Patient Billing System - Basic Example

This example was a request to AI to generate a basic patient billing system.

Disclaimer: This is just an example, do not use for real-world processing

Example:

1. **Patient Information**:

- Patient ID - First Name - Last Name - Date of Birth - Gender - Contact Information (Phone, Email) - Address - Emergency Contact Information 2. **Insurance Information**: - Insurance Provider Name - Policy Number - Group Number - Policy Holder's Name - Relationship to Policy Holder - Coverage Details - Pre-authorization Number (if needed) 3. **Medical Information**: - Medical History - Primary Care Physician - Referring Physician (if any) - Diagnosis Code(s) (ICD-10 codes) - Procedure Code(s) (CPT or HCPCS codes) 4. **Billing Information**: - Date of Service - Billing Date - Total Charges - Insurance Adjustments - Patient Payments - Balance Due - Invoice Number - Payment Status (Paid, Unpaid, Partially Paid) 5. **Visit Information**: - Visit Date - Visit Type (Inpatient, Outpatient, Emergency) - Room Number (if Inpatient) - Discharge Date (if Inpatient) 6. **Service Information**: - Service ID - Service Description - Service Unit - Service Price It's important to remember that maintaining the privacy and security of patient health information is crucial in any healthcare-related system, and all of these systems need to adhere to regulatory standards such as HIPAA (Health Insurance Portability and Accountability Act) in the United States. Sure, I can expand the example with 3 more fictional patients. Patient ID First Name Last Name DOB Gender Contact Number Address Insurance Provider Policy Number Visit Date Diagnosis Code Procedure Code Total Charges Payment Status 1 John Doe 1/1/1980 M 1234567890 1234 Maple St. Insurance A ABC123 5/1/2023 J06.9 99214 $200 Paid 2 Jane Smith 2/2/1990 F 0987654321 5678 Oak Ave. Insurance B XYZ456 6/1/2023 M54.5 99213 $150 Unpaid 3 Alice Johnson 3/3/2000 F 1122334455 9101 Pine Rd. Insurance C DEF789 4/15/2023 B34.2 99203 $175 Paid 4 Bob Williams 4/4/1970 M 5566778899 1213 Cedar Ln. Insurance D GHI101 5/20/2023 S86.1 99215 $225 Partially Paid 5 Carol Brown 5/5/1985 F 9988776655 1415 Elm St. Insurance E JKL202 6/10/2023 M15.9 99204 $200 Unpaid Again, the information in this table is entirely fictional and is designed to demonstrate the potential structure of a patient billing system. In practice, a patient billing system would likely use unique identifiers for patient ID, policy numbers, etc., and not simple sequential integers or alphanumeric strings. Example: import pandas as pd # Create a list of dictionaries where each dictionary represents a patient patients = [ {"Patient ID": 1, "First Name": "John", "Last Name": "Doe", "DOB": "1/1/1980", "Gender": "M", "Contact Number": "1234567890", "Address": "1234 Maple St.", "Insurance Provider": "Insurance A", "Policy Number": "ABC123", "Visit Date": "5/1/2023", "Diagnosis Code": "J06.9", "Procedure Code": "99214", "Total Charges": "$200", "Payment Status": "Paid"}, {"Patient ID": 2, "First Name": "Jane", "Last Name": "Smith", "DOB": "2/2/1990", "Gender": "F", "Contact Number": "0987654321", "Address": "5678 Oak Ave.", "Insurance Provider": "Insurance B", "Policy Number": "XYZ456", "Visit Date": "6/1/2023", "Diagnosis Code": "M54.5", "Procedure Code": "99213", "Total Charges": "$150", "Payment Status": "Unpaid"}, {"Patient ID": 3, "First Name": "Alice", "Last Name": "Johnson", "DOB": "3/3/2000", "Gender": "F", "Contact Number": "1122334455", "Address": "9101 Pine Rd.", "Insurance Provider": "Insurance C", "Policy Number": "DEF789", "Visit Date": "4/15/2023", "Diagnosis Code": "B34.2", "Procedure Code": "99203", "Total