Commentary

The psychiatric clinic of the future

Current Psychiatry. 2020 April;19(4):31-32,37-39A

References

1. Pirmohamed M. Pharmacogenetics and pharmacogenomics. Br J Clin Pharmacol. 2001;52(4):345-347.
2. Benitez J, Cool CL, Scotti DJ. Use of combinatorial pharmacogenomic guidance in treating psychiatric disorders. Per Med. 2018;15(6):481-494.
3. Cannon TD. Candidate gene studies in the GWAS era: the MET proto-oncogene, neurocognition, and schizophrenia. Am J Psychiatry. 2010;167(4):4,369-372.
4. Greenwood J, Chamberlain C, Parker G. Evaluation of a rural telepsychiatry service. Australas Psychiatry. 2004;12(3):268-272.
5. Hubley S, Lynch SB, Schneck C, et al. Review of key telepsychiatry outcomes. World J Psychiatry. 2016;6(2):269-282.
6. Cheng KM, Siu BW, Yeung CC, et al. Telepsychiatry for stable Chinese psychiatric out-patients in custody in Hong Kong: a case-control pilot study. Hong Kong Med J. 2018;24(4):378-383.
7. Frankish K, Ryan C, Harris A. Psychiatry and online social media: potential, pitfalls and ethical guidelines for psychiatrists and trainees. Australasian Psychiatry. 2012;20(3):181-187.
8. de la Torre Díez I, Alonso SG, Hamrioui S, et al. IoT-based services and applications for mental health in the literature. J Med Syst. 2019;43(1):4-9.
9. Topol E. Deep Medicine. New York, NY: Basic Books; 2019:168.
10. Adams RA, Huys QJM, Roiser JP. Computational Psychiatry: towards a mathematically informed understanding of mental illness. J Neurol Neurosurg Psychiatry. 2016;87(1):53-63.
11. Insel TR. Bending the curve for mental health: technology for a public health approach. Am J Public Health. 2019;109(suppl 3):S168-S170.
12. Just MA, Pan L, Cherkassky VL, et al. Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nat Hum Behav. 2017;1:911-919.
13. Pestian J, Nasrallah H, Matykiewicz P, et al. Suicide note classification using natural language processing: a content analysis. Biomed Inform Insights. 2010;2010(3):19-28.
14. Walsh CG, Ribeiro JD, Franklin JC. Predicting risk of suicide attempts over time through machine learning. Clinical Psychological Science. 2017;5(3):457-469.
15. Pestian JP, Sorter M, Connolly B, et al; STM Research Group. A machine learning approach to identifying the thought markers of suicidal subjects: a prospective multicenter trial. Suicide Life Threat Behav. 2017;47(1):112-121.
16. Corcoran CM, Carrillo F, Fernández-Slezak D, et al. Prediction of psychosis across protocols and risk cohorts using automated language analysis. World Psychiatry. 2018;17(1):67-75.
17. Bedi G, Carrillo F, Cecchi GA, et al. Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophr. 2015;1:15030. doi: 10.1038/npjschz.2015.30.
18. Kalanderian H, Nasrallah HA. Artificial intelligence in psychiatry. Current Psychiatry. 2019;18(8):33-38.
19. Drysdale AT, Grosenick L, Downar J, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 2017;23(1):28-38.
20. Chekroud AM, Zotti RJ, Shehzad Z, et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry. 2016;3(3):243-250.
21. Grisanzio KA, Goldstein-Piekarski AN, Wang MY, et al. Transdiagnostic symptom clusters and associations with brain, behavior, and daily function in mood, anxiety, and trauma disorders. JAMA Psychiatry. 2018;75(2):201-209.
22. Lucas G, Gratch J, King A, et al. It’s only a computer: virtual humans increase willingness to disclose. Computers in Human Behavior. 2014;37:94-100.
23. Nasir M, Baucom BR, Georgiou P, et al. Predicting couple therapy outcomes based on speech acoustic features. PLoS One. 2017;12(9):e0185123. doi: 10.1371/journal.pone.0185123.
24. Huguet A, Rao S, McGrath PJ, et al. A systematic review of cognitive behavioral therapy and behavioral activation apps for depression. PLoS One. 2016;11(5):e0154248. doi: 10.1371/journal.pone.0154248.
25. Scholten MR, Kelders SM, Van Gemert-Pijnen JE. Self-guided web-based interventions: scoping review on user needs and the potential of embodied conversational agents to address them. J Med Internet Res. 2017;19(11):e383.
26. Topol E. Deep Medicine. New York, NY: Basic Books; 2019:283-310.
27. Abramson EL, McGinnis S, Edwards A, et al. Electronic health record adoption and health information exchange among hospitals in New York State. J Eval Clin Pract. 2012;18(6):1156-1162.
28. Meeks DW, Smith MW, Taylor L, et al. An analysis of electronic health record-related patient safety concerns. J Am Med Inform Assoc. 2014;21(6):1053-1059.
29. Kho AN, Rasmussen LV, Connolly JJ, et al. Practical challenges in integrating genomic data into the electronic health record. Genet Med. 2013;15(10):772-778.
30. Ornstein SM, Oates RB, Fox GN. The computer-based medical record: current status. J Fam Pract. 1992;35(5):556-565.
31. Corea F. Machine ethics and artificial moral agents. In: Applied artificial intelligence: where AI can be used in business. Basel, Switzerland: Springer; 2019:33-41.
32. Beauchamp T, Childress J. Principles of biomedical ethics. 7th ed. New York, NY: Oxford University Press; 2012:44.

