How do you turn messy, unstructured healthcare data into real-time intelligence that actually improves care? In this podcast hosted by Mphasis Vice President of Products Chenny Solaiyappan, Datycs CEO Dr. Srini Rao speaks on transforming unstructured clinical data into real-time healthcare intelligence. The conversation explores interoperability, NLP versus GenAI in regulated environments, and why real progress in value-based care depends on transforming clinical notes into actionable, standards-based data.
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Show Notes
- Srini Rao’s career spans major technological revolutions: speech recognition and machine learning at IBM, wireless at Motorola, and healthcare AI.
- His entrepreneurial mindset was shaped by his early startup experience, which later led to Motorola’s acquisition.
- Transition to healthcare was motivated both by technological opportunity (big data, sensors) and personal/family ties.
- Datex was founded to address the challenge of transforming unstructured healthcare data into actionable intelligence.
- The company leverages NLP and machine learning to extract valuable insights from messy, unstructured clinical data.
- Unlike many competitors, Datex’s focus is on real, messy clinical notes rather than only structured healthcare data.
- Datex has processed over 15 million clinical charts, gaining extensive exposure to diverse data sets.
- They partner with organizations such as the Massachusetts Health Data Consortium to enable real-time, FHIR-based data exchange for better interoperability.
- Interoperability in healthcare is compared to similar challenges in telecom—slow progress, many silos, and the need for common standards.
- Adoption of FHIR API and transforming unstructured docs into FHIR resources are central to Datex’s approach.
- There are significant business model challenges due to healthcare’s fragmented ecosystem and slow adoption cycles.
- Datex’s initial product summarized patient charts to give clinicians single-view access to key patient information.
- Their services expanded from summarization to document processing for quality reporting, risk adjustment, and prior authorization automation.
- The company frequently reassesses market direction—waiting for sufficient, real market demand before scaling new solutions.
- Fine-tuning or building healthcare-specific language models (small language models/SLMs) is still an open challenge, with industry limitations and data access restrictions.
- Customer needs and market trends shape product prioritization, ensuring resource-constrained startups avoid spreading themselves too thin.
- The “desirability, feasibility, viability” framework drives solution development and prioritization at Datex.
- For entrepreneurs: focus on real, concrete problems and plan for a long-term journey in healthcare tech.
- AI is expected to play a transformative role not only in care delivery, but also in research and clinical trials through real-world evidence.
- Continuous adaptation, partnership, and learning are essential to succeeding in healthcare innovation.
About the speaker
Srini Rao is the founder and Chief Executive Officer of Datycs. In this role, he leads the execution of the company’s business plan, strategic relationships, and product strategy. Srini is a seasoned technology executive with more than 30 years of experience in healthcare technology, wireless communications, and artificial intelligence. Under Srini’s leadership, Datycs' solutions are enabling health plans and health systems to improve the quality and operational efficiency of healthcare delivery. Datycs is also working with the Massachusetts Health Data Consortium (MHDC) on a FHIR standard-based solution to enhance clinical data sharing between health plans and providers across Massachusetts, for quality measurement and reporting. Srini collaborated with national healthcare policy experts and physician researchers at Boston Medical Center, Cambridge Health Alliance, and Harvard Medical School faculty on inpatient readmission risk at the two largest safety-net hospitals in Massachusetts. He also worked with Community Health Centers and Associations in Massachusetts, Texas, South Dakota, and Puerto Rico, to accelerate their adoption of healthcare analytics. Srini is enthusiastic about the potential impact of interoperability and AI in making healthcare more accessible and improving patient outcomes through enhanced diagnostics and decision support. Earlier in his career, he served as the vice chairman of one of the 3G wireless standards committees, and as the editor of the 3G network Interoperability Specifications (IOS). Later, he worked with Motorola’s strategic partners to develop one of the earliest open ecosystems in the world for IP Multimedia Subsystem (IMS), which paved the way for convergence of mobile voice, video, and data networks. Srini was recognized by Motorola as a Science Advisory Board Associate. He serves on the Board of IEEE Communications Society Boston chapter. Srini received his M.S. and Ph.D. from Rice University, Houston and bachelor’s degree from the Indian Institute of Technology (IIT) Madras, all in Electrical Engineering.