Aesop Technology launches CDI tool to help avoid patient record errors


California-based medical AI startup Aesop Technology, which has an R&D office in Taiwan, has recently unveiled its latest clinical documentation improvement tool that helps coders spot incorrectly coded diagnoses or procedures.

DxPrime provides suggestions to support medical data entry. The CDI tool is based on a machine learning model that has been trained based on a data set of some 3.2 billion patient visits. 

According to Aesop Technology, their latest solution for medical coding harnesses AI to “efficiently compensate for traditional CDSS and NLP weaknesses to find correct or missed diagnoses”.


Now available on digital health marketplace Olive Library, DxPrime provides information on missing and wrongly coded diagnoses or procedures to easily correct patients’ charts. 

With incorrect patient records, Aesop claims, patients could be given improper discharge instructions, thus receiving poor after-discharge care. For providers, this could lead to a wrong estimate of their patients’ length of stay and wrong code insurance claims, which could ultimately result in denials and revenue losses.

Aesop emphasised that errors in diagnosis input are difficult for physicians to avoid due to the gap in their knowledge of coding systems. Currently, the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) has 14,400 diseases included in its base classification, 68,000 diagnosis codes under ICD-10-CM and 87,000 procedural codes under ICD-10-PCS.


Last month, Aesop’s medication decision support tool RxPrime was launched on Olive Helps, a desktop platform for healthcare IT developers. The solution analyses inpatient data using patterns from prescriptions and flags potentially inappropriate prescriptions that do not match a patient’s diagnosis. 

In other news, Aesop partnered with Taipei Medical University, Harvard Medical School and Brigham and Women’s Hospital last year for a study that ran its machine learning model in EHR systems in the United States. It was found that the model, which provides adaptive suggestions to help doctors better complete their prescriptions, had demonstrated good international transferability.


Jim Long, CEO of Aesop, said: “Physicians, CDI team, and coders have to spend a lot of time poring through medical records to find the key clinical diagnoses among the vast amount of information available. After that, they have to follow a series of inefficient steps on the computer to complete the input process, and search functionality for ICD codes often is not helpful. The whole process is complex, time-consuming, and error-prone”.

When using DxPrime, he claimed, doctors were able to notice incorrect code complications. “By assisting them in inputting the proper diagnoses, our partners have seen an increase in revenue of 5-10% per inpatient,” Long said.


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