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, US

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

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

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

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
  • US
  • Phone: 1 734-936-4787
  • Email: jpiette@umich.edu