📊 Situation

A New Patient Analytics Dashboard 📈 to help us understand patient data better.

      To provide quick insights ✨ into things like patient groups 👨‍👩‍👧‍👦, treatment results ✅, 
      and costs 💲,to support data-driven decision-making 🧠.

🎯 Task

Build this comprehensive dashboard from scratch 🏗️. This involved developing and visualising key performance indicators (KPIs) 📊

    such as patient distribution by indication 🌍, treatment success rates 🏆, cost per patient 💰, 
    readmission rates 🔄, length of stay ⏱️, treatment efficacy comparisons ⚖️, and mortality rates 💔,         
    applying various filters for detailed analysis 🔬.

💻Action

I began by breaking down each required KPI 🧩 to understand its underlying data requirements and relationships.

    This involved identifying all necessary data points like age 👵👴, ethnicity 🧑‍🤝‍🧑, payer type 💳, 
    indication 🤒, treatment 💊, outcomes 👍, and costs 💵. 

I created detailed charts for patient demographics 📊, utilised heatmaps 🔥 to highlight readmission risks,

    and developed comparative views for treatment efficacy and mortality. 

I implemented all specified filters 🔍, such as age group, ethnicity, payer type, provider type,

    and pharmacy type, to allow for granular data exploration. 

✅ Result

I successfully delivered a fully functional 👍 and insightful Patient Analytics Dashboard 🚀

    It effectively presented all the specified KPIs and filters, enabling stakeholders to visualise critical patterns📈, 
    understand treatment effectiveness ✅, identify cost variations 💲, and pinpoint areas of high risk 🚩. 

This dashboard significantly enhanced our ability to derive meaningful insights from patient data 🧠,

Supporting more informed decision-making in patient care 🌟.