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Clinical wisdom in the age of computer apps: a systematic review of four functions that may complement clinical treatment

Published online by Cambridge University Press:  19 August 2022

Catalina R. Pacheco Open the ORCID record for Catalina R. Pacheco [Opens in a new window]  and
Michael S. Scheeringa
Catalina R. Pacheco*
Affiliation:
Department of Psychiatry and Behavioral Sciences, Tulane University School of Medicine, New Orleans, LA, USA
Michael S. Scheeringa
Affiliation:
Department of Psychiatry and Behavioral Sciences, Tulane University School of Medicine, New Orleans, LA, USA
*
*Corresponding author. Email: [email protected]
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Abstract

Mental health clinicians perform complex tasks with patients that potentially could be improved by the massive computing power available through mobile apps. This study aimed to analyse commercially available mobile and computer applications (apps) focused on treating psychiatric disorders. Apps were analysed by two independent raters for whether they took advantage of computer power to process data in a fashion that augments four main elements of clinical treatment including (1) assessment/diagnosis, (2) treatment planning, (3) treatment fidelity monitoring, and (4) outcome tracking. The evidence base for each of these apps was also explored via PsychINFO, Research Gate and Google Scholar. Searches of the Google Play Store, the Apple App Store, and the One Mind PsyberGuide found 722 apps labelled for mental health use, of which 163 apps were judged relevant to clinical work with patients with psychiatric disorders. Fifty-nine of these were determined to contain a computer-driven function for at least one of the four main elements of clinical treatment. The most common element was assessment/diagnosis (55/59 apps), followed by outcome tracking (34/59 apps). Six apps updated treatment plans using user input. Only one app tracked treatment fidelity. None of the apps contained computer-driven functions for all four elements. Twelve apps were supported in randomized clinical trials to show greater efficacy compared with either wait-list or other active treatments. Results showed that these four clinical elements can be meaningfully augmented, but the full potential of computer processing appears unreached in mental health-related apps.

Key learning aims

  1. (1) To understand what apps are currently available to treat clinical-level psychiatric problems.

  2. (2) To understand how many of the commercially available mental health-focused apps can be used for the treatment of clinical populations.

  3. (3) To understand how mental health services can be complemented by utilizing computer processing power within apps.

Keywords

appscomputerized mental health caremental healthmobile applicationsmobile appsprogressive web appstelemedicine
Type
Original Research
Information
the Cognitive Behaviour Therapist , Volume 15 , 2022 , e40
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the British Association for Behavioural and Cognitive Psychotherapies

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References

Further reading

Liu, F. F., Cruz, R. A., Rockhill, C. M., & Lyon, A. R. (2019). Mind the gap: considering disparities in implementing measurement-based care. Journal of the American Academy of Child and Adolescent Psychiatry, 58, 459–461. https://doi.org/10.1016/j.jaac.2018.11.015 CrossRefGoogle ScholarPubMed
Imel, Z. E., Caperton, D. D., Tanana, M., & Atkins, D. C. (2017). Technology-enhanced human interaction in psychotherapy. Journal of Counseling Psychology, 64, 385–393. https://doi.org/10.1037/cou0000213 CrossRefGoogle ScholarPubMed

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