
Opmed
Automates resource planning and allocation process to be resilient and optimized.
Date | Investors | Amount | Round |
---|---|---|---|
* | $15.0m | Series A | |
Total Funding | 000k |
USD | 2021 | 2022 |
---|---|---|
Revenues | 0000 | 0000 |
% growth | - | 150 % |
EBITDA | 0000 | 0000 |
Profit | 0000 | 0000 |
EV | 0000 | 0000 |
EV / revenue | 00.0x | 00.0x |
EV / EBITDA | 00.0x | 00.0x |
R&D budget | 0000 | 0000 |
Source: Dealroom estimates
Related Content
Opmed.ai offers an AI-powered optimization platform tailored for the healthcare sector, with a specific focus on enhancing the operational efficiency of hospitals and other healthcare facilities. The company's core product is a sophisticated engine that leverages artificial intelligence and network science to address complex scheduling and resource allocation challenges. Initially targeting operating rooms (OR), the system optimizes scheduling to maximize utilization, reduce patient wait times, and improve overall throughput.
The platform's capabilities extend beyond the OR to other critical areas such as inpatient care, cath labs, and rehabilitation centers. By analyzing vast amounts of data, the system provides real-time, data-driven insights that empower hospital administrators to make informed decisions. It can predict demand, balance patient loads across different units, and optimize staff schedules, including those for nurses, based on real-time operational data. This approach helps to alleviate bottlenecks, prevent staff burnout, and ensure that resources are allocated where they are most needed.
The business model appears to be B2B, selling its SaaS solution directly to healthcare providers, including large hospital systems like Geisinger. The company generates revenue by licensing its platform. By demonstrating a clear return on investment through cost savings, increased revenue from higher patient throughput, and improved patient outcomes, Opmed.ai positions itself as a critical tool for modern healthcare management.
Keywords: healthcare optimization, operating room scheduling, AI in healthcare, hospital operations, patient flow, resource allocation, network science, clinical analytics, workforce management, predictive analytics