scholarly journals Development of an explicit and implicit knowledge identification tool for the analysis of the decision-making process of traditional Asian medicine doctors

2021 ◽  
Author(s):  
Musun Park ◽  
Min Hee Kim ◽  
So-young Park ◽  
Minseo Kang ◽  
Inhwa Choi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Explicit Knowledge ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Implicit Knowledge ◽  
Decision Making Process ◽  
Analysis Tool ◽  
Asian Medicine ◽  
Explicit And Implicit Knowledge

Background and objectives: While pattern identification (PI) is an essential process for diagnosis and treatment in traditional Asian medicine (TAM), it is difficult to objectify since it relies heavily on implicit knowledge. Here, we propose a machine learning-based analysis tool to objectify and evaluate the clinical decision-making process of PI in terms of explicit and implicit knowledge. Methods: Clinical data for the development of the analysis tool were collected using a questionnaire administered to allergic rhinitis (AR) patients and the diagnosis and prescription results of TAM doctors based on the completed AR questionnaires. Explicit knowledge and implicit knowledge were defined based on the explicit and implicit importance scores of the AR questionnaire, which were obtained through doctors′ explicit scoring and feature evaluations of machine learning models, respectively. The analysis tool consists of eight evaluation indicators used to compare, analyze and visualize the explicit and implicit knowledge of TAM doctors. Results: The analysis results for 8 doctors showed that our tool could successfully identify explicit and implicit knowledge in the PI process. We also conducted a postquestionnaire study with the doctors who participated to evaluate the applicability of our tool. Conclusions: This study proposed a tool to evaluate and compare decision-making processes of TAM doctors in terms of their explicit and implicit knowledge. We identified the differences between doctors′ own explicit and implicit knowledge and the differences among TAM doctors. The proposed tool would be helpful for the clinical standardization of TAM, doctors′ own clinical practice, and intern/resident training.

scholarly journals Trends in Using IoT with Machine Learning in Health Prediction System

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 181-206
Author(s):  
Amani Aldahiri ◽  
Bashair Alrashed ◽  
Walayat Hussain
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Healthcare Sector ◽  
Decision Making Process ◽  
Comprehensive Overview ◽  
Optimal Method ◽  
Healthcare Data ◽  
Security Business

Machine learning (ML) is a powerful tool that delivers insights hidden in Internet of Things (IoT) data. These hybrid technologies work smartly to improve the decision-making process in different areas such as education, security, business, and the healthcare industry. ML empowers the IoT to demystify hidden patterns in bulk data for optimal prediction and recommendation systems. Healthcare has embraced IoT and ML so that automated machines make medical records, predict disease diagnoses, and, most importantly, conduct real-time monitoring of patients. Individual ML algorithms perform differently on different datasets. Due to the predictive results varying, this might impact the overall results. The variation in prediction results looms large in the clinical decision-making process. Therefore, it is essential to understand the different ML algorithms used to handle IoT data in the healthcare sector. This article highlights well-known ML algorithms for classification and prediction and demonstrates how they have been used in the healthcare sector. The aim of this paper is to present a comprehensive overview of existing ML approaches and their application in IoT medical data. In a thorough analysis, we observe that different ML prediction algorithms have various shortcomings. Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data. The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.

Machine learning in clinical decision making

Med ◽  
2021 ◽  
Author(s):  
Lorenz Adlung ◽  
Yotam Cohen ◽  
Uria Mor ◽  
Eran Elinav
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision

Benner’s Framework and Clinical Decision-Making in the Critical Care Environment

2016 ◽  
Vol 30 (1) ◽  
pp. 52-57 ◽  
Author(s):  
Kristi J. Stinson
Keyword(s):  
Decision Making ◽  
Critical Care ◽  
Clinical Experience ◽  
Strong Correlation ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Decision Making Process ◽  
Clinical Experiences ◽  
Care Environment ◽  
Decision Making Processes

Completed as part of a larger dissertational study, the purpose of this portion of this descriptive correlational study was to examine the relationships among registered nurses’ clinical experiences and clinical decision-making processes in the critical care environment. The results indicated that there is no strong correlation between clinical experience in general and clinical experience in critical care and clinical decision-making. There were no differences found in any of the Benner stages of clinical experience in relation to the overall clinical decision-making process.

scholarly journals Pet owners’ and veterinarians’ perceptions of information exchange and clinical decision-making in companion animal practice

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245632
Author(s):  
Natasha Janke ◽  
Jason B. Coe ◽  
Theresa M. Bernardo ◽  
Cate E. Dewey ◽  
Elizabeth A. Stone
Keyword(s):  
Decision Making ◽  
Focus Groups ◽  
Information Exchange ◽  
Clinical Decision Making ◽  
Current Knowledge ◽  
Exchange Process ◽  
Clinical Decision ◽  
Decision Making Process ◽  
Pet Owners ◽  
Pet Owner

