Preop.ai is built for hospitals, surgical centers, and procedural departments to simplify one of the most fragmented corners of healthcare operations: the preoperative stage of the patient record.
The company
Preop.ai is operated by MMF Systems, Inc. — a company that has spent more than two decades working alongside hospitals on preoperative information management. Preop.ai is our flagship platform for organizing and delivering preoperative patient documentation across surgical and procedural departments.
What we do
Hospitals and their affiliated surgeons, physician offices, and procedural teams use Preop.ai to handle preoperative patient information end to end. We centralize and simplify the collection of required documentation — from smaller hospitals to major academic medical centers — improving communication between the hospital, its perioperative personnel, and the physician offices that feed it.
The outcome: fewer last-minute surprises, cleaner audit trails, and documentation that arrives where it needs to be, when it needs to be there. Our platform supports hospital compliance with Joint Commission standards and integrates directly with existing EHR and document management systems.
Focus and experience
Preop.ai is a widely adopted preoperative information solution, with continued investment in AI-assisted capabilities that help documentation teams catch gaps earlier, route information faster, and keep cases moving. Our focus has always been on one problem done well.
Security and reliability
MMF continuously invests in the infrastructure that underpins Preop.ai. We operate with the same security standards used in online banking, with geographically redundant hosting and audited access controls. All data is encrypted at rest and in transit. We are HIPAA-compliant and SOC 2 Type II certified.
Research
Our team has contributed to peer-reviewed research on preoperative patient information and its downstream clinical impact. For example:
Prothrombin Time and Activated Partial Thromboplastin Time Testing: A Comparative Effectiveness Study in a Million-Patient Sample — Capoor, M. N., Stonemetz, J. L., Baird, J. C., Ahmed, F. S., Awan, A., Birkenmaier, C., Inchiosa, M. A., Jr., Magid, S. K., McGoldrick, K., Molmenti, E., Naqvi, S., Parker, S. D., Pothula, S. M., Shander, A., Steen, R. G., Urban, M. K., Wall, J., & Fischetti, V. A. (2015).