As cloud technologies become sought-after for key clinical and operational systems of healthcare organizations, the healthcare industry is seeking to develop digitally enhanced, data-driven healthcare.
And, unstructured healthcare data, available within clinical summaries and documents, continues to remain a key source of insights to support operational and clinical excellence of healthcare practices.
Nonetheless, there is a high volume of important unstructured data, which is beyond manual digging and manipulation by clinicians. This requires automation.
Machine learning to enable Healthcare to render Value-based Care efficiently
“Meanwhile, there is a huge shift from volume of care to value-based care. In this scenario, 54% of CEOs of hospitals see the shift from volume to value as their biggest financial question. And, two-thirds of IT budgets of healthcare institutions is used to keep the lights on,” says a senior associate at Amazon web Services, co-presenter of a HIMSS20 Digital presentation on machine learning and unstructured data.
Machine learning plays an interesting role that necessarily do not intend to replace workflows. Nonetheless, it gives a superpower to healthcare workers and allow them to perform their jobs more efficiently.
Meanwhile, value-based care creates lot of data to find use for health IT leaders. When a patient undergoes various stages of care, lot of data is created as a result of immense documentation.
The question is how to make use of available resources so healthcare is rendered in a more streamlined manner. Conversely, a lot of currently used healthcare IT models lack agility to keep pace with technology. Reiterating, it’s about accentuating position of people in this space and giving them a superpower. This is to help them bring forth the right data and use it to make benefitting clinical decisions.