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Three Decision Points Vetting a More Ideal Online Technical Platform for Monitoring and Treating Mental Health Problems like Depression and Schizophrenia

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Intelligent Human Computer Interaction (IHCI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13184))

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Abstract

To improve Information Communication Technology for Development (ICT4D) in e-health applications means getting the maximum advantage at the minimum cost to purveyors and consumers. This is a conceptual and empirical paper arguing three informed decision points, taken in order, should happen whenever maximizing advantages of health diagnostics and/or treatment facilitation via online platforms. Three kinds of data are important to know beforehand to create informed decisions to maximize beneficial uses of online technology to aid mental health: (1) how do you define the etiology of mental health problems; (2) how do you define who should be helped as a priority, and (3) how do you make decisions technically to fit the former two points? Since serving patients is more important than technical profiteering, these three critical decision points mean primary medical choices of diagnosis and secondary social demographic research should guide a more conditioned tertiary technical use, instead of vice versa that regularly leads to cutting patients to fit a pre-determined and thus misaligned technological investment. Due to limitations of space, only the third vetting point is analyzed in detail. The current state of the art for how to reach patients best via Internet-connected technologies for monitoring and treating depression and schizophrenia is analyzed. Policy advice on platform design is given from this vetting procedure that might later be scaled worldwide.

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Notes

  1. 1.

    This research was supported under the National Research Foundation (NRF) Korea (2020K1A3A1A68093469) funded by the Ministry of Science and ICT (MSIT) Korea and by the Department of Biotechnology (India) (DBT/IC-12031(22)-ICD-DBT).

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Author information

Authors and Affiliations

  1. Department of Technology and Society, Stony Brook University, State University of New York, Korea (SUNY Korea), Incheon, Songdo, South Korea

    Mark D. Whitaker, Nara Hwang, Durdonakhon Usmonova & Nara Park

  2. Cognitive Science Department, Yonsei University, Seoul, Sinchon, South Korea

    Kangrim Cho

Authors
  1. Mark D. Whitaker
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  2. Nara Hwang
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  3. Durdonakhon Usmonova
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  4. Kangrim Cho
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  5. Nara Park
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Corresponding author

Correspondence to Mark D. Whitaker .

Editor information

Editors and Affiliations

  1. Kent State University, Kent, OH, USA

    Jong-Hoon Kim

  2. University of Tartu, Tartu, Estonia

    Madhusudan Singh

  3. Kent State University, Kent, OH, USA

    Javed Khan

  4. Indian Institute of Information Technology, Allahabad, India

    Uma Shanker Tiwary

  5. Massachusetts Institute of Technology, Cambridge, MA, USA

    Marigankar Sur

  6. Hankuk University of Foreign Studies, Seoul, Korea (Republic of)

    Dhananjay Singh

Appendices

Appendices

By the editor’s recommendation, appendices are at an external link or by contacting the authors since the appendices are not crucial for describing results of this study, though are raw data in the interests of transparency demonstrating empirical points already summarized in this paper: https://mega.nz/file/kwZBhIib#lnhIwRs34NcK86KpR34_CAOXycopLOUUgvCQMF3wxiE.

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Whitaker, M.D., Hwang, N., Usmonova, D., Cho, K., Park, N. (2022). Three Decision Points Vetting a More Ideal Online Technical Platform for Monitoring and Treating Mental Health Problems like Depression and Schizophrenia. In: Kim, JH., Singh, M., Khan, J., Tiwary, U.S., Sur, M., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2021. Lecture Notes in Computer Science, vol 13184. Springer, Cham. https://doi.org/10.1007/978-3-030-98404-5_8

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  • DOI: https://doi.org/10.1007/978-3-030-98404-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98403-8

  • Online ISBN: 978-3-030-98404-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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