Chapter 10 AB 4

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Health IT and EHRs: Principles and Practice, Sixth Edition

Chapter 10: Data Infrastructure Assessment

Data Infrastructure

Data infrastructure refers to what data are needed to operate an enterprise and how they are defined (vocabulary), structured and processed (architecture) and quality-assured.

Data architecture for health IT supports ability to create the data-information-knowledge-wisdom continuum.

Data Architecture supports D-I-K-W

Data = raw facts and figures that make up communication

Information = data that have been combined to produce value

Knowledge = information enhanced with experience

Wisdom = knowledge with insight

Heuristic thought is processing of data by humans that gives them their intelligence

Knowledge management is a discipline associated with those who work primarily with their minds (knowledge workers).

Learning organization is one where knowledge management is central to organizational performance.

Types of EHR Data

Formats of Data Stored in Computer

Structured data

Values of variables

Stored in databases

Can have significant operations performed on them

Reflections of original data (aka image data, unstructured data)

Narrative text

Video & audio

Images

Computers enhance their availability and access

Both Types of Data are Necessary

Structured data support clinical decision making; but narrative data support clinicians’ understanding of the patient story

Data entry aids are helping to blend structured and unstructured data.

Natural language processing (NLP) would convert narrative data to structured data.

Improving in maturity, but due to contextual nature of healthcare data still difficult to fully achieve

Discrete Reportable Transcription

Combines dictation of narrative notes with NLP that tags data elements so they can be placed into structured data collection templates

Traditional Dictation

Note Produced

Speech

Dictation

Structured Data for EHR

Note Transcribed

(or Reviewed)

Follows EHR Template

System Tags Data for EHR

Vocabulary Standards

Codes - Representation of words to enable machine processing

Classification, or taxonomy - Grouping of terms with similar meanings used for a specific purpose

Vocabulary, Terminology, and Nomenclature

Vocabulary – all terms within a domain

Terminology – prescribed set of terms

Nomenclature – system of naming

Language - System of communication

Data mapping - process of identifying relationships between two distinct data models, which may be used to coordinate data among different classification systems, mediate between sources and destinations of data, and when transitioning from one version of a system to another

Vocabulary server - software that enables multiple vocabularies to be used across different applications

Broad

Specific

Data Mapping Goal and Examples

Ultimate goal:

Capture clinically specific data

Once at the point of care, and

Derive information there from for

Every other legitimate use

Primary Purpose	Secondary Use	Mapping From	Mapping To
Clinical documentation	Service reimbursement	SNOMED CT	ICD-10
Lab orders	Billing	LOINC	CPT
Documentation of ADE/ADR	Regulatory reporting	SNOMED CT	MedDRA
Clinical problem list	Literature search for decision support	SNOMED CT	MeSH

Codes and Coding

Codes are used to represent words in machine processing

Codes may be structured into a classification system (e.g., ICD-10-CM), or be random representations of words or concepts (e.g., SNOMED CT)

Coding is the process of assigning codes to words.

Medical coding (with ICD-10-CM and CPT) has largely been a manual process. Note: automated code books that help a coder locate codes is not computer-assisted coding.

Computer-assisted coding using NLP can assign codes from an EHR. This is the primary way in which SNOMED CT codes are assigned.

Note also that ‘coding’ can refer to the development of software, where code refers to representation of the instructions in a computer

Code Sets and Data Sets

Code set refers to a group of associated codes.

Most medical classifications (e.g., ICD-10-CM, SNOMED CT, CPT) are code sets.

Where an entire language or terminology has a set of codes, the set of codes is generally called a lexicon.

Code sets exist for many types of data used in healthcare; and not all are medical code sets. For example, there is a Claim Adjustment Reason Code (CARC) set that is used to describe why changes have been made in reimbursement from what is requested on a claim. Another common code set is the Zip Code set.

Data set is a predefined list of data that need to be collected for a registry or special data set. The data collected may or may not be encoded.

Certified EHR Technology Code Set Requirements

ICD-10-CM or SNOMED CT are the code sets required for problem lists

LOINC is a code set required for documenting lab data

May be used for other observation data such as vital signs and nursing data

RxNorm is a group of code sets required for describing medications, developed by the National Library of Medicine, Veterans Administration, and Food and Drug Administration.

Vendors providing these code sets (and often accompanying clinical decision support for drug alerting) include: Multum, Micromedex, First Databank, Gold Standard Drug Database, and MediSpan

Also included in RxNorm is the VA’s terminology (National Drug File-Reference Terminology [NDF-RT]) used to code clinical drug properties

SNOMED CT

SNOMED CT is a clinical reference terminology

Enables consistent capture of detailed clinical information

It is largely used to code concepts, descriptions, and relationships

Originally developed by the College of American Pathologists as a multi-axial system to describe the etiology, topography, morphology, and function of pathological tissue; later adding other axes to form Systematized Nomenclature of Medicine (SNOMED)

Today, SNOMED CT is an international standard maintained by The International Health Terminology Standards Development Organization, based in Denmark

College of American Pathologists provides SNOMED Terminology Solutions that aid:

Implementing SNOMED CT into systems

Building SNOMED CT subsets

Extending content (guidance on extensions)

Modeling content

Mapping local code sets to SNOMED CT

SNOMED CT Concepts

SNOMED CT has over 344,000 concepts with unique meanings and definitions organized into hierarchies.

