Understanding frictions in patient-reported outcome-based community healthcare - An institutional logics perspective.

Objective: Patient-reported outcome (PRO) measures in community-based healthcare play a significant role in the emerging field of digital health. This qualitative study explored healthcare professionals' (HCPs') experiences of integrating 'MyPROfile' as a dialogue tool in consultations in community healthcare.

Methods: Adopting a qualitative approach with a social constructivist perspective, the study utilised qualitative interviews, participant observations, and focus group interviews.

A systematic review of whether the number of linguistic errors in medical interpretation is associated with the use of professional vs ad hoc interpreters.

Medical consultations depend on a shared linguistic understanding between the patient and physician. When language concordance is not possible, interpretation is required. Prior studies have revealed that professional in-person interpretation (PIPI) results in patients reporting higher satisfaction and a better understanding of things the physician explained.

Predictive utility of artificial intelligence on schizophrenia treatment outcomes: A systematic review and meta-analysis.

Identifying optimal treatment approaches for schizophrenia is challenging due to varying symptomatology and treatment responses. Artificial intelligence (AI) shows promise in predicting outcomes, prompting this systematic review and meta-analysis to evaluate various AI models' predictive utilities in schizophrenia treatment. A systematic search was conducted, and the risk of bias was evaluated.

Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data.

Background: Involuntary admissions to psychiatric hospitals are on the rise. If patients at elevated risk of involuntary admission could be identified, prevention may be possible. Our aim was to develop and validate a prediction model for involuntary admission of patients receiving care within a psychiatric service system using machine learning trained on routine clinical data from electronic health records (EHRs).

Association of Psychotropic Prescriptions With Non-Registered Indications and the Risk of Mortality in Older Adults: A Danish Nationwide Cohort Study.

Background And Objective: Psychotropic drug use is common in older adults, with off-label use reported despite limited understanding of the safety outcomes compared to on-label use. Incomplete recordings of treatment indications in the Danish National Prescription Register (DNPR) raise concerns about potential off-label medication use, particularly among older adults. We, therefore, investigated the association between psychotropic prescriptions with non-registered indications in DNPR and the 1-year all-cause mortality in older adults, including subgroups with any psychiatric disorders, depression, or dementia.

Linking heart and mind: development and evaluation of online information material on mental health disorders in children and adolescents with CHDs.

Psychiatric comorbidity is common in children and adolescents with CHDs. Early recognition and evidence-based treatments are crucial to prevent long-term consequences. To support early identification and reduce stigma, we 1) developed and 2) tested the usability and acceptability of online information material on common mental health disorders targeted healthcare professionals and affected families.

Use of generative artificial intelligence (AI) in psychiatry and mental health care: a systematic review.

Objectives: Tools based on generative artificial intelligence (AI) such as ChatGPT have the potential to transform modern society, including the field of medicine. Due to the prominent role of language in psychiatry, e.g.

Depression Treatment Trajectories and Associated Social Determinants: A Three-Year Follow-Up Study in 66,540 Older Adults Undergoing First-Time Depression Treatment in Denmark.

Objective: This study aimed to describe depression treatment patterns, identify unique trajectory groups using a group-based trajectory approach, and explore associated social determinants in older adults undergoing first-time depression treatment during a 3-year follow-up.

Methods: This Danish register-based cohort study included all adults aged ≥ 65 who initiated depression treatment by redeeming first-time antidepressant prescriptions (no prescriptions in the last 10 years) between 2006 and 2015. The outcome of interest during the 2-year follow-up was depression treatment, assessed as antidepressant prescriptions redemptions and psychiatric hospital contacts for depression.

Does health anxiety and vaccine concern predict self-reported adverse events following COVID-19 vaccination?-A Danish national cohort study.

Objectives: The nocebo effect refers to an expectation of sickness that leads to sickness in the expectant. Studies have found COVID-19 vaccines to be associated with the nocebo effect. However, the literature in this field is sparse yet important with the continuation of booster vaccines.

Optimizing the Prediction of Depression Remission: A Longitudinal Machine Learning Approach.