Charges": "$175", "Payment Status": "Paid"}, {"Patient ID": 4, "First Name": "Bob", "Last Name": "Williams", "DOB": "4/4/1970", "Gender": "M", "Contact Number": "5566778899", "Address": "1213 Cedar Ln.", "Insurance Provider": "Insurance D", "Policy Number": "GHI101", "Visit Date": "5/20/2023", "Diagnosis Code": "S86.1", "Procedure Code": "99215", "Total Charges": "$225", "Payment Status": "Partially Paid"}, {"Patient ID": 5, "First Name": "Carol", "Last Name": "Brown", "DOB": "5/5/1985", "Gender": "F", "Contact Number": "9988776655", "Address": "1415 Elm St.", "Insurance Provider": "Insurance E", "Policy Number": "JKL202", "Visit Date": "6/10/2023", "Diagnosis Code": "M15.9", "Procedure Code": "99204", "Total Charges": "$200", "Payment Status": "Unpaid"} ] # Convert the list of dictionaries to a DataFrame df = pd.DataFrame(patients) # Display the DataFrame print(df) ========================================================================= This script creates a pandas DataFrame and populates it with the patient data. You can then use pandas' various functions to manipulate and analyze this data. Please note that this script requires the pandas library, which you can install using pip: sh Copy code pip install pandas If you are looking to store this data persistently, you would need to use a database. Python has several libraries such as sqlite3, psycopg2, pyodbc, and SQLAlchemy which can User I would like to have the script generate a .csv file that use a comma as the field delimiter. The script should create a file called Patients.csv ChatGPT Sure, you can use the pandas library's to_csv method to accomplish this. Here's how you can modify the previous script to generate a .csv file: python Copy code import pandas as pd # Create a list of dictionaries where each dictionary represents a patient patients = [ {"Patient ID": 1, "First Name": "John", "Last Name": "Doe", "DOB": "1/1/1980", "Gender": "M", "Contact Number": "1234567890", "Address": "1234 Maple St.", "Insurance Provider": "Insurance A", "Policy Number": "ABC123", "Visit Date": "5/1/2023", "Diagnosis Code": "J06.9", "Procedure Code": "99214", "Total Charges": "$200", "Payment Status": "Paid"}, {"Patient ID": 2, "First Name": "Jane", "Last Name": "Smith", "DOB": "2/2/1990", "Gender": "F", "Contact Number": "0987654321", "Address": "5678 Oak Ave.", "Insurance Provider": "Insurance B", "Policy Number": "XYZ456", "Visit Date": "6/1/2023", "Diagnosis Code": "M54.5", "Procedure Code": "99213", "Total Charges": "$150", "Payment Status": "Unpaid"}, {"Patient ID": 3, "First Name": "Alice", "Last Name": "Johnson", "DOB": "3/3/2000", "Gender": "F", "Contact Number": "1122334455", "Address": "9101 Pine Rd.", "Insurance Provider": "Insurance C", "Policy Number": "DEF789", "Visit Date": "4/15/2023", "Diagnosis Code": "B34.2", "Procedure Code": "99203", "Total Charges": "$175", "Payment Status": "Paid"}, {"Patient ID": 4, "First Name": "Bob", "Last Name": "Williams", "DOB": "4/4/1970", "Gender": "M", "Contact Number": "5566778899", "Address": "1213 Cedar Ln.", "Insurance Provider": "Insurance D", "Policy Number": "GHI101", "Visit Date": "5/20/2023", "Diagnosis Code": "S86.1", "Procedure Code": "99215", "Total Charges": "$225", "Payment Status": "Partially Paid"}, {"Patient ID": 5, "First Name": "Carol", "Last Name": "Brown", "DOB": "5/5/1985", "Gender": "F", "Contact Number": "9988776655", "Address": "1415 Elm St.", "Insurance Provider": "Insurance E", "Policy Number": "JKL202", "Visit Date": "6/10/2023", "Diagnosis Code": "M15.9", "Procedure Code": "99204", "Total Charges": "$200", "Payment Status": "Unpaid"} ] # Convert the list of dictionaries to a DataFrame df = pd.DataFrame(patients) # Write