One of the most complex aspects of the veterinarian-client-patient interaction is the clinical decision-making process. Research suggests that the approach to communication used by veterinarians can impact veterinary clients’ involvement in the decision-making process and their ultimate satisfaction. Using different approaches to the decision-making process may affect how information is exchanged and consequently how decisions are made. The objective of this study was to determine pet owners’ expectations with respect to information exchange and decision-making during veterinarian-client-patient interactions and to compare veterinarians’ perceptions of those expectations and the challenges they face in meeting them. Five pet owner focus groups (27 owners) and three veterinarian focus groups (24 veterinarians) were conducted with standardized open-ended questions and follow-up probes. Thematic analysis of the transcribed data was conducted to identify trends and patterns that emerged during the focus groups. Three pet owner-based themes were identified: 1) understanding the client; 2) providing information suitable for the client; and 3) decision-making. In addition, three barriers for veterinarians affecting information exchange and decision-making were identified: 1) time constraints; 2) involvement of multiple clients; and 3) language barriers. Results suggest that pet owners expect to be supported by their veterinarian to make informed decisions by understanding the client’s current knowledge, tailoring information and educating clients about their options. Breakdowns in the information exchange process can impact pet owners’ perceptions of veterinarians’ motivations. Pet owners’ emphasis on partnership suggests that a collaborative approach between veterinarians and clients may improve client satisfaction.

scholarly journals Factors Influencing Physicians' Clinical Decision-making at Upazila Health Complexes in Bangladesh

2020 ◽  
Vol 3 (4) ◽  
pp. 125-133
Author(s):  
M. Aminul Islam ◽  
M. Abdul Awal
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Clinical Decision Making ◽  
Average Length ◽  
Clinical Decision ◽  
Primary Data ◽  
Decision Making Process ◽  
Cross Sectional ◽  
The Right ◽  
Consultation Session

ABSTRACT Introduction Selecting the most appropriate treatment for each patient is the key activity in patient-physician encounters and providing healthcare services. Achieving desirable clinical goals mostly depends on making the right decision at the right time in any healthcare setting. But little is known about physicians' clinical decision-making in the primary care setting in Bangladesh. Therefore, this study explored the factors that influence decisions about prescribing medications, ordering pathologic tests, counseling patients, average length of patient visits in a consultation session, and referral of patients to other physicians or hospitals by physicians at Upazila Health Complexes (UHCs) in the country. It also explored the structure of physicians' social networks and their association with the decision-making process. Methods This was a cross-sectional descriptive study that used primary data collected from 85 physicians. The respondents, who work at UHCs in the Rajshahi Division, were selected purposively. The collected data were analyzed with descriptive statistics including frequency, percentage, one-way analysis of variance, and linear regression to understand relationships among the variables. Results The results of the study reveal that multiple factors influence physicians' decisions about prescribing medications, ordering pathologic tests, length of visits, counseling patients, and referring patients to other physicians or hospitals at the UHCs. Most physicians prescribe drugs to their patients, keeping in mind their purchasing capacity. Risk of violence by patients' relatives and better management are the two key factors that influence physicians' referral decisions. The physicians' professional and personal social networks also play an influential role in the decision-making process. It was found that physicians dedicate on average 16.17 minutes to a patient in a consultation session. The length of visits is influenced by various factors including the distance between the physicians' residence and their workplace, their level of education, and the number of colleagues with whom they have regular contact and from whom they can seek help. Conclusion The results of the study have yielded some novel insights about the complexity of physicians' everyday tasks at the UHCs in Bangladesh. The results would be of interest to public health researchers and policy makers.

Clinical Problem Solving: Decreased Level of Consciousness and Unexplained Hydrocephalus

2022 ◽  
pp. 194187442110567
Author(s):  
Naomi Niznick ◽  
Ronda Lun ◽  
Daniel A. Lelli ◽  
Tadeu A. Fantaneanu
Keyword(s):  
Decision Making ◽  
Differential Diagnosis ◽  
Clinical Decision Making ◽  
Clinical Problem ◽  
Clinical Decision ◽  
Decision Making Process ◽  
Clinical Problem Solving ◽  
Level Of Consciousness ◽  
History Of

We present a clinical reasoning case of 42-year-old male with a history of type 1 diabetes who presented to hospital with decreased level of consciousness. We review the approach to coma including initial approach to differential diagnosis and investigations. After refining the diagnostic options based on initial investigations, we review the clinical decision-making process with a focus on narrowing the differential diagnosis, further investigations, and treatment.

Sign in / Sign up

Export Citation Format

Share Document