Medical Imaging is the process of scanning and obtaining images of a specific target like a medical device or a certain organ/region in the human body. These images are a rich data source of medical information which can be leveraged by:
- Medical practitioners to interpret and obtain critical health information about human anatomy which is critical in diagnosis of a medical condition
- Research Institutions and Healthcare Organizations as crucial data sources for research and technological innovations.
The ultimate goal is to maximize the amount of information obtained from these medical images without increasing the exposure and imaging duration. To achieve this goal, the key factors are the quality of information in the image, quantity of information in the image and lastly the quality of the image itself. Ways to enable this improvement include:
- Introduction of new and innovative imaging modalities
- Advancements in medical imaging equipment (scanners)
- Improving workflows and processes around medical imaging
- Application of AI/ML technologies in medical imaging
However, there are a number of challenges faced during implementation.
Image Volume Overload
Hardware and software innovations have resulted in medical imaging scanner’s improved capability to capture a large number of high quality images for the imaging software to process and generate final images of top quality. Although it results in high quality images, this also causes an increase in load on the IT infrastructure, thereby giving rise to the need for higher computational power and efficient medical image management systems.
Increase in Costs
To implement these improvements, there will be a need for an increase in the size of the IT infrastructure and, as majority of these healthcare IT infrastructures are on-premise, they will incur huge implementation costs.
Decentralization of Resources
Although there are various modalities available in medical imaging, there is a lack of a platform capable of combining resources from different modalities when required for diagnosis. To tackle this issue, complex workflows and infrastructures have been implemented.
Increase in Need for Care Quality
Patient satisfaction is regarded as a top priority. However, due to the increase in demand for medical imaging, there is an increase in the duration of medical imaging, report generation and treatment planning. This is causing a hindrance to the quality of care and comfort providers are intending to provide.
Data Privacy and Compliance
With the ever increasing concern for data privacy, several compliance laws and standards have been introduced by the authorities. Remaining in compliance requires implementation of steps by data stakeholders to protect sensitive data at all times. These stakeholders have to abide by specific standardizations when handling data containing sensitive information.
To overcome these challenges associated with volume, integration, and quality, healthcare organizations must address the need for a comprehensive cloud-based enterprise data management service. Adopting a cloud-based medical imaging solution will help healthcare organizations tackle these challenges by providing a virtual IT infrastructure with key features like:
Achieved through a centralized vendor-neutral repository that can manage multiple types of content in a cost-effective way, and without a decline in performance. At scale, organizations can leverage a mix of on-premise and cloud storage, to free resources and enable new ways to deliver better care.
Gained through a single platform that aggregates study worklists and patient data, which can lead to productivity gains for radiologists and referring physicians, resulting in greater job satisfaction.
Collaboration can lead to improved quality. When physicians share images more easily, they communicate more effectively—and create consensus around the best course of care to their patients.
Healthcare organizations that have a cloud-based enterprise medical imaging management system with these attributes can improve care and enhance both provider and patient experiences.
SpringML provides a robust cloud-based infrastructure to support high computational power and the ability to manage large volumes of medical image data. This solution is built on Google Cloud Platform and is powered by Google Healthcare API. In the second Blog, we will discuss more on this solution, its features and capabilities.