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Microsoft

Monitor Healthcare AI Implementation with Power BI

Microsoft via Coursera

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Overview

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Use Power BI to monitor how healthcare AI implementations are performing—focused on consuming, filtering, interpreting, and communicating insights from dashboards prepared by analysts. This course builds the data-monitoring literacy that entry-level healthcare AI program managers need to contribute meaningfully to project status meetings. You'll navigate Power BI reports using filters, slicers, drill-through, and page navigation; distinguish chart-type choices for trends, comparisons, distributions, and exceptions; interpret dashboard indicators to monitor adoption, task progress, and operational exceptions; identify anomalies in healthcare AI implementation metrics; and prepare annotated status snapshots for recurring project meetings. Building or modifying Power BI report visuals is not in scope—learners consume and interpret analyst-built reports rather than construct them. This course is suitable for learners who are familiar with basic spreadsheets and want to add data-driven monitoring to their program management toolkit. No prior Power BI experience is required.

Syllabus

  • Identify Power BI Report Components
    • Build the vocabulary and recognition foundation for Power BI reports before interacting with them. Learn to identify the components that make up a report, datasets, visuals, filters, slicers, and report pages, to navigate reports built by BI analysts with confidence and understand what each part is doing.
  • Apply Power BI Interaction Features for Status Reviews
    • Turn component recognition into interaction. Apply Power BI's filter and slicer interactions, including plus cross-filtering, drill-through, and page navigation, to pull the specific data a weekly status review needs. Learn to extract role-relevant insights, flag anomalies for the senior PM lead, and prepare filtered views that make the report useful for status-review conversations.
  • Distinguish Chart Types for Healthcare AI Metrics
    • This section builds chart-type recognition: which chart types work for which data patterns, how to read reports faster, and how to recognize when a chart type doesn't fit its metric. It closes with applying that recognition to a monitoring report and flagging any mismatches for the business intelligence (BI) team.
  • Interpret Visual Formatting for Threshold Breaches and Status Changes
    • This section turns chart-type recognition into visual formatting interpretation: reading the semantic colors, threshold indicators, conditional formatting, and status change markers that surface healthcare artificial intelligence (AI) workflow issues, breached thresholds, and shifted statuses at a glance, and spotlighting them for the senior program manager lead in a weekly review.
  • Interpret Healthcare AI Dashboard Indicators
    • This module builds an interpretation of the four indicator categories common to healthcare AI monitoring dashboards, adoption, task progress, review outcomes, and operational exceptions, read against established operational targets. It distinguishes a Power BI dashboard, a single-page tiled summary, from a Power BI report, a multi-page interactive analysis tool.
  • Apply Dashboard Navigation and Annotation for Status Snapshots
    • This module turns indicator interpretation into meeting preparation. It covers navigating a healthcare artificial intelligence (AI) monitoring dashboard efficiently, drilling through tiles to source reports when detail is needed, and producing annotated status snapshots that give weekly meeting attendees an at-a-glance view of what's on target, approaching threshold, or already breached.
  • Analyze Healthcare AI Data for Trends and Anomalies
    • This section builds the skill of drilling into healthcare AI implementation data to identify trends, anomalies, and basic performance patterns, using Power BI drill-down to move from an aggregate view to the level where a pattern first appears. It focuses on recognizing and documenting observations for senior review, not on determining causation.
  • Comparative Analysis and Informatics Validation
    • This section turns pattern identification into defensible comparative analysis, using filtered views, time-period comparisons, and benchmark thresholds to evaluate current healthcare artificial intelligence (AI) metrics against reference values. It then applies health informatics terminology at the recognition level to confirm that filtered views represent the clinical data types they claim to show, producing a clear, evidence-based comparison ready for senior review.
  • Project Module: Prepare a Healthcare AI Status Readout from Power BI
    • Bring together everything from reading Power BI reports; interpreting chart types and visual formatting; navigating dashboards and interpreting indicators against operational targets; and analyzing data for trends, anomalies, and comparative analysis with informatics validation—to produce a complete healthcare AI status readout for the Senior AI Program Manager's use in the executive weekly. You'll build a five-section readout that gives a Senior AI Program Manager and executive attendees an at-a-glance summary, deep-dive detail on indicators needing attention, drill-down anomaly findings, defensible comparative analysis, and a consolidated escalation summary, all scoped to entry-level PM authority as observations and analysis for senior review.

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Microsoft

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