scholarly journals Documenting Patient Data in Psoriasis Clinical Practice—Patient Focus Groups Supporting Psoriasis Experts’ Decision-making

2021 ◽  
Vol Volume 15 ◽  
pp. 549-557
Author(s):  
Marina Otten ◽  
Matthias Augustin
Keyword(s):  
Decision Making ◽  
Clinical Practice ◽  
Focus Groups ◽  
Patient Data ◽  
Practice Patient

scholarly journals An Intensive Care Unit Team Reflects on End-of-Life Experiences With Patients and Families in Chile

2022 ◽  
Vol 31 (1) ◽  
pp. 24-32
Author(s):  
Alejandra Palma ◽  
Verónica Aliaga-Castillo ◽  
Luz Bascuñan ◽  
Verónica Rojas ◽  
Fernando Ihl ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Decision Making ◽  
Intensive Care ◽  
Clinical Practice ◽  
Focus Groups ◽  
End Of Life ◽  
Emotional Impact ◽  
Urban Hospital ◽  
Communication Difficulties ◽  
Eol Care

Background Deaths in the intensive care unit (ICU) represent an experience of suffering for patients, their families, and professionals. End-of-life (EOL) care has been added to the responsibilities of the ICU team, but the evidence supporting EOL care is scarce, and there are many barriers to implementing the clinical recommendations that do exist. Objectives To explore the experiences and perspectives of the various members of an ICU care team in Chile regarding the EOL care of their patients. Methods A qualitative study was performed in the ICU of a high-complexity academic urban hospital. The study used purposive sampling with focus groups as a data collection method. A narrative analysis based on grounded theory was done. Results Four discipline-specific focus groups were conducted; participants included 8 nurses, 6 nursing assistants, 8 junior physicians, and 6 senior physicians. The main themes that emerged in the analysis were emotional impact and barriers to carrying out EOL care. The main barriers identified were cultural difficulties related to decision-making, lack of interprofessional clinical practice, and lack of effective communication. Communication difficulties within the team were described along with lack of self-efficacy for family-centered communication. Conclusion These qualitative findings expose gaps in care that must be filled to achieve high-quality EOL care in the ICU. Significant emotional impact, barriers related to EOL decision-making, limited interprofessional clinical practice, and communication difficulties were the main findings cross-referenced.

An introduction to advance care planning: practice at the frontline

2018 ◽  
Author(s):  
Anjali Mullick ◽  
Jonathan Martin
Keyword(s):  
Decision Making ◽  
Clinical Practice ◽  
Communication Skills ◽  
Advance Care Planning ◽  
Use Case ◽  
Care Planning ◽  
Practical Advice ◽  
Future Care ◽  
Case Vignettes ◽  
Advance Care

Advance care planning (ACP) is a process of formal decision-making that aims to help patients establish decisions about future care that take effect when they lose capacity. In our experience, guidance for clinicians rarely provides detailed practical advice on how it can be successfully carried out in a clinical setting. This may create a barrier to ACP discussions which might otherwise benefit patients, families and professionals. The focus of this paper is on sharing our experience of ACP as clinicians and offering practical tips on elements of ACP, such as triggers for conversations, communication skills, and highlighting the formal aspects that are potentially involved. We use case vignettes to better illustrate the application of ACP in clinical practice.

scholarly journals Text Classification in Clinical Practice Guidelines Using Machine-Learning Assisted Pattern-Based Approach

2021 ◽  
Vol 11 (8) ◽  
pp. 3296
Author(s):  
Musarrat Hussain ◽  
Jamil Hussain ◽  
Taqdir Ali ◽  
Syed Imran Ali ◽  
Hafiz Syed Muhammad Bilal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Machine Learning Algorithms ◽  
Nominal Group ◽  
Specific Information ◽  
Matching Techniques ◽  
Disease Specific

