Objective: Visual hallucinations are under-reported by patients and are often undiscovered by health professionals. There is no gold standard available to assess hallucinations. Our objective was to develop a reliable, valid, semi-structured interview for identifying and assessing visual hallucinations in older people with eye disease and cognitive impairment.

Methods: We piloted the North-East Visual Hallucinations Interview (NEVHI) in 80 older people with visual and/or cognitive impairment (patient group) and 34 older people without known risks of hallucinations (control group). The informants of 11 patients were interviewed separately. We established face validity, content validity, criterion validity, inter-rater agreement and the internal consistency of the NEVHI, and assessed the factor structure for questions evaluating emotions, cognitions, and behaviours associated with hallucinations.

Results: Recurrent visual hallucinations were common in the patient group (68.8%) and absent in controls (0%). The criterion, face and content validities were good and the internal consistency of screening questions for hallucinations was high (Cronbach alpha: 0.71). The inter-rater agreements for simple and complex hallucinations were good (Kappa 0.72 and 0.83, respectively). Four factors associated with experiencing hallucinations (perceived control, pleasantness, distress and awareness) were identified and explained a total variance of 73%. Informants gave more 'don't know answers' than patients throughout the interview (p = 0.008), especially to questions evaluating cognitions and emotions associated with hallucinations (p = 0.02).

Conclusions: NEVHI is a comprehensive assessment tool, helpful to identify the presence of visual hallucinations and to quantify cognitions, emotions and behaviours associated with hallucinations.

Download full-text PDF

Source
http://dx.doi.org/10.1002/gps.1965DOI Listing

Publication Analysis

Top Keywords

visual hallucinations
24
older people
16
hallucinations
13
semi-structured interview
8
hallucinations older
8
patient group
8
internal consistency
8
questions evaluating
8
behaviours associated
8
cognitions emotions
8

Similar Publications

AI-Powered Neurogenetics: Supporting Patient's Evaluation with Chatbot.

Genes (Basel)

December 2024

Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy.

Background/objectives: Artificial intelligence and large language models like ChatGPT and Google's Gemini are promising tools with remarkable potential to assist healthcare professionals. This study explores ChatGPT and Gemini's potential utility in assisting clinicians during the first evaluation of patients with suspected neurogenetic disorders.

Methods: By analyzing the model's performance in identifying relevant clinical features, suggesting differential diagnoses, and providing insights into possible genetic testing, this research seeks to determine whether these AI tools could serve as a valuable adjunct in neurogenetic assessments.

View Article and Find Full Text PDF

Background: Resistant auditory verbal hallucination (AVH) remains a disabling symptom in schizophrenia. Transcranial direct current stimulation (tDCS) and its more targeted variant, high-definition tDCS (HD-tDCS), have shown promising results in reducing AVH. We aimed to determine the effects of adjunctive HD-tDCS on various dimensions of AVH in patients with schizophrenia.

View Article and Find Full Text PDF

The aim of this scoping review was to map intervention programmes for first-episode psychosis by identifying their characteristics, participants, and specific contexts of implementation. It seems reasonable to suggest that early intervention may be beneficial in improving recovery outcomes and reducing the duration of untreated psychosis (DUP). Despite the expansion of these programmes, there are still some significant variations and barriers to access that need to be addressed.

View Article and Find Full Text PDF

Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI.

Bioengineering (Basel)

January 2025

Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.

Recent advancements in deep learning have significantly improved medical image segmentation. However, the generalization performance and potential risks of data-driven models remain insufficiently validated. Specifically, unrealistic segmentation predictions deviating from actual anatomical structures, known as a Seg-Hallucination, often occur in deep learning-based models.

View Article and Find Full Text PDF

Researchers today face significant challenges reshaping the landscape of academic, government, and industry research due to the exponential growth of global research outputs and the advent of Generative Artificial Intelligence (GenAI). The annual increase in published works has made it difficult for traditional literature review and data analysis methods to keep pace, often rendering reviews outdated by the time of publication. In response, this methods article introduces a suite of new tools designed to automate a number of stages for systematic literature reviews.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!