Clinical Data Visualization: The Current State and Future Needs.

The growing volume of clinical data in modern medical practice creates difficulties for clinicians when attempting to come to a full understanding of each patient's overall health status. Many different approaches to computer-based visualizations have been taken in an attempt to alleviate this burden; however, no single approach has been widely adopted. As a step towards optimization and standardization of data visualization in healthcare, this paper presents a diverse set of approaches to visualization for multiple organ systems. To do so we summarize best practices in design and evaluation while proposing usability testing methodology. We then review and illustrate the goals of various clinical data visualization techniques.

Development of a Natural Language Processing Algorithm to Identify and Evaluate Transgender Patients in Electronic Health Record Systems.

Objective: To create a natural language processing (NLP) algorithm to identify transgender patients in electronic health records. Design: We developed an NLP algorithm to identify patients (keyword + billing codes). Patients were manually reviewed, and their health care services categorized by billing code. Setting: Vanderbilt University Medical Center. Participants: 234 adult and pediatric transgender patients. Main Outcome Measures: Number of transgender patients correctly identified and categorization of health services utilized. Results: We identified 234 transgender patients of whom 50% had a diagnosed mental health condition, 14% were living with HIV, and 7% had diabetes. Largely driven by hormone use, nearly half of patients attended the Endocrinology/Diabetes/Metabolism clinic. Many patients also attended the Psychiatry, HIV, and/or Obstetrics/Gynecology clinics. The false positive rate of our algorithm was 3%. Conclusions: Our novel algorithm correctly identified transgender patients and provided important insights into health care utilization among this marginalized population.

Developing image sets for inducing obsessive-compulsive checking symptoms.

Visual stimuli are often used for obsessive-compulsive (OC) symptom provocation in research studies. We tested the induction of anxiety and OC checking symptoms across different types of checking provocation stimuli in three populations: individuals with obsessive compulsive disorder (OCD), individuals with checking symptoms but without a diagnosis of OCD, and control individuals with neither checking symptoms nor a clinical diagnosis. One set of provocative images depicted objects that are commonly associated with checking anxiety. Another set ('enhanced provocative images') depicted similar objects but also included contextual cues suggesting a specific harmful scenario that could occur. As expected, the enhanced provocative images were more effective at inducing anxiety and OC symptoms than the standard provocative images. Future studies requiring checking symptom provocation should therefore consider incorporating similarly suggestive images. Individuals with clinical OCD reported the greatest provocation in response to these images, followed by those with nonclinical checking, followed by control individuals. Thus, these stimuli are able to provoke OC checking symptoms and anxiety differentially across groups, with the intensity of provocation reflecting diagnostic status. All groups demonstrated a similar qualitative pattern of provocation across images. Finally, in all groups, reported anxiety closely tracked intrusive thoughts and checking urges.