scholarly journals Conceptualizations of Clinical Decision-Making: A Scoping Review in Geriatric Emergency Medicine

2020 ◽  
Author(s):  
Maria Louise Gamborg ◽  
Mimi Mehlsen ◽  
Charlotte Paltved ◽  
Gitte Tramm ◽  
Peter Musaeus
Keyword(s):  
Decision Making ◽  
Emergency Medicine ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
Empirical Studies ◽  
Clinical Decision ◽  
Future Research ◽  
Study Results ◽  
Geriatric Emergency Medicine

Abstract Background: Clinical decision-making (CDM) is an important competency for young doctors, especially under complex and uncertain conditions, which is present in geriatric emergency medicine (GEM). Research in this field is however characterized by an unclear conceptualization of CDM. To evolve and evaluate evidence-based knowledge of CDM, it is thus important to identify different definitions and their operationalisations in studies on GEM.Objective: A scoping review of empirical articles was designed to provide an overview of the documented evidence of findings and conceptualizations of CDM in GEM.Methods: A detailed search for empirical studies focusing on CDM in a GEM setting was conducted in PubMed, ProQuest, Scopus, EMBASE and Web of Science. In total, 52 publications were included in the analysis, utilizing a data extraction sheet, following the PRISMA guidelines. Reported outcomes were summarized.Results: Four themes of operationalization of CDM emerged; CDM as dispositional decisions, CDM as cognition, CDM as a model, and CDM as clinical judgement. Study results and conclusions differed according to how CDM was conceptualized. It was evident how especially frailty- heuristics lead to biases in treatment of geriatric patients, and that the complexity of this patient group was seen as a challenge for CDM.Conclusions: This scoping review summarizes how different studies in GEM use the term CDM. It provides a snapshot of findings in GEM. Potentially, findings from CDM research can guide implementation of adequate CDM strategies in clinical practice but this requires application of more stringent definitions of CDM in future research.

scholarly journals Conceptualizations of clinical decision-making: a scoping review in geriatric emergency medicine

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Maria Louise Gamborg ◽  
Mimi Mehlsen ◽  
Charlotte Paltved ◽  
Gitte Tramm ◽  
Peter Musaeus
Keyword(s):  
Decision Making ◽  
Emergency Medicine ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
Empirical Studies ◽  
Clinical Decision ◽  
Future Research ◽  
Study Results ◽  
Geriatric Emergency Medicine

Abstract Background Clinical decision-making (CDM) is an important competency for young doctors especially under complex and uncertain conditions in geriatric emergency medicine (GEM). However, research in this field is characterized by vague conceptualizations of CDM. To evolve and evaluate evidence-based knowledge of CDM, it is important to identify different definitions and their operationalizations in studies on GEM. Objective A scoping review of empirical articles was conducted to provide an overview of the documented evidence of findings and conceptualizations of CDM in GEM. Methods A detailed search for empirical studies focusing on CDM in a GEM setting was conducted in PubMed, ProQuest, Scopus, EMBASE and Web of Science. In total, 52 publications were included in the analysis, utilizing a data extraction sheet, following the PRISMA guidelines. Reported outcomes were summarized. Results Four themes of operationalization of CDM emerged: CDM as dispositional decisions, CDM as cognition, CDM as a model, and CDM as clinical judgement. Study results and conclusions naturally differed according to how CDM was conceptualized. Thus, frailty-heuristics lead to biases in treatment of geriatric patients and the complexity of this patient group was seen as a challenge for young physicians engaging in CDM. Conclusions This scoping review summarizes how different studies in GEM use the term CDM. It provides an analysis of findings in GEM and call for more stringent definitions of CDM in future research, so that it might lead to better clinical practice.

scholarly journals Conceptualizations of Clinical Decision-Making: A scoping review in geriatric emergency medicine

2020 ◽  
Author(s):  
Maria Louise Gamborg ◽  
Mimi Mehlsen ◽  
Charlotte Paltved ◽  
Gitte Tramm ◽  
Peter Musaeus
Keyword(s):  
Decision Making ◽  
Emergency Medicine ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
Empirical Studies ◽  
Clinical Decision ◽  
Future Research ◽  
Study Results ◽  
Geriatric Emergency Medicine

