📊 Healthcare Descriptive Analytics – Explained Simply

Descriptive analytics in healthcare is like looking in the rear-view mirror to understand what just happened — using data to summarize past events and uncover patterns.

🧠 What It Means

Descriptive analytics focuses on “what happened?” in a healthcare setting. It takes raw data from:

• Electronic Health Records (EHRs)

• Insurance claims

• Lab results

• Patient satisfaction surveys
  …and turns it into easy-to-understand summaries and visuals.

🏥 Examples in Healthcare

• Hospital readmission rates 📈

• Patient demographics and disease prevalence 👥

• Length of hospital stays 🛏️

• Medication usage trends 💊

• Emergency room visit patterns 🚑

📌 Why It Matters

• Helps administrators make better decisions

• Identifies inefficiencies or bottlenecks

• Tracks quality and safety metrics

• Forms the foundation for more advanced analytics (predictive or prescriptive)

🛠️ Tools Used

• Dashboards (e.g., Tableau, Power BI)

• Excel-based reports

• Healthcare information systems

• SQL databases

🚦 The First Step in Data-Driven Care

Descriptive analytics doesn’t predict or prescribe. But it’s the starting point for spotting problems, finding strengths, and ultimately improving patient outcomes and operational performance.