The tool can predict and measure the incompleteness of EHRs in lab results, disease diagnosis, medical history and prescription records. Hospitals can lose an average of $5 to $8 million a year due to incomplete EHR data that impacts insurance reimbursement, according to a Nov. 17 UCF news release.
Dr. Gurupur’s algorithm works by identifying medical attributes that are more likely to be incomplete.
