MTI Viewpoints Insights shared by industry relative to healthcare and the advancement of medical technology. Jaimon Jose, senior director of engineering and compliance at CharmHealth, is a software architect and technical leader with more than 18 years of product development experience in the areas of identity and security technologies, distributed systems, cloud security, and data…
Traditionally, contract negotiation has been viewed as a financial or legal responsibility, typically led by finance teams, legal counsel, or executive leadership, with operations stepping in afterward to execute against decisions that have already been made, but that model is evolving as healthcare delivery becomes more interconnected and operationally complex, requiring administrators to engage earlier in the process, not only to understand what is being agreed to but to help define how those agreements will function in real-world environments.
As organizations introduce additional technologies, including AI-enabled tools and CRM platforms, these gaps become more pronounced. Those that are seeing meaningful improvements are taking an end-to-end view, evaluating how systems interact within a unified workflow and ensuring that data flows seamlessly and actions are triggered in a coordinated manner, thereby reducing the need for manual intervention and follow-up.
In this part 2 of a 4-part series, Melissa highlight how the moment AI begins to influence decisions or workflows, it is operating within a regulated environment. Aligning with documentation standards, keeping data within established pathways, and ensuring actions are consistently recorded are all ways to support that reality early, rather than adapting to it later. Seen this way, integration is not just a technical milestone.
In this part 1 of a 4-part series, we look at how the burden of digital transformation is impacting the entire healthcare ecosystem, and that while burnout in healthcare is most often framed around clinicians, it does not reflect where the full transformation work is actually being carried…or its impact across healthcare.
How does combining AI with Lean Management significantly improve efficiency in MedTech engineering? Much like IDEs or CAD tools before it, AI is becoming an essential enabler in reducing friction throughout the product development lifecycle—from onboarding and requirements generation to coding and testing—ultimately enhancing both productivity and innovation. Identifying and targeting inefficiencies using Lean principles, MedTech engineering organizations can unlock AI’s full potential to accelerate development and deliver higher-quality healthcare technologies.
This silent crisis has dire consequences. Patients face delays, errors increase and the entire system suffers. This cannot continue. But in the face of such crippling challenges, how can healthcare practices look to improve the interoperability of their systems?
Up until now, the lack of interoperable health-at-home devices has inhibited health technology integration into the home for use in wellness, prevention, managing chronic conditions, and clinical trials.
Billions of dollars are lost annually in medical billing errors resulting from data entry mistakes, outdated coding practices, and duplicated charges. Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the process of claims processing, diagnostics, eliminating errors, streamlining workflow, and increasing the accuracy of claims submissions. Together with human oversight to ensure precision and safety, AI augments healthcare professionals to improve patient care and outcomes.
AI and real-time data enhance care efficiency and access. And with healthcare workers in short supply, the rapid advancements in AI, IoMT, and related innovation offer patient access freedom, enhanced care delivery, and better outcomes.