Abstract Background Clinical decision-making (CDM) is an important competency for young doctors especially under complex and uncertain conditions in geriatric emergency medicine (GEM). However, research in this field is characterized by a vague conceptualization of CDM. To evolve and evaluate evidence-based knowledge of CDM, it is important to identify different definitions and their operationalisations in studies on GEM. Objective A scoping review of empirical articles was conducted to provide an overview of the documented evidence of findings and conceptualizations of CDM in GEM. Methods A detailed search for empirical studies focusing on CDM in a GEM setting was conducted in PubMed, ProQuest, Scopus, EMBASE and Web of Science. In total, 52 publications were included in the analysis, utilizing a data extraction sheet, following the PRISMA guidelines. Reported outcomes were summarized. Results Four themes of operationalization of CDM emerged: CDM as dispositional decisions, CDM as cognition, CDM as a model, and CDM as clinical judgement. Study results and conclusions naturally differed according to how CDM was conceptualized. Thus, frailty-heuristics lead to biases in treatment of geriatric patients and the complexity of this patient group was seen as a challenge for young physicians engaging in CDM. Conclusions This scoping review summarizes how different studies in GEM use the term CDM. It provides an analysis of findings in GEM and call for more stringent definitions of CDM in future research, so that it might lead to better clinical practice.

scholarly journals P071: Artificial intelligence in emergency medicine: A scoping review

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S90-S90
Author(s):  
A. Kirubarajan ◽  
A. Taher ◽  
S. Khan ◽  
S. Masood
Keyword(s):  
Artificial Intelligence ◽  
Emergency Medicine ◽  
Language Processing ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
A Priori ◽  
Clinical Decision ◽  
Hospital Environment ◽  
Clinical Implementation

Introduction: The study of artificial intelligence (AI) in medicine has become increasingly popular over the last decade. The emergency department (ED) is uniquely situated to benefit from AI due to its power of diagnostic prediction, and its ability to continuously improve with time. However, there is a lack of understanding of the breadth and scope of AI applications in emergency medicine, and evidence supporting its use. Methods: Our scoping review was completed according to PRISMA-ScR guidelines and was published a priori on Open Science Forum. We systematically searched databases (Medline-OVID, EMBASE, CINAHL, and IEEE) for AI interventions relevant to the ED. Study selection and data extraction was performed independently by two investigators. We categorized studies based on type of AI model used, location of intervention, clinical focus, intervention sub-type, and type of comparator. Results: Of the 1483 original database citations, a total of 181 studies were included in the scoping review. Inter-rater reliability for study screening for titles and abstracts was 89.1%, and for full-text review was 77.8%. Overall, we found that 44 (24.3%) studies utilized supervised learning, 63 (34.8%) studies evaluated unsupervised learning, and 13 (7.2%) studies utilized natural language processing. 17 (9.4%) studies were conducted in the pre-hospital environment, with the remainder occurring either in the ED or the trauma bay. The majority of interventions centered around prediction (n = 73, 40.3%). 48 studies (25.5%) analyzed AI interventions for diagnosis. 23 (12.7%) interventions focused on diagnostic imaging. 89 (49.2%) studies did not have a comparator to their AI intervention. 63 (34.8%) studies used statistical models as a comparator, 19 (10.5%) of which were clinical decision making tools. 15 (8.3%) studies used humans as comparators, with 12 of the 15 (80%) studies showing superiority in favour of the AI intervention when compared to a human. Conclusion: AI-related research is rapidly increasing in emergency medicine. AI interventions are heterogeneous in both purpose and design, but primarily focus on predictive modeling. Most studies do not involve a human comparator and lack information on patient-oriented outcomes. While some studies show promising results for AI-based interventions, there remains uncertainty regarding their superiority over standard practice, and further research is needed prior to clinical implementation.

scholarly journals Article Commentary: Calling for Help? Considering Function and Meaning when Patients Drunk-Dial Psychotherapists

2013 ◽  
Vol 7 ◽  
pp. SART.S12423
Author(s):  
Kelly Serafini ◽  
Donna LaPaglia ◽  
Matthew Steinfeld
Keyword(s):  
Decision Making ◽  
Substance Use ◽  
Popular Culture ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Substance Use Treatment ◽  
Future Research ◽  
Research And Practice ◽  
Use Of Technology ◽  
Literature Base

