Clinical Decision
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Brittany Humphries ◽  
Jafna L. Cox ◽  
Ratika Parkash ◽  
Lehana Thabane ◽  
Gary A. Foster ◽  

Background The IMPACT‐AF (Integrated Management Program Advancing Community Treatment of Atrial Fibrillation) trial is a prospective, randomized, cluster design trial comparing atrial fibrillation management with a computerized clinical decision support system with usual care (control) in the primary care setting of Nova Scotia, Canada. The objective of this analysis was to assess and compare patient‐reported health‐related quality of life and patient‐reported experience with atrial fibrillation care between clinical decision support and control groups. Methods and Results Health‐related quality of life was measured using the EuroQol 5‐dimensional 5‐level scale, whereas patient‐reported experience was assessed using a self‐administered satisfaction questionnaire, both assessed at baseline and 12 months. Health utilities were calculated using the Canadian EuroQol 5‐dimensional 5‐level value set. Descriptive statistics and generalized estimating equations were used to compare between groups. Among 1145 patients enrolled in the trial, 717 had complete EuroQol 5‐dimensional 5‐level data at baseline. The mean age of patients was 73.53 years, and 61.87% were men. Mean utilities at baseline were 0.809 (SD, 0.157) and 0.814 (SD, 0.157) for clinical decision support and control groups, respectively. At baseline, most patients in both groups reported being “very satisfied” with the care received for their atrial fibrillation. There were no statistically significant differences in utility scores or patient satisfaction between groups at 12 months. Conclusions Health‐related quality of life of patients remained stable over 12 months, and there was no significant difference in patient satisfaction or utility scores between clinical decision support and control groups. Registration information Identifier: NCT01927367.

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 100569
Irene Y. Zhang ◽  
Joshua M. Liao

Carmen Fernández Aguilar ◽  
José-Jesús Martín-Martín ◽  
Sergio Minué-Lorenzo ◽  
Alberto Fernández Ajuria

Rationale, aims and objectives: The available evidence on the existence and consequences of the use of heuristics in the clinical decision process is very scarce. The purpose of this study is to measure the use of the Representativeness, Availability and Overconfidence heuristics in real conditions with Primary Care physicians in cases of dyspnea and to study the possible correlation with diagnostic error. Methods: A prospective cohort study was carried out in 4 Primary Care centers in which 371 new cases or dyspnea were registered. The use of the three heuristics in the diagnostic process is measured through an operational definition of the same. Subsequently, the statistical correlation with the identified clinical errors is analyzed. Results: In 9.97% of the registered cases a diagnostic error was identified. In 49.59% of the cases, the physicians used the representativeness heuristic in the diagnostic decision process. The availability heuristic was used by 82.38% of the doctors and finally, in more than 50% of the cases the doctors showed excess confidence. None of the heuristics showed a statistically significant correlation with diagnostic error. Conclusion: The three heuristics have been used as mental shortcuts by Primary Care physicians in the clinical decision process in cases of dyspnea, but their influence on the diagnostic error is not significant. New studies based on the proposed methodology will allow confirming both its importance and its association with diagnostic error.

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Jun Chen ◽  
Chao Lu ◽  
Haifeng Huang ◽  
Dongwei Zhu ◽  
Qing Yang ◽  

Importance. The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies. From the diagnosis of diseases till the generation of treatment plans, cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making. This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade. Highlights. (1) A holistic review of both research papers and industrial practice about cognitive computing-based CDSS is conducted to identify the necessity and the characteristics as well as the general framework of constructing the system. (2) Several of the typical applications of cognitive computing-based CDSS as well as the existing systems in real medical practice are introduced in detail under the general framework. (3) The limitations of the current cognitive computing-based CDSS is discussed that sheds light on the future work in this direction. Conclusion. Different from medical content providers, cognitive computing-based CDSS provides probabilistic clinical decision support by automatically learning and inferencing from medical big data. The characteristics of managing multimodal data and computerizing medical knowledge distinguish cognitive computing-based CDSS from other categories. Given the current status of primary health care like high diagnostic error rate and shortage of medical resources, it is time to introduce cognitive computing-based CDSS to the medical community which is supposed to be more open-minded and embrace the convenience and low cost but high efficiency brought by cognitive computing-based CDSS.

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