Reporting patient experiences within elective perioperative care: a scoping review.

Patient experience is an important descriptor of the human experience of healthcare. Specifically, it is described as the sum of all interactions, shaped by an organization's culture, that influence patient perceptions, across the continuum of care. Currently, patient experience is measured with a focus on the hospital experience.

Refining our understanding of depressive states and state transitions in response to cognitive behavioural therapy using latent Markov modelling.

Background: It is increasingly recognized that existing diagnostic approaches do not capture the underlying heterogeneity and complexity of psychiatric disorders such as depression. This study uses a data-driven approach to define fluid depressive states and explore how patients transition between these states in response to cognitive behavioural therapy (CBT).

Methods: Item-level Patient Health Questionnaire (PHQ-9) data were collected from 9891 patients with a diagnosis of depression, at each CBT treatment session.

Improving the translation of search strategies using the Polyglot Search Translator: a randomized controlled trial.

Background: Searching for studies to include in a systematic review (SR) is a time- and labor-intensive process with searches of multiple databases recommended. To reduce the time spent translating search strings across databases, a tool called the Polyglot Search Translator (PST) was developed. The authors evaluated whether using the PST as a search translation aid reduces the time required to translate search strings without increasing errors.

Quantifying the Association Between Psychotherapy Content and Clinical Outcomes Using Deep Learning.

Importance: Compared with the treatment of physical conditions, the quality of care of mental health disorders remains poor and the rate of improvement in treatment is slow, a primary reason being the lack of objective and systematic methods for measuring the delivery of psychotherapy.

Objective: To use a deep learning model applied to a large-scale clinical data set of cognitive behavioral therapy (CBT) session transcripts to generate a quantifiable measure of treatment delivered and to determine the association between the quantity of each aspect of therapy delivered and clinical outcomes.

Design, Setting, And Participants: All data were obtained from patients receiving internet-enabled CBT for the treatment of a mental health disorder between June 2012 and March 2018 in England.

Demographic and clinical predictors of response to internet-enabled cognitive-behavioural therapy for depression and anxiety.

Background: Common mental health problems affect a quarter of the population. Online cognitive-behavioural therapy (CBT) is increasingly used, but the factors modulating response to this treatment modality remain unclear.

Aims: This study aims to explore the demographic and clinical predictors of response to one-to-one CBT delivered via the internet.