Drunk-dialing is a term documented in both popular culture and academic literatures to describe a behavior in which a person contacts another individual by phone while intoxicated. In our collective clinical experience we have found that clients drunk-dial their clinicians too, particularly while in substance use treatment, and yet there is a noticeable absence of research on the topic to guide clinical decision-making within a process-based understanding of these events. As the parameters within which psychotherapy takes place become increasingly technologized, a literature base to document clients’ idiosyncratic use of technology will become increasingly necessary and useful. We provide a brief review of the existing research on drunk-dialing and conclude with specific questions to guide future research and practice.

scholarly journals Economic evaluations of big data analytics for clinical decision-making: a scoping review

2020 ◽  
Vol 27 (9) ◽  
pp. 1466-1475
Author(s):  
Lytske Bakker ◽  
Jos Aarts ◽  
Carin Uyl-de Groot ◽  
William Redekop
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Economic Impact ◽  
Scoping Review ◽  
Data Analytics ◽  
Clinical Decision Making ◽  
Big Data Analytics ◽  
Clinical Decision ◽  
Economic Evaluations ◽  
Definition Of

Abstract Objective Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making. Materials and Methods We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed “big data analytics” based on a broad definition of this term. Results The search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined “big data analytics” and only 7 reported both cost-savings and better outcomes. Discussion The promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of “big data” limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.

scholarly journals Predicting infection and sepsis; what predictors have been used to train machine learning algorithms? A systematic review

2021 ◽  
Vol 29 (Supplement_1) ◽  
pp. i18-i18
Author(s):  
N Hassan ◽  
R Slight ◽  
D Weiand ◽  
A Vellinga ◽  
G Morgan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Decision Making ◽  
Quality Assessment ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
Learning Algorithms ◽  
Clinical Decision ◽  
Machine Learning Algorithms

Abstract Introduction Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients who may be at risk of developing infection and subsequent sepsis and assist clinicians with their care management. Aim To identify the optimal set of predictors used to train machine learning algorithms to predict the likelihood of an infection and subsequent sepsis and inform clinical decision making. Methods This systematic review was registered in PROSPERO database (CRD42020158685). We searched 3 large databases: Medline, Cumulative Index of Nursing and Allied Health Literature, and Embase, using appropriate search terms. We included quantitative primary research studies that focused on sepsis prediction associated with bacterial infection in adult population (>18 years) in all care settings, which included data on predictors to develop machine learning algorithms. The timeframe of the search was 1st January 2000 till the 25th November 2019. Data extraction was performed using a data extraction sheet, and a narrative synthesis of eligible studies was undertaken. Narrative analysis was used to arrange the data into key areas, and compare and contrast between the content of included studies. Quality assessment was performed using Newcastle-Ottawa Quality Assessment scale, which was used to evaluate the quality of non-randomized studies. Bias was not assessed due to the non-randomised nature of the included studies. Results Fifteen articles met our inclusion criteria (Figure 1). We identified 194 predictors that were used to train machine learning algorithms to predict infection and subsequent sepsis, with 13 predictors used on average across all included studies. The most significant predictors included age, gender, smoking, alcohol intake, heart rate, blood pressure, lactate level, cardiovascular disease, endocrine disease, cancer, chronic kidney disease (eGFR<60ml/min), white blood cell count, liver dysfunction, surgical approach (open or minimally invasive), and pre-operative haematocrit < 30%. These predictors were used for the development of all the algorithms in the fifteen articles. All included studies used artificial intelligence techniques to predict the likelihood of sepsis, with average sensitivity 77.5±19.27, and average specificity 69.45±21.25. Conclusion The type of predictors used were found to influence the predictive power and predictive timeframe of the developed machine learning algorithm. Two strengths of our review were that we included studies published since the first definition of sepsis was published in 2001, and identified factors that can improve the predictive ability of algorithms. However, we note that the included studies had some limitations, with three studies not validating the models that they developed, and many tools limited by either their reduced specificity or sensitivity or both. This work has important implications for practice, as predicting the likelihood of sepsis can help inform the management of patients and concentrate finite resources to those patients who are most at risk. Producing a set of predictors can also guide future studies in developing more sensitive and specific algorithms with increased predictive time window to allow for preventive clinical measures.

scholarly journals The Impact of Artificial Intelligence on Health Equity in Oncology: A Scoping Review

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4934-4934
Author(s):  
Paul Istasy ◽  
Wen Shen Lee ◽  
Alla Iansavitchene ◽  
Ross Upshur ◽  
Bekim Sadikovic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Health Equity ◽  
Scoping Review ◽  
Clinical Decision Making ◽  
Ethical Issues ◽  
Clinical Decision ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
The Impact

