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

Viewpoints and Perspectives (514) Electronic Health Records (1053) Precision Medicine (75) Decision Support for Health Professionals (1185) Clinical Information and Decision Making (1407) Tools, Programs and Algorithms (266) Clinical Informatics (1022) Design and Usability of Clinical Software and EHRs (268) Data Science (270) Big Data (240)

Published on in Vol 24, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34560, first published .
Building a Precision Medicine Delivery Platform for Clinics: The University of California, San Francisco, BRIDGE Experience

Building a Precision Medicine Delivery Platform for Clinics: The University of California, San Francisco, BRIDGE Experience

Building a Precision Medicine Delivery Platform for Clinics: The University of California, San Francisco, BRIDGE Experience

Authors of this article:

Riley Bove1 Author Orcid Image ;   Erica Schleimer1 Author Orcid Image ;   Paul Sukhanov1 Author Orcid Image ;   Michael Gilson1 Author Orcid Image ;   Sindy M Law1 Author Orcid Image ;   Andrew Barnecut1 Author Orcid Image ;   Bruce L Miller1 Author Orcid Image ;   Stephen L Hauser1 Author Orcid Image ;   Stephan J Sanders1 Author Orcid Image ;   Katherine P Rankin1 Author Orcid Image
Article Authors Cited by (7) Tweetations (11) Metrics

Journals

  1. Block V, Koshal K, Wijangco J, Miller N, Sara N, Henderson K, Reihm J, Gopal A, Mohan S, Gelfand J, Guo C, Oommen L, Nylander A, Rowson J, Brown E, Sanders S, Rankin K, Lyles C, Sim I, Bove R. A Closed-Loop Falls Monitoring and Prevention App for Multiple Sclerosis Clinical Practice: Human-Centered Design of the Multiple Sclerosis Falls InsightTrack. JMIR Human Factors 2024;11:e49331 View
  2. Gonzalez R, Saha A, Campbell C, Nejat P, Lokker C, Norgan A. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities. Journal of Pathology Informatics 2024;15:100347 View
  3. Henderson K, Reihm J, Koshal K, Wijangco J, Miller N, Sara N, Doyle M, Mallory A, Sheridan J, Guo C, Oommen L, Feinstein A, Mangurian C, Lazar A, Bove R. Pragmatic phase II clinical trial to improve depression care in a real-world diverse MS cohort from an academic MS centre in Northern California: MS CATCH study protocol. BMJ Open 2024;14(2):e077432 View
  4. Henderson K, Reihm J, Koshal K, Wijangco J, Sara N, Miller N, Doyle M, Mallory A, Sheridan J, Guo C, Oommen L, Rankin K, Sanders S, Feinstein A, Mangurian C, Bove R. A Closed-Loop Digital Health Tool to Improve Depression Care in Multiple Sclerosis: Iterative Design and Cross-Sectional Pilot Randomized Controlled Trial and its Impact on Depression Care. JMIR Formative Research 2024;8:e52809 View
  5. Demuth S, Ed‐Driouch C, Dumas C, Laplaud D, Edan G, Vince N, De Sèze J, Gourraud P. Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data. European Journal of Neurology 2024 View
  6. Schmajuk G, Nasrallah C, Berrean B, Prugh J, Wilson C, Hamblin A, Young C, Jacobsohn L, Kay J, Li J, Kersey E, Subash M, Murray S, Yazdany J. A step-by-step roadmap for the development and deployment of an electronic health record sidecar application that tracks patient outcomes: The RA PRO dashboard. DIGITAL HEALTH 2024;10 View

Books/Policy Documents

  1. Patel D, Borgstadt J. Digital Health. View