Highly significant reductions in service use were associated with steady employment. Given supported employment's well-established contributions to recovery, evidence of long-term reductions in the cost of mental health services should lead policy makers and insurers to promote wider implementation.

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. Specifically, variation in the central tendency of the frequency spectrum of a stimulus—its spectral centroid—is used by signal senders to express emotional arousal, and by signal receivers to make emotional arousal judgements. We show that this code is used across sounds, shapes, speech and human body movements, providing a strong multi-sensory signal that can be used to efficiently estimate an agent's level of emotional arousal.

Highly significant reductions in service use were associated with steady employment. Given supported employment's well-established contributions to recovery, evidence of long-term reductions in the cost of mental health services should lead policy makers and insurers to promote wider implementation.

BackgroundProviding patients with recordings of their clinic visits enhances patient and family engagement, yet few organizations routinely offer recordings. Challenges exist for organizations and patients, including data safety and navigating lengthy recordings. A secure system that allows patients to easily navigate recordings may be a solution.ObjectiveThe aim of this project is to develop and test an interoperable system to facilitate routine recording, the Open Recording Automated Logging System (ORALS), with the aim of increasing patient and family engagement. ORALS will consist of (1) technically proficient software using automated machine learning technology to enable accurate and automatic tagging of in-clinic audio recordings (tagging involves identifying elements of the clinic visit most important to patients [eg, treatment plan] on the recording) and (2) a secure, easy-to-use Web interface enabling the upload and accurate linkage of recordings to patients, which can be accessed at home.MethodsWe will use a mixed methods approach to develop and formatively test ORALS in 4 iterative stages: case study of pioneer clinics where recordings are currently offered to patients, ORALS design and user experience testing, ORALS software and user interface development, and rapid cycle testing of ORALS in a primary care clinic, assessing impact on patient and family engagement. Dartmouth’s Informatics Collaboratory for Design, Development and Dissemination team, patients, patient partners, caregivers, and clinicians will assist in developing ORALS.ResultsWe will implement a publication plan that includes a final project report and articles for peer-reviewed journals. In addition to this work, we will regularly report on our progress using popular relevant Tweet chats and online using our website, www.openrecordings.org. We will disseminate our work at relevant conferences (eg, Academy Health, Health Datapalooza, and the Institute for Healthcare Improvement Quality Forums). Finally, Iora Health, a US-wide network of primary care practices (www.iorahealth.com), has indicated a willingness to implement ORALS on a larger scale upon completion of this development project.ConclusionsUpon the completion of this project we will have developed a novel recording system that will be ready for large-scale testing. Our long-term goal is for ORALS to seamlessly fit into a clinic’s and patient’s daily routine, increasing levels of patient engagement and transparency of care.

This column describes a best practice for dissemination and implementation used by the Johnson & Johnson-Dartmouth Program: a national learning collaborative among community mental health programs on supported employment. In this two-tiered learning collaborative, researchers meet regularly with mental health and vocational rehabilitation leaders in 12 states and the District of Columbia, and state leaders oversee more than 130 individual programs in their respective states. Participants share educational programs, implementation and intervention strategies, practice experiences, outcome data, and research projects. The national learning collaborative facilitates implementation, dissemination, standardization, and sustainability of supported employment.