The combination of artificial intelligence with cloud-based computing offers a lot of promise for commercial applications. Providence St. Joseph Health based in Renton, WA has taken steps to implement a healthcare delivery system that incorporates HL7’s Fast Healthcare Interoperability Resources (FHIR) to do the processing of data sources from a secure Microsoft Azure-based cloud server. FHIR uses theoretical and logical models to provide a system for the exchange of data among healthcare applications. Microsoft’s Azure server enables the use of AI and machine learning to quickly sift through large volumes of collected data to generate useful information.
Providence St. Joseph Health is not the first company to adopt a Microsoft cloud server for use in its health facility. Rush Medical Center announced last month that they had embraced the use of Google Cloud to help in the delivery of their medical services. By taking their unstructured client data stored on their proprietary SNOMED CT system, and processing it using Google Cloud and Maven Wave, the company was able to generate actionable insights which personnel could apply to their clients in real time.
Combining New Technology with Existing Architecture
The solution to most problems in healthcare is access to enough information to make a diagnosis. Through the use of AI and cloud servers, medical facilities make it much easier for their personnel to get pertinent data that affects a particular user or group of users. Rush Medical Center’s application used AI in the form of natural language processing (NLP) to administer cancer screenings for early detection, to verify the completion of medical physicals, and to raise the level of their clinical documentation.
The idea of combining existing siloed data with the processing power of artificial intelligence opens the door to a lot of possibilities for the medical fraternity. Insights can be generated not just from localized data, but, thanks to the cloud server, from data collected from different areas of the country. Big data thrives on having more data points to work with and produces better results with larger pools of data. As more companies adopt cloud computing, the pool of available public data (and by extension the accuracy of results) is likely to get better.