Investigative data journalism: quantifying fixable waste in US healthcare, one issue at a time. Open-source analysis of CMS, OECD, and federal datasets. $98.6B in savings identified so far.

🏥Medicare Locator - Open source starter pack for developers to build contextual chatbots and AI assistants in healthcare

MediCareAI 是一个基于人工智能的智能疾病管理系统，采用现代化的全栈架构设计，整合了医疗指南、AI 智能诊断、文档处理和邮件通知等功能。项目已完成主要功能开发阶段，具备生产环境部署能力。

🏆 World Runner-Up project of International Flutter Hackthon 2020. "Donate Plasma" is a prototype apps built with flutter for the purpose of connecting COVID-19 patients and the patients recently recovered from COVID-19 willing to donate their plasma for effective treatment. In this application, plasma donors will also be able to share their story of fighting COVID-19 to motive others staying strong mentally.

Project to understand pharmaceutical spending, currently focused on US government programs.

An API designed to serve Medicare beneficiaries' demographic, enrollment, and claims data using the HL7® FHIR® Standard format.

Examples, libraries, and tools for working with bulk FHIR data.

drgpy is a Python library for assigning a combination of diagnosis and procedure codes to Diagnosis Related Groups (MS-DRG) that is used in Medicare inpatient reimbursement today.

CMS (Centers for Medicare and Medicaid Services) eAPD - Modernizing the APD experience

A demo of how to use PageRank with Hadoop and SociaLite to identify anomalies in Healthcare Data

4 different Big Datasets joined to get single table for final data analysis. Fraud Detection by taken consideration of different key features with applying different Machine Learning Algorithm to see which one performs better.

HCC Algorithm that works with FHIR and 837

Welcome to the Medicare/Medicaid Provider Enrollment Screening Portal

Public Healthcare Provider APIs :stethoscope:

Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) skills in a real case analysis setting. You are expected to clean and process data and then apply various ML techniques like Linear and no linear models like regularized regression, MARS, and Partitioning methods. You are expected to use at least two of R, Python and JMP software. Data details: Medical claims file for 2016 contains ~17 millions rows and ~60 columns of data, containing ~6.5 million individual medical claims. These claims are all commercial claims that were filed by healthcare providers in 2016 in the state of NH. These claims were ~88% for residents of NH and the remaining for out of state visitors who sought care in NH. Each claim consists of one or more line items, each indicating a procedure done during the doctor’s visit. Two columns indicating Billed amount and the Paid amount for the care provided, are of primary interest. The main objective is to predict “Paid amount per procedure” by mapping a plethora of features available in the dataset. It is also an expectation that you would create new features using the existing ones or external data sources. Objectives: Step 1: Take a random sample of 1 million unique claims, such that all line items related to each claim are included in the sample. This will result in a little less than 3 million rows of data. Step 2: Clean up the data, understand the distributions, and create new features if necessary. Step 3: Run predictive models using validation method of your choice. Step 4: Write a descriptive report (less than 10 pages) describing the process and your findings.

Prediction or detection of various medical ailments

CMS Medicare Fraud Detection

Process and analyze WA State Medicaid, Medicare, and All-Payer Claims Database eligibility and claims data

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Unified Medicare documentation in a single responsive website

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Databricks and John Snow Labs Solution Accelerator for Medicare Risk Adjustment automates the extraction of undiagnosed member conditions from unstructured clinical notes with NLP models, improving downstream reimbursements.

Source Code for Calculating QPP/MIPS Quality Measures from Medicare Claims Data

Hierarchical Condition Category (HCC) Risk Models from the Centers for Medicare and Medicaid Services (CMS) and the Department of Health and Human Services (HHS)

MediCare Plus and E-commerce Site

A collection of data sets for data entrepreneurs from the Centers for Medicare and Medicaid Services synthetic public use files

A python implementation of Medicare's Risk Adjustment model based on Hierarchical Condition Categories (HCCs).

This connector is a dbt project that maps Medicare CCLF claims data to the Tuva Input Layer.

a Pharmacy Inventory Management system made with ReactJS and Firebase