Healthcare providers in the US work with very complex IT systems. Even though many use electronic health records (EHRs) and other digital tools, lots still use old systems. A 2021 survey by HIMSS showed that 73% of US healthcare providers rely on outdated software and hardware. These old systems include older EHRs, lab information systems (LIS), picture archiving and communication systems (PACS), and hospital information systems (HIS) made many years ago.
One reason these old systems stay is because they work steadily and people know how to use them. But these systems also have problems:
These problems get worse when healthcare groups try to add new AI tools to automate or improve clinical and admin tasks. Old systems may not support modern data exchange methods, which makes real-time data sharing hard.
To make AI work well in healthcare systems that mix old and new technology, following interoperability standards is very important. Standards like HL7 (Health Level Seven) and FHIR (Fast Healthcare Interoperability Resources) give set ways for different systems to communicate. HL7 V2 messages are used by over 90% of Health Information Exchanges daily because they are simple and widely supported. More recently, FHIR has become popular because it uses modern web tools, RESTful APIs, and clear data structures.
FHIR is helpful when adding AI because it supports detailed, real-time data exchange. It lets AI access and update patient records, schedules, and clinical notes quickly. But changing old systems to work with FHIR comes with challenges:
Organizations wanting to add AI must spend time testing and validating to make sure AI and hospital IT systems talk to each other without errors.
Good AI integration respects how healthcare organizations work. It should not overwhelm IT teams or suddenly change workflows. One good way is to use a phased strategy. This means adding AI step by step with growing levels of integration. For example, Tucuvi, a healthcare AI platform, uses a three-phase plan for its AI agent called LOLA:
Healthcare groups in the US report better AI growth and less IT strain using these phased steps. Marcos Rubio from Tucuvi says this method builds trust between IT and clinical teams, cuts disruption, and lets AI’s benefits be tested step by step.
Data privacy and security are top concerns in healthcare IT, especially with AI handling private patient information. Following rules like HIPAA and GDPR (for international data with US links) is required.
Leading AI platforms like Tucuvi use strong security measures such as:
US hospitals have strict IT security rules. New AI tools usually must pass detailed security checks before being used. Successful AI projects work closely with hospital IT and compliance teams to meet all requirements.
Linking AI with old and new hospital IT systems means fixing technical issues such as:
One way to handle these is using middleware or integration engines. Programs like Infor Cloverleaf act as data bridges by standardizing formats like HL7 V2, FHIR, CDA, and X12. They translate data to keep different systems working and safe.
New AI platforms, like ENTER, use semantic mapping with RESTful APIs to cut down on costly HL7 custom work. AI also uses natural language processing (NLP) to pull important info from unstructured clinical notes. These methods lower manual data work and cut integration costs while improving data sharing.
Apart from technical links, AI’s real strength is in automating clinical and admin tasks to reduce staff work and help patients.
Many healthcare workers spend a lot of time answering common calls, scheduling, and follow-ups. AI can help by:
For example, Tucuvi’s LOLA AI fits into usual clinical workflows instead of forcing new ones. It puts automated notes where clinicians expect and adds alerts and task triggers inside EHR tools. This keeps usual routines, making staff more comfortable and cutting disruptions.
AI integration also follows business rules, like prioritizing patient groups and respecting local scheduling policies.
While phased AI integration works, healthcare groups must also think about updating old systems to use AI fully. Old systems limit scaling, security, and use of advanced data analysis:
Updating methods include wrapping legacy systems with APIs, moving apps to cloud platforms, rewriting code, rebuilding or replacing systems, and redesigning for growth. Each needs careful planning, expert partners, and step-by-step rollouts to avoid disrupting care.
The average healthcare organization uses about 976 unique applications (per a 2022 report), making modernization tough but needed to ensure smooth data flow and AI use.
Many US hospitals find success by using phased, standards-based methods to add AI:
Pravin Uttarwar, CTO of Mindbowser, says detailed data mapping, testing, and training are key for switching from old systems to modern FHIR platforms, especially with big Epic setups.
Jordan Kelley, CEO of ENTER, points out that AI-based semantic mapping plus RESTful APIs lowers expensive HL7 integration fees. This makes interoperability work more cost-effective.
Administrators, practice owners, and IT managers in US healthcare face special tasks when adding AI into old and new hospital systems:
AI integration in US healthcare IT needs a careful mix of new tools, security, following rules, and respecting current workflows. By using interoperability standards, phased integration, thoughtful legacy system updates, and automating tasks, healthcare providers can improve efficiency, patient care, and staff satisfaction. This way, modern healthcare IT can safely use AI advancements while avoiding common problems with complexity and risk.
LOLA is Tucuvi’s clinically validated AI agent designed to automate clinical phone calls, integrating into healthcare workflows to enhance patient management without disruption, such as automating follow-up calls and documenting interactions directly into the EHR.
There are three phases: Phase 0 (standalone use without integration), Phase 1 (secure automated batch data exchange via sFTP), and Phase 2 (full real-time API/FHIR integration offering seamless bi-directional data flow and embedded UI within the EHR.
Phase 0 requires no IT workload and enables quick deployment by using a standalone AI that automates calls based on uploaded patient lists, producing structured call summaries with SNOMED-CT and FHIR standards ensuring future integration and immediate ROI.
Phase 1 automates data transfers via secure sFTP, allowing scheduled batch export/import of patient data and call results, reducing manual efforts and integrating with existing HL7 interface engines, improving efficiency with minimal IT changes.
Phase 2 enables real-time updates from AI calls into EHRs, single sign-on with embedded AI dashboard, automated clinical documentation within patient records, and expanded data access via FHIR APIs for personalized patient interactions, enhancing workflow and clinical decision-making.
Tucuvi supports healthcare interoperability standards like HL7 and FHIR, adapts to legacy and modern systems, ensures secure encrypted data transfers, complies with HIPAA/GDPR, and undergoes rigorous security and medical device certifications to navigate complex healthcare IT environments.
Tucuvi AI automates inbound call handling by using natural language understanding to schedule, modify, or confirm appointments directly via integration with scheduling systems or EHR modules, improving patient experience and reducing front-desk workload while honoring business rules.
Tucuvi is ISO 27001 certified, HIPAA and GDPR compliant, encrypting data in transit and at rest, maintaining audit trails, controlling data residency, and passing rigorous hospital IT security reviews to ensure patient privacy and trustworthy operations.
Tucuvi aligns documentation and alerts within existing EHR sections, preserves clinical workflows, integrates alerts and task triggers, and uses a phased rollout to get stakeholder buy-in, ensuring clinicians perceive AI as a seamless extension of their routine rather than additional burden.
Tucuvi’s experience includes handling HL7 variant mismatches, firewall and VPN configurations, EHR-specific implementation quirks like unsupported FHIR fields, and limits on note length. Proactive validation and customization minimize integration risks, leading to faster, smoother deployments across diverse healthcare settings.
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