Lebogang Mohau
Data specialist dealing with Excel Data Cleaning, data entry
New to Contra
Lebogang is building their profile!
The dashboard provides an overview of 1,200 healthcare patients, their billing, insurance providers, diagnoses, and blood pressure measurements.
Total patients: 1,200.
Total revenue: $26,257,784.79, showing substantial billing across the healthcare records.
Average weight: 87.5 kg.
Average height: 165.3 cm.
Insurance and admissions
Blue Cross has the highest number of admissions at around 180, followed by Kaiser, Medicare, and UnitedHealth at roughly 170 each. Medicaid has the lowest admissions among the major providers at about 140. There are also around 50 records with no insurance provider listed.
Revenue
Blue Cross generates the highest revenue at approximately $4.4 million. Kaiser contributes about $3.9 million, while Medicare and UnitedHealth generate around $3.8 million and $3.7 million respectively. Records without an insurance provider generate the least revenue, at roughly $600,000.
Diagnoses and billing
The dashboard includes a wide range of diagnoses, including asthma, atrial fibrillation, chronic kidney disease, heart failure, hypertension, obesity, pneumonia, and diabetes. Billing is distributed across these diagnoses, with no single diagnosis completely dominating the total.
Blood pressure
Average systolic blood pressure generally ranges from about 118–129 mmHg, while average diastolic pressure is around 75–86 mmHg across diagnoses. This shows variation between conditions, although the values remain relatively consistent overall.
Overall insight
The dashboard shows that Blue Cross is the strongest insurance provider in both admissions and revenue. The patient population has diverse diagnoses, while average blood pressure varies by diagnosis. The dashboard also highlights missing insurance information that could be cleaned or investigated further.
2
40
This clean healthcare patient dataset contains structured and standardised information about patients, including demographic, physical, clinical, hospitalisation, insurance, billing, and readmission data. The dataset includes variables such as patient ID, full name, date of birth, gender, height, weight, systolic and diastolic blood pressure, glucose level, cholesterol level, admission and discharge dates, primary diagnosis, insurance provider, billing amount, and readmission status.
The data has been cleaned to improve its quality, consistency, and accuracy. Inconsistent formats have been standardised, invalid or incorrect values have been identified and addressed, and categorical variables such as gender and readmission status have been made consistent. Date and numerical fields have also been formatted appropriately to ensure that the dataset is suitable for analysis.
The cleaned dataset can be used to analyse patient characteristics, health conditions, hospital admissions, billing patterns, and readmission trends. It provides a more reliable and organised dataset for healthcare data analysis and visualisation.
0
18
This dataset is a raw healthcare dataset containing demographic, clinical, hospitalisation, insurance, billing, and readmission information for patients. The dataset includes variables such as patient ID, demographic details, height, weight, blood pressure, glucose, cholesterol, admission and discharge dates, diagnosis, insurance provider, billing amount, and readmission status. As the dataset was collected before cleaning, it contains data-quality issues such as inconsistent formats, invalid values, and inconsistently recorded categorical and date variables. The purpose of cleaning the dataset is to standardise the data and correct or handle these issues so that it can be used for accurate analysis
0
22