Patient-sharing networks among Finnish primary healthcare professionals taking care of patients with mental health or substance use problems: a register study.

Objectives: Patient-sharing networks based on administrative data are used to understand the organisation of healthcare. We examined the patient-sharing networks between different professionals taking care of patients with mental health or substance use problems.

Design: Register study based on the Register of Primary Health Care visits (Avohilmo) that covers all outpatient primary health care visits in Finland.

General medical comorbidities in psychotic disorders in the Finnish SUPER study.

Schizophrenia (SZ), schizoaffective disorder (SZA), bipolar disorder (BD), and psychotic depression (PD) are associated with premature death due to preventable general medical comorbidities (GMCs). The interaction between psychosis, risk factors, and GMCs is complex and should be elucidated. More research particularly among those with SZA or PD is warranted.

Patient-Reported Experience Measures for In- and Outpatients in Mental Health and Substance Use Services: Psychometric Properties and Results From a Nationwide Survey in Finland.

Objectives: We implemented the first national patient experience survey, with novel patient-reported experience measures (PREMs), in out- and inpatient mental health and substance use services in Finland.

Methods: The Outpatient Experience Scale (OPES) and the Inpatient Experience Scale (IPES) were co-designed with experts by experience and professionals. The survey was carried out in 2021 in 435 treatment facilities.

Multimodal prediction of the need of clozapine in treatment resistant schizophrenia; a pilot study in first-episode psychosis.

As many as one third of the patients diagnosed with schizophrenia do not respond to first-line antipsychotic medication. This group may benefit from the atypical antipsychotic medication clozapine, but initiation of treatment is often delayed, which may worsen prognosis. Predicting which patients do not respond to traditional antipsychotic medication at the onset of symptoms would provide fast-tracked treatment for this group of patients.

Artificial intelligence aided serum protein electrophoresis analysis of Finnish patient samples: Retrospective validation.

Background And Aims: Serum protein electrophoresis interpretation requires a substantial amount of manual work. In 2020, Chabrun et al. created a machine learning method called SPECTR for the task.