607 - Bringing Consistency to Complexity Through AI-Driven Decision Support

In this episode of Talking Health Tech, host Peter Birch sits down with Simon Taylor-Cross and Dr. Daniel Stiglitz from Atidia Health to explore their AI-driven Patient Optimiser Platform (POP), a solution designed to standardize and improve perioperative care. Drawing from Daniel’s clinical experience as an anaesthetist, the conversation highlights the alarming variability in pre-surgical patient preparation—especially for older patients with multiple comorbidities—leading to preventable complications, cancellations, and inefficiencies. The team explains how POP uses evidence-based machine learning and workflow-integrated decision support to identify high-risk patients early, such as those with undiagnosed heart valve issues, thereby preventing serious adverse events and optimizing surgical outcomes. The platform’s success is rooted in a unique triad of clinician, technologist, and commercial expertise, ensuring the solution is both clinically relevant and commercially viable. A major case study at Princess Alexandra Hospital in Brisbane demonstrates how POP helped standardize 20 disparate surgical pathways, improve clinic efficiency, and enhance team collaboration through deep change management. The episode also delves into broader healthcare challenges: the difficulty of scaling innovation due to fragmented systems, workforce burnout, and financial constraints. Atidia’s approach—focusing on rapid ROI, nurse satisfaction, and patient experience—proves that technology must fit seamlessly into existing workflows to be adopted. The hosts emphasize that true transformation comes not from isolated tools, but from deep, ongoing partnerships between clinicians, technologists, and healthcare leaders. The takeaway is clear: innovation in healthcare must be human-centered, collaborative, and embedded in real-world workflows to drive meaningful, sustainable change.