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

Proposals (eHealth) (79) Public (e)Health, Digital Epidemiology and Public Health Informatics (713) Secondary Use of Clinical Data for Research and Surveillance (362) Protocols for Public Health Research and Surveillance (61) Surveillance Systems (457) Antimicrobial Stewardship and Antibiotic Resistance (43) Data Science (263) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (831) Open Source and Data for Public Health (21) Datasets (9) Machine Learning (1450)

Published on in Vol 13 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58116, first published .
Combating Antimicrobial Resistance Through a Data-Driven Approach to Optimize Antibiotic Use and Improve Patient Outcomes: Protocol for a Mixed Methods Study

Combating Antimicrobial Resistance Through a Data-Driven Approach to Optimize Antibiotic Use and Improve Patient Outcomes: Protocol for a Mixed Methods Study

Combating Antimicrobial Resistance Through a Data-Driven Approach to Optimize Antibiotic Use and Improve Patient Outcomes: Protocol for a Mixed Methods Study

Authors of this article:

Jonathan Mayito1 Author Orcid Image ;   Conrad Tumwine1 Author Orcid Image ;   Ronald Galiwango1, 2 Author Orcid Image ;   Elly Nuwamanya1 Author Orcid Image ;   Suzan Nakasendwa1 Author Orcid Image ;   Mackline Hope1 Author Orcid Image ;   Reuben Kiggundu1 Author Orcid Image ;   Dathan M Byonanebye1 Author Orcid Image ;   Flavia Dhikusooka1 Author Orcid Image ;   Vivian Twemanye1 Author Orcid Image ;   Andrew Kambugu1 Author Orcid Image ;   Francis Kakooza1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

There are no citations yet available for this article according to Crossref .