Top Tweets for #HEALFICAHealthDataChallenge
Over the next 90 days, I want to introspect, ask better questions, see patterns, show and not just tell, so data helps people make better decisions about care.
By the end, I want to show skill and the value I bring to a team.
@The_healfica #healficahealthdatachallenge
3οΈβ£ Findings & Impact: Identified an 11.39% baseline readmission rate and a 4-factor risk score capturing 68.35% of readmissions. This helps leadership optimize staffing to 23.0 FTEs.
#healficahealthdatachallenge
@The_healfica My Actionable Recommendations & Project Links
#healficahealthdatachallenge
Put the Risk Flag in the Electronic Health Record(EHR): Integrate this 4-point score into our electronic records so a red badge pops up automatically on high-risk patients at discharge.
Step 5: Findings & Insights
Digging into hospital data for the @healfica Data Challenge. My goal? Figure out why diabetic patients keep coming back to the hospital within 30 days of leaving, and to stop it without burning out staff.
Dashboard shows what the data told me:
Safer Transitions Home: Because post-acute care transfers carry higher risk, hospital teams can build specific handoff checklists for those facilities to make sure no patient falls through the cracks.
#healficahealthdatachallenge
Add 1 point for each applicable trigger:
ποΈ Prior Visit: Hospital stay in the past year.
β³ Length of Stay: 5+ days this visit.
π Medications: 15+ current medications.
π¨ Admission Type: Admitted through the ER.
#healficahealthdatachallenge
β’ Where They Go After Discharge: Patients sent to Skilled Nursing Facilities or Home Health Care were much more likely to come back to the hospital than those who went straight home, no matter how long their initial stay was.
#healficahealthdatachallenge
#DataCollection
After completing Step 4 of the #healficahealthdatachallenge I did a second-pass data audit before finalizing the PowerBI dashboard visuals.
Here are the 3 adjustments I made β My updated Cleaning Log and Data Dictionary: https://t.co/eGDsF6T35Yπ
Answering the Questions with DAX for #healficahealthdatachallenge
@The_healfica Iβve translated those 7 business questions into a high-performance data model. Here is how DAX answers the call:
ππ HEALFICA 30-Day Readmission Data Challenge - Final Submission!
I took 101,766 healthcare encounters from raw data to an interactive Power BI dashboard using PostgreSQL,SQL and Power BI.
Thank you @HEALFICA, for the challenge!π
#healficahealthdatachallenge #SQL #PowerBI
Today, I was able to wrap up with cleaning the data, formatting, checking for any irregularities/inconsistencies, removing duplicates.
I split the IDS Mapping into three different files to ease the...
@The_healfica
@kiki_blacc
#healficahealthdatachallenge
I'm currently working on a healthcare data challenge by @The_healfica
This is my first time I'll be analysing this kind of data.
The purpose of this challenge is to help us think like healthcare analyst by...
#healficahealthdatachallenge
Answering the Questions with DAX for #healficahealthdatachallenge
@The_healfica Iβve translated those 7 business questions into a high-performance data model. Here is how DAX answers the call:
@The_healfica
#healficahealthdatachallenge
π Step 2: Ask the Questions
For this step, I defined the questions. Rather than merely tracking if numbers increased, the focus is on actionable insights:
1. Baseline: What is the overall 30-day diabetic readmission rate? π
Final Cleaned Dataset: 99,343 encounter records ready for Power BI reporting! π
https://t.co/CP8oehaMBV
#DataAnalytics #healficahealthdatachallenge
#healficahealthdatachallenge
Quick Power Query lesson I learned today while cleaning messy mapping files π
I had 3 distinct lookup tables trapped in ONE CSV file. Here is how I cleaned it up:
1. Set ID columns to Whole Number to prevent dropped rows during merges.
My reasoning: Standardizing text columns like race, weight, and medical specialty keeps the data clean for analysis, while tracking patient re-entries prevents skewed results. #healficahealthdatachallenge
I cleaned readmission data from #healficahealthdatachallenge using Power Query in PowerBI π
1οΈβ£ Loaded the data: Imported diabetic_encounter_raw.csv and IDS_mapping.csv into the Power Query Editor.
I'm currently working on a healthcare data challenge by @The_healfica
This is my first time I'll be analysing this kind of data.
The purpose of this challenge is to help us think like healthcare analyst by...
#healficahealthdatachallenge
Exactly the right place to start: understand the healthcare problem before getting lost in the data.
Looking forward to seeing where your analysis takes you π
#healficahealthdatachallenge
Working on a healthcare data analytics challenge with @The_healfica
Weβre focusing on 30-day readmissions among diabetics patients.
The dataset contains 101,766 inpatient encounters from 130 Hospitals in the US.
The dataset covers demographics, admissions, diagnoses, medications, procedures, and readmission outcomes.
The Aim is to understand the patterns that are associated with patients returning within 30 days after discharge, and where additional care coordination could make the most difference.
@MoonChi222 This is exactly what we want to see from the #healficahealthdatachallenge.
You didnβt jump into Power BI and start building. You started with the problem, translated it into questions, then mapped those questions back to the data.
Thatβs healthcare analytics thinking
Weldoneππ½
β’ Age Format: Is age continuous or already binned into bands like [50-
60)? Already binned into ranges like [50-60)
NEXT: Data cleaning. Standardizing IDs, joining lookup tables, and isolating true 30-day readmissions
(<30)
#healficahealthdatachallenge #PowerBl #DataAnalytics
Once the investigative questions were defined, the next step was to understand the raw data. #healficahealthdatachallenge @The_healfica
Step 3: The Data
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