Operations | Monitoring | ITSM | DevOps | Cloud

Israel’s healthcare system is widely recognized as one of the most digitally advanced in the world. Electronic health records are deeply embedded across hospitals, and platforms like Chameleon sit at the center of clinical operations. Patient data is captured, structured and accessible at nearly every stage of care delivery. But digital maturity alone does not guarantee operational efficiency.

Platform engineering has emerged as a response to the growing complexity of modern software delivery. As organizations adopt Kubernetes, microservices, CI/CD pipelines, and infrastructure as code, they are creating dedicated teams responsible for building and operating the internal platforms that power developer workflows.

Healthcare is shifting fast, and artificial intelligence is no longer a future concept sitting in research labs or pilot programs. It’s already embedded in clinical workflows, operational systems, and patient interactions, often in ways that feel subtle, uneven, and sometimes uncomfortable.

You’ve done it. Your machine learning model is live in production. It’s serving predictions, powering features, and quietly doing its job. Dashboards are green. There are no errors in the logs. Nothing appears broken. And yet, something is wrong. Predictions are getting less reliable. Users are waiting a little longer for responses. Conversion rates are slipping. Trust is eroding, but no alert fires, no system crashes, and no one knows there’s a problem until the damage has been done.

Over the past couple of weeks, as snowstorms and extreme cold swept across much of the Northeast, something interesting started happening on our end at OnPage. Our phones lit up. Not from healthcare teams or IT operations/tech teams, which is where many people expect us to be used, but from HVAC companies, contractors, and property management firms scrambling to prepare for what they knew was coming.