Finding the Value: Identifying the Key Elements of Recorded Clinic Visits From the Perspective of Patients, Clinicians, and Caregivers.

Objective: We aimed to understand what patients, caregivers and clinicians identified as the most important information from their audio-recorded clinic visits and why.

Methods: We recruited patients, caregivers and clinicians from primary and speciality care clinics at an academic medical centre in New Hampshire, U.S.

An Audio Personal Health Library of Clinic Visit Recordings for Patients and Their Caregivers (HealthPAL): User-Centered Design Approach.

Background: Providing digital recordings of clinic visits to patients has emerged as a strategy to promote patient and family engagement in care. With advances in natural language processing, an opportunity exists to maximize the value of visit recordings for patients by automatically tagging key visit information (eg, medications, tests, and imaging) and linkages to trustworthy web-based resources curated in an audio-based personal health library.

Objective: This study aims to report on the user-centered development of HealthPAL, an audio personal health library.

Natural language processing for automated annotation of medication mentions in primary care visit conversations.

Objectives: The objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians.

Materials And Methods: Eight clinicians contributed to a data set of 85 clinic visit transcripts, and 10 transcripts were randomly selected from this data set as a development set. Our approach utilizes Apache cTAKES and Unified Medical Language System controlled vocabulary to generate a list of medication candidates in the transcribed text and then performs multiple customized filters to exclude common false positives from this list while including some additional common mentions of the supplements and immunizations.

Algorithm-Driven Substance Use Disorder Treatment for Inner-City Clients With Serious Mental Illness and Multiple Impairments.

Mental health clients with serious mental illness in urban settings experience multiple chronic stresses related to poverty, unemployment, discrimination, homelessness, incarceration, hospitalization, posttraumatic stress disorder, pain syndromes, traumatic brain injury, and other problems. Substance use disorder exacerbates these difficulties. This study examined the efficacy of algorithm-driven substance use disorder treatments for 305 inner-city mental health clients with multiple challenges.

A multi-sensory code for emotional arousal.

People express emotion using their voice, face and movement, as well as through abstract forms as in art, architecture and music. The structure of these expressions often seems intuitively linked to its meaning: romantic poetry is written in flowery curlicues, while the logos of death metal bands use spiky script. Here, we show that these associations are universally understood because they are signalled using a multi-sensory code for emotional arousal.