From easing burdens on burned-out clinicians to helping streamline administrative tasks and speed up clinical decisions and diagnoses, AI solutions are powering healthcare transformation.
AI solutions are a prescription for progress in healthcare. Today, AI can enhance genomic analysis, medical imaging, and drug discovery. And the rapid advancements in AI aren't only improving health outcomes; they're also reducing clinician burnout and driving significant cost savings.
But building an AI-ready infrastructure in the highly regulated healthcare environment is anything but straightforward. For AI to thrive, data must flow swiftly and securely from diagnostic solutions at the edge, throughout clinical applications, and to cloud environments. Whether you're a provider, payer, or research institution, NetApp can remove data silos and prescribe a highly effective course for AI success. It's a course built on real-time, market-ready analytics and proven AI solutions.
Cumbersome administrative tasks weigh down staff and squeeze margins. Fortunately, up to 40% of support staff tasks and 33% of practitioner staff tasks are good candidates for automation."Automation and Artificial Intelligence," Metropolitan Policy Program at Brookings, January 2019. Automating these tasks with AI-powered solutions improves efficiency, freeing staff to do more high-value work. Self-scheduling and natural language processing solutions buy time and reduce frustrations for staff and patients while lowering operating costs and supporting comfortable margins.
For diagnoses and treatment plans, AI is game changing. Doctors who use AI can analyze MRI scans and biopsy images incredibly fast and with remarkable accuracy. But that’s only the beginning. Doctors who use AI can also triage critical findings in medical imaging, flag acute abnormalities, prioritize life-threatening cases, help manage chronic diseases and treatment plans—and much more.
The needle-in-a-haystack nature of drug discovery has led to staggering and ever-rising R&D costs. AI is resetting the status quo. Machine learning applications slash the time it takes to identify promising molecule candidates, helping researchers focus their efforts where it counts. With faster drug discovery at a lower cost, everyone wins.
With ready access to data for myriad variables, and with predictive analytics, risk prediction has come of age in healthcare. At the patient level, AI-driven risk assessment can help with early interventions against devastating and costly diseases. On the macro level, big data and predictive analytics can even forecast epidemics. The challenge is to effectively manage the massive amounts of data that's being generated by wearables and clinical trials and getting it to the right place at the right time.
NetApp was immensely valuable in providing performance and reliability to gain sustained flows of data. It perfectly matches our compute power and, thus, allows us to improve the image quality, accelerate the data pipeline, and run five times more data in the same time.
Dr. Hinrich Winther, Resident, Institute for Diagnostic and Interventional Radiology, MHH
How do we bring the data into the algorithms, and how do we bring the outputs of the algorithms back into the clinical systems? That’s really the issue we are trying to solve, and that’s where partnering with NetApp has been extremely helpful.
Jorge Cardoso, CTO, London Medical Imaging, Kings College
Data management for AI in healthcare is a big topic, so you undoubtedly have questions. Our AI solution specialists would love to talk through them (don’t worry, we don’t route your inquiry through sales). Just pick a contact option, and we’ll get back to you stat.