Clinical Scorecard: Exploring the Use of Natural Language Processing in Analyzing Psychiatric Clinical Documentation: A Scoping Review

At a Glance

Key Highlights

• NLP offers a shift from subjective clinical judgment to measurement-based care.
• Clinical notes contain valuable information for advancing mental health research.
• Recent advances in deep learning have enhanced NLP applications in psychiatry.
• PLMs like BERT improve performance in text-based models for mental illness.
• Challenges remain in the application of NLP to clinical notes.

Guideline-Based Recommendations

Diagnosis

• Utilize NLP for disease diagnosis, including suicide screening and depression identification.

Management

• Implement NLP tools to assist in clinical decision-making and personalized treatment strategies.

Monitoring & Follow-up

• Employ automated language analysis for robust tracking of patient symptoms and progress.

Risks

• Address challenges related to the heterogeneity and complexity of clinical notes.

Patient & Prescribing Data

Patients with various psychiatric disorders

NLP can enhance the understanding of patient narratives and treatment responses.

Clinical Best Practices

• Adopt standardized measures for patient assessment in clinical practice.
• Leverage EHRs for systematic analysis of unstructured clinical data.
• Stay updated on advancements in NLP technologies for mental health applications.

Related Resources & Content

• Joanna Briggs Institute guidance for scoping reviews
• PRISMA-ScR checklist