Clinical Practice Guidelines (CPGs) aim to optimize patient care by assisting physicians during the decision-making process. However, guideline adherence is highly affected by its unstructured format and aggregation of background information with disease-specific information. The objective of our study is to extract disease-specific information from CPG for enhancing its adherence ratio. In this research, we propose a semi-automatic mechanism for extracting disease-specific information from CPGs using pattern-matching techniques. We apply supervised and unsupervised machine-learning algorithms on CPG to extract a list of salient terms contributing to distinguishing recommendation sentences (RS) from non-recommendation sentences (NRS). Simultaneously, a group of experts also analyzes the same CPG and extract the initial patterns “Heuristic Patterns” using a group decision-making method, nominal group technique (NGT). We provide the list of salient terms to the experts and ask them to refine their extracted patterns. The experts refine patterns considering the provided salient terms. The extracted heuristic patterns depend on specific terms and suffer from the specialization problem due to synonymy and polysemy. Therefore, we generalize the heuristic patterns to part-of-speech (POS) patterns and unified medical language system (UMLS) patterns, which make the proposed method generalize for all types of CPGs. We evaluated the initial extracted patterns on asthma, rhinosinusitis, and hypertension guidelines with the accuracy of 76.92%, 84.63%, and 89.16%, respectively. The accuracy increased to 78.89%, 85.32%, and 92.07% with refined machine-learning assistive patterns, respectively. Our system assists physicians by locating disease-specific information in the CPGs, which enhances the physicians’ performance and reduces CPG processing time. Additionally, it is beneficial in CPGs content annotation.

scholarly journals The use of multiple-criteria decision-making theory to measure students’ perceptions of high-fidelity simulation

2017 ◽  
Vol 3 (3) ◽  
pp. 88-93 ◽  
Author(s):  
Maureen Anne Jersby ◽  
Paul Van-Schaik ◽  
Stephen Green ◽  
Lili Nacheva-Skopalik
Keyword(s):  
Decision Making ◽  
Clinical Practice ◽  
Repeated Measures ◽  
Clinical Decision Making ◽  
Learning Experience ◽  
Clinical Decision ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Initial Exposure ◽  
Repeated Measures Design

BackgroundHigh-Fidelity Simulation (HFS) has great potential to improve decision-making in clinical practice. Previous studies have found HFS promotes self-confidence, but its effectiveness in clinical practice has not been established. The aim of this research is to establish if HFS facilitates learning that informs decision-making skills in clinical practice using MultipleCriteria DecisionMaking Theory (MCDMT).MethodsThe sample was 2nd year undergraduate pre-registration adult nursing students.MCDMT was used to measure the students’ experience of HFS and how it developed their clinical decision-making skills. MCDMT requires characteristic measurements which for the learning experience were based on five factors that underpin successful learning, and for clinical decision-making, an analytical framework was used. The study used a repeated-measures design to take two measurements: the first one after the first simulation experience and the second one after clinical placement. Baseline measurements were obtained from academics. Data were analysed using the MCDMT tool.ResultsAfter their initial exposure to simulation learning, students reported that HFS provides a high-quality learning experience (87%) and supports all aspects of clinical decision-making (85%). Following clinical practice, the level of support for clinical decision-making remained at 85%, suggesting that students believe HFS promotes transferability of knowledge to the practice setting.ConclusionOverall, students report a high level of support for learning and developing clinical decision-making skills from HFS. However, there are no comparative data available from classroom teaching of similar content so it cannot be established if these results are due to HFS alone.

scholarly journals Plain Language Summary: Opioid Prescribing for Analgesia After Common Otolaryngology Operations

2021 ◽  
Vol 164 (4) ◽  
pp. 704-711
Author(s):  
Samantha Anne ◽  
Sandra A. Finestone ◽  
Allison Paisley ◽  
Taskin M. Monjur
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Clinical Practice ◽  
Pain Management ◽  
Health Care Providers ◽  
Clinical Practice Guideline ◽  
Shared Decision ◽  
Care Providers ◽  
Plain Language ◽  
Opioid Prescribing

This plain language summary explains pain management and careful use of opioids after common otolaryngology operations. The summary applies to patients of any age who need treatment for pain within 30 days after having a common otolaryngologic operation (having to do with the ear, nose, or throat). It is based on the 2021 “Clinical Practice Guideline: Opioid Prescribing for Analgesia After Common Otolaryngology Operations.” This guideline uses available research to best advise health care providers, and it includes recommendations that are explained in this summary. Recommendations may not apply to every patient but can be used to facilitate shared decision making between patients and their health care providers.

