Most innovation in medical imaging tends to be incremental in nature, aimed at the modification or improvement of an existing technology. This tendency toward incremental innovation is largely due to market forces and the relatively risk-averse nature of large technology providers [6
]. Simply stated, most vendors want to be able to accurately predict a return on investment for a proposed innovation, and in the absence of an established market (i.e., sales) for the technology, this becomes difficult to quantify.
On occasion, an innovation is groundbreaking, offering a uniquely different and new approach to performing a given task or process. Examples of such innovations in medical imaging include the creation of a new image acquisition technology (e.g., MRI), transition from analog to digital imaging data (e.g., PACS), and creation of new reporting transcription technologies (e.g., speech recognition). In all of these cases, the innovation offered improved deliverables; which could take the form of quality (e.g., improved diagnosis), workflow (e.g., automation), or timeliness (e.g., report turnaround time). In order for the innovations to achieve success, it is essential that the innovation provides measurable and reproducible improvements over its predecessor and is compatible with the values and needs of the adopting community of end-users.
As one postulates future innovation opportunity in medical imaging, it is important that the scientific principles of innovation diffusion be considered and incorporated into the product development and adoption process. This requires an understanding of the differences and similarities of the end-user population, which can differ according to occupation, education/training, clinical experience, computer proclivity, practice type, and personality. In order to achieve a widespread adoption within a large and diverse population of end-users, the innovation must be perceived as having workflow, quality, or economic improvement while also possessing some degree of adaptability and flexibility in order to accommodate to end-user variability.
While the importance of social interactions has been historically established in innovation adoption, a number of unique changes must be considered in the current market. In medical imaging, the transition from analog to digital practice has produced profound changes in interpersonal communication. As imaging data have become widely accessible, physicians no longer are required to travel to the imaging department to access imaging and report data, resulting in decreased physician-radiologist consultations [7
]. At the same time, the digitization of medical imaging has led to the emergence of teleradiology, which has provided improved quality of life for the radiologist community while also producing new commoditization and outsourcing pressures on service delivery [8
]. As radiologists have become more isolated from their clinician colleagues, it has become apparent that new practice strategies are required to ensure that radiologists are seen as valuable and indispensable contributors to patient care.
While medical imaging and information system technologies have produced alterations in professional social interactions, social networking technologies and the Internet have also had a profound impact on communication. The ability to communicate on a 24/7 basis has dramatically transformed expectations on practice deliverables, with healthcare consumers expecting enhanced communication with their care providers, improved access to medical data, and greater involvement in clinical decision making. This has led to the creation of a number of new technologies which provide patients with single-source storage of individualized electronic medical data and decision support (www.healthvault.com
Technology innovation in healthcare will continue to focus on data mining, which offers opportunities to improve workflow, quality, safety, and economics. In medical imaging, innovation possibilities extend throughout all steps in the imaging cycle including exam ordering, image acquisition, data retrieval, image processing, quality assurance, interpretation, reporting, and communication. This newfound ability to automate data collection and analysis in real time not only decreases the development time requirements for innovation but also changes the way a given innovation is observed and analyzed for predictability of success. This will have a profound impact on the success of a number of quality-oriented healthcare initiatives such as evidenced- based medicine (EBM) and pay for performance (P4P). The same data-driven analytics which provide the requisite infrastructure for EBM and P4P will also create new innovation opportunities for automated feedback and alerts, customizable education, workflow automation, computerized decision support, and clinical outcomes analyses.