Published on in Vol 15, No 7 (2013): July

Maximizing the Value of Mobile Health Monitoring by Avoiding Redundant Patient Reports: Prediction of Depression-Related Symptoms and Adherence Problems in Automated Health Assessment Services

Maximizing the Value of Mobile Health Monitoring by Avoiding Redundant Patient Reports: Prediction of Depression-Related Symptoms and Adherence Problems in Automated Health Assessment Services

Maximizing the Value of Mobile Health Monitoring by Avoiding Redundant Patient Reports: Prediction of Depression-Related Symptoms and Adherence Problems in Automated Health Assessment Services

John D Piette   1 , PhD, ScM ;   Jeremy B Sussman   1 , MD ;   Paul N Pfeiffer   2 , MD ;   Maria J Silveira   1 , MD ;   Satinder Singh   3 , PhD ;   Mariel S Lavieri   4 , PhD

1 VA Center for Clinical Management Research and Division of General Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States

2 VA Center for Clinical Management Research and Department of Psychiatry, Ann Arbor VA Healthcare System and University of Michigan, Ann Arbor, MI, United States

3 Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, United States

4 Deparment of Industrial and Operations Engineering, College of Engineering, University of Michigan, Ann Arbor, MI, United States

Corresponding Author:

  • John D Piette, PhD, ScM
  • VA Center for Clinical Management Research and Division of General Medicine
  • Department of Internal Medicine
  • University of Michigan
  • PO Box 130170
  • Ann Arbor, MI, 48113-0170
  • United States
  • Phone: 1 734-936-4787
  • Fax: 1 734-936-8944
  • Email: jpiette@umich.edu