Healthcare IT leaders and employees work hard to update outdated devices, which is a pain. Is a full development of heath IT a distant dream?
Artificial intelligence is exploding in care. There have been a lot of operational applications for it, but there haven’t been many medical ones however. What might occur to encourage hospitals and health systems ‘ regular use of AI in the popular?
Everyone in the healthcare sector is aware that value-based worry is gradually becoming prevalent in the sector. But what can render it the economy standard? AI maybe?
Robert Connely has answers to all three of these questions, and he claims that there will be changes for all three this time.
Connely is the worldwide leader in the healthcare sector at Pega, a supplier with a focus on business AI decision-making and process automation. He has worked for multinational corporations like McKesson and Aetna and for successful health IT start-ups like Medicity for more than three decades in the areas of innovative, innovation, and proper leadership.
Redesign and change, please
Healthcare service businesses this year will leave the “wrap and renew” approach to legacy systems in favor of targeted “reimagine and change” strategies, Connely predicts.
” The change in 2025 from updating legacy techniques to full development requires a fine equilibrium”, he said. The goal is to help businesses use AI to handle complex processes while reducing the professional debt associated with the upkeep of legacy systems.
” This change is likely to affect two key areas: the creation of development strategies themselves, and the rise of AI orchestration platforms, “he continued”. These components are unfolding together, but a specific pattern is emerging.”
Modernization has shifted away from the traditional” blow and replace “approach, which swaps archaic systems for newer ones – while effective, this method is very difficult, costly and time-consuming, often taking months just in the discovery phase only, he added.
He added that it frequently ignores the complex processes that modern businesses must go through, such as provider lifecycle management and holistic patient care. These interconnected workflows were not intended to be handled by legacy systems.
” A subsequent approach,’ wrap and renew,’ sought to extend the life of legacy systems by integrating them with newer technologies, enabling their participation in complex workflows”, he added. This approach, however, still leaves technical debt unresolved and just drags the issue forward to solve in the future.
According to him, the emerging trend involves dividing up old systems into modular components and dispersing them between different technology layers, which will give rise to more flexibility and scalability.
” This is an approach we’re calling’ rethink and replace,’ and it uses generative AI to quickly and cost-effectively align business and IT to design and automate new workflow improvements”, he said. ” This capability increases low code technology, allowing organizations to reduce development efforts traditionally required to program AI to perform complex orchestrations.
” AI-powered orchestration platforms are a key enabler of this new approach, “he noted”. These platforms enable the integration of outdated systems with contemporary workflows, satisfying current business, regulatory, and security requirements without requiring time-consuming and laborious retrofits. By creating a bridge between old and new, these platforms allow gradual modernization.”
He advised businesses to implement modern workflows while also charting a path to the demise of outdated systems. Then, when does the value of maintaining the legacy system outweigh its technical debt?
Organizations will create phased roadmaps that ultimately lead to the retirement of legacy systems as their functions continue to exist on and evolve in newer platform environments in response to constantly evolving needs, he said.
AI’s future depends on security
The future of AI in healthcare hinges on overcoming security concerns, especially around managing private patent data, and 2025 will be the tipping point, Connely said.
” I see a security breakthrough from two angles: technology and technique, “he predicted”. Currently, most AI in automation and decision making relies on statistical AI to predict, decide and automate workflows. Security concerns place a premium on model usage and output auditing to monitor behavior, improve performance, find biases, and ensure responsible use.
” On the technology side, barriers to AI adoption include securing data for, and effectively managing and auditing statistical AI,” he continued. Retrieval-augmented generation frameworks enhance prompts by incorporating information system private data, according to generative AI.
He explained that this involves breaking data into chunks, vectorizing it, and embedding it in the prompt sent to the large language model. There is a mathematical chance of decoding the prompt, but LLMs can be instructed not to use the data for training.
” A promising solution lies in homomorphic encryption, a technique that allows data to remain encrypted while being processed by the AI model”, he explained. LLMs can use encrypted data that is then decrypted upon return to the source to create augmented responses with this technology. However, this method is still a few years away from practical implementation. In the interim, more advanced strategies are being developed to secure AI use.
” One emerging technique involves the adoption of private LLMs, “he added”. Organizations are increasingly creating their own vector databases, incorporating proprietary data that generative AI can access without compromising the security of the organization. This approach enables businesses to benefit from generative AI without the drawbacks of using public tools like ChatGPT.
In addition, developers and integrators are applying AI narrowly within specific process workflows, he noted.
” This focused use limits exposure, reduces security risks and makes it easier to measure value,” Connely said”. Executives are finding ways to safely adopt AI while unlocking its potential value by combining these approaches, such as private LLMs, vector databases, and targeted AI applications.
AI boosts value-based care
In 2025, AI will be the catalyst that transforms value-based care from a pilot initiative to the standard model across healthcare, Connely predicted.
” Dissatisfaction with U. S. healthcare payers is at an all-time high, “he noted”. The U. S. is unique in having the most technically advanced – and expensive – medical system globally, yet it often serves as a safety net for non-medical challenges such as aging populations, social inequities, environmental factors and behavioral health issues. These realities are driving a shift from fee-for-service models to value-based care contracts.
“, requiring payers to adopt a more patient- and member-centric approach“, he continued. This model recognizes that a lot of medical waste can be avoided. Often, minor issues–“ papercuts“ – escalate into costly medical problems when left unaddressed“.
Care management initiatives have already demonstrated that regular communication and proactive interventions can lower costs by lowering hospital stays, hospital stays, and other high-cost services. However, there aren’t enough care managers to scale these efforts across entire populations.
” This is where AI steps in”, Connely said. ” AI can augment care management by engaging with members and their broader support networks, including caregivers, family, social services and providers. Through AI-driven orchestration, education and proactive intervention, health systems can address fragmented processes. Agentic AI platforms, which are more sophisticated systems capable of managing complex healthcare challenges and workflows, are examples of this.
” As VBC reshapes the payer’s role, requiring them to take greater responsibility for patient outcomes and journeys, technology becomes a critical enabler, “he continued”. For healthcare payer CEOs, this is top of mind. AI-driven systems allow for better engagement and coordination among providers, members and others in the healthcare ecosystem.”
These advancements, he said, are accelerating the transition to VBC by allowing payers to act as true collaborators in enhancing outcomes while limiting costs.
” Agentic AI also has the potential to address one of the most persistent political divides in U. S. healthcare: the tension between individual and collective solutions,” Connely said”. Healthcare dollars have traditionally been allocated to broad population cohorts because of the inability to make decisions at an individual level, which is inefficient and prone to fraud and waste.
” AI changes this dynamic by enabling real-time hyper-personalization”, he added. It enables payers to assess individual circumstances in context and implement customary rules and interventions that are tailored to the individual’s needs at the time. This strategy combines the effectiveness of targeted, data-driven care with fairness of policies intended to help everyone.
By enabling precise, personalized decision making, AI aligns individual care with broader social justice goals, ensuring resources are used more effectively, he said.
” AI technology is evolving rapidly, offering payers new ways to engage with providers, members and social systems”, Connely concluded. ” These advancements are laying the groundwork for value-based care to become the standard in U. S. healthcare, delivering on its promise to improve outcomes, reduce costs and transform the system for the better”.
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