Stein2022 - Psychiatric diagnosis and treatment in the 21st century, paradigm shifts versus incremental integration
- Date read: 2022-09-13
- Bibtex: @stein2022
- Bibliography: Stein, D. J., Shoptaw, S. J., Vigo, D. V., Lund, C., Cuijpers, P., Bantjes, J., Sartorius, N., & Maj, M. (2022). Psychiatric diagnosis and treatment in the 21st century: Paradigm shifts versus incremental integration. World Psychiatry, 21(3), 393–414. https://doi.org/10.1002/wps.20998
- They review a bunch of recent advances
- clinical neuroscience and personalized pharmacotherapy. RDoC - Insel2015 - Brain disorders Precisely
- novel statistical approaches to psychiatric nosology, notably Network theory of psychopathology and HiTOP krueger_2018_progress_in_achieving_quantitative_classification_of_psychopathology.pdf
- deinstitutionalization and community mental health care
- scale-up of evidence-based psychotherapy, lots of IAPT talk
- digital phenotyping and digital therapies (both ICBT and smartphone apps)
- global mental health and task-sharing approaches
- I’ll read the parts that are unfamiliar and summarize below.
Deinstitutionalization and community mental health care
- First half of 1900s saw growth of psychiatric hospitals
- 80-90% reduction in many western countries between mid 1950s and 1990s
- Three components
- Discharging patients from hospitals to community care
- Diverting new admissions to alternative facilities
- Community-based services
- What were the forces behind this change?
- The driving forces behind deinstitutionalization in psychiatry was access to better medication, high costs of psychiatric hospitals, and public concerns about the practices in psychiatric hospitals
- Medications made it possible for people with schizophrenia and bipolar disorder to live reasonably well in community settings
- High costs of psychiatric hospitals
- Human rights movement in 1950s-1960s -> increasing public concern about what was going on in psychiatric institutions. One Flew over the Cuckoo’s Nest and other movies generated public attention.
- Deinstitutionalization is not seen in the same extent in low-income countries, where psychiatric beds are increasing.
- Success of deinstitutionalization is varied. When things go poorly we see “revolving door” patterns of care: acute episodes lead to inpatient care, discharge without appropriate care and support in the community, relapse and readmission.
- Some arguments around community-based care and the underlying structural issues that should be addressed, e.g. Lower demand for psychological treatments, due to non-medical interpretations of symptoms and structural issues in underserved areas, should not be ignored
Digital phenotyping and digital therapies
- CCTV images to identify suicide attempts in progress at hotspots (ref 160)
- Refs 81-83 seem interesting
- Dwyer DB, Falkai P, Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol 2018;14:91 118.
- Rutledge RB, Chekroud AM, Huys QJ. Machine learning and big data in psychiatry: toward clin ical applications. Curr Opin Neurobiol 2019;55: 1529.
- Koppe G, MeyerLindenberg A, Durstewitz D. Deep learning for small and big data in psychia try. Neuropsychopharmacology 2021;46:17690.
- Checkroud AM, Bondar J, Delgadillo J et al. The promise of machine learning in predicting treat ment outcomes in psychiatry. World Psychiatry 2021;20:15493.
The ability to access, store and manipulate data, together with the use of machine learning algorithms, promises to advance the practice of individualized medicine in psychiatry by allowing matching of patients with the most appropriate therapies
- Active data collection vs passive data collection
- Passive data collection can be used for digital phenotyping
- Limitations in app-based trt (beyond security issues and lack of evidence-base)
- High data costs
- Unstable Internet connections
- Bandwidth limitations