Checklist ; Clinical Decision-Making ; Decision Making ; Humans ; Metacognition

Introduction : The purpose of this study was to identify the relationship of sleep quality to fatigue and its effect on the clinical decision making of staff nurses Methods : This study correlated the effect of sleep quality and fatigue on the clinical decision making among staff nurses at the UERM Memorial Hospital using the Pittsburgh Sleep Quality Index, Fatigue Assessment Scale, and Clinical Decision Making in Nursing Scale for sleep quality, fatigue and clinical decision making, respectively. Spearman rho coefficient was computed to determine the relationship between sleep quality and fatigue, and between sleep quality and clinical decision making. The chance of poor clinical decision making among nurses with and without fatigue were computed. Results : Twenty-eight nurses were included in the study, of which 75% had poor sleep quality, 25% suffered from fatigue and one of five had good decision making. The chance of fatigue among nurses with poor sleep quality over the chance of fatigue among nurses with a good quality of sleep is one (OR 1.0. The chance of good decision making among nurses with fatigue over the chance of good decision making among nurses without fatigue is two out of five (OR - 0.18). Spearman rho shows a moderate, significant correlation between the Fatigue Assessment Scale and Pittsburgh Sleep Ouality Index scores (r - 0.547, p < 0.05) and a weak, non-significant correlation between Clinical Decision Making in Nursing Scale and Pittsburgh Sleep Quality Index scores (r - 0.151, p = 0.44). Conclusion Poor sleep quality is moderately correlated with fatigue but it may not necessarily translate into poor decision making among the staff nurses in the study. Fatigue decreases the chance of good decision making by 80%.
Sleep Quality ; Fatigue ; Clinical Decision-Making

PURPOSE: The purpose of this study was to construct and test a hypothetical model of clinical decision-making ability of nurses based on the Decision Making Process model and the Cognitive Continuum theory. METHODS: The data were collected from nurses working at 11 hospitals in Busan, Daejeon, and South Gyeongsang Province from June 30 to August 1, 2017. Finally, the data from 323 nurses were analyzed. RESULTS: The goodness-of-fit of the final model was at a good level (χ²/df=2.46, GFI=.87, AGFI=.84, IFI=.90, CFI=.90, SRMR=.07, RMSEA=.07) and 6 out of 10 paths of the model were supported. The clinical decision-making ability was both directly and indirectly affected by task complexity and indirectly affected by experiences, autonomy, and work environment. Specifically, it was strongly directly affected by analytical competency but was insignificantly affected by intuitive competency. These variables accounted for 66.0% of clinical decision-making ability. CONCLUSION: The nurses' clinical decision-making ability can be improved by improving their analytical competency. Therefore, it is necessary to organize nursing work, create a supportive work environment, and develop and implement various education programs.
Busan ; Clinical Competence ; Clinical Decision-Making ; Decision Making ; Education ; Intuition ; Nursing

A prescriptive model approach in decision making could help achieve better diagnostic accuracy in clinical practice through methods that are less reliant on probabilistic assessments. Various prescriptive measures aimed at regulating factors that influence heuristics and clinical reasoning could support clinical decision-making process. Clinicians could avoid time-consuming decision-making methods that require probabilistic calculations. Intuitively, they could rely on heuristics to obtain an accurate diagnosis in a given clinical setting. An extensive literature review of cognitive psychology and medical decision-making theory was performed to illustrate how heuristics could be effectively utilized in daily practice. Since physicians often rely on heuristics in realistic situations, probabilistic estimation might not be a useful tool in everyday clinical practice. Improvements in the descriptive model of decision making (heuristics) may allow for greater diagnostic accuracy.
Clinical Decision-Making ; Decision Making ; Diagnosis ; Heuristics ; Problem Solving ; Psychology ; Thinking*

Artificial Intelligence (AI) to support the medical decision-making process has long been both an interest and concern of physicians and the public. However, the introduction of open source software, supercomputers, and a variety of industry innovations has accelerated the progress of the development of AI in clinical decision support systems. This article summarizes the current trends and challenges in the medical field, and presents how AI can improve healthcare systems by increasing efficiency and decreasing costs. At the same time, it emphasizes the centrality of the role of physicians in utilizing AI as a tool to supplement their decisions as they provide patient-oriented care.
Artificial Intelligence* ; Clinical Decision-Making ; Decision Support Systems, Clinical ; Delivery of Health Care

PURPOSE: The aim of this research was to compare different methods of calibrating multiple choice question (MCQ) and clinical decision making (CDM) components for the Medical Council of Canada's Qualifying Examination Part I (MCCQEI) based on item response theory. METHODS: Our data consisted of test results from 8,213 first time applicants to MCCQEI in spring and fall 2010 and 2011 test administrations. The data set contained several thousand multiple choice items and several hundred CDM cases. Four dichotomous calibrations were run using BILOG-MG 3.0. All 3 mixed item format (dichotomous MCQ responses and polytomous CDM case scores) calibrations were conducted using PARSCALE 4. RESULTS: The 2-PL model had identical numbers of items with chi-square values at or below a Type I error rate of 0.01 (83/3,499 or 0.02). In all 3 polytomous models, whether the MCQs were either anchored or concurrently run with the CDM cases, results suggest very poor fit. All IRT abilities estimated from dichotomous calibration designs correlated very highly with each other. IRT-based pass-fail rates were extremely similar, not only across calibration designs and methods, but also with regard to the actual reported decision to candidates. The largest difference noted in pass rates was 4.78%, which occurred between the mixed format concurrent 2-PL graded response model (pass rate= 80.43%) and the dichotomous anchored 1-PL calibrations (pass rate= 85.21%). CONCLUSION: Simpler calibration designs with dichotomized items should be implemented. The dichotomous calibrations provided better fit of the item response matrix than more complex, polytomous calibrations.
Calibration ; Canada ; Clinical Decision-Making ; Dataset ; Educational Measurement ; Licensure

