In a medical practice, timely documentation of patient encounter is a very
critical factor. Equally important is the accuracy of the data in the patient
encounter notes. Apart from the medical terminologies, it is extremely
important that the patient demographic information as well as the encounter
specific details is entered accurately. It has medical, financial as well as
legal implications. Inaccurate information can result in denial of insurance
claims or even land the provider in a legal suit. Hence it is very important
that due diligence is maintained to ensure that the patient demographic as well
as encounter specific information is entered correctly in patient reports.
The Challenge
One of the most important encounter specific information is the Date of
Service (DOS). A wrong DOS on the patient’s medical transcription report can
land the provider in problems and also result in denial of insurance claims,
resulting in revenue loss.
Every medical practice has its own system of managing the medical records.
This includes the handling of dictations on patient encounters. The providers
use different modes to dictate on patient encounters. It could be a phone-in
dictation, digital records or even the latest technology of smart phones
(including iPhones). Some practices and providers have the habit of dictating
the patient notes at the end of the day of the encounter and uploading on the
same day. However some others dictate on the same day of the encounter but they
upload the dictations for transcription in bulk later. Usually it could be
twice in a week or even once in a week. At times, even within the same
practice, different providers could have different ways. However, in most of
the cases, there will be regularity in the way dictations are handled.
It was found that the mistakes were because of inaccurate data sent to
Transcription Company from the practice. At the time of sending the dictations
(especially using digital recorders), the person in the medical records
department, who upload the dictations have to select the DOS for the set of
dictations. At times, by mistake they happen to select the wrong DOS, resulting
in wrong encounter data being typed in the report.
When this is brought to the notice of the provider by the insurance
company, the providers blame the Transcription Company for the errors. This
leads to a loss of trust in the quality of service provided by the
Transcription Company.
AI can play a major role in overcoming this problem. Most of the practices
and/or providers have a definite pattern of sending dictations. There were
slight divergences, but on the whole a pattern could be mapped out.
Based on the result of the pattern mapping, the AI could arrive at a matrix
which gave us the most probable pattern of dictation for a provider. It could
be same day dictation, next day dictation, twice in a week or once in week etc.
This pattern can be incorporated into transcription system and the value
included in the provider/practice profile.
Whenever Transcription Company receives the dictations, the transcribers would enter the DOS based on the information provided from the practice side. AI system would compare the date of dictation and DOS and match it with the value against the provider/practice profile. If there was a divergence which was more than the accepted one, the AI system would throw an alert to the next level (editor or quality analyst) to recheck the DOS. The Editor or QA could reconfirm the DOS and in the case of a doubt flag it to the attention of the practice or provider to take due care.
This artificial intelligence based approach will result in substantial
reduction in the usage of wrong encounter specific data, especially the DOS.
This also resulted in major confidence boost among the providers with regard to
transcription service.
