Physicians should not have to rely upon memory to go back and make sure that an abnormality is followed up on, Moore says.
Providers will also be given options on how to follow up. In some cases, this analysis can trigger an alert in an EHR. In others, a daily report could be routed to nurse care managers to look through the report and make sure all patients have had proper follow up care, Moore says.
The same NLP techniques UNCHC is using to scrutinize mammography reports and pathology reports could also be used to scan other kinds of radiology reports, or any other type of free text reports.
By taking the next step and converting its results into structured tables that can be incorporated into patient EHRs, UNCHC and other IBM customers will avoid being locked into some proprietary NLP system that would sit alongside the traditional EHR.
That's not to say IBM doesn't want customers to stay with its solution. But by getting into that structured format, it does tilt the balance of power back toward the customer, because structured data is inherently more interoperable and portable than unstructured data. As IBM's Ed Macko, worldwide CTO for healthcare and life sciences puts it, the newly-structured data, extracted by NLP, becomes part of the patient's longitudinal record.