A description table contains more than 913,000 English-language and 660,000 Spanish language descriptions or synonyms for flexibility in expressing clinical concepts.

A relationship table contains approximately 1.3 million relationships to enable reliability and consistency of data retrieval.

Example of a SNOMED CT Code

284196006: Burn of skin

246112005 (Severity) = 24484000: Severe

113185004: Structure of skin between fourth and fifth toes

272741003 (Laterality) = 7771000: Left

Other Classifications & Terminologies

ABC Coding Solutions for complementary medicine

International Classification of Functioning, Disability, and Health

MEDCIN is a proprietary vocabulary primarily for physician office use to describe symptoms, history, physical exam results, and other data

MedDRA is a Medical Dictionary for Regulatory Activities

Nursing Terminologies (see next slide)

National Drug Code (NDC) is a universal product identifier for drugs

Unique Device Identification (UDI) helps encode information in medical device adverse event reporting

Universal Medical Device Nomenclature System (UMDNS) is an international standardized nomenclature and coding system relating to unique medical device concepts and definitions

Nursing Terminologies

American Nurses Association recognizes nursing terminology and supports their mapping in SNOMED CT.

Unified Medical Language System (UMLS)

National Library of Medicine (NLM) provides the nation’s principal biomedical bibliographic citation database, MEDLINE/PubMed.

To index its journals for the database, it developed the Medical Subject Headings (MeSH) controlled-vocabulary thesaurus.

NLM has been a strong supporter of facilitating the development of EHRs, distinguishing between:

Semantics - the study of meaning, including ways meaning changes over time

Syntax - the study of patterns of formation of sentences and phrases from words and grammar

For effective use of EHRs, the meaning of terms and their format must work together

UMLS Knowledge Sources

Aid retrieval and integration of biomedical information from bibliographic databases, EHRs, and other sources

These include:

UMLS Metathesaurus links over 100 biomedical vocabularies and classifications

SPECIALIST Lexicon contains syntactic information for terms not in the Metathesaurus

UMLS Semantic Network contains information about concepts and their permissible relationships

Data Architecture

Specific way each individual data element is used in the information system

Data sets

Predefined group of data elements

Data registries and data registry functionality

Registries:

Separate databases existing apart from a provider’s EHR, and often outside of a given provider setting

Examples: cancer registries, immunization registries

Registry Functionality - functions that can be performed on a panel of patients simultaneously, rather than one-by-one. Registry functionality in an EHR enables the EHR to process data from a registry

Big data refers to the massive amount of data available to study

Standardized Data Sets

Uniform Hospital Discharge Data Set (UHDDS)

National Quality Forum (NQF) measures

ORYX (Joint Commission)

Healthcare Effectiveness Data and Information Set (HEDIS)

Continuity of Care Record (CCR) from ASTM International

Many others

Databases

Databases: A data structure for information processing Files of related information

Database Management Systems (DBMS) Software and data structure to support databases Types of databases

Flat file

Hierarchical

Relational

Object-oriented

Multi-dimensional

Hadoop

Data repository

Relational database designed with an open structure not dedicated to software of any one vendor, which collects and organizes data to provide an integrated, multidisciplinary view

Used for online transaction processing (OLTP)

May also be called:

Transactional database

Operational database

Data warehouse

Hierarchical or multi-dimensional database that collects data on which complex analysis is performed

Used for online analytical processing (OLAP)

May also be structured into data marts and operational data stores

Data Repository

Primary means to collect and provide data for transactions performed in an EHR

Requires the following data integration functions:

Data transformation

Data cleansing

Linkage

Data Warehouse

Collection of data that can be reorganized into more suitable formats for ad hoc querying and analytical processing

Data warehouse management system (DWMS) extracts data from a repository or application database and applies data integrity routines to the data so they are suitable for the type of processing to performed in the warehouse:

Data normalization eliminates redundancy

Data denormalization creates intentional redundancies to support multiple uses, often in segments of the data warehouse (i.e., data marts)

Data Warehousing

Data Management

Data Modeling

Entity-relationship

Relational

Object

Data Dictionary

Captures the results of data modeling

Supplies metadata (data about data)

Knowledge Representation

Processing data to support clinical decision making

Ontology is the representation of knowledge in a given domain

Metadata (ISO/IEC 11179 standard

Descriptive metadata

Describes data elements to be captured and processed in an application

Describes data attributes

Provides processing rules

Identifies relationships among data

Provides keys (or links) to a data model

A database (called a data dictionary) usually is used to store this metadata (see next slide)

Structural metadata

Describes how the data for each data element are captured, processed, stored, and displayed. A data model is used for this purpose (see following slides)

Administrative metadata

Metadata programmed into the software to be generated by the software.

Provides information about how and when data were created and used.

Examples:

Audit log of access to data

Decision support rules used to alert EHR users of potential issues with a patient

Data provenance identified where data have originated from and where data may have moved between databases

Data Dictionary and Example Entry

Data Model Examples

Knowledge Representation

Encoding of knowledge on computers to enable systems to reason automatically (“machine learning”). Examples:

Artificial intelligence (such as Amazon suggests other products based on your past buying patterns)

Expert systems (such as clinical protocols developed with data from a very large number of patients)

Ontology is a structural framework, or representation of knowledge, that helps model and create knowledge

Data, Information, and Knowledge Governance

Governance is the establishment of policies and continual monitoring of their proper implementation for managing organization assets to enhance the viability of the organization

Key assets include data

Governance processes ensures quality data and data collection strategies