Decisions about when to change antidepressant treatment are complex and benefit from accurate prediction of treatment outcome. Prognostic accuracy can be enhanced by incorporating repeated assessments of symptom severity collected during treatment. Participants (n = 714) from the Genome-Based Therapeutic Drugs for Depression study received escitalopram or nortriptyline over 12 weeks.

Language, Motor Ability and Related Deficits in Children at Familial Risk of Schizophrenia or Bipolar Disorder.

Background: It is known that impairments in linguistic ability and motor function tend to co-occur in children, and that children from families with parental mental illness such as schizophrenia tend to perform poorly in both domains, but the exact nature of these links has not yet been fully elucidated.

Design: In this study, we leveraged the first wave of the Danish High Risk and Resilience Study (VIA 7), which includes both genetic data and measures covering multiple developmental domains. The VIA 7 cohort comprises 522 7-year-old children born to parents with schizophrenia (N = 202), bipolar disorder (N = 120) or neither (N = 200).

Patient trust in the use of machine learning-based clinical decision support systems in psychiatric services: A randomized survey experiment.

Background: Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore, the aim was to examine whether receiving basic information about ML-based CDSS increased trust in them.

Psychiatric co-morbidities and feasibility of an online psychiatric screening measure in children and adolescents with the Fontan circulation.

Background: Guidelines recommend screening for psychiatric co-morbidities in patients with congenital heart defects alongside cardiac outpatient follow-ups. These recommendations are not implemented in Denmark. This study aimed to investigate the psychiatric co-morbidities in children and adolescents with Fontan circulation in Denmark and to evaluate the feasibility of an online screening measure for psychiatric disorders.

Can the Whiteley Index be used to assess health anxiety in adolescents from the general population?

Health anxiety (HA) is characterized by worry about being or becoming ill. The Whiteley Index (WI) is a valid and frequently used measure for HA in adults. We examined item response distribution, floor and ceiling effects, and construct validity of four different one-factor models of the WI (an 8-item model, the widely used WI-7, a revised 7-item version (WI-7-R), and a 6-item version (WI-6-R)) in a population-based sample of adolescents, using data from the 16-17-year follow-up of the Copenhagen Child Cohort 2000 (N = 2521, 16-17 years old).

Precision psychiatry needs causal inference.

Objective: Psychiatric research applies statistical methods that can be divided in two frameworks: causal inference and prediction. Recent proposals suggest a down-prioritisation of causal inference and argue that prediction paves the road to 'precision psychiatry' (i.e.

Exploring the potential link between ΔFosB and -acetylcysteine in craving/relapse dynamics: can -acetylcysteine stand out as a possible treatment candidate?

From a neuroscientific point of view, one of the unique archetypes of substance use disorders is its road to relapse, in which the reward system plays a crucial role. Studies on the neurobiology of substance use disorders have highlighted the central role of a protein belonging to the Fos family of transcription factors, ΔFosB. Relying on the roles ΔFosB plays in the pathophysiology of substance use disorders, we endeavour to present some evidence demonstrating that -acetylcysteine, a low-cost and well-tolerated over-the-counter medicine, may influence the downstream pathway of ΔFosB, thereby serving as a treatment strategy to mitigate the risk of relapse in cases of substance use.

The risk of diabetes and HbA1c deterioration during antipsychotic drug treatment: A Danish two-cohort study among patients with first-episode schizophrenia.

Background: Antipsychotics increase the risk of developing diabetes, but clinical trials are not generalizable with short follow-up, while observational studies often lack important information, particularly hemoglobin A1c (HbA1c).

Methods: We followed two Danish cohorts with schizophrenia. First, using Danish nationwide registers, we identified all individuals diagnosed with first-episode schizophrenia (FES) between 1999 and 2019 (n = 31,856).

Associations between physical diseases and subsequent mental disorders: a longitudinal study in a population-based cohort.

People with physical diseases are reported to be at elevated risk of subsequent mental disorders. However, previous studies have considered only a few pairs of conditions, or have reported only relative risks. This study aimed to systematically explore the associations between physical diseases and subsequent mental disorders.