BACKGROUND: Although a growing number of guidelines and clinical researches are available for immunosuppressive treatment of post-transplantation, there is no clinical practice guideline for the care of kidney transplant recipients in Korea. Selection of a researchable question is the most important step in conducting qualified guideline development. Thus, we aimed to formulate key questions for Korean guideline to aid clinical decision-making for immunosuppressive treatment. METHODS: Based on previous published guidelines review, a first survey was constructed with 29 questions in the range of immunosuppressive treatments. The experts were asked to rate the clinical importance of the question using a 5-point Likert scale. The questions reached 60% or more from the first survey and additional new questions were included in the second survey. In analyzing the responses to items rated on the 9-point scale, consensus agreement on each question was defined as 75% or more of experts rating 7 to 9. RESULTS: In the first survey, 50 experts were included. Among the 29 questions, 27 were derived to get 60% or more importance and 3 new questions were additionally identified. Through the second survey, 9 questions were selected that experts reached consensus on 75% and over of the options. Finally, we developed key questions using PICO (patient, intervention, comparison, and outcome) methodology. CONCLUSION: The experts reached a high level of consensus on many of key questions in the survey. Final key questions provide direction for developing clinical practice guideline in the immunosuppressive treatment of transplantation.
Clinical Decision-Making ; Consensus ; Kidney Transplantation ; Kidney ; Korea ; Transplant Recipients

OBJECTIVETo initially optimize comprehensive treatment program for treating and preventing unstable angina (UA) by integrative medicine (IM).

METHODSBased on partially observable Markov decision process model (POMDP), we chose 3 syndrome elements, i.e., qi deficiency, blood stasis, and phlegm turbidity from UA inpatients. The efficacy of treating UA by IM was objectively assessed by in-depth data mining and analyses.

RESULTSThe treatment programs for UA patients of qi deficiency syndrome, blood stasis syndrome, and phlegm turbidity syndrome were recommended as follows: nitrates +statins +clopidogrel +angiotensin II receptor blockers +heparins +Astragalus membranaceus +Condonopsis + poria and large-head atractylodes rhizome (ADR = 0.85077869); nitrates + aspirin + clopidogrel + statins + heparins + Astragalus membranaceus + safflower + peach seed + red peony root (ADR = 0.70773000); nitrates + aspirin + statins + angiotensin-converting inhibitors + snakegourd fruit + onion bulb + ternate pinellia + tangerine peel (ADR = 0.72509600).

CONCLUSIONAs a POMDP based optimized treatment programs for UA, it can be used as a reference for further standardization and formulation of UA program by integrative medicine.

Clinical decision support systems are interactive computer systems for situational decision making and can improve decision efficiency and safety of care. We investigated the role of these systems in enhancing prehospital care. This narrative review included full-text articles published since 2000 that were available in databases/e-journals including Web of Science, PubMed, Science Direct, and Google Scholar. Search keywords included “clinical decision support system,” “decision support system,” “decision support tools,” “prehospital care,” and “emergency medical services.” Non-journal articles were excluded. We revealed 14 relevant studies that used such a support system in prehospital emergency medical service. Owing to the dynamic nature of emergency situations, decision timing is critical. Four key factors demonstrated the ability of clinical decision support systems to improve decision-making, reduce errors, and improve the safety of prehospital emergency activity: computer-based, offer support as a natural part of the workflow, provide decision support in the time and place of decision making, and offer practical advice. The use of clinical decision support systems in prehospital care resulted in accurate diagnoses, improved patient triage and patient outcomes, and reduction of prehospital time. By improving emergency management and rescue operations, the quality of prehospital care will be enhanced.
Computer Systems ; Decision Making ; Decision Support Systems, Clinical ; Diagnosis ; Emergencies ; Emergency Medical Services ; Humans ; Triage

OBJECTIVES: The aim of this study was to develop a decision support model for the prediction of occlusal force from the size and color of articulating paper markings in bruxism patients. METHODS: We used the information from the datasets of 30 bruxism patients in which digital measurements of the size and color of articulating paper markings (12-µm Hanel; Coltene/Whaledent GmbH, Langenau, Germany) on canine protected hard stabilization splints were measured in pixels (P) and in red (R), green (G), and blue (B) values using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA). The occlusal force (F) was measured using T-Scan III (Tekscan Inc., South Boston, MA, USA). The multiple regression equation was applied to predict F from the P and RGB. Model evaluation was performed using the datasets from 10 new patients. The patient's occlusal force measured by T-Scan III was used as a ‘gold standard’ to compare with the occlusal force predicted by the multiple regression model. RESULTS: The results demonstrate that the correlation between the occlusal force and the pixels and RGB of the articulating paper markings was positive (F = 1.62×P + 0.07×R –0.08×G + 0.08×B + 4.74; R 2 = 0.34). There was a high degree of agreement between the occlusal force of the patient measured using T-Scan III and the occlusal force predicted by the model (kappa value = 0.82). CONCLUSIONS: The results obtained demonstrate that the multiple regression model can predict the occlusal force using the digital values for the size and color of the articulating paper markings in bruxism patients.
Bite Force* ; Bruxism* ; Dataset ; Decision Making ; Decision Support Systems, Clinical* ; Decision Support Techniques ; Humans ; Logistic Models ; Occlusal Splints ; Splints