the DataFrame to a .csv file df.to_csv('Patients.csv', index=False) This script will create a .csv file named "Patients.csv" in the same directory as your script. If you want to specify a different directory, you can provide the full path to the to_csv method. Also note that the index=False argument is used to prevent pandas from writing the DataFrame's index to the . User Ok great. Now I want a way to import the "Patients.csv" file to a Microsoft SQL 2022 database. Provide the script and step by step instructions please. ChatGPT To import data from a CSV file into a Microsoft SQL Server database, we'll use the pyodbc library, which allows Python to connect to an ODBC database, like SQL Server. If you don't have it installed already, you can install it using pip: bash Copy code pip install pyodbc Now, let's create the script to import the data: python Copy code import pandas as pd import pyodbc # Create a connection string (this would depend on your server, database, and login details) conn_str = ( r'DRIVER={ODBC Driver 17 for SQL Server};' r'SERVER=your_server_name;' r'DATABASE=your_database_name;' r'UID=your_username;' r'PWD=your_password' ) # Establish the connection cnxn = pyodbc.connect(conn_str) # Read the data from CSV file df = pd.read_csv('Patients.csv') # Insert the data into the SQL Server database for index, row in df.iterrows(): cursor = cnxn.cursor() cursor.execute("INSERT INTO Patients([Patient ID],[First Name],[Last Name],[DOB],[Gender],[Contact Number],[Address],[Insurance Provider],[Policy Number],[Visit Date],[Diagnosis Code],[Procedure Code],[Total Charges],[Payment Status]) values(?,?,?,?,?,?,?,?,?,?,?,?,?,?)", row['Patient ID'], row['First Name'], row['Last Name'], row['DOB'], row['Gender'], row['Contact Number'], row['Address'], row['Insurance Provider'], row['Policy Number'], row['Visit Date'], row['Diagnosis Code'], row['Procedure Code'], row['Total Charges'], row['Payment Status']) cnxn.commit() cursor.close() cnxn.close() Please replace 'your_server_name', 'your_database_name', 'your_username', 'your_password' with your actual SQL Server details. Also make sure the 'DRIVER' in your connection string matches the ODBC driver installed on your system. It is important to note that the table "Patients" should already exist in your SQL Server database with the structure matching the data from the CSV file. If it doesn't exist, you need to create it first. This script works by establishing a connection to your SQL Server database, reading the data from the CSV file into a pandas DataFrame, then iterating over the rows of the DataFrame and inserting each one into the database. If you experience any issues with the ODBC Driver, you may need to install or update it. You can download the ODBC Driver for SQL Server from the official Microsoft website. As of my knowledge cutoff in September 2021, the latest version was "ODBC Driver 17 for SQL Server".

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Patient ID	First Name	Last Name	DOB	Gender	Contact Number	Address	Insurance Provider	Policy Number	Visit Date	Diagnosis Code	Procedure Code	Total Charges	Payment Status
1	John	Doe	1980-01-01	M	1234567890	1234 Maple St.	Insurance A	ABC123	2023-05-01	J06.9	99214	200.00	Paid
2	Jane	Smith	1990-02-02	F	0987654321	5678 Oak Ave.	Insurance B	XYZ456	2023-06-01	M54.5	99213	150.00	Unpaid
3	Alice	Johnson	2000-03-03	F	1122334455	9101 Pine Rd.	Insurance C	DEF789	2023-04-15	B34.2	99203	175.00	Paid
4	Bob	Williams	1970-04-04	M	5566778899	1213 Cedar Ln.	Insurance D	GHI101	2023-05-20	S86.1	99215	225.00	Partially Paid
5	Carol	Brown	1985-05-05	F	9988776655	1415 Elm St.	Insurance E	JKL202	2023-06-10	M15.9	99204	200.00	Unpaid