🏥 Pharos Launches: Automate Reporting and Prevent Patient Harm with AI

Pharos recently launched!

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^{"AI for hospital quality teams"}

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^{tl;dr:The data hospital teams need to improve patient safety is buried in unstructured medical records. Today, clinicians spend thousands of hours manually ‘abstracting’ it for reporting and analysis. Pharos automates the entire process and uses the data to show them where and why avoidable harm is happening.}_‍^‍
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_‍Founded by Felix Brann & Matthew Jones

Felix and Matthew spent the past 5 years deploying patient and clinician-facing AI into over 70 hospitals together.

As VP of Data Science, Felix published papers in major medical journals on sepsis prediction and medical record summarization using LLMs. Matthew has years of experience integrating software into EHRs and previously built another startup from inception to international expansion.

Alex joined the team after working as a doctor in the UK and then as a medical AI researcher at Imperial College London and Meta’s Reality Labs. He experienced this problem firsthand, spending years of his residency frustrated at the manual abstraction required for quality improvement.

They believe enabling quality teams with AI represents a huge opportunity to save lives and prevent harm.

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The problem:

Avoidable harm happens in hospitals all the time. Wards are busy, clinician turnover is high, and an aging population means increasingly complex patients. Sepsis alone kills 350,000 patients a year in the US, and a significant number of those deaths are preventable.

Hospitals have teams dedicated to preventing harm. They track avoidable events, identify the process failures that cause them, and report performance data to clinical registries. This means identifying harm events, risk factors and process adherence from patient journeys composed of pages of unstructured clinical notes.

Today, this is an entirely manual process. Producing structured quality metrics from a single complex patient case can take up to 8 hours of clinical time. A single hospital can spend $5m per year extracting this data, and it still arrives weeks after discharge, on a small sample of their patients.

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The solution:

Pharos AI extracts the data quality teams need from every patient record in real-time. It produces verifiable quality metrics, with references into the original medical record.

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They use this data to:

• Automate reporting for clinical registries and value-based reimbursement contracts, saving thousands of clinical hours.
• Identify and surface process failures that are contributing to patient harm, letting teams take action on issues like sepsis, hospital-acquired infections, and pressure ulcers.
• Measure the impact of quality improvement projects in real-time rather than months after implementation.

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Learn More

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^{🌐 Visit pharos.health to learn more.}

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^{🤝 Do you know anyone working at a senior level at a US hospital (They’ll ask them for an intro to their quality team)? Email the team here.}

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^{🤝 Do you know anyone working in healthcare with a title that includes “Quality”, “Patient Safety,” or “(Sepsis, Stroke, …) Coordinator"? Email the team here.}

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^{🤝 Do you know academics and clinicians working at the intersection of data and clinical quality? Email the team here.}

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^{📅 Discover how your data could protect your patients today. Schedule a demo.}

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