New medical imaging software applications and AI algorithms have historically been purchased and implemented one at a time which is typically a long, drawn-out, and painstaking process involving a diverse cross-functional team. For every new application, this time-consuming acquisition cycle of activities is repeated, severely limiting the number of new applications that can reasonably be adopted by an organization.

In general, the key issues associated with a traditional approach to deploying new imaging applications fall into two broad categories:

Business Operations

The purchase and implementation of a new software application is a complex process that involves the healthcare organization’s clinical, legal, IT, finance, procurement, senior administration, and potentially other departments. Even after the selection process is completed and a contract is negotiated and signed, all applications require detailed implementation, configuration, and testing.

In addition to the human capital and the financial resources required, there are also significant soft costs associated with the application process – the most significant being time. Time for the team to assess, compare, discuss, and learn ahead of the purchase; and then time to integrate, test, train, and support the software. It can take anywhere from nine months to several years from initial discussions with the application provider to “go live”. All this time equates to a considerable financial cost to the organization, far beyond the cost of the application itself.

Technical Deployment

The deployment of an application is also associated with multiple technical challenges that require involvement from numerous departments that may have conflicting priorities. These challenges include software implementation, integration, configuration, troubleshooting, support, technical and clinical staff training, and many more.

For example, organizations have growing security and application control requirements that must be individually addressed. In addition, a conventional deployment requires interfacing work to be performed with each individual application, which is time-consuming and costly to achieve. There can also be inconsistent adoption of commercial standards, and back-end integration challenges to overcome, or varying hardware and software requirements and deployment approaches. All of these create implementation delays and have significant costs associated with them.

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