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What is Panalgo?

Panalgo’s Instant Health Data platform serves as a comprehensive software solution for healthcare analytics, aimed at alleviating programming challenges and speeding up the analysis of real-world data across multiple domains, such as life sciences, pharmaceuticals, payers, providers, government, and academia. By integrating diverse health data sources—including claims, electronic health records, registries, and other real-world datasets—this platform converts the information into a unified, analysis-ready format through a healthcare-specific data model combined with an extensive library of algorithms. This transformation facilitates rapid, scalable, and transparent analytics, eliminating traditional coding barriers. Users are equipped with point-and-click analytics, tailored dashboards, statistical evaluations, machine learning forecasts, automated documentation, and collaborative reporting tools, enabling stakeholders to effectively explore, understand, and share insights. Moreover, features like Ella AI provide natural-language, generative AI support that aids in cohort creation, insight development, and informed decision-making processes, significantly boosting the platform's functionality for its users. Consequently, Panalgo’s IHD not only simplifies the analytical process but also cultivates an environment conducive to collaboration among various healthcare stakeholders, ultimately enhancing the quality of healthcare decisions. The innovative approach of this platform sets a new standard for how health data can be utilized in the industry.

What is ArborMetrix?

At ArborMetrix, we strive to improve healthcare by leveraging the capabilities of data science, and we are committed to delivering impactful and user-friendly technology alongside analytics. Our top-tier healthcare analytics offerings have demonstrated their ability to provide targeted, clinically relevant insights that not only improve clinical outcomes but also enhance financial performance, increase stakeholder value, and evaluate the real-world effectiveness of a range of treatments, procedures, medical devices, and pharmaceuticals. Patient registries serve a vital function across various medical fields, with post-market surveillance being among their most prominent uses. In order to help you create a dependable data resource, we implement a methodical strategy. It is crucial to ensure a streamlined and secure process for collecting large volumes of real-world data and preparing it for in-depth analysis. By utilizing the right technology, you can efficiently acquire data from a variety of sources, including medical records, regulatory information, patient-reported outcomes, and other resources that support post-market surveillance efforts. This extensive data collection not only supports compliance but also nurtures innovation in the realms of treatment and patient care, ultimately contributing to a more effective healthcare system. Furthermore, our commitment to excellence drives us to continuously refine our methodologies, ensuring that we meet the evolving needs of healthcare providers and stakeholders alike.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub
Webinars

Company Facts

Organization Name

Panalgo

Date Founded

1996

Company Location

United States

Company Website

panalgo.com

Company Facts

Organization Name

ArborMetrix

Date Founded

2011

Company Location

United States

Company Website

www.arbormetrix.com/registryx

Categories and Features

Healthcare AI

Not specified

Healthcare Analytics

Not specified

Categories and Features

Healthcare Analytics

Not specified

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