Increasingly, hospitals are adopting AI tools to improve surgical operations, minimize delays, and facilitate more efficient clinical team workflows. But if you’re an OR director, head of perioperative services, or innovation lead considering what else to evaluate for an AI solution and where the dependencies are, look no further than the software systems your teams interact with every day. Before any AI or machine learning system meant for the OR can deliver real value, it must connect with the hospital's electronic health record (EHR), where most patient and surgical case documentation lives. Popular EHRs include Epic Systems and Cerner - Oracle Health.
Why EHR integration matters
AI tools generate massive volumes of new data and often rely on machine learning models that require extensive training and input to produce meaningful insights. The EHR is where organizations schedule cases, assign staff, complete key surgical documentation, and a huge variety of other relevant details. Integrating with the EHR allows machine learning systems to:
- See the surgery schedule in real time
- Know which staff members are present in the room during procedures
- Identify and understand patterns in scheduling and performance
- Learn from historical data to make smart, useful predictions
How integration with the EHR works
Generally, AI and machine learning tools connect to EHRs in two key ways. First, hospitals share data files, such as historical schedules and staffing patterns, that provide a descriptive baseline of what the models need to know. Second, live data feeds send updates about surgeries as they’re scheduled and performed. These two data sources work together to help the AI system understand what's happening in the operating room and create a sort of language from which machine learning will draw as it becomes more prescriptive and predictive.
How long does EHR integration take?
An entire integration process typically takes between three and five months. The timeline depends on the healthcare organization's internal systems and their prioritization of implementation objectives. In parallel, the set up of supporting hardware and software that round out the AI system (such as sensors, databases, and SDKs) may occur. After installation, there is typically a learning period during which the AI models and machine learning systems are trained before end users access the system.
Check out our case study on How Houston Methodist Reduced Long Turnover Times by 16% and unlocked capacity for an estimated 18 additional cases per OR per year.
Step-by-step: what happens during integration
1. Setting up the connection
First, a secure connection is established, allowing data to travel safely from the hospital’s EHR to the AI or machine learning system. This is like building a private tunnel just for your information.
2. Testing the data flow
Next, teams test how information moves between systems. This often involves walking through real-world clinical workflows to ensure that the correct data is captured and transmitted.
3. Sharing extra data
In addition to live data feeds, organizations typically send files containing necessary information, such as historical schedules or staffing patterns. Clear specifications guide this process.
4. Training the model
Once data flows, the system learns how operations run at that hospital. This ensures the insights it delivers are accurate and relevant.
Who's involved
Successful integration usually involves collaboration between several groups:
- Clinical Application Specialists help ensure workflows are accurately represented.
- Interface Engine Teams set up connections and support system testing.
- Reporting Analysts generate and deliver the necessary data reports and files.
- Network Security Teams approve, set up and monitor secure tunnels or channels.
- Project Managers coordinate timelines and resources across teams.
Common roadblocks (and how to avoid them)
Integrating AI tools with an electronic health record like Epic Systems or Cerner - Oracle Health can present challenges. One common issue is limited availability and project prioritization — key hospital staff, especially on the IT and interface teams, usually have a full slate of other responsibilities to balance.
Miscommunications can also arise if data and security requirements aren’t clearly explained. In some cases, hospitals have separate processes to approve the network connections and the sharing. These processes may have committees that report to a Privacy Office or CISO. When hospitals have custom or unique workflows, extra time may be required to adapt the integration plan.
Strong planning and clear communication are the most effective ways to avoid these challenges. It helps to have a formal timeline with specific milestones that can be revisited often and updated as necessary during project team meetings.
Tips for a smooth experience
Begin by ensuring your team understands how clinical actions translate to EHR documentation. When possible, stick with standard formats and protocols to streamline the process. Also, make sure key team members have the time and support they need to contribute throughout the integration.
After integration: what happens next?
Once integration is complete, the connection between the EHR and most AI tools is designed to run with little to no ongoing maintenance. The systems are built to be stable and self-sufficient. Occasionally, teams may revisit the setup to support new features or data needs, but ongoing support is minimal.
Effectively integrating with your EHR is key to achieving the surgical performance and patient outcome improvements envisioned by the adoption of AI and machine learning tools. While it takes time and coordination, the benefits are worth the effort. In fact, a well-planned AI strategy will create new value from an existing EHR investment. With careful planning and the right support, healthcare organizations can gain valuable insights that lead to better, faster decisions in the operating room, improving patient care and increasing revenue.
In the next part of our series on AI implementation in the OR, we'll outline how to ensure your physical infrastructure deployment stays on track.
Check out our case study on How Houston Methodist Reduced Long Turnover Times by 16% and unlocked capacity for an estimated 18 additional cases per OR per year.