Imran Qureshi

Chief Software Development Officer

Imran Qureshi is the Chief Software Development Officer at Health Catalyst where he is responsible for all software development in the company. He also leads the Engineering team building the Data Operating System (DOS). Before Health Catalyst, Imran was the Chief Technology Officer at Acupera where he led the team that built the care management platform that was successfully implemented in Ascension, Montefiore, Kaiser, and other health systems. Prior to that, Imran was VP of Engineering at CareAnyware, where he led development of the largest cloud-based EHR for Home Health and Hospice. He spent 12 years at Microsoft, including building the slideshow for PowerPoint and building the email experience for Hotmail. Imran holds several patents and has a Computer Science degree from Stanford University.

See content from Imran Qureshi

Healthcare Analytics Platform: DOS Delivers the 7 Essential Components

The Data Operating System (DOS™) is a vast data and analytics ecosystem whose laser focus is to rapidly and efficiently improve outcomes across every healthcare domain. DOS is a cornerstone in the foundation for building the future of healthcare analytics. This white paper from Imran Qureshi details the seven capabilities of DOS that combine to unlock data for healthcare improvement:

1. Acquire
2. Organize
3. Standardize
4. Analyze
5. Deliver
6. Orchestrate
7. Extend

These seven components will reveal how DOS is a data-first system that can extract value from healthcare data and allow leadership and analytics teams to fully develop the insights necessary for health system transformation.

How to Turn Data Analysts into Data Scientists

Healthcare data scientists are in high demand. This shortage limits the ability of healthcare organizations to leverage the power of artificial intelligence (AI). Health systems must better utilize their data analysts, and, where possible, turn some data analysts into data scientists.

This report covers the following:

• Healthcare use cases and which ones data analysts can take the lead on.
• Specific steps for turning data analysts into data scientists.
• How to identify the best candidates among your data analysts.
• Recommended resources to get started on an AI journey.

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