[Personalised psychiatry: no substitute for personal care]

Area of Science:

• Psychiatry
• Personalised Medicine
• Bioinformatics

Background:

• Personalised psychiatry promises clinical prediction through biological stratification and big data analysis.
• Current approaches face significant hurdles in delivering on this promise.

Purpose of the Study:

• To analyze the promises and challenges of personalised medicine in psychiatry.
• To evaluate the clinical utility and cost-effectiveness of biological stratification.

Main Methods:

• Analysis of current challenges in biological psychiatry and personalised medicine.
• Review of existing literature on data quality, cost, and clinical impact.

Main Results:

• Biological psychiatry provides weak diagnostic findings with negligible predictive value.
• Biological stratification may lead to high healthcare costs with limited impact.
• Public mental health investment could yield greater health gains.
• Data quality is crucial, potentially more so than data volume.
• Overemphasis on stratification may neglect holistic patient care.

Conclusions:

• Personalised care should integrate unique patient narratives and resilience-building.
• Within-person (n=1) predictive measurements (emotional, biological) are key.
• This integrated approach may best align psychiatric practice with personalised medicine goals.