Turn a functional healthcare AI project workspace into a well-organized, audit-ready environment. This module covers approved folder conventions, SharePoint taxonomy application, version history for traceability, and PHI-aware access controls so the right people see the right things.

Produce the three types of documentation that carry the most weight in an entry-level healthcare AI role: requirements captures, workflow-change notes, and implementation-decision records. Apply a structured standard so senior leads can pick up your documents and act on them without a follow-up conversation.

Turn a set of project documents into an audit-ready record. Apply approved version-control, controlled-access, and traceability practices to healthcare AI documentation so senior leads and reviewers can trust that what's in SharePoint Online reflects the current state of the initiative, and can be defended in a compliance review.

Learn to read a healthcare AI project’s stakeholder landscape and identify what each participant category—clinical, operational, and compliance—needs to know at each stage of the initiative.

Turn a stakeholder’s identified information needs into an actual message they will read and act on. Apply the three core communication templates—Teams update, Outlook email, and meeting readout—to deliver concise, audience-tuned healthcare AI project updates.

Analyze healthcare workflows from a Healthcare AI Coordinator perspective by identifying AI output touchpoints, required human review checkpoints, and potential exception paths. This foundation supports accurate workflow tracking, risk identification, and effective issue management.

Turn the analyzed workflow into an operational tracking system that a team can actually use. Apply workflow tracking methods to document task handoffs, review checkpoints, and exception paths, and maintain a routine risk register that surfaces operational risks to senior leads before they compound into blockers.

Build a single healthcare AI project workbook in Excel with three tabs, an expanded RAID log (risks, assumptions, issues, dependencies, actions, and decisions), a stakeholder list that evolves into a RACI matrix, and a cost and resource summary (covering labor and non-labor resources, with CapEx vs OpEx and blended vs standard rate concepts introduced at recognition level), using core Excel structures (tables, filters, basic formulas). This is the foundational workbook work that Module 2 builds on with data validation, formatting, and sharing practices.

Turn a working tracker into one that stays accurate over time. Apply data validation to prevent entry errors, conditional formatting to make status visible at a glance, and shared-workbook practices, such as access controls, edit protection, and change tracking, to keep the tracker useful when a team is contributing to it.

Produce the artifact package an entry-level Healthcare AI Coordinator delivers on their first initiative: a configured project workspace, a foundational requirements capture, a targeted stakeholder update, an annotated workflow map, and a working task and decision log. You'll take the same discovery inputs a real coordinator would receive and produce five integrated artifacts that a Senior AI Program Manager could pick up and act on without rework.