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.

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 Shared clinical decision-making experiences in nursing: a qualitative study

BMC Nursing ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Fen-Fang Chung ◽  
Pao-Yu Wang ◽  
Shu-Chuan Lin ◽  
Yu-Hsia Lee ◽  
Hon-Yen Wu ◽  
...  
Keyword(s):  
Decision Making ◽  
Nursing Education ◽  
Nursing Staff ◽  
Information Exchange ◽  
Clinical Decision Making ◽  
Medical Center ◽  
Clinical Decision ◽  
Sociocultural Factors ◽  
Patient Centered ◽  
Clinical Nursing

Abstract Background Shared decision making (SDM) is a patient-centered nursing concept that emphasizes the autonomy of patients. SDM is a co-operative process that involves information exchange and communication between medical staff and patients for making treatment decisions. In this study, we explored the experiences of clinical nursing staff participating in SDM. Methods This study adopted a qualitative research design. Semistructured interviews were conducted with 21 nurses at a medical center in northern Taiwan. All interview recordings were transcribed verbatim. Content analysis was performed to analyze the data. Results The findings yielded the following three themes covering seven categories: knowledge regarding SDM, trigger discussion and coordination, and respect of sociocultural factors. Conclusions The results of this study describe the experiences of clinical nursing staff participating in SDM and can be used as a reference for nursing education and nursing administrative supervisors wishing to plan and enhance professional nursing SDM in nursing education.

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.

scholarly journals Exploring the role of the Care and Health Information Exchange (CHIE) in clinical decision-making: a realist evaluation

2020 ◽  
Author(s):  
Philip Scott ◽  
Elisavet Andrikopoulou ◽  
Haythem Nakkas ◽  
Paul Roderick
Keyword(s):  
Decision Making ◽  
Health Information ◽  
Information Exchange ◽  
Healthcare Professionals ◽  
Clinical Decision Making ◽  
Social Care ◽  
Health Information Exchange ◽  
Clinical Decision ◽  
Health And Social Care ◽  
The Impact

Background: The overall evidence for the impact of electronic information systems on cost, quality and safety of healthcare remains contested. Whilst it seems intuitively obvious that having more data about a patient will improve care, the mechanisms by which information availability is translated into better decision-making are not well understood. Furthermore, there is the risk of data overload creating a negative outcome. There are situations where a key information summary can be more useful than a rich record. The Care and Health Information Exchange (CHIE) is a shared electronic health record for Hampshire and the Isle of Wight that combines key information from hospital, general practice, community care and social services. Its purpose is to provide clinical and care professionals with complete, accurate and up-to-date information when caring for patients. CHIE is used by GP out-of-hours services, acute hospital doctors, ambulance service, GPs and others in caring for patients. Research questions: The fundamental question was How does awareness of CHIE or usage of CHIE affect clinical decision-making? The secondary questions were What are the latent benefits of CHIE in frontline NHS operations? and What is the potential of CHIE to have an impact on major NHS cost pressures? The NHS funders decided to focus on acute medical inpatient admissions as the initial scope, given the high costs associated with hospital stays and the patient complexities (and therefore information requirements) often associated with unscheduled admissions. Methods: Semi-structured interviews with healthcare professionals to explore their experience about the utility of CHIE in their clinical scenario, whether and how it has affected their decision-making practices and the barriers and facilitators for their use of CHIE. The Framework Method was used for qualitative analysis, supported by the software tool Atlas.ti. Results: 21 healthcare professionals were interviewed. Three main functions were identified as useful: extensive medication prescribing history, information sharing between primary, secondary and social care and access to laboratory test results. We inferred two positive cognitive mechanisms: knowledge confidence and collaboration assurance, and three negative ones: consent anxiety, search anxiety and data mistrust. Conclusions: CHIE gives clinicians the bigger picture to understand the patient's health and social care history and circumstances so as to make confident and informed decisions. CHIE is very beneficial for medicines reconciliation on admission, especially for patients that are unable to speak or act for themselves or who cannot remember their precise medication or allergies. We found no clear evidence that CHIE has a significant impact on admission or discharge decisions. We propose the use of recommender systems to help clinicians navigate such large volumes of patient data, which will only grow as additional data is collected.

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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