This document discusses information integrity and data quality in healthcare information systems. It identifies various data sources that populate electronic health records, such as primary sources that come directly from the patient and secondary sources derived from primary sources. The document also discusses data capture methods, data integrity, data quality management characteristics, and common data integrity issues. The overall goal is to emphasize the importance of accurate, timely, and complete data for high quality patient care and business decisions.

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Introduction to Information Systems for
Health Information Technology
Chapter 2: Information Integrity and Data Quality
© 2020 American Health Information Management Association

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Learning Objectives
Identify the various data sources that populate the electronic health record
List and give an example of each of the AHIMA data quality management
model characteristics
Choose the appropriate field type for a data element
Make recommendations to address data quality and data integrity issues

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Introduction
Data must be accurate, timely, and complete

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Data Sources
Primary data sources
• Come directly from source
• Health record
Secondary data sources
• Derived from the primary data sources
• Examples
• Indices
• Registries
• Other databases

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Screen Design
Clear design assist users with data entry
Poor design can confuse users

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Data Capture
Process of recording healthcare-related data in a health record system or
clinical database
Poor data capture
• Poor quality care
• Poor business decisions

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Data Capture—Direct Data Entry, 1
Keyboard, mouse or other device
Hot spot
Unstructured data fields
• Free text
• Textbox
Structured data fields: Guide the user during the data entry process,
limiting what a user can enter into the field

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Data Capture—Direct Data Entry, 2
Field types
• Drop-down menu
• Check box
• Radio buttons
• Numeric field
• Date field
• Time field
• Autonumbering

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Data Capture, 1
Template-based data entry
• Free text and structured data entry
Speech recognition
• Technology that translates speech to text
• Front-end speech recognition
• Back-end speech recognition

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Data Capture, 2
Natural language processing: Technology that converts human language
(structured or unstructured) into data that can be translated then
manipulated by computer systems

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Data Integrity, 1
Data integrity is the extent to which healthcare data are complete,
accurate, consistent, and timely
Data quality is the reliability and effectiveness of data for its intended uses
in operations, decision making, and planning

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Data Integrity, 2
Required fields
Edit check
• Illogical data
• Format

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Data Quality Management, 1
“The business processes that ensure the integrity of an organization’s data
during collection, application (including aggregation), warehousing, and
analysis” (Davoudi et al.)

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Data Quality Management, 2
Data accessibility
Data accuracy
• Quantitative analysis
• Qualitative analysis
Data comprehensiveness
• Physician advisor
• Peer review

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Data Quality Management, 3
Data consistency
Data currency
Data definition
Data granularity

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Data Quality Management, 4
Data precision
Data relevancy
Data timeliness

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Data Integrity Issues, 1
Data cleansing: Process of checking internal consistency and duplication as
well as identifying outliers and missing data
Data mapping: Connections between two systems

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Data Integrity Issues, 2
Documentation integrity errors
• Patient identification
• Authorship
• Dictation errors without editing
• Copying and pasting
• Amendments to the health record
• Version control

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Data Integrity Issues, 3
Data quality measure
• Mechanism to assign a quantitative figure to quality of care
• Quantitative figure compared to a criterion

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References
Davoudi, S., J. A. Dooling, B. Glondys, T. D. Jones, L. Kadlec, S. M.
Overgaard, K. Ruben, and A. Wendicke. 2015. Data Quality Management
Model (2015 update).
http://bok.ahima.org/doc?oid=107773#.WjxlEORy5jo.