Between a logic of disruption and a logic of continuation: Negotiating the legitimacy of algorithms used in automated clinical decision-making

2021 ◽  
pp. 136345932199674
Author(s):  
Rikke Torenholt ◽  
Henriette Langstrup
Keyword(s):  
Decision Making ◽  
Clinical Practice ◽  
Patient Reported Outcomes ◽  
Patient Involvement ◽  
Clinical Decision Making ◽  
Diagnostic Procedures ◽  
Clinical Decision ◽  
Patient Reported ◽  
Pro Tools

In both popular and academic discussions of the use of algorithms in clinical practice, narratives often draw on the decisive potentialities of algorithms and come with the belief that algorithms will substantially transform healthcare. We suggest that this approach is associated with a logic of disruption. However, we argue that in clinical practice alongside this logic, another and less recognised logic exists, namely that of continuation: here the use of algorithms constitutes part of an established practice. Applying these logics as our analytical framing, we set out to explore how algorithms for clinical decision-making are enacted by political stakeholders, healthcare professionals, and patients, and in doing so, study how the legitimacy of delegating to an algorithm is negotiated and obtained. Empirically we draw on ethnographic fieldwork carried out in relation to attempts in Denmark to develop and implement Patient Reported Outcomes (PRO) tools – involving algorithmic sorting – in clinical practice. We follow the work within two disease areas: heart rehabilitation and breast cancer follow-up care. We show how at the political level, algorithms constitute tools for disrupting inefficient work and unsystematic patient involvement, whereas closer to the clinical practice, algorithms constitute a continuation of standardised and evidence-based diagnostic procedures and a continuation of the physicians’ expertise and authority. We argue that the co-existence of the two logics have implications as both provide a push towards the use of algorithms and how a logic of continuation may divert attention away from new issues introduced with automated digital decision-support systems.

scholarly journals 307 - Exploratory Study on Healthcare Decision-Making Capacity Assessment

2020 ◽  
Vol 32 (S1) ◽  
pp. 65-65
Author(s):  
Ana Saraiva Amaral ◽  
Rosa Marina Afonso ◽  
Mário R. Simões ◽  
Sandra Freitas
Keyword(s):  
Decision Making ◽  
Content Analysis ◽  
Focus Groups ◽  
Exploratory Study ◽  
Assessment Instrument ◽  
Funding Agency ◽  
Capacity Assessment ◽  
Healthcare Decision ◽  
Healthcare Decision Making ◽  
Decision Making Capacity

Mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) prevalence is expected to continue to increase, due to the population ageing. MCI and AD may impact patients’ decision-making capacities, which should be assessed through the disease course. These medical conditions can affect the various areas of decision-making capacity in different ways. Decision-making capacity in healthcare is particularly relevant among this population. Elders often suffer from multimorbidity and are frequently asked to make healthcare decisions, which can vary from consenting a routine diagnostic procedure to decide receiving highly risk treatments.To assess this capacity in elders with MCI or AD, we developed the Healthcare Decision-Making Capacity Assessment Instrument (IACTD-CS - Instrumento de Avaliação da Capacidade de Tomada de Decisão em Cuidados de Saúde). This project is funded by Portuguese national funding agency for science, research and technology, FCT (SFRH/BD/139344/2018). IACTD-CS was developed based on Appelbaum and Grisso four abilities model, literature review and review of international assessment instruments. After IACTD-CS first version development, an exploratory study with focus groups was conducted. This study included focus groups with healthcare professionals and nursing homes’ professionals.The focus groups main goals were: 1) understand the participants perception regarding healthcare decision-making capacity, 2) distinguish relevant aspects of decision-making, 3) discuss the abilities and items included in IACTD-CS and 4) identify new aspects or items to be added to IACTD-CS. A content analysis of the focus groups results, with resource to MAXQDA, was conducted afterwards. This exploratory study allowed to identify professionals’ perceptions on healthcare decision-making and its results were a significant contribute to IACTD-CS development. The proposed communication aims to describe the methodology used and present the results of content analysis.

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