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Welcome to the Empowered Patient Podcast with Karen Jagoda.  This show is a window into the latest innovations in applying generative AI, novel therapeutics and vaccines, and the changing dynamics in the medical and healthcare environment. One focus is on how providers, pharmaceutical companies, and payers are empowering patients.  In addition, conversations are often about how providers, care facilities, pharmaceutical companies, and payers are being empowered by technology to improve patient outcomes and reduce friction across the healthcare landscape.

Popular Topics

  • Virtual and digital health
  • Use of AI, ML, and LLM in healthcare and drug discovery, development, trials
  • Value-based healthcare 
  • Precision and stratified medicine
  • Integration of digital technology into existing workflow and procedures 
  • Next-generation immuno, cell, and gene therapies
  • Vaccines
  • Biomarkers, sequencing, and imaging
  • Rare diseases
  • MedTech and medical devices
  • Clinical trials
  • Addressing Social Determinants of Health
  • Treating chronic conditions like obesity and pain
  • Clinician and staff burnout

The audience includes life science leaders, researchers, medical professionals, patient advocates, digital health entrepreneurs, patients, caregivers, healthcare solution providers, students, journalists, and investors. 


Check out our new EmpoweredPatient.Solutions site where you can quickly search all of the Empowered Patient Podcast interviews by any word or phrase to identify useful resources, potential partners, and insights about the life sciences landscape.

Empowered Patient Solutions

Jan 23, 2024

Dr. Michael Gao, Co-Founder and CEO of SmarterDx, discusses the challenges hospitals face in getting paid for their services. He explains that the revenue cycle, which involves generating accurate receipts for patient care, is a complex process that requires manual work from healthcare professionals. SmarterDx is streamlining the process by using AI to scan and analyze medical data, allowing professionals to focus on making judgments based on clinically validated data to report and bill more accurately for actions taken.  

Michael explains, "One of the biggest challenges in the healthcare environment today is that hospitals have a hard time getting paid to provide the services they do for sick patients. And we've all seen through COVID, and certainly, in modern-day, these organizations are operating off of 1% operating margins or even losing money year over year. I think, very fundamentally, the problem that we're trying to solve is how do we make sure that the hospital can tell a complete patient story and represent all the care they provided so that they can be paid fairly for the care they provided."

"Revenue cycle is very complicated, but at the end of the day, it's just about generating a receipt for what happened during that patient's visit to the hospital. And that receipt, we think of it as a bill, is used by insurance companies to sort of size the payment. It's also used by CMS, US News and World Report, and other organizations to figure out what happens so they can grade the quality of the care provided. From our vantage point of the revenue cycle, there are a lot of different names for different steps of the revenue cycle, but at the end of the day, it's just about generating an accurate receipt. Our company's North Star is to help revenue cycle leaders ensure that the items on that receipt are as accurate and complete as possible."

"If I were to outline a bunch of steps within the revenue cycle, there are two steps today that people do in the middle. One is clinical documentation improvement, and the other is medical coding. And for your listeners on the call who maybe haven't been a documentation improvement nurse themselves, it's an incredibly difficult job. What they're doing is opening up charts, looking through thousands of labs, medications, vital signs, notes, radiology reports, pathology reports, and they're looking for the absence of something."

#SmarterDx #HealthcareAI #RCM #ClinicalAI #Hospitals #RevenueCycle

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