Excel Medical Data Comparison
Budget: ₹600 – ₹1,500 INR
I have a set of anonymised patient health records already organised in Excel and I want to turn those rows and columns into clear, decision-ready insights. The objective is a comparative analysis between the different medical conditions represented in the file—no need to break results down by age or gender at this stage, the spotlight is firmly on the conditions themselves.
Here is what I need from you:
• A clean, well-documented workbook that compares key outcome metrics across each condition. This can be done with pivot tables, dynamic formulas or Power Query—whatever gives reliable, refreshable results.
• Appropriate statistical tests (t-test, chi-square or similar as warranted) so we know whether observed differences are meaningful. Please note the test used and its assumptions inside the sheet.
• A concise dashboard or set of charts that lets a non-technical clinician grasp the differences at a glance.
• A short written summary (a page or two) highlighting the most significant findings and any recommendations for further exploration.
The raw data are ready to share as soon as we start, and I can clarify field names or coding conventions right away. If you routinely work with tools like Excel’s Analysis ToolPak, Power Pivot, or even R/Python for heavier stats but present final outputs in Excel, that’s perfect—just make sure everything is reproducible inside the workbook.
Accuracy, transparent methodology and a tidy final file matter more to me than flashy visuals, so please plan on a brief walkthrough once you are done so I can follow every step. I look forward to seeing how you can pull actionable insights out of these records.
Here is what I need from you:
• A clean, well-documented workbook that compares key outcome metrics across each condition. This can be done with pivot tables, dynamic formulas or Power Query—whatever gives reliable, refreshable results.
• Appropriate statistical tests (t-test, chi-square or similar as warranted) so we know whether observed differences are meaningful. Please note the test used and its assumptions inside the sheet.
• A concise dashboard or set of charts that lets a non-technical clinician grasp the differences at a glance.
• A short written summary (a page or two) highlighting the most significant findings and any recommendations for further exploration.
The raw data are ready to share as soon as we start, and I can clarify field names or coding conventions right away. If you routinely work with tools like Excel’s Analysis ToolPak, Power Pivot, or even R/Python for heavier stats but present final outputs in Excel, that’s perfect—just make sure everything is reproducible inside the workbook.
Accuracy, transparent methodology and a tidy final file matter more to me than flashy visuals, so please plan on a brief walkthrough once you are done so I can follow every step. I look forward to seeing how you can pull actionable insights out of these records.