The COVID-19 pandemic has massively accelerated the adoption and normalization of new technology in healthcare. Here are five mistakes Imaging Centers should look out for -- and what to do now.
1. Messy data
Lacking standardization in your data will directly impact your ability to retrospectively look back on your data and extract insights as digital health solutions evolve.
Having standardized data means you’ll be able to pick an AI solution from the market and efficiently run it on your real-world data and assess results without a long implementation burdon.
The principle of using standard systems and processes exists to help ensure organizations can scale efficiently (with more people). It’s important to now also think about how your data will scale with your organization.
In order to take advantage of new and innovative AI algorithms with short integrations and onboarding, organizations should strategically prioritize standardizing their data. One example is the RadLex radiology lexicon by RSNA. RadLex is a comprehensive set of radiology terms for use in radiology reporting, decision support, data mining, data registries, education, and research – and is licensed freely for commercial and non-commercial users.
Search beyond the trends, too. While a large part of the commercial market is focused on image processing and image interpretation, there is significant untapped potential in other areas such as:
- Worklist management
- Automated measurements
- Operational efficiencies
2. Siloed teams and ideas
When teams are disjointed, ideas are formed compartmentally and don’t take into account the wider picture of stakeholders and end-to-end workflows.
Multi-disciplinary teams working together fosters a deep and shared understanding of pain points in existing workflows and underlines the gaps that exist which should be tackled first.
With the rise of remote work, teams should adopt and operate with a remote-first culture. At the heart of this lies clear and transparent communication – documenting company conversations, research, and decisions, in easy-to-search places. Organizations should spin up multidisciplinary teams consisting of Radiologists, Rad Techs, Clinicians, IT, and more, to identify opportunities for improvement in existing workflows.
The Data Science Institute by ACR has published DSI USE CASES, which gives guidance on scenarios where AI might improve medical imaging care for both interpretative and non-interpretative use cases. This is a great place for your multi-disciplinary team to start, to help evaluate AI tools and ensure they provide needed information, can be implemented into workflows, and have the potential to improve patient care.
The Data Science Institute has also launched AI-LAB, a data science toolkit designed to help radiologists learn the basics of AI and then use these skills in their practices, and a web-based catalog called AI Central which radiologists can use to learn more about AI algorithms that have clearance from the FDA.
These resources allow teams to first ‘dip their toes’ by exploring the use of basic AI tools applied to a variety of clinical settings.