Introduction
In 2018, a clinical informaticist launched a tool to handle intake forms and clinical notes so doctors could spend less time typing and more time doctoring. A small study with 18 medical students suggested that the Cydoc smart intake form could substantially reduce note-writing time while maintaining note quality, although broader validation in practicing clinicians was still needed. By August 2025, the company was gone.
The postmortem names the main reason: Cydoc lived outside the EHR. Doctors had to copy the notes from the Cydoc interface and paste them into the EHR, which meant working in two windows and adding an extra workflow step for routine clinical documentation. The founder later described the lack of EHR integration as a fatal adoption mistake.
Cydoc isn’t an exception. Even with a strong model, healthcare AI projects can fail when they add friction to already complex clinical workflows. A Gartner survey of infrastructure and operations leaders conducted in late 2025 found that only 28% of AI use cases fully succeeded and met ROI expectations, while 20% failed outright; poor data quality, limited data availability, and weak workflow integration were among the reported barriers. From pre-build through pilot and scale, the same mistakes are made, and the good news is that they are not inevitable.
