This paper is in the following e-collection/theme issue:

Mobile Health in Psychiatry (426) Cost-Effectiveness and Economics (252) Economic Evaluations of mHealth Programs and Infrastructures (35)

Published on in Vol 7, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18812, first published .
A Patient-Centered Framework for Measuring the Economic Value of the Clinical Benefits of Digital Health Apps: Theoretical Modeling

A Patient-Centered Framework for Measuring the Economic Value of the Clinical Benefits of Digital Health Apps: Theoretical Modeling

A Patient-Centered Framework for Measuring the Economic Value of the Clinical Benefits of Digital Health Apps: Theoretical Modeling

Authors of this article:

Adam Powell1 Author Orcid Image ;   John Torous2 Author Orcid Image
Article Authors Cited by (13) Tweetations (16) Metrics

Journals

  1. van Kessel R, Kyriopoulos I, Wong B, Mossialos E. The Effect of the COVID-19 Pandemic on Digital Health–Seeking Behavior: Big Data Interrupted Time-Series Analysis of Google Trends. Journal of Medical Internet Research 2023;25:e42401 View
  2. Balcombe L, De Leo D. Digital Mental Health Amid COVID-19. Encyclopedia 2021;1(4):1047 View
  3. Powers B, Bucher A. An Economic Impact Model for Estimating the Value to Health Systems of a Digital Intervention for Diabetes Primary Care: Development and Usefulness Study. JMIR Formative Research 2022;6(9):e37745 View
  4. Torous J, Bucci S, Bell I, Kessing L, Faurholt‐Jepsen M, Whelan P, Carvalho A, Keshavan M, Linardon J, Firth J. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 2021;20(3):318 View
  5. Clarke P, Neffa-Creech D, Evans S. Cooking app engages kids as well as moms: Inviting secondary users into health outreach. Health Education Journal 2022;81(1):85 View
  6. Gensorowsky D, Witte J, Batram M, Greiner W. Market access and value-based pricing of digital health applications in Germany. Cost Effectiveness and Resource Allocation 2022;20(1) View
  7. Powell A, Dolan P. Moving to Personalized Medicine Requires Personalized Health Plans. Journal of Participatory Medicine 2022;14(1):e35798 View
  8. Groene N, Schneck L. Covering digital health applications in the public insurance system: how to foster innovation in patient care while mitigating financial risks—evidence from Germany. Frontiers in Digital Health 2023;5 View
  9. Nève de Mévergnies C, Verhaeghe N, Koster E, Baeken C, Vander Zwalmen Y, Hoorelbeke K. Health Economic Evaluation of Cognitive Control Training for Depression: Key Considerations. JMIR Mental Health 2023;10:e44679 View
  10. Bera S, Bhattacharya S. Exploring the importance of mobile app attributes based on consumers' voices using structured and unstructured data. IIM Ranchi Journal of Management Studies 2024;3(1):4 View
  11. Smith K, Hardy A, Vinnikova A, Blease C, Milligan L, Hidalgo-Mazzei D, Lambe S, Marzano L, Uhlhaas P, Ostinelli E, Anmella G, Zangani C, Aronica R, Dwyer B, Torous J, Cipriani A. Digital Mental Health for Schizophrenia and Other Severe Mental Illnesses: An International Consensus on Current Challenges and Potential Solutions. JMIR Mental Health 2024;11:e57155 View
  12. Freitag B, Uncovska M, Meister S, Prinz C, Fehring L. Cost-effectiveness analysis of mHealth applications for depression in Germany using a Markov cohort simulation. npj Digital Medicine 2024;7(1) View

Books/Policy Documents

  1. . Experience in Healthcare Innovation. View