Digital psychiatry

Integrating computer-based technology into psychiatric practice has given birth to a new frontier that could be called digital psychiatry. This might encompass the following:

telepsychiatry
social media with a mental health focus
web-based applications/devices
artificial intelligence (AI).

Telepsychiatry. Videoconferencing is the most widely used form of telepsychiatry. It provides patients with easier access to mental health treatment.⁴ Telepsychiatry has the potential to match patients and clinicians with similar cultural backgrounds, thus minimizing cultural gaps and misunderstandings. Most importantly, it is comparable to face-to-face interviews in terms of the reliability of assessment and treatment outcomes.⁵

Telepsychiatry might be particularly helpful for patients with restricted mobility, such as those who live in remote areas, nursing homes, or correctional facilities. In correctional settings, transferring prisoners is expensive and carries the risk of escape. In a small study (N = 86) conducted in Hong Kong, Chen et al⁶ found that using videoconferencing to conduct clinical interviews of inmates was cost-efficient and scored high in terms of patient acceptability.

Social media. Social media could be a powerful platform for early detection of mental illness. Staying connected with patients on social media could allow psychiatrists to be more aware of their patient’s mood fluctuations, which might lead to more timely assessments. Physicians could be automatically notified about changes in their patients’ social media activity that indicate changes in mental state, which could solicit immediate intervention and treatment. On the other hand, such use of social media could blur professional boundaries. Psychiatrists also could use social media to promote awareness of mental health and educate the public on ways to improve or maintain their mental well-being.⁷

Web-based applications/devices. Real-time monitoring through applications or internet-based smart devices creates a new avenue for patients to receive personalized assessments, treatment, and intervention.⁸ Smartwatches with internet connectivity may offer a glimpse of the wearer’s sleep architecture and duration, thus providing real-time data on patients who have insomnia. We can now passively collect objective data from devices, such as smartphones and laptops, to phenotype an individual’s mood and mental state, a process called digital phenotyping. The Table⁹lists examples of the types of mental health–related metrics that can be captured by smartphones, smartwatches, and similar technology. Information from these devices can be accumulated to create a database that can be used to predict symptoms.¹⁰ For example, the way people use a smartphone’s keyboard, including latency time between space and character types, can be used to generate variables for data. This type of information is being studied for use in screening depression and passively assessing mood in real time.¹¹

Continue to: Artificial intelligence

The psychiatric clinic of the future

References

Digital psychiatry

Pages

Recommended Reading