# Presentation: 638462

## Evaluating Drug Names Similarities applying  Handwriting Recognition Technologies Kaz JaszczakDirector, Product Planning and OperationsParascript, LLCWashington, DC    June 26, 2003

**Notes:**
Welcome to Parascript’s corporate and product overview. My name is...

## Goal: evaluate proprietary drug names to reduce medication errors due to similarity in drug names.  
Answer: Parascript’s proprietary Total RecognitionTM Technology can   
Compare graphic patterns of writing a proposed drug name against  the patterns of writing the existing drug names.

- Answer: Parascript’s proprietary Total RecognitionTM Technology can  ** **
- Compare graphic patterns of writing a proposed drug name against  the patterns of writing the existing drug names.

**Evaluating Drug Names for Similarities**

**Notes:**
The FDA has determined that many of the medication errors reported to the agency result from medical products having proprietary names that look or sound like the names of other medical products. The goal of this presentation is to show how comprehensive pattern recognition technology may contribute to testing the proposed drug names and help to reduce medication errors due to this problem. 

## Parascript Overview

**Colorado**

**Moscow**

**Russia**

**Boulder**

- Incorporated March 1996
- Profitable since 1997
- Recognized industry leader
- Two decades of recognition research, development and innovation
- First to introduce natural handwriting recognition
- Recognizing more than 100,000,000 forms daily
- Employees: 100+
- Locations:
- Niwot, Colorado
- Moscow, Russia
- Sunnyvale, CA
- Leading users in a variety of industries

**Notes:**
Couple words to introduce the company...

## Comprehensive  recognition technology able to read any style of cursive handwriting. 
Built on the newest scientific ideas
Human-like, holistic approach to handwriting recognition. 
Uses a special descriptive language. 

Analytical approach
Neural Networks

- Built on the newest scientific ideas
**Human-like, holistic approach to handwriting recognition. **
**Uses a special descriptive language.**** **
**Analytical approach**
**Neural Networks**

- Total Recognition Technology

**clear**

**dear**

**Notes:**
Total Recognition Technology is based on the most advanced ideas and approaches existing today in recognition industry. It is built on multiple engines including:
unique engine for cursive recognition, which uses a special proprietary developed language that describes any handwriting; In particular it applies a human-like, holistic approach to handwriting recognition: a word is interpreted as a whole without segmentation to the character level. 
It is achieved with a help of a special descriptive language based on a set of formative characters that embody the essence of all styles of cursive handwriting (The slide presents just 8 basic elements, though the the total number of XR-elements is 64 to deal with any variation in writing style).  
Additionally, it involves
an algorithm of matching input symbol graphs with previously defined symbol prototypes (analytical approach) 
nineteen neural-networks-based learning and interpretation agents based on different features that are used to recognize the same character
All these methods complement each other to provide superior reliability and accuracy of results and allow a universal solution for interpreting any type of text without preliminary learning and training.

Beside unique technology Total Recognition offers a unique application oriented approach that allows the extraordinary read rate, accuracy and efficiency for each specific application.

## Parascript offers two approaches:
Compare graphic patterns of writing a proposed drug name against  the drug names existing in a database.
	Input Data Requirements:
A set of patterns of writing a proposed drug name (10 to 50 variations received from different people)
Database of existing drug names 
Compare graphic patterns of writing a proposed drug name against  the graphic patterns of drug names existing in a database.
	Input Data Requirements:
A set of patterns of writing a proposed drug name (10 to 50 variations)
Database of graphic patterns for all existing drug names (at least 10 variations for each entry)

- Compare graphic patterns of writing a proposed drug name against  the drug names existing in a database.
- 	Input Data Requirements:
- A set of patterns of writing a proposed drug name (10 to 50 variations received from different people)
- Database of existing drug names
- Compare graphic patterns of writing a proposed drug name against  the graphic patterns of drug names existing in a database.
- 	Input Data Requirements:
- A set of patterns of writing a proposed drug name (10 to 50 variations)
- Database of graphic patterns for all existing drug names (at least 10 variations for each entry)

**Applying Total Recognition Technology to  Evaluate Drug Names for Similarities**

**Notes:**
We propose two approaches. The first approach is more feasible for implementation and is easier for deployment as it is less demanding from the point of view of the initial requirements. So we will mainly focus on it in the presentation. Second approach may be considered for a second step as an option to improve the efficiency of the existing solution in future.

## Solution Workflow

**Easprin **
**Empirin **
**Ephedrine**
**.****.****. **
**Eulexin **
**Exelderm **
**Exelon **
**Extendryl **

**Vocabulary of Drug Names**

**Handwriting prototypes**

- Fuzzymatching

**XR_representations of the proposed name**

**Similar Names **
**with **
**Similarity Scores**

**Notes:**
How our technology works:
The input of the system is a set of versions of writing a proposed drug name. Total Recognition technology interprets cursive entry as a progression of motions. The motions are extracted as XR elements, a series of hieroglyphic shapes that form a recognition alphabet. Vocabulary contains existing drug names, which may have multiple representations through XR-elements. The XR-versions of the proposed drug name  is compared against XR-versions of the existing drug names. The result of a match is a list of names similar to the proposed name along with the similarity scores provided for each result.

## Handwriting distortions:
Parascript Total Recognition Technology deals with any type and quality of writing, including sloppy handwriting. No training is required.
Spelling Errors and Abbreviations:
Total Recognition Technology provides for a mechanism that deals with word abbreviations and will help to anticipate errors that may occur due to the similarity of the abbreviated forms of the proposed drug name and existing ones. This mechanism is called  aliases mechanism.
Similarly, the technology may anticipate medication errors aroused by typical spelling mistakes.

- Parascript Total Recognition Technology deals with any type and quality of writing, including sloppy handwriting. No training is required.
- _Spelling Errors and Abbreviations:_
- Total Recognition Technology provides for a mechanism that deals with word abbreviations and will help to anticipate errors that may occur due to the similarity of the abbreviated forms of the proposed drug name and existing ones. This mechanism is called  aliases mechanism.
- Similarly, the technology may anticipate medication errors aroused by typical spelling mistakes.

**Managing Additional Problems **

**Notes:**

An alias list defines words that are spelled differently but have the same meaning. The word pair “Center”/“Centre” is an example of this. Use an alias list to include all possible spellings of a word. 

## Legacy Forms

**Notes:**
Some examples of actual usage of the technology, types of writing it may process: 
NOAA Legacy Forms
Ability to process legacy forms – Weather Observation data collected since the end of the 19th century.
Deteriorating paper
Forms filled out with pencil
Condensed lines
Corrections
Extra high requirements to the error rate

## Envelopes, Letters, Postcards, Flats

_**Case Study: Address Recognition for United States Postal Service**_
**250+ locations**
**Parascript recognition solutions process more than 68 million envelopes/day**
**The United States Postal Service saves 12 million work hours/year for a total of $350 million savings**

**Notes:**
Address recognition example

## A Selection of Parascript Users

- ALLTEL
- BancTec
- Bank of New York
- Bell Atlantic
- Chase Manhattan Bank
- Control Diabetes
- First American Bank in Nashville
- Frost Bank
- Nova Information Systems

-   Regions Bank
-   Republic of South Africa
-   Scan Optics
-   TIAA-CREF
-   TRW
-   Union Bank of California
-   UNISYS
-   United Missouri Bank
-   U.S. Postal Service
-   Wachovia Bank

***And the list keeps growing!!!!***

**Notes:**
Also, Parascript has a variety of customers that benefit from our recognition technology.