AiThority Interview with Eli Ben-Joseph, CEO at Regard

Eli Ben-Joseph, CEO at Regard, discusses his pivot from medicine to tech, driven by the need to alleviate doctors’ administrative burdens through AI. 

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Eli, can you share your learnings in the AI and tech journey as a CEO of Regard?

Some of the biggest lessons I’ve learned on my journey as founder and CEO of Regard honestly came before the company was even started. My path to the tech world was sparked by my early ambition to pursue medicine. Growing up with a grandfather who was a dedicated physician, I initially followed the pre-med path, eager to make a difference in healthcare. However, during my hospital shadowing experiences, I witnessed firsthand the mounting frustrations of doctors drowning in administrative tasks and data overload. The reality on the ground was that doctors who once loved their jobs were burning out because of all the non-medical tasks being added to their roles. The conversations that I had with these doctors led me to pivot away from practicing medicine and toward building technology that would alleviate these burdens, restoring the care team’s ability to focus on patient care first and foremost. The insights from these early first-hand experiences still drive Regard’s product vision today.

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Tell us about how the recent $61 million Series B funding will accelerate Regard’s growth and product development.

We recently announced a $61M funding round to help close the clinical insights gap in healthcare. This round was led by Oak HC/FT + Cedars- Sinai Ventures with participation from TenOneTen Ventures, Calibrate Ventures, and Techstars. Regard is one of the first investments in AI made by Oak HC/FT, which underscores how unique Regard’s technology is within the market and re-affirms the value that we know it can bring to the health system.

This investment will enable us to accelerate product development with our core clinical insights platform, invest in fundamental research in large language models (LLMs), and move beyond beta testing, and expand beyond inpatient facilities. Additionally, it will help us continue to recruit high-quality talent and grow our team. Regard is moving beyond the medical scribe movement and this will be the next area for innovation and investment.

Regard leverages AI to analyze vast amounts of healthcare data. Can you explain how your platform uses machine learning and natural language processing to generate clinically actionable insights?

Regard’s AI-powered technology reviews and analyzes the entirety of the medical record, including notes from past visits, medication histories, scan results and more, and alerts doctors to potential diagnoses that may have been otherwise missed. From a technical perspective, this is done by checking the available data against a predetermined set of guardrails and criteria. Each recommended diagnosis is presented with supporting data from the EHR and then reviewed by the care team, who can then approve or decline the recommendation. By providing these valuable insights from within the EHR, Regard is empowering clinicians to improve patient outcomes with data-driven insights, save valuable time by streamlining diagnosing and care planning, and protecting and enhancing hospital revenue through complete care and accurate documentation.

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Large language models (LLMs) are a focus of your research. How do you see LLMs evolving and impacting clinical decision-making in the future?

Right now LLMs are still a bit too early to be doing actual clinical work. They are fantastic for helping automate some of the non-clinical tasks (eg: writing a pre-auth letter to an insurance company). Over time, I expect LLMs to continue improving to the point where they could essentially operate like an AI resident – diagnosing, recommending treatments, and writing up notes for their human partners.

The World Economic Forum states that less than 3% of available patient data is used by physicians. What are the biggest challenges in unlocking the full potential of this data?

The biggest barrier to unlocking the full potential of EHR data for health systems is the sheer volume of the data— it’s overwhelming! The EHR was initially created for b****** purposes, not care delivery. Over time, the technology has evolved into a patient care platform that stores mountains of data, and it’s created an administrative nightmare for doctors who can’t possibly review all of the available information. By some estimates, as many as 900,000 data points on a critical care bed go unused – that’s per patient, per hour. This gap between the vast amount of data  available and the actionable insights, is called the clinical insights gap, and it’s what Regard’s technology solves for.

How does Regard handle data privacy and security within Regard’s AI platform?

Privacy is a top concern for Regard, and our technology was built to protect the privacy of both the patients and the physicians. We are fully HIPAA compliant, and SOC2 and ONC certified. Since Regard is embedded directly into the EHR, no patient data is ever shared or stored externally.

Also Read: AI and Big Data Governance: Challenges and Top Benefits

How do you see AI transforming the healthcare industry over the next five years, particularly in terms of clinical decision support and patient care?

Over the next five years, I anticipate AI will continue its transformative impact on the healthcare industry. At Regard, we are leading the integration of AI into healthcare workflows to enhance clinician experiences and ensure safer, more personalized care for patients across all specialties. Looking forward, it’s clear that a future where computers play a pivotal role in clinical decision making is inevitable. The majority of diagnoses will likely be computer-driven, enabling a shift towards highly personalized healthcare that hinges on robust data processing.

Our ongoing efforts are dedicated to developing advanced algorithms that support both clinicians and administrative staff. With AI-driven systems, we aim to move away from standardized healthcare practices towards tailored approaches that better meet individual patient needs. By leveraging AI, we aim to break down barriers for providers and patients, enabling clearer insights and improved understanding of health data.

 Finally, what book or resource has most influenced your approach to leading an AI-driven healthcare company?

“The AI Revolution in Medicine” by Peter Lee has been a huge influence on the capabilities of AI in healthcare and also in painting a picture of what the future may look like.

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