The conversation around artificial intelligence (AI) and machine learning (ML) in healthcare continues to grow. Research in cutting-edge areas like machine learning continues to demonstrate that computers have the potential to predict outcomes and optimize clinical operations in a wide variety of settings.
Healthcare stands poised for a transformation driven by AI and ML, and fueled by an abundance of data sources – electronic health records, claims data, genomic sequences, medical imaging, and even embedded sensor data.
Data is the fundamental raw material required to power AI and ML systems, and is an essential ingredient that enables healthcare organizations to increase efficiency, improve outcomes, and enhance quality of life for both patients and providers.
While the demands of treating patients and developing new therapies often relegate data collection and analysis to a back burner in healthcare, new tools enable developers to integrate ML and other capabilities easily into the routine process of developing and delivering treatments. Far from being an exclusive province of researchers and technology companies, AI and ML is now accessible to all.
As these use cases expand, success is dependent on several ingredients. First, such initiatives require large quantities of carefully curated, high-quality data, which may be hard to come by in healthcare where data is often complex and unstructured. High-quality data sets are required not only to operate AI and ML-driven systems, but even more importantly, to feed the training models upon which they are built.
Second, these systems need to be optimized for the compute-intensive jobs typically required by AI applications. And finally, IT resources supporting AI applications must comply with industry standards and regulations and adhere to the highest security and privacy standards to protect patient and other sensitive data.
Flatiron Health is an example of a company that has done this successfully. Flatiron Health links clinical data from 265 oncology practices with a growing network of large academic centers and other healthcare organizations that collectively document more than 2 million cancer cases. Then, by integrating these data into AI systems, Flatiron is able to optimize care, develop new treatment regimens and discover potential new therapies. In February 2018, the Swiss pharmaceutical company Roche, an investor since 2015, acquired Flatiron outright for $1.9 billion proving the value of AI based healthcare delivery.
A crucial technology that provides storage capacity, compute elasticity, security, and analytic capabilities needed to implement AI and ML – and drive innovation – is cloud computing. Cloud computing platforms make it easy to ingest and process data, whether structured, unstructured, or streaming, and simplifies the process of building, training, and deploying machine learning-based models. Healthcare organizations that can use cloud computing to make themselves more efficient and effective will be the most successful in coming years, particularly as the industry shifts to value-based care.
For example, the Dutch technology company Philips is building a cloud-based healthcare platform called HealthSuite Digital Platform. HealthSuite is built on more than 21 petabytes of data from 390 million medical images, medical records, and patient generated data. HealthSuite gives providers, clinicians, data scientists, and software developers access to both the quality data and AI tools they can use to improve care in real time.
Healthcare companies, whether established or new startups, are increasingly looking to AI and ML to drive innovation and transformation at their company and across the healthcare industry. These organizations share a common goal of reducing time to discovery and insight, improving care quality and enhancing the patient and provider experience. As the availability and volume of data sources continue to grow, the essential ingredients for AI and ML success will remain the same: high-quality data, cloud computing to remove undifferentiated heavy lifting, and ML services accessible to everyday developers. Once these foundational elements are established, AI and ML have the potential to power more efficient and effective care, enhanced decision making and the ability to drive greater value for patients and providers.
By Shez Partovi MD, CM, Director of Global Business Development, Healthcare, Life Sciences and Agricultural Technology at Amazon Web Services (AWS)