Victor Otani
Santa Casa Medical School – Brazil
Biography:
Dr Victor Otani is a psychiatrist, psychotherapist and professor at Santa Casa Medical School – Sao Paulo, Brazil. He has expertise in adult general psychiatry, liaison-consultant and is an enthusiast of health education, innovation and research as cornerstones to a better patient centered experience
Abstract:
Background: Liaison consulting has been previously associated with significant costs as well as potential adverse patient outcomes if quality processes are not adequately handled. High disagreement rates between requester and liaison consultants are among these quality metrics.
Aim: To evaluate predictors of disagreement between the reasons for a psychiatric consult request and the final psychiatric diagnosis by a specialist.
Methods: A registry was established at a tertiary, academic hospital. Disagreement was evaluated between reported symptoms and a final diagnosis of depression, anxiety, withdrawal, psychosis, or delirium. Evaluated specialties included Surgery, Orthopedics, Obstetrics and Gynecology, Pediatrics, Bariatric Surgery, Internal Medicine, Adult Intensive Care Unit, Pediatric Intensive Care Unit, Intensive Care Unit for the Emergency Room, Speech Pathology, Bone Marrow Transplant, and Ophthalmology. Models were used to predict disagreement regarding individual and combined diagnoses.
Results: Most of our patients were female, in their early to mid-40s, single, unemployed and with an average six years of education. Highest disagreement rates were reported for services classified as others (88.2%), General Surgery (78.5%) and Bone Marrow Transplant (77.7%). Disagreement rates varied widely across different diagnoses, with Anxiety having the highest disagreement kappa values (46.0%), while psychosis had the lowest disagreement rate. When evaluating kappa coefficients, highest agreement occurred with diagnoses of withdrawal and psychosis (0.66 and 0.51, respectively), while anxiety and depression presented the lowest values (0.31 and 0.11). Finally, the best performing predictive model for most outcomes was random forest, with the most important predictors being specialties other than the ones focused on single systems, older age, lack of social support and the requester being a resident.
Conclusion: By identifying disagreement rates and their predictors, Quality Improvement and Safety programs can specifically target areas that would lead to better patient care.
Key words: liaison consulting, mental health, psychiatry, agreement, diagnosis