Patient Record Comparative Analysis Excel
Budget: ₹600 – ₹1,500 INR
I have a set of patient records in raw spreadsheet form and need an experienced Excel analyst to turn them into clear, actionable comparisons. The dataset contains demographic information alongside detailed medical histories; treatment details are not included.
Your task is to clean, structure, and compare these two data dimensions so I can quickly spot variations between age groups, genders, or other demographic slices versus key medical-history indicators. I expect you to leverage pivot tables, lookup formulas, charts, or any other native Excel features that keep the workbook fully transparent and editable on my end—no external add-ins, please.
Deliverables
• One well-organized Excel workbook with separate tabs for:
– the cleaned source data (with your transformation steps documented in comments)
– comparative pivot tables and slicers for interactive filtering
– at least two clear charts that visualise the most significant differences you uncover
• A short read-me sheet outlining your methodology and pointing to where each insight can be found.
Acceptance criteria
• All formulas must resolve without errors when I refresh the file.
• Demographic and medical-history fields remain intact and traceable back to the original rows.
• Comparative findings are obvious at a glance, supported by labelled charts and concise headings.
If this aligns with your skill set and you can turn it around promptly, I look forward to working with you.
Your task is to clean, structure, and compare these two data dimensions so I can quickly spot variations between age groups, genders, or other demographic slices versus key medical-history indicators. I expect you to leverage pivot tables, lookup formulas, charts, or any other native Excel features that keep the workbook fully transparent and editable on my end—no external add-ins, please.
Deliverables
• One well-organized Excel workbook with separate tabs for:
– the cleaned source data (with your transformation steps documented in comments)
– comparative pivot tables and slicers for interactive filtering
– at least two clear charts that visualise the most significant differences you uncover
• A short read-me sheet outlining your methodology and pointing to where each insight can be found.
Acceptance criteria
• All formulas must resolve without errors when I refresh the file.
• Demographic and medical-history fields remain intact and traceable back to the original rows.
• Comparative findings are obvious at a glance, supported by labelled charts and concise headings.
If this aligns with your skill set and you can turn it around promptly, I look forward to working with you.