Role of intuitive knowledge in the diagnostic reasoning of hospital specialists: a focus group study.

BACKGROUND AND OBJECTIVE: Intuition is an important part of human decision-making and can be explained by the dual-process theory where analytical and non-analytical reasoning processes continually interact. These processes can also be identified in physicians' diagnostic reasoning. The valuable role of intuition, including gut feelings, has been shown among general practitioners and nurses, but less is known about its role among hospital specialists. This study focused on the diagnostic reasoning of hospital specialists, how they value, experience and use intuition. DESIGN AND PARTICIPANTS: Twenty-eight hospital specialists in the Netherlands and Belgium participated in six focus groups. The discussions were recorded, transcribed verbatim and thematically coded. A circular and iterative analysis was applied until data saturation was achieved. RESULTS: Despite initial reservations regarding the term intuition, all participants agreed that intuition plays an important role in their diagnostic reasoning process. Many agreed that intuition could guide them, but were cautious not to be misguided. They were especially cautious since intuition does not have probative force, for example, in medicolegal situations. 'On-the-job experience' was regarded as a precondition to relying on intuition. Some participants viewed intuition as non-rational and invalid. All participants said that intuitive hunches must be followed by analytical reasoning. Cultural differences were not found. Both the doctor as a person and his/her specialty were seen as important determinants for using intuition. CONCLUSIONS: Hospital specialists use intuitive elements in their diagnostic reasoning, in line with general human decision-making models. Nevertheless, they appear to disagree more on its role and value than previous research has shown among general practitioners. A better understanding of how to take advantage of intuition, while avoiding pitfalls, and how to develop 'skilled' intuition may improve the quality of hospital specialists' diagnostic reasoning.

Machine Learning Algorithms Utilizing Functional Respiratory Imaging May Predict COPD Exacerbations.

RATIONALE AND OBJECTIVES: Acute chronic obstructive pulmonary disease exacerbations (AECOPD) have a significant negative impact on the quality of life and accelerate progression of the disease. Functional respiratory imaging (FRI) has the potential to better characterize this disease. The purpose of this study was to identify FRI parameters specific to AECOPD and assess their ability to predict future AECOPD, by use of machine learning algorithms, enabling a better understanding and quantification of disease manifestation and progression. MATERIALS AND METHODS: A multicenter cohort of 62 patients with COPD was analyzed. FRI obtained from baseline high resolution CT data (unenhanced and volume gated), clinical, and pulmonary function test were analyzed and incorporated into machine learning algorithms. RESULTS: A total of 11 baseline FRI parameters could significantly distinguish ( p < 0.05) the development of AECOPD from a stable period. In contrast, no baseline clinical or pulmonary function test parameters allowed significant classification. Furthermore, using Support Vector Machines, an accuracy of 80.65% and positive predictive value of 82.35% could be obtained by combining baseline FRI features such as total specific image-based airway volume and total specific image-based airway resistance, measured at functional residual capacity. Patients who developed an AECOPD, showed significantly smaller airway volumes and (hence) significantly higher airway resistances at baseline. CONCLUSION: This study indicates that FRI is a sensitive tool (PPV 82.35%) for predicting future AECOPD on a patient specific level in contrast to classical clinical parameters.