an actual health checkup dataset comprising data from 3132 participants, to lower systolic blood pressure and risk of chronic kidney disease at the individual level. We confirm that the computed treatment processes are actionable and consistent with clinical knowledge for improving these values. We also show that the improvement processes presented by the framework can be clinically informative. These results demonstrate that our framework can contribute toward decision-making in the medical field, providing clinicians with deeper insights.

scholarly journals Clinical Decision-Making: Developing a 4 C Model Using Graph Theoretic Approach

2020 ◽  
Vol 7 (1) ◽  
pp. 30
Author(s):  
Rajesh P. Mishra ◽  
Nidhi Mundra ◽  
Girish Upreti ◽  
Marcela Villa-Marulanda
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Theoretic Model ◽  
Specific Situation ◽  
Theoretic Approach ◽  
Graph Theoretic ◽  
Treatment Processes ◽  
Graph Theoretic Approach ◽  
Major Factors

The purpose of this paper is to propose a graph-theoretic mathematical model to measure how conducive the environment of a hospital is for decision-making. We propose a 4-C model, developed from four interacting factors: confidence, complexity, capability, and customer. In this graph-theoretic model, abstract information regarding the system is represented by the directed edges of a graph (or digraph), which together depict how one factor affects another. The digraph yields a matrix model useful for computer processing. The net effect of different factors and their interdependencies on the hospital's decision-making environment is quantified and a single numerical index is generated. This paper categorizes all the major factors that influence clinical decision-making and attempts to provide a tool to study and measure their interactions with each other. Each factor and each interaction among factors are to be quantified by healthcare experts according to their best judgment of the magnitude of its effect in a local hospital environment.A hospital case study is used to demonstrate how the 4-C model works. The graph-theoretic approach allows for the inclusion of new factors and generation of alternative environments by a combination of both qualitative and quantitative modeling. The 4-C model can be used to create both a database and a simple numerical scale that help a hospital set customized guidelines, ranging from patient admittance procedures to diagnostic and treatment processes, according to its specific situation. Implementing this methodology systematically can allow a hospital to identify factors that will lead to improved decision-making as well as identifying operational factors that present roadblocks.

Abstract TMP105: Location Does Matter: Comparing the Location of Blood Pressure Management in an Intensive Care Unit Setting

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Kathrina B Siaron ◽  
Michaela Cortes ◽  
Aardhra Venkatachalam ◽  
Sonja Stutzman ◽  
Khalid M Ahmed ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Intensive Care Unit ◽  
Decision Making ◽  
Intensive Care ◽  
Repeated Measures ◽  
Clinical Decision Making ◽  
Pearson Correlation ◽  
Clinical Decision ◽  
Repeated Measures Anova ◽  
Measurement Site

Background and Purpose: Blood pressure (BP) management is a vital part of acute stroke care. Both over- and under-correction of BP are associated with increased morbidity and mortality. Non-invasive BP (NIBP) and intra-arterial BP (ABP) measurements are commonly used, yet both methods yield inconsistent results. Measurement error can hinder optimal clinical management in neurological conditions where BP directly affects cerebral perfusion pressures. Discrepancy in clinical decision-making associated with BP measurement is rarely reported. This prospective, non-randomized, cross-sectional study aims to address the gap by correlating simultaneous within-subject BP readings from multiple sites. Methods: NIBP was simultaneously measured from 4 sites (both arms, both wrists) and ABP (when available) in 80 intensive care unit subjects. Correlation matrices and repeated measures ANOVA were used to explore for differences in BP by measurement site. Results: Of 80 subjects, 41 were male, mean age = 52.8, and mean BMI = 30. Pearson Correlation Coefficients for SBP ranged from 0.67 to 0.83; DBP from 0.77 to 0.84; and MAP from 0.76 to 0.88. (Figure 1). SBP differences ranged from 0 to 57 mmHg and MAP differences ranged from 0 to 36 mmHg. One-way repeated measures ANOVA revealed significantly different values for SBP (p=0.0319); DBP (p=0.0002); and MAP (p=0.0001). Conclusions: BP values vary significantly when measured simultaneously in different sites. Unpredictable inter-site comparisons of BP warrant significant research in larger prospective trials. Nurses should consider standardizing a measurement site for consistent BP readings and better clinical decision-making.

