Ashwin Moduga, Research Manager for Health Insights Asia/Pacific notes, “Hospitals will not just use AI/Cognitive for automation or accuracy anymore. Implementation will aim to augment the low availability of healthcare specialists in the region. Emerging solutions are already helping hospitals improve on medical image diagnoses using deep learning and allowing for large scale diagnostic efforts with minimal human inputs. AI/Cognitive solutions in the next ten years will look to bolster decision making and empower clinicians with tools that validate clinical decision confidence – helping improve disease progression.”

“In the next 2-3 years, hospitals will have passed the stage of experimenting with simple automation and begin to invest on deep learning algorithms that make sense and generates actionable insight out of the geometrically growing, unstructured data sets, from personal and hospital health information management systems,” Moduga adds.

Implementation efforts, made possible by involving stakeholders from the line of business and end users (physicians, nursing staff), are focused based on hospital needs – the private health sector investing on specific clinical decision support systems for diseases like Oncology and Neurology, while the smaller and public hospital systems explore areas related to automation of data entry and patient engagement.

“While most hospitals in the region, apart from large private chains, are still evaluating large scale solutions, it is important to begin in-house efforts for a select set of individual processes. These in-house efforts have, for many hospitals, resulted in improved understanding of key process stakeholders, project scopes, and importantly the ability to showcase ROI for future, larger investments." said Jessie Cai, Senior Research Manager, AI/Cognitive.