🌞 Is Your Health Data Safe? How to Confirm

Leader Spotlight: Eli Ben-Joseph, Co-founder & CEO, Regard


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Welcome to your briefing:

  • AI ROUNDUP: What we’re seeing is not telehealth: alarm over doctors using AI and prescribing without seeing patients

  • INDUSTRY ROUNDUP: EPA imposes first national limits on forever chemicals in drinking water

  • WELLNESS BYTES: Why do we die? The latest on aging and immortality from a Nobel Prize-winning scientist

  • INSIGHTS CORNER: Leader Spotlight: Eli Ben-Joseph. Co-founder & CEO, Regard

  • TRIVIA: Which AI program, developed in the 1980s, was considered a significant advancement in medical AI for its ability to simulate the decision-making ability of a human expert cardiologist?



AI-powered mental health chatbots developed as a therapy support tool

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Leader Spotlight

Leader spotlight image

How does Regard specifically target and reduce the administrative tasks that contribute to clinician burnout, and what measurable impacts has this had on clinician workload and satisfaction?

  • Clinicians need 26.7 hours per day just to meet documentation requirements. As a result, two out of three clinicians report that they experience burnout, which is associated with a 2x increase in adverse patient safety events, and a 50% reduction in patient satisfaction. Even the most skilled clinicians don’t have enough time to pour through all of the data and documentation in the EMR before seeing a patient. 

  • Regard’s AI automatically scans data from the electronic medical record recommends diagnoses, and drafts the note for the clinician. Each recommended diagnosis is presented with supporting data in a curated draft note and then reviewed, edited, and signed by clinicians. This enables them to accurately diagnose patients and automate clinical workflows like chart reviews and notes. While most AI solutions generate more data, Regard helps health systems get the most value out of data they already have, saving physicians time, improving patient care, and rejuvenating hospital finances. 56% of Regard users report a reduction in measures of burnout and 47% say that they are more satisfied with the amount of time they spend documenting. 

Can you provide a case study or example where Regard significantly improved outcomes or efficiency in a clinical setting?

  • We recently expanded our partnership with Sentara Health to provide clinicians access to Regard's AI technology in all 12 of Sentara's hospitals. Before using Regard, Sentara was facing a few major challenges in relation to patient care and safety: inability to fully leverage data in the EHR, inefficient EHR workflows, data overload, and missed revenue opportunities due to incomplete nonspecific documentation. 

  • Colin Findlay, MD hospitalist with Sentara recently saw a patient admitted for uncontrolled heart failure and atrial fibrillation. He suspected medication nonadherence was the cause and didn’t see or read anything that indicated otherwise. Regard performed a comprehensive review of data in the patient's EHR and surfaced a diagnosis of hyperthyroidism, a condition that when left untreated causes a rapid heart rate. Because of Regard's diagnosis, Findlay was able to see that the patient's medication had been paused several months earlier and had never been resumed. 

  • Aside from catching critical diagnoses such as the one in Dr. Findlay's case, Regard has also increased CC/MCC capture by 17% enabling a 4x ROI per user at Sentara. Additionally, Regard has consistently reduced the severity of illness and risk of mortality, prevented patient harm, and promoted operational and financial sustainability. 

What have been the primary technical and operational challenges in developing Regard to integrate seamlessly with existing EHR systems, and how have these challenges been addressed?

  • Some of the biggest technical and operational challenges we have faced in developing Regard have been the desire for customization around notes and documentation preferences. Different hospitals have different requirements and standards as well as individual preferences which has in some instances been a barrier. Once clinicians have worked through the documentation workflow, however, we see strong adoption and usage. 

  • We are constantly working to better our technology and include new diagnoses in our system. We strive to show what physicians need and care about in the notes, while also providing them a reference to additional information. Our goal is to provide our users with a complete picture of their patients before they meet with them so that they can provide the highest quality care.

In what ways does Regard customize its support for various medical specialties, and how does it adapt to the unique needs of different practices?

  • We intentionally built Regard to first focus on inpatient facilities as a platform to enable hospitalists. We have seen that this is a huge area where AI can have an impact and cover a broad set of diagnoses. We have plans in the future to expand to other care settings and users based on our customer feedback and collaboration but want to ensure that we provide the gold standard to inpatient settings first.

  • Today with our existing customers we have started to build new functionality that will support the unique preferences of different hospitals based on their note standards and documentation requirements, but a huge part of our work is to keep as much of our platform standard as that has enabled the largest impact for our customers. 

Looking towards the future, how does Regard plan to evolve its AI capabilities to further support clinicians and administrative staff?

  • As we look towards the future it is impossible to imagine a world where the majority of medicine is not being done by a computer. Most diagnoses will be done by a computer. Medicine will become personalized, and that can only happen when computer systems can process all that data.

  • We’re constantly working to develop more algorithms to support clinicians and administrative staff. There’s enough data that with AI-enabled systems, we can make careless like an assembly line and more tailored toward individuals and their needs. We want our AI to remove barriers for both providers and patients to make sense of health data. 


Which AI program, developed in the 1980s, was considered a significant advancement in medical AI for its ability to simulate the decision-making ability of a human expert cardiologist?

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