Miami University

Epic Training Lead

Miami, FL, USA · Staff+ · Contract

Sponsorship not specifiedDetected 15 days ago
HRISEpicPatient CareRadiologyResearchCommunicationMentoringMicrosoft Office

About the role

  • To learn how to apply for a faculty or staff position, please review this tip sheet.
  • This list of duties and responsibilities is not intended to be all-inclusive and may be expanded to include other duties or responsibilities as necessary.
  • UHealth-University of Miami Health System, South Florida's only university-based health system, provides leading-edge patient care powered by the ground breaking research and medical education at the Miller School of Medicine.

Responsibilities

  • Managing the full training lifecycle, including curriculum, environment readiness, and go‑live support.
  • Collaborating with Instructional Designer to identify training gaps and drive continuous improvement to ensure effective Epic adoption.
  • Leads the communication of new policies and procedures related to training.
  • Collaborates with analysts on application changes and new features to develop training strategies.
  • Maintains regular communication with team members and leads weekly project team meetings.
  • Analyzes, design s, build s, test s, and troubleshoot application and technical issues related to training environments (MST).

Requirements

  • Bachelor's degree in relevant field
  • EpicCare Ambulatory/Kaleidoscope Certifications required
  • Experience supporting outpatient environments, with a strong understanding of physician, nurse and ancillary roles' workflows, hospital outpatient departments, and end-to-end patient experiences.
  • Familiarity with ophthalmology physician practices' workflows and patient experiences.
  • Ability to communicate effectively in both oral and written form.
  • Ability to recognize, analyze, and solve a variety of problems.
  • Proficiency in computer software (i.e. Microsoft Office).

This listing is sourced directly from Miami University's careers page and normalized into a canonical job model.