The increased digitalization of medical device manufacturing makes lean practices more applicable than ever. Keep these factors in mind to put them into action.
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…
No longer a horizon technology, Artificial Intelligence in healthcare has reached a level of real-world performance that makes clinical value demonstrable at a critical time in healthcare – a time of clinician shortages, backlogs, and rising costs that have made access to treatment a challenge.
Protect patient data with medical IoT security. Learn how AI, Zero Trust, and encryption can prevent cyber threats and secure healthcare IoT devices.
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.
Human factors engineering plays a critical role in the design of AI-enabled medical devices and whether they might improve care or introduce new risks.
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.
Artificial intelligence is moving quickly into mainstream medical devices, and the industry has become fluent in a familiar set of concerns: bias, transparency, and cybersecurity. These topics matter, but they don’t capture the risks most likely to shape patient safety in the coming decade. The deeper challenges lie in the interactions between algorithms, clinical workflows, data pipelines, and human decision making. Those interactions are where safety is won or lost, and they remain the least examined part of AI adoption.