{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/15"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"}],"enrichment":{"capability":"Carelytics provides healthcare data cleaning, validation, and predictive analytics for revenue cycle management, including denial prediction, readmission risk modeling, and FHIR-compliant data standardization.","skillfed_tags":["healthcare-analytics","revenue-cycle-management","clinical-data"],"use_cases":["Clean and validate EHR datasets before loading into hospital performance dashboards or analytics platforms.","Predict claim denial risk on incoming claims to flag high-risk submissions for manual review before submission.","Identify patients at high readmission risk using demographics and vitals to enable proactive care interventions.","Standardize lab values and vital signs across multiple data sources for statistical analysis or clinical research.","De-identify patient datasets to remove PHI before sharing with external researchers or analytics vendors.","Compute RCM key performance indicators (denial rates, collection efficiency, AR aging) for revenue cycle dashboards."],"what_it_does":"Carelytics is a Python library for healthcare data analytics focused on revenue cycle management (RCM) workflows. It wraps pandas, numpy, and scikit-learn to provide domain-specific functions for validating and cleaning healthcare datasets, standardizing formats for interoperability (FHIR-ready), and running predictive models such as claim denial and hospital readmission prediction. The package includes utilities for handling protected health information (PHI) de-identification, analyzing patient encounters and lab data, and computing RCM metrics like average accounts receivable days and net collection rates.\n\nThe library is organized into modular components: validators and cleaners for data preparation, predictive models for denial and readmission risk, claim-level and patient-level analytics, and FHIR parsing for standards compliance. It is intended for healthcare analysts, researchers, and developers working with EHR or claims data who want to accelerate analytics workflows without building these functions from scratch.","worth_installing":"Yes, with conditions. Install if you are working on healthcare data analytics or RCM workflows and want pre-built validation, cleaning, and predictive modeling functions. The low install friction and permissive MIT license are favorable. However, the package is early-stage (270 days old, no visible recent commits), so verify that the FHIR parsing, de-identification, and prediction models meet your accuracy and compliance requirements before deploying to production. No known vulnerabilities."},"id":"carelytics","links":{"html":"https://skillfed.io/packages/carelytics","md":"https://skillfed.io/packages/carelytics.md","pypi":"https://pypi.org/project/carelytics/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-11-17","license_spdx":null,"license_treatment":"permissive","name":"carelytics","python_support":"supports_current","summary":"A Python library for Healthcare Data Analytics and Revenue Cycle Management."},"popularity":{"monthly_downloads":217136,"position":9365,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.3"}