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AI: Making Clinicians Jobs a Little Easier

With the shift to a value-based reimbursement model, hospitals and clinicians are looking for ways to increase efficiencies and improve patient outcomes. Artificial intelligence can help to streamline diagnoses and treatments by culling through volumes of data and pinpointing specific disease types or other patient data. Patients who need to be seen are seen quicker because a doctor or nurse wasn’t spending time looking through reams of reports, which in turn increases satisfaction all around.

The goal of cognitive computing is to make knowledge workers more effective, not to replace them, says Hal Andrews, president of healthcare at software company Digital Reasoning. “Any workflow that requires humans to read or skim or scan vast amounts of data, technology can make that more efficient,” Andrews tells Healthcare Dive. “And there are better outcomes for the patients, there are lower costs for the system as a whole, and there’s improved job satisfaction for the knowledge workers.”

Use of AI is growing in healthcare, with the market poised to reach $6 billion by 2021, up from $600 million in 2014, according to Frost & Sullivan. Cognitive solutions such as IBM’s Watson system are capable of sifting through huge volumes of patient data and providing guidance and decision support to improve workflow.

Also, Vice President Biden cancer moonshot has plans to harness big data to provide precision medicine solutions for veterans and to expand the use of mobile and wearable technologies for cancer diagnosis and treatment. The initiative also is developing a tool that converts narrative into standardized data, making clinical data more available for research.

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