Evaluation of health services and treatment alliance among extensively hospitalized patients due to severe self-harm - results from the Extreme Challenges project.

Background: Extensive psychiatric hospitalization due to repeated severe self-harm (SH), is a poorly researched area, but a challenge within health services (HS). Recent studies have demonstrated high levels of involuntary treatment among patients with severe personality disorder (PD) and complex comorbidity. Keeping focus on extensively hospitalized SH patients, this study aimed to investigate patients' and clinicians' evaluation of HS and treatment alliance.

Mental health disorders, functioning and health-related quality of life among extensively hospitalized patients due to severe self-harm - results from the Extreme Challenges project.

Background: Severe self-harm leading to extensive hospitalization generates extreme challenges for patients, families, and health services. Controversies regarding diagnoses and health care often follow. Most evidence-based treatments targeting self-harm are designed for borderline personality disorder (BPD).

Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records.

Background: Data mining of electronic health records (EHRs) has a huge potential for improving clinical decision support and to help healthcare deliver precision medicine. Unfortunately, the rule-based and machine learning-based approaches used for natural language processing (NLP) in healthcare today all struggle with various shortcomings related to performance, efficiency, or transparency.

Methods: In this paper, we address these issues by presenting a novel method for NLP that implements unsupervised learning of word embeddings, semi-supervised learning for simplified and accelerated clinical vocabulary and concept building, and deterministic rules for fine-grained control of information extraction.

Machine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospital.

Background: Natural language processing (NLP) based clinical decision support systems (CDSSs) have demonstrated the ability to extract vital information from patient electronic health records (EHRs) to facilitate important decision support tasks. While obtaining accurate, medical domain interpretable results is crucial, it is demanding because real-world EHRs contain many inconsistencies and inaccuracies. Further, testing of such machine learning-based systems in clinical practice has received limited attention and are yet to be accepted by clinicians for regular use.

Knowledge, safety, and teamwork: a qualitative study on the experiences of anaesthesiologists and nurse anaesthetists working in the preanaesthesia assessment clinic.

Background: The preanaesthesia assessment clinic (PAC) has been shown to contribute to safe anaesthesia assessment in hospitals. In the PAC, patients are assessed with an interview and can also ask relevant questions about anaesthesia. The intention is to ensure that patients are comprehensively prepared for the surgery and hospital stay.