Health systems are increasingly turning to cloud vendors to secure and analyze data, with the end goal of pushing novel insights back to the bedside. Just this year alone, we’ve seen major healthcare organizations partner with public cloud vendors to bring AI to the point of care. Providence St. Joseph Health has partnered with Azure to accelerate its digital transformation, while Mayo Clinic recently announced a partnership with Google to transform patient and clinician experiences and accelerate medical research.

We’re crossing the chasm as an industry, and the time is ripe for healthcare and life sciences (HLS) organizations to invest in enterprise health clouds to win, serve, and retain customers. Our recently published report, “The Forrester Wave™: Enterprise Health Clouds, Q3 2019,” identifies and evaluates the nine most significant players: Amazon Web Services (AWS), Atos, Google, IBM, Microsoft, NTT DATA, Philips, Rackspace, and SAP. Based on the research, the following questions are those that HLS organizations need to think about when evaluating health cloud solutions:

  • How robust is secure backup and disaster recovery? The cybersecurity war continues to rage in healthcare, including the most recent ransomware attack on Campbell County Health, which affected 1,500 computers and forced the hospital to divert patients from the emergency room. HLS organizations must invest in services that will reduce their downtime in the event of an attack. Many cloud vendors are answering the call, offering native disaster-recovery-as-a-service (DRaaS) solutions within HIPAA-compliant, HITRUST CSF-certified environments.
  • How does the solution support healthcare data ingestion and preparation? FHIR and DICOM are now table stakes for data ingestion and preparation. Do the cloud vendors offer native support for data ingestion from EHRs, picture archiving and communications system (PACS), claims, genomic, streaming device, and social-determinants-of-health data? Is natural language processing supported, and can it be used to analyze, extract, and classify data elements in unstructured medical data? Are there services available to support patient matching, database deduplication, and data deidentification?
  • Are pretrained healthcare-specific AI services supported? Public cloud vendors are increasingly offering pretrained AI models designed to support healthcare and life science use cases, including precision medicine research, benefits design analysis, and provider performance benchmarking. This cuts time to market and costs down dramatically. For those non-data scientists, the cloud offers capabilities that allow them to work on custom machine learning and image analysis.

This Forrester Wave is a follow-up to the “Now Tech: Enterprise Health Clouds, Q2 2019” report.