Requires Adobe Acrobat NDDF Plus Brochure   Products: Enhanced Therapeutic Classification System First DataBank Medical Lexicon First DataBank Medical Test Lexicon Tall Man Plus

Applications and users of computerized drug information have widely varying requirements for database navigation and drug information access. With this in mind, First DataBank has developed adaptable drug concepts, integral to its knowledge bases, to accommodate the information needs of various audiences, such as physicians, pharmacists and nurses. Multiple Access Points (MAPs) include a family of medication name concepts, traditional First Databank identifiers, and flexible clinical classifications to support applications that incorporate drug content.

Developers using NDDF Plus can leverage an enhanced set of name-based identifiers, allowing for more flexibility in implementation options suitable for a range of healthcare audiences and information needs.

Flexibility in choosing from a variety of identifiers is essential for meeting disparate information needs across the healthcare continuum. When retail pharmacists dispense medications in the U.S., for example, they select brand- or generic-name packaged products, and use the related National Drug Code (NDC) to process claims with payers. In contrast, when a healthcare professional interviews a patient to obtain a medical history, they may need to capture only the product name (Med Name).

In both situations, however, healthcare professionals need timely access to medication concepts and clinical information to identify possible drug allergies, interactions and contraindications. The right collection of drug concepts will allow users to intuitively access relevant information.

Enhanced Therapeutic Classification System
ETC is an advanced NDDF Plus drug classification system with virtually unlimited levels of specificity, for easy formulary maintenance and drug selection. It allows drugs to reside in multiple therapeutic classes, with links to drug concepts at any level of the hierarchy.

First DataBank Medical Lexicon
The First DataBank Medical Lexicon (FML) is a specialized medical vocabulary relating drug products to various diagnoses and health-related concepts in numerous First DataBank disease decision-support and dosing modules.

First DataBank Medical Test Lexicon
The First DataBank Medical Test Lexicon is a controlled vocabulary developed for the specific purpose of supporting the population of drug-lab interference records in the Drug-Lab Interference Module. Hospitals, pharmacies, physicians and clinical laboratories use the Medical Test Lexicon in conjunction with DLIM to identify drugs that may falsely alter laboratory test results.

Tall Man Plus
Tall Man Plus drug identification data helps medical professionals detect drugs that could be confused because of similarly spelled names. It uses alternating upper- and lower-case spelling of drug names to visually distinguish look-alike, sound-alike medication names. The confusion of drug names accounts for approximately 15% of all reported medication errors. Such errors can waste time, raise healthcare costs and cause serious injury or even death.

MAPs are designed to conform to "good vocabulary practice," characterized by the following features.

Stable Identifiers. MAPs concepts are represented by stable numeric identifiers, which point to one and only one medication concept. This ensures stability for user files. Once these identifiers are retired or replaced, they are never used again. This means users can confidently rely on recorded information, significantly reducing the usual burden of change management.

"Dumb" Numbers. Best practices dictate that numbers used for representing concepts do not have meaning built into them. This "dumb number" approach means numbers carry no significance beyond their literal values and do not need to be continually updated to keep up with drug information that is constantly changing. Following this methodology translates into stability and helps users to avert the change management headaches created when there are changes to a specific value or position that has meaning associated with it.

Single-Purposed. A third aspect of good vocabulary practice concerns the question of how many different concepts a single identifier should represent. Experience has shown that identifiers that represent only one concept enhance precision of meaning as well as stability. For this reason, FDB identifiers are single purposed.

Optimizing Pick Lists. Users need clean pick lists, not those made unnecessarily long by duplicate items, spelling errors, or other variations in representation. For example, duplications due to minor variations in representation ("acetaminophen and codeine" vs. "codeine and acetaminophen") can frustrate users. Using MAPs concepts, developers can minimize unnecessary scrolling and mouse clicking-allowing for information to be displayed in clean, short lists at a task-appropriate level of specificity.

Documenting Patient-Reported Medications. Usually this is done at the most general concept level (Med Name). When taking a medical history, for example, the interviewer may find out only the product name. To capture the medication concept and perform drug screening, the application would use the Med Name, which would link the drug to the appropriate clinical data for that level of specificity.

Connecting to Formulary Data. MAPs enable the user to build or connect to formularies at the most appropriate level. One benefit is that the end user doesn't have to specify parameters that won't make a difference in determining whether an item is covered or not. For example, if all Clomid® is off formulary, why should the user have to take the time to specify and enter Clomid 50 mg tablet oral, when just entering Clomid would suffice?

Providing Access into Clinical Information. Medication name concepts can be used to access clinical information without the need to specify strength or dosage form for a drug. This view into our clinical content reflects expanding contexts where computerized drug knowledge bases are leveraged, and is particularly valuable for next-generation applications that are physician-based, such as computerized prescriber order entry (CPOE).