The healthcare industry is undergoing a significant shift towards personalized medicine, with patient-specific programs playing an integral role in this transformation. While the benefits of these programs are clear, medical device companies face several challenges when trying to start or scale them. One of the most significant barriers is how to convert 2D patient data into 3D using segmentation solutions at scale, with consistently high quality and at an affordable cost.
Segmentation is an essential part of the workflow that allows companies to convert 2D medical images into 3D, patient specific files, to identify and isolate specific structures or regions of interest within a patient’s dataset. It is a critical component of patient-specific programs as it provides the foundation for developing personalized treatment plans and creating patient-specific medical devices. However, not all segmentation solutions are able to scale with the increasing demand for innovation in patient specific solutions, and many medical device companies find that their current solution is holding them back from starting or scaling these programs.
1. Costs
One of the most common issues medical device companies face is that the cost of their current segmentation solution does not allow them to scale. The current segmentation software options available on the market vary widely in price, with some software licenses costing tens of thousands of dollars per year. This high cost can be prohibitive, especially for smaller medical device companies, those just starting with patient-specific programs, or those looking to scale quickly. Each license also requires additional costs for someone to run the software and with each new license the costs grow at the price of a new seat. Other solutions are provided on a cost per use basis, saving companies many overhead and license costs. Providers may struggle to justify the expense of a segmentation solution, which can prevent them from getting started in the first place or limit their ability to scale their programs over time.
2. Resources
Another challenge medical device companies face is that they may have limited resources available. For companies that offer software licenses that enable users to segment their 2D data, it requires a highly skilled engineer to manually segment the data. Each engineer can segment one set of data at a time on one software license. If you want to scale the amount of data processed, additional human resources are required. Whereas AI powered segmentation services can be scaled quickly and easily without the need for additional employees.
3. Time
Time is another factor that impacts a medical device companies’ ability to scale. By purchasing software licenses, it requires onsite installation by IT, training on how to use, and a learning curve to get comfortable with the tool. Using an AI powered, cloud-based solution, there is no installation required and users can upload data immediately. Software that requires manual segmentation of data means hours for each patient data set and is limited to working hours. AI driven segmentation can process data in minutes, handle multiple datasets at a time and work 24/7.
Evaluating and finding the right segmentation solutions for your company is essential to start or scale patient-specific programs. Perhaps most frustratingly, companies may find that their current segmentation solution simply is not up to the task of starting or scaling their patient-specific programs. Providers may find that their current solution is holding them back, either because of budget constraints, limited resources, or time urgency.
A scalable solution should be cost-effective, with pricing that allows providers to get started and scale their programs over time. It should be powerful enough to handle the high volumes of data quickly, without human limitations, and without compromising on quality or accuracy. To overcome these challenges and achieve success with patient-specific programs providers need a scalable segmentation solution that meets their needs both now and in the future.
