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

RCTs - Protocols/Proposals (funded, already peer-reviewed, eHealth) (307)

Published on in Vol 5 , No 2 (2016) :Apr-Jun

Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: Protocol for a Randomized Study Funded by the US Department of Veterans Affairs Health Services Research and Development Program

Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: Protocol for a Randomized Study Funded by the US Department of Veterans Affairs Health Services Research and Development Program

Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: Protocol for a Randomized Study Funded by the US Department of Veterans Affairs Health Services Research and Development Program

Authors of this article:

John D Piette 1, 2, 3 Author Orcid Image ;   Sarah L Krein 1, 3 Author Orcid Image ;   Dana Striplin 2, 3 Author Orcid Image ;   Nicolle Marinec 2, 3 Author Orcid Image ;   Robert D Kerns 4, 5 Author Orcid Image ;   Karen B Farris 6 Author Orcid Image ;   Satinder Singh 7 Author Orcid Image ;   Lawrence An 1 Author Orcid Image ;   Alicia A Heapy 4, 5 Author Orcid Image
Article Authors Cited by (14) Tweetations (5) Metrics

Journals

  1. Fiske A, Henningsen P, Buyx A. Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy. Journal of Medical Internet Research 2019;21(5):e13216 View
  2. Schofield P, Shaw T, Pascoe M. Toward Comprehensive Patient-Centric Care by Integrating Digital Health Technology With Direct Clinical Contact in Australia. Journal of Medical Internet Research 2019;21(6):e12382 View
  3. Brandt C, Søgaard G, Clemensen J, Sndergaard J, Nielsen J. General Practitioners’ Perspective on eHealth and Lifestyle Change: Qualitative Interview Study. JMIR mHealth and uHealth 2018;6(4):e88 View
  4. Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Advanced Drug Delivery Reviews 2019;151-152:169 View
  5. Forman E, Kerrigan S, Butryn M, Juarascio A, Manasse S, Ontañón S, Dallal D, Crochiere R, Moskow D. Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss?. Journal of Behavioral Medicine 2019;42(2):276 View
  6. Higgins D, Buta E, Williams D, Halat A, Bair M, Heapy A, Krein S, Rajeevan H, Rosen M, Kerns R. Internet‐Based Pain Self‐Management for Veterans: Feasibility and Preliminary Efficacy of the Pain EASE Program. Pain Practice 2020;20(4):357 View
  7. Sadeh-Sharvit S, Hollon S. Leveraging the Power of Nondisruptive Technologies to Optimize Mental Health Treatment: Case Study. JMIR Mental Health 2020;7(11):e20646 View
  8. Piette J, Newman S, Krein S, Marinec N, Chen J, Williams D, Edmond S, Driscoll M, LaChappelle K, Kerns R, Maly M, Kim H, Farris K, Higgins D, Buta E, Heapy A. Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools. JAMA Internal Medicine 2022;182(9):975 View
  9. Siontis G, Sweda R, Noseworthy P, Friedman P, Siontis K, Patel C. Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials. BMJ Health & Care Informatics 2021;28(1):e100466 View
  10. Lin Y, Richardson C, Dobrin I, Pop-Busui R, Piatt G, Piette J. Accessibility and Openness to Diabetes Management Support With Mobile Phones: Survey Study of People With Type 1 Diabetes Using Advanced Diabetes Technologies. JMIR Diabetes 2022;7(2):e36140 View

Books/Policy Documents

  1. Fiske A, Henningsen P, Buyx A. Ethics of Digital Well-Being. View
  2. MacMartin-Moglia K, Mahler M. Precision Medicine and Artificial Intelligence. View
  3. Raman R, Varshney L. Handbook on Blockchain. View