VALIDITY AND AGREEMENT OF CLINICAL DECISION MAKING IN PREVENTIVE BLOOD PRESSURE MEASURES

2004 ◽  
Vol 22 (Suppl. 2) ◽  
pp. S158-S159
Author(s):  
V. Gil ◽  
D. Orozco ◽  
A. Lattour ◽  
J. Belmonte ◽  
F. Mas ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals Usefulness of a radial applanation tonometry device in blood pressure related clinical decision making by anesthesiologists

2020 ◽  
Author(s):  
Xiaoshuang Zhou ◽  
Bin Liu ◽  
Haidan Lan ◽  
Jin Liu ◽  
Xiao Li ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Elective Surgery ◽  
Clinical Decision ◽  
Applanation Tonometry ◽  
Percentage Error ◽  
Arterial Blood ◽  
Efficient Information ◽  
The Right

Abstract Purpose: Radial artery tonometry (AT) can continuously measure arterial blood pressure (ABP) noninvasively. This study aimed to evaluate AT for continuous ABP monitoring during anesthesia and compared AT to invasive (IBP) and non-invasive (NIBP) ABP measurements at clinical decision-making moments. Methods: 243 patients undergoing elective surgery were prospectively included in the study and AT was applied on the right or left arm while IBP and NIBP were recorded simultaneously. At moments when the IBP signal required a clinical decision by the anesthesiologist for situations of hyper- or hypotension, comparison was made whether AT and NIPB signals would require a clinical decision as well. Agreement/discrepancy of clinical decision-making was analyzed, additionally bias, precision, and percentage error of AT was compared to IBP at these moments. Results: 513 clinical decision moments were recorded. Decision moments based on AT signal did not differ significantly from decision moments based on IBP (1 vs. 1; IQR, 1 – 2 vs. 0 – 3, P = 0.06), while NIBP based decision moments showed significant differences (0 vs. 1; IQR, 0 – 2 vs. 0 – 3, P＜0.001). Subgroup analysis of patients divided by age, BMI and surgery time also showed no significant differences between IBP and AT. Conclusions: ABP measurement using AT is feasible and safe. AT provides relevant and efficient information to anesthesiologists; at moments when IBP called for action, AT called for action as well, but not NIBP. AT also showed clinically satisfactory agreement with IBP at moments of hypo- and hypertension.

scholarly journals Usefulness of a radial applanation tonometry device in blood pressure related clinical decision making by anesthesiologists

2019 ◽  
Author(s):  
Guo Chen ◽  
Xiaoshuang Zhou ◽  
Bin Liu ◽  
Haidan Lan ◽  
Jin Liu ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Elective Surgery ◽  
Clinical Decision ◽  
Applanation Tonometry ◽  
Percentage Error ◽  
Arterial Blood ◽  
Efficient Information ◽  
The Right

Abstract Purpose: Radial artery tonometry (AT) can continuously measure arterial blood pressure (ABP) noninvasively. This study aimed to evaluate AT for continuous ABP monitoring during anesthesia and compared AT to invasive (IBP) and non-invasive (NIBP) ABP measurements at clinical decision-making moments. Methods: 243 patients undergoing elective surgery were prospectively included in the study and AT was applied on the right or left arm while IBP and NIBP were recorded simultaneously. At moments when the IBP signal required a clinical decision by the anesthesiologist for situations of hyper- or hypotension, comparison was made whether AT and NIPB signals would require a clinical decision as well. Agreement/discrepancy of clinical decision-making was analyzed, additionally bias, precision, and percentage error of AT was compared to IBP at these moments. Results: 513 clinical decision moments were recorded. Decision moments based on AT signal did not differ significantly from decision moments based on IBP (1 vs. 1; IQR, 1 – 2 vs. 0 – 3, P = 0.06), while NIBP based decision moments showed significant differences (0 vs. 1; IQR, 0 – 2 vs. 0 – 3, P＜0.001). Subgroup analysis of patients divided by age, BMI and surgery time also showed no significant differences between IBP and AT. Conclusions: ABP measurement using AT is feasible and safe. AT provides relevant and efficient information to anesthesiologists; at moments when IBP called for action, AT called for action as well, but not NIBP. AT also showed clinically satisfactory agreement with IBP at moments of hypo- and hypertension.

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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