Integration of Smart Home Health Data in the Clinical Decision Making Process

2014 ◽  
pp. 354-366
Author(s):  
Axel Helmer ◽  
Frerk Müller ◽  
Okko Lohmann ◽  
Andreas Thiel ◽  
Friedrich Kretschmer ◽  
...  
Keyword(s):  
Decision Making ◽  
Smart Home ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Health Data ◽  
Home Health ◽  
Decision Making Process

Usefulness of magnetic resonance enterography in the clinical decision-making process for patients with inflammatory bowel disease

2020 ◽  
Vol 43 (8) ◽  
pp. 439-445
Author(s):  
Laura Ramos López ◽  
Alejandro Hernández Camba ◽  
Iago Rodríguez-Lago ◽  
Marta Carrillo Palau ◽  
Luis Cejas Dorta ◽  
...  
Keyword(s):  
Decision Making ◽  
Inflammatory Bowel Disease ◽  
Magnetic Resonance ◽  
Bowel Disease ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Magnetic Resonance Enterography ◽  
Decision Making Process ◽  
Inflammatory Bowel

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 Integrating Patient-Generated Health Data Into Clinical Care Settings or Clinical Decision-Making: Lessons Learned From Project HealthDesign

2016 ◽  
Vol 3 (2) ◽  
pp. e26 ◽  
Author(s):  
Deborah J Cohen ◽  
Sara R Keller ◽  
Gillian R Hayes ◽  
David A Dorr ◽  
Joan S Ash ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Clinical Decision ◽  
Health Data ◽  
Lessons Learned

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.

Improving the evidence-based clinical decision-making process: Interactive classification and topic discovery on diabetes-related biomedical literature (Preprint)

2021 ◽  
Author(s):  
Adrian Ahne ◽  
Guy Fagherazzi ◽  
Xavier Tannier ◽  
Thomas Czernichow ◽  
Francisco Orchard
Keyword(s):  
Decision Making ◽  
Active Learning ◽  
Hierarchical Clustering ◽  
Domain Knowledge ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Biomedical Literature ◽  
Decision Making Process ◽  
Evidence Based ◽  
Text Data

BACKGROUND The amount of available textual health data such as scientific and biomedical literature is constantly growing and it becomes more and more challenging for health professionals to properly summarise those data and in consequence to practice evidence-based clinical decision making. Moreover, the exploration of large unstructured health text data is very challenging for non experts due to limited time, resources and skills. Current tools to explore text data lack ease of use, need high computation efforts and have difficulties to incorporate domain knowledge and focus on topics of interest. OBJECTIVE We developed a methodology which is able to explore and target topics of interest via an interactive user interface for experts and non-experts. We aim to reach near state of the art performance, while reducing memory consumption, increasing scalability and minimizing user interaction effort to improve the clinical decision making process. The performance is evaluated on diabetes-related abstracts from Pubmed. METHODS The methodology consists of four parts: 1) A novel interpretable hierarchical clustering of documents where each node is defined by headwords (describe documents in this node the most); 2) An efficient classification system to target topics; 3) Minimized users interaction effort through active learning; 4) A visual user interface through which a user interacts. We evaluated our approach on 50,911 diabetes-related abstracts from Pubmed which provide a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against three other strategies: random selection of training instances, uncertainty sampling which chooses instances the model is most uncertain about and an expected gradient length strategy based on convolutional neural networks (CNN). RESULTS For the hierarchical clustering performance, we achieved a F1-Score of 0.73 compared to scikit-learn’s of 0.76. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1-Score over all MeSH codes resulted in satisfying 0.62 F1-Score of our approach, compared to 0.61 of the uncertainty strategy, 0.61 the CNN and 0.45 the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents but increased execution time. CONCLUSIONS We proposed an easy to use tool for experts and non-experts being able to combine domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore our approach is very memory efficient and highly parallelizable making it interesting for large Big Data sets. This approach can be used by health professionals to rapidly get deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process.

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