Psychiatric trainees in Finland 2001

This study examined Finnish psychiatric trainees’ views on their education. This was a survey study of nationwide data on Finnish psychiatric trainees in 2001. The quality of training was considered at least moderate by 84% of the respondents. Training on epidemiology, on taking history and status, and on psychopharmacology was considered the best. Quality was rated bad for training in leadership and administration, and educating the community. Research was done by 20%, and a personal clinical supervisor was appointed to 52% of the respondents. Offensive treatment had been experienced by 49% of the trainees in this study. Generally, studies of training also reflect strengths and weaknesses of the profession. Based on our results, it seems especially that training in leadership and in educating the community need to be improved; both of these are quintessential skills to survive in the struggle for economic and human resources. Furthermore, treatment of the trainees could still be better; attention should be paid to supervision of all trainees. Moreover, research must become more attractive. Psychiatry can be developed by the development of psychiatric training.     

Temporary Health Impact of Prostate MRI and Transrectal Prostate Biopsy in Active Surveillance Prostate Cancer Patients

PurposeTo assess the temporary health impact of prostate multiparametric MRI (mpMRI) and transrectal prostate biopsy in an active surveillance prostate cancer population.MethodsA two-arm institutional review board–approved HIPAA-compliant prospective observational patient-reported outcomes study was performed from November 2017 to July 2018. Inclusion criteria were men with Gleason 6 prostate cancer in active surveillance undergoing either prostate mpMRI or transrectal prostate biopsy. A survey instrument was constructed using validated metrics in consultation with the local patient- and family-centered care organization. Study subjects were recruited at the time of diagnostic testing and completed the instrument by phone 24 to 72 hours after testing. The primary outcome measure was summary testing-related quality of life (summary utility score), derived from the testing morbidities index (TMI) (scale: 0 = death and 1 = perfect health). TMI is stratified into seven domains, with each domain scored from 1 (no health impact) to 5 (extreme health impact). Testing-related quality-of-life measures in the two cohorts were compared with Mann-Whitney U test.ResultsIn all, 122 subjects were recruited, and 90% (110 of 122 [MRI 55 of 60, biopsy 55 of 62]) successfully completed the survey instrument. The temporary quality-of-life impact of transrectal biopsy was significantly greater than that of prostate mpMRI (0.82, 95% confidence interval [CI] 0.79-0.85, versus 0.95, 95% CI 0.94-0.97; P < .001). The largest mean domain-level difference was for intraprocedural pain (transrectal biopsy 2.6, 95% CI 2.4-2.8, versus mpMRI 1.3, 95% CI 1.1-1.5; P < .001).ConclusionTransrectal prostate biopsy has greater temporary health impact (lower testing-related quality-of-life measure) than prostate mpMRI.     

Natural Language Processing of Radiology Reports in Patients With Hepatocellular Carcinoma to Predict Radiology Resource Utilization

ObjectiveRadiology is a finite health care resource in high demand at most health centers. However, anticipating fluctuations in demand is a challenge because of the inherent uncertainty in disease prognosis. The aim of this study was to explore the potential of natural language processing (NLP) to predict downstream radiology resource utilization in patients undergoing surveillance for hepatocellular carcinoma (HCC).Materials and MethodsAll HCC surveillance CT examinations performed at our institution from January 1, 2010, to October 31, 2017 were selected from our departmental radiology information system. We used open source NLP and machine learning software to parse radiology report text into bag-of-words and term frequency–inverse document frequency (TF-IDF) representations. Three machine learning models—logistic regression, support vector machine (SVM), and random forest—were used to predict future utilization of radiology department resources. A test data set was used to calculate accuracy, sensitivity, and specificity in addition to the area under the curve (AUC).ResultsAs a group, the bag-of-word models were slightly inferior to the TF-IDF feature extraction approach. The TF-IDF + SVM model outperformed all other models with an accuracy of 92%, a sensitivity of 83%, and a specificity of 96%, with an AUC of 0.971.ConclusionsNLP-based models can accurately predict downstream radiology resource utilization from narrative HCC surveillance reports and has potential for translation to health care management where it may improve decision making, reduce costs, and broaden access to care.     

A Road Map to Foster Wellness and Engagement in Our Workplace—A Report of the 2018 Summer Intersociety Meeting

The 2018 radiology Intersociety Committee reviewed the current state of stress and burnout in our workplaces and identified approaches for fostering engagement, wellness, and job satisfaction. In addition to emphasizing the importance of personal wellness (the fourth aim of health care), the major focus of the meeting was to identify strategies and themes to mitigate the frequency, manifestations, and impact of stress. Strategies include reducing the stigma of burnout, minimizing isolation through community building and fostering connectivity, utilizing data and benchmarking to guide effectiveness of improvement efforts, resourcing and training “wellness” committees, acknowledging value contributions of team members, and improving efficiency in the workplace. Four themes were identified to prioritize organizational efforts: (1) collecting, analyzing, and benchmarking data; (2) developing effective leadership; (3) building high-functioning teams; and (4) amplifying our voice to increase our influence.