The modern concept of “triage” dates back to the Napoleonic Wars. At the time, French battlefield surgeons developed a system for assessing the injured and prioritizing treatment in order to manage proper medical care amidst the chaos and chaos of war. Efficiently utilize limited resources under difficult circumstances, increase the number of survivors, strengthen the military’s continued combat capability, and transform healthcare delivery strategies in ways that still impact the healthcare sector today. I did.
Amidst the chaos and confusion of healthcare IT in the AI era, that concept of triage may be the best guide for organizations struggling to navigate a strong and capable path in 2025.
Challenging and dynamic landscape
Technically, the medical industry currently operates along a wide range of capabilities and limitations. However, recent research shows that more than half of healthcare organizations are using AI tools for some purpose, demonstrating that incredibly sophisticated data-driven technology is already widely deployed. is shown. Conversely, hospitals lose approximately $8 billion annually due to inefficient IT systems, and the industry as a whole remains one of the most expensive in business, reflecting constrained technological resources that are constantly under siege. We are regularly plagued by cybersecurity crises.
Healthcare IT is a difficult puzzle to solve. In most cases, data infrastructure must be compliant, secure, and operational 24/7 (with limited budgets and high costs), and must be able to protect itself from new threats and emerging threats. You should try to adapt your data infrastructure to “required” functionality. I’m knocking on the door every day.
For example, most hospitals and healthcare organizations have only really started their cloud journey in the past five years. Although the landscape is already moving towards multiple clouds, they may still be trying to achieve their initial roadmap and refine their cloud strategy. You’re a healthcare system that’s moved to the cloud on AWS, Microsoft is now releasing new Azure and Fabric tools that can integrate with Epic EHR, and oh yeah, your research department wants to use Google Cloud’s Healthcare Data Engine. Suppose you have You probably have different business units in different domains with unique tools for health plan management, the care delivery side of the organization, procurement and supply chain, and so on. You’re also probably using Snowflake or Databricks as your data platform going forward. Both are announcing open source Apache Iceberg-related tools and a suite of new features that support data lakehouses and various analytics engines that can promote autonomy and simplify data architecture. How do we manage that sensitive, regulated data, and how do we support and build the best systems for using all three public clouds, all the great tools and features, and everything else that’s coming tomorrow? Is it?
And all of this barely touches on the elephant in the room: AI.
Impact of AI
Each year, Matt Turck publishes the popular “State of the Union” map, a collection of logos representing important companies in the data, analytics, machine learning, and AI ecosystem. The first version in 2012 had only 139 logos. Last year, there were 1,416 people. And this year there are 2,011 pieces! The last 12 months alone have been a veritable arms race, with AI tools and capabilities being added and enhanced across every conceivable platform. It’s almost impossible to keep up with all the features. One of them might change the game, but it might not be the one that screams the loudest. And perhaps organizations should really focus their budget and resources on practical things like optimizing cloud storage and setting up a more resilient architecture to handle all the other tools. It’s not sexy, but it’s really important.
We know we’re preaching to the choir about the weight of complexity and the continued onslaught of innovation, but approaching the new year with a triage mindset is how organizations can best deal with this moment. It may help clarify: What survives with or without intervention? What makes a positive difference in outcome when taken care of? What perishes regardless of your efforts? Is it? Here are some examples of what triage might look like in an IT practice.
Prioritize your evaluation: Measuring the ROI of data initiatives in healthcare IT has traditionally been something data teams either don’t do or do poorly. This is even more pronounced with AI. Because your organization is actually building multiple “new muscles” and you need to understand the impact. This means you need to use new technology, understand how to predict its usage, and have a strong framework for measuring and validating whether it’s appropriate. It either benefits the organization as intended or becomes an outrage that drains resources from the organization’s other IT priorities. Healthcare facilities cannot afford to spend another year experimenting without results. Focus on data strategy and architecture: As the number of technology vendors grows and existing vendors add new capabilities, you need a strong approach to help you choose the right tools for the right jobs. It may not be available from one vendor. Open source data lakehouses such as Iceberg provide the cornerstone of a “bring your own tools” architecture, so you can think outside the box and be completely data layer agnostic as your landscape evolves and changes. It has a data layer. Multiple analysis engines will be available. Many of the tools that power that functionality have only been available to the public for only a few months, and there is a tremendous amount of new activity taking place in this area, but this is something we will continue to monitor repeatedly in the new year and plan to develop our strategy and roadmap. It’s a topic worth analyzing in light of. . Build expertise in design thinking and automation: This is a highly sought-after IT skillset in healthcare and every other industry in 2025 and beyond. AI offers tremendous opportunities, but there’s nothing worse than automating processes that shouldn’t exist in the first place. Teams that can quickly grasp the current state of affairs, understand the technology enablement of new tools, weigh build vs. buy opportunities, and consistently translate that into business outcomes in a practical way are highly valued. It’s precious.
The essence of triage is pragmatism in the face of chaos and uncertainty. And that’s what healthcare IT really needs in the age of AI. Build a roadmap and make it actionable by providing direction by systematically connecting it to your specific organization’s context, resources, and desired outcomes. You don’t have to decide everything at once. All you need to know is the right “next thing” you need to create and why it’s important.
Photo: Neverup, Getty Images
Chris Puuri, VP, Global Head of Healthcare and Life Sciences at Hakkoda, uses his deep understanding of healthcare IT and regulatory challenges to solve healthcare-specific data and analytics problems. Masu. Chris has over 18 years of experience as a data architect for organizations across health systems, pharmaceutical companies, payers, and biotech companies, building, integrating, and launching data solutions for some of the nation’s largest healthcare organizations. I’m here.
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