Intelligent CIO APAC Issue 25 - Page 58



However , its former system did not have consumable backend data or the ability to inject AI / ML models into streaming data on the fly . Additionally , the hospital struggled to automate its clinician workflows , which was inhibiting employee productivity .
To achieve operational excellence and continue to offer the best care possible , NUHS needed to leverage its data and generate predictive analytics – moving to an emphasis on value-based , community care .
Dr . Ngiam states : “ Value-driven outcomes are a core piece of our strategy to move beyond quality to value . We want to move upstream into preventive community health by incentivizing healthy patient behaviors through the use of AI and automation at scale .”
Analytics accelerates hospital transformation
NUHS started its transformation journey by deploying TIBCO solutions to address clinical needs – extracting the correct data to build the right models for clinical problems . The hospital system chose TIBCO technology for its end-to-end capabilities from data sources to deployment – and the platform ’ s incorporation of validated AI models into clinical workflows to improve workforce productivity and clinical outcomes . These features are deployed on NUHS ’ s new platform , called ENDEAVOUR AI , which is a core system that houses multiple AI tools and technologies , including TIBCO Connect technology for messaging , microservices and other capabilities .
Dr . Ngiam said : “ For example , high volume clinical data is converted from our source electronic health record ( EHR ) system to Kafka messages and consumed by various AI microservices in TIBCO Streaming software .
“ We use TIBCO Streaming and its TIBCO ModelOps capabilities to operationalize these Python scripts containing AI models that have been trained and validated on historic data . The output of these AI models can then be sent to our AI chatbot services , which can be subscribed to by multiple downstream service applications to deliver messages to providers and patients .”
The goals of these AI tools are to augment the work of doctors by applying better diagnostic and disease models to improve patient safety and reduce complications . The team has also implemented discovery applications to predict the efficacy of drug therapies and optimize patient wait times .
Dr . Ngiam said : “ We can deploy AI tools at population scale to screen patients for particular conditions such as breast cancer . This model uses a natural language processing tool to predict the risk of breast cancer of any patient presenting to our health system . We are also able to automate the process of following up at-risk patients for mammograms , as well as referrals for treatment if suspicious features are found .” As ENDEAVOUR AI aggregates live data throughout the health system , this data can be read by the TIBCO Spotfire system to display outcomes in a command center format so smarter decisions can be made in real time .
Dr . Ngiam said : “ We leverage AI to improve healthcare practices and outcomes , enabling clinical practitioners to make faster , more accurate diagnoses and precise treatments . Healthcare institutions aggregate vast quantities of data , but most of this data is analyzed retrospectively . TIBCO technology enables the NUHS ENDEAVOUR AI platform to stream data in real time , feeding live data into AI models that produce actionable insights on the fly .”
Better operations , better diagnostics
With this new platform , NUHS can now deploy novel AI and automation tools that were not possible before . New services , such as pharmacogenomics , can now be introduced and would support the optimization of patients ’ medication according to their genetic profile .
NUHS plans to operationalize as many as 150 distinct AI and automation tools as microservices on ENDEAVOUR AI . These AI tools incorporate multidomain patient information , such as demographics , clinical text , images , lab data and medications to make predictions .
These tools range from improving diagnosis at admission , to predicting length of stay in the hospital .
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