Concept Processing

Concept Processing is a type of technology used in some Electronic Medical Record (EMR) software applications, as an alternative to the more widespread template-based technology. Concept Processing is a new and innovative approach to Electronic Medical Recording. This is a highly integrated technology that incorporates artificial intelligence and Boolean logic.

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Some methods of data entry in electronic medical records

The most widespread methods of data entry into an EMR are templates, voice recognition, transcription, and concept processing.

Templates

The physician uses a general template pre-fabricated for the type of case at that moment, making it specific through use of forms, pick-lists, check-boxes and free-text boxes.

Voice recognition

The physician dictates into a computer voice recognition device that enters the data directly into a free-text area of the EMR.

Transcription

The physician dictates the case into a recording device, which is then sent to a transcriptionist for entry into the EMR, usually into free text areas.

Concept Processing

Based on artificial intelligence technology and Boolean logic, Concept Processing attempts to mirror the mind of each physician by recalling elements from past cases that are the same or similar to the case being seen at that moment.

The theory behind Concept Processing

For years the US healthcare industry has been heralding the widespread adoption of Electronic Medical Records throughout the nation. However, the reality is that up to now most doctors have not been convinced the technology will help their specific clinical, needs.

One reason behind the slow EMR adoption rate among physicians is the lack of an acceptable user interface. Filling out electronic forms, searching through pick-lists, and forcing patients into preset boilerplates have all put physicians off the switch to electronic charting.

Template-based software can slow physicians down, rather than making documentation easier. Most significantly, if the software is made to serve physicians, they should not be forced to conform to the computer; the computer must conform to the physician. Concept Processing is a reaction to this age-old user interface problem.

How Concept Processing works

For every physician the bell-shaped curve effect is found, representing a frequency distribution of case types. Some cases are so rare that physicians will have never handled them before. The majority of other cases become repetitive, and are found on top of this bell shape curve.

A Concept Processor brings forward the closest previous encounter in relation to the one being seen at that moment, putting that case in front of the physician for fine-tuning.

There are only three possibilities of cases : The closest encounter could be identical to the current encounter (not an impossible event). It could be similar to the current note, or it could be a rare new case.

If the closest encounter is identical to your present one, the physician has effectively completed charting. A Concept Processor will pull through all the related information needed.

If the encounter is similar but not identical, the physician modifies the differences from the closest case using hand-writing recognition, voice recognition, or keyboard. A Concept Processor then memorizes all the changes, so that when the next encounter falls between two similar cases, the editing is cut in half, and then by a quarter for the next case, and then by an eighth....and so on. In fact, the more a Concept Processor is used, the faster and smarter it becomes.

Concept Processing also can be used for rare cases. These are usually combinations of SOAP note elements, which in themselves are not rare. If the text of each element is saved for a given type of case, there will be elements available to use with other cases, even though the other cases may not be similar overall.

The role of a concept processor is simply to reflect that thinking process accurately in a doctor’s own words.

See also

• Electronic health record
• Electronic medical record
• Health informatics
• Medical record