Abstract Introduction: The expanding use of Artificial Intelligence (AI) in hematology and oncology research and practice creates an urgent need to consider the potential impact of these technologies on health equity at both local and global levels. Fairness and equity are issues of growing concern in AI ethics, raising problems ranging from bias in datasets and algorithms to disparities in access to technology. The impact of AI on health equity in oncology, however, remains underexplored. We conducted a scoping review to characterize, evaluate, and identify gaps in the existing literature on AI applications in oncology and their implications for health equity in cancer care. Methodology: We performed a systematic literature search of MEDLINE (Ovid) and EMBASE from January 1, 2000 to March 28, 2021 using key terms for AI, health equity, and cancer. Our search was restricted to English language abstracts with no restrictions by publication type. Two reviewers screened a total of 9519 abstracts, and 321 met inclusion criteria for full-text review. 247 were included in the final analysis after assessment by three reviewers. Studies were analysed descriptively, by location, type of cancer and AI application, as well as thematically, based on issues pertaining to health equity in oncology. Results: Of the 247 studies included in our analysis, 150 (60.7%) were based in North America, 57 (23.0%) in Asia, 29 (11.7%) in Europe, 5 (2.1%) in Central/South America, 4 (1.6%) in Oceania, and 2 (0.9%) in Africa. 71 (28.6%) were reviews and commentaries, and 176 were (71.3%) clinical studies. 25 (10.1%) focused on AI applications in screening, 42 (17.0%) in diagnostics, 46 (18.6%) in prognostication, and 7 (2.9%) in treatment. 40 (16.3%) used AI as a tool for clinical/epidemiological research and 87 (35.2%) discussed multiple applications of AI. A diverse range of cancers were represented in the studies, including hematologic malignancies. Our scoping review identified three overarching themes in the literature, which largely focused on how AI might improve health equity in oncology. These included: (1) the potential for AI reduce disparities by improving access to health services in resource-limited settings through applications such as low-cost cancer screening technologies and decision support systems; (2) the ability of AI to mitigate bias in clinical decision-making through algorithms that alert clinicians to potential sources of bias thereby allowing for more equitable and individualized care; (3) the use of AI as a research tool to identify disparities in cancer outcomes based on factors such as race, gender and socioeconomic status, and thus inform health policy. While most of the literature emphasized the positive impact of AI in oncology, there was only limited discussion of AI's potential adverse effects on health equity . Despite engaging with the use of AI in resource-limited settings, ethical issues surrounding data extraction and AI trials in low-resource settings were infrequently raised. Similarly, AI's potential to reinforce bias and widen disparities in cancer care was under-examined despite engagement with related topics of bias in clinical decision-making. Conclusion: The overwhelming majority of the literature identified by our scoping review highlights the benefits of AI applications in oncology, including its potential to improve access to care in low-resource settings, mitigate bias in clinical decision-making, and identify disparities in cancer outcomes. However, AI's potential negative impacts on health equity in oncology remain underexplored: ethical issues arising from deploying AI technologies in low-resources settings, and issues of bias in datasets and algorithms were infrequently discussed in articles dealing with related themes. Disclosures No relevant conflicts of interest to declare.

A Clinical Decision Making Example: Implementing Intensive Speech Sound Intervention for School-Age Students Through Telepractice

2017 ◽  
Vol 2 (18) ◽  
pp. 15-26
Author(s):  
Vicki Pullins ◽  
Sue Grogan-Johnson
Keyword(s):  
Decision Making ◽  
Speech Intelligibility ◽  
Intervention Program ◽  
Clinical Decision Making ◽  
Speech Sound ◽  
Clinical Decision ◽  
Future Research ◽  
Decision Making Process ◽  
School Age ◽  
Efficient Manner

The purpose of this article is to describe the clinical decision making process that was utilized in implementing a speech sound intervention program delivered five times a week in individual telepractice therapy sessions lasting six minutes to school-age students in kindergarten through fifth grade (K-5). We utilized a clinical decision making process (Kamhi, 2006) to help establish this alternate way to provide speech sound intervention that resulted in improved speech intelligibility in the classroom in an efficient manner. Initial data collection on student outcomes will be provided. Limitations of this initial experiment are discussed with guidance provided for possible future research studies.

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