Carelytics – Healthcare Data Analytics Library

A modular Python package for healthcare data cleaning, validation, and revenue cycle insights.

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Overview

Carelytics is a Python library designed to simplify data analytics and automation in the healthcare domain, especially focusing on Revenue Cycle Management (RCM) workflows.

It provides functions to:

• Validate and clean large healthcare datasets
• Analyze patient encounters, lab data, and vitals
• Standardize formats for interoperability (FHIR-ready)
• Support predictive modeling such as readmission and denial prediction

Built with Pandas, NumPy, and Scikit-learn, Carelytics empowers analysts, researchers, and developers to derive actionable insights from healthcare data quickly and efficiently.

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Package Architecture

carelytics/
│
├── data/                     # (Placeholder for sample data or CSVs)
│
├── fhir/                     # Handles healthcare interoperability (FHIR parsing)
│   ├── parser.py
│   └── validator.py
│
├── models/                   # Predictive models for healthcare analytics
│   ├── denial_prediction.py
│   └── readmission.py
│
├── utils/                    # Utility functions for data processing
│   ├── __init__.py
│   ├── cleaner.py
│   ├── deid.py               # De-identification utilities for PHI data
│   ├── validator.py          # Schema and datatype validation
│   └── __init__.py
│
├── claims.py                 # Claim-level metrics and KPIs
├── encounter.py              # Patient encounter analytics
├── lab.py                    # Lab result standardization
├── patient.py                # Patient-level summaries
└── vitals.py                 # Vital signs normalization and aggregation

Each module is reusable and can be independently imported.

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⚙️ Core Functionalities

1. carelytics.utils.validator

Provides schema and datatype validation for healthcare datasets.

Example:

from carelytics.utils.validator import validate_columns, validate_datatypes

validate_columns(df, ["patient_id", "age", "diagnosis"])
validate_datatypes(df, {"age": "int64", "diagnosis": "object"})

Output:

all required columns validated.
Column data types validated successfully.

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2. carelytics.utils.cleaner

Includes data cleaning utilities like missing value handling, standardization, and column renaming.

from carelytics.utils.cleaner import fill_missing

df = fill_missing(df, strategy="median")

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3. carelytics.models.denial_prediction

Predicts claim denial probabilities based on payer data, CPT/ICD codes, and historical denials.

from carelytics.models.denial_prediction import predict_denials
pred = predict_denials(df)
print(pred.head())

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4. carelytics.models.readmission

Predicts hospital readmission likelihood using patient demographics and vitals.

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5. carelytics.claims

Analyzes RCM claim metrics such as:

• Average AR days
• Net collection rate
• Denial rates

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6. carelytics.lab & carelytics.vitals

Helps in normalizing patient lab values and vitals for statistical analysis.

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7. carelytics.utils.deid

Supports data anonymization to remove or mask PHI (Protected Health Information) before analysis.

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Example Workflow

import pandas as pd
from carelytics.utils import validator, cleaner
from carelytics.models import denial_prediction

# Load your healthcare dataset
df = pd.read_csv("claims.csv")

# Validate structure
validator.validate_columns(df, ["claim_id", "payer", "amount", "denial_flag"])

# Clean and prepare
df = cleaner.fill_missing(df, "median")

# Run prediction
pred = denial_prediction.predict_denials(df)
print(pred.head())

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Key Use Cases

Use Case	Description
Hospital Analytics	Clean and validate EHR data for performance dashboards
RCM Optimization	Predict denials, track collection efficiency
Clinical Research	Analyze patient lab results and vitals
Data Interoperability	FHIR parser ensures standard formats for sharing
PHI Handling	Built-in data de-identification ensures HIPAA compliance

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Dependencies

• Python ≥ 3.7
• pandas
• numpy
• scikit-learn

Install all dependencies with:

pip install carelytics

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Authors & Contributors

Rohan Desai Dallas, Texas, USA Email: rohan.acme@gmail.com GitHub: https://github.com/rohan-desai LinkedIn: https://www.linkedin.com/in/rohandesai07/

Vaishnavi Sanjay Gadve Irving, Texas, USA Email: vaishnavigadve143@gmail.com GitHub: https://github.com/vaish2412 LinkedIn: https://www.linkedin.com/in/vaishnavi-gadve-4b577512a/

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License

MIT License © 2025 Rohan Desai & Vaishnavi Sanjay Gadve