Introduction to Healthcare Informatics, Second Edition

Chapter 5:

Data and Information

Objectives

Describe the types of data, as well as the relationship between data and information

Practice using health information standards

Create a data map for a given scenario

Compare and contrast the methods of data collection

Demonstrate the use of data quality practices

Design appropriate data presentations

Support and evaluate EHRs, HIEs, and RECs

Data Basics

Singular or plural data

Numerical or alphabetical

Categorical or discrete

Nominal or ordinal

Continuous

Ratio or interval

Questions to Ask about Data or Information

For which purpose or use was the data originally collected?

Are there any data standards related to the data?

How was the data collected?

What is the quality of the data?

How will the data be analyzed to produce information?

How can the data or information be presented so that it is meaningful to users?

Data Format

Raw data

unformatted combination of text, symbols, and words

E.g. “Green, 25”

Formatted database field

Last name = Green

Age in Years = 25

Data Standard Development

Ad hoc standards

De facto standards

Government mandate

Consensus standards

LOINC

UHDDS

Microsoft Windows

ICD

Caitlin Wilson (CW) – Not sure what the examples on the right are supposed to line up with. Can these be removed or put in a different location?

ISO Standards Development Process

Proposal

Preparatory

Committee

Enquiry

Approval

Publication

US Health Information Technology Standards

Health IT Policy Committee (sets overall health IT policy)

Health IT Standards Committee (recommends standards to meet health IT policy)

Health Insurance Portability and Accountability Act (informs policy and standards)

Data Set Standards

Vital statistics

London Bills of Mortality

Immunization registries

Health Plan Employer Data and Information Set

Continuity of Care Document (CCD) (ASTM Standard 2005)

Classification, Code Set, and Terminology Standards

Classification

Code set

Terminology

Concepts

Data Mapping

Definition

Creation of a map

Purpose

Source

Target

Forward maps

Reverse maps

Relationship

Equivalence

Transaction Standards

Data-interchange standards

Transaction set

Types of transaction standards

Accredited Standards Committee X12 5010

National Committee on Prescription Drug Programs

Health Level 7

Electronic Health Record Standards and Certification

Certification Commission on Health Information Technology

Health Information Technology for Economic and Clinical Health Act mandated

ONC-Authorized Testing and Certification Bodies

National Institute for Standards and Technology

Data Collection Considerations

Why is the organization entering these data?

What data need to be entered?

Who will enter the data?

What will be the most efficient method of data entry for each type of user?

Under what circumstances will users enter data?

What use does the organization expect to make of the data?

Data Entry

Structured data entry

Easily computable

Controlled values

Inflexible

Potential loss of meaning

Unstructured data entry

Expressivity

Post-hoc text processing

Natural language processing

Narrative-text string

Concept identification

Negation and temporal issues

Data Measurement

Nominal data

Ordinal data

Interval data

Ratio data

Management and Governance

Data management

Data governance

Discover

Design

Enable

Maintain

Archive or retire

Data Quality

Data Quality Management Model

Application

Collection

Warehousing

Analysis

Data Quality Characteristics

Accuracy

Accessibility

Comprehensiveness

Consistency

Currency

Definition

Granularity

Precision

Relevance

Timeliness

DATA QUALITY CHARACTERISTIC AND DEFINITION	HEALTHCARE EXAMPLE
Accuracy—The extent to which the data are free of identifiable errors	The data element gender is completed for all patients and a random check of 500 records performed annually revealed only one demographic data element in conflict with the documentation.
Accessibility—Data items that are easily obtainable and legal to access with strong protections and controls built into the process	All persons with access to the EHR have the ability to search the master patient index and the search function is designed such that the wrong patient is rarely accessed.
Comprehensiveness—All required data items are included; ensures that the entire scope of the data is collected with intentional limitations documented	Providers may decide that recording external cause data is not useful. If so, their data dictionary for the diagnoses data elements would need to include this as an intentional limitation.
Consistency—The extent to which the healthcare data are reliable and the same across applications	Within the EHR data such as allergies must be consistently displayed within different applications or screens to prevent confusion.
Currency—The extent to which data are up-to-date; a datum value is up-to-date if it is current for a specific point in time, and it is outdated if it was current at a preceding time but incorrect at a later time	Patient age is generally current when the care is delivered. If age is not reentered the software needs to be have functionality to automatically update the age when appropriate.

Data Quality Characteristics

DATA QUALITY CHARACTERISTIC AND DEFINITION	HEALTHCARE EXAMPLE
Definition—The specific meaning of a healthcare-related data element	Address as a data element label can mean the street address or it can mean the entire address to include city, state, and zip code.
Granularity—The level of detail at which the attributes and values of healthcare data are defined	Adult weights are usually only recorded in pounds, possibly tenths of a pound. Newborn weights must be recorded in terms of ounces for accuracy.
Precision—Data values should be strictly stated to support the purpose	Diagnosis Related Group values are carried out to four digits behind the decimal. It would be inaccurate to have the system only use two digits behind the decimal.
Relevance—The extent to which healthcare-related data are useful for the purposes for which they were collected	Recording a primary diagnosis for hospital inpatients would be irrelevant because coding guidelines mandate collection of the principal diagnosis, which can be entirely different.
Timeliness—Concept of data quality that involves whether the data is up-to-date and available within a useful time frame; timeliness is determined by the manner and context in which the data are being used	It would be inappropriate for blood pressure readings from intensive-care unit (ICU) monitors to only be updated each hour.

Data Quality Characteristics

Data Quality Assessment and Management Process

Who are the data consumers?

What are the needs of the data consumers?

What are the required features and quality characteristics?

How well do our current information products meet the needs and requirements?

Where are the gaps and how important are they?

Data Consumer Needs Assessment Tool

Data Analysis

Understanding the data

Data dictionary

Methods of collection

Cleaning the data

Identify errors

Descriptive statistics

Categorical data

Use of crosstabs

Determine correct values or impute

If uncorrectable delete the record

Data Cleaning—Continuous Data

Data Cleaning—Categorical Data

Data Cleaning—Crosstab

Data Analysis

Analyzing the data

Goals and objectives

Level of analysis of the study

Limitations of the data

Tools available

Analysis needs

Use of the results

Data Analysis Evaluation

Review new data elements from data set merges

Review new derived or computed data elements

Verification of analysis timeframe and any data subject to change over time

Are statistical analyses correct?

Do counts, sums, averages make sense?

Imputation

Method for dealing with missing data

Remove the record from data analysis

Coded missing data

E.g. -999 for missing data

Imputation

Replace missing values with median of data set or mean of points near the missing data

Clear Data Presentation

Words—concise and accurate

Tables

The table should be a logical unit that is self-explanatory and stands on its own

The source of the data in the table should be specified

Headings for rows and columns should be understandable

Blank cells should contain a zero or a dash

Formatting for headings and cell contents should be consistent so that the eye is not confused (Horton 2013, 508)

Caitlin Wilson (CW) – Update reference here and on remaining slides

Charts and Graphs

Guiding Principles

Distortion—The representation of numbers or percentages should be proportional to the quantities represented.

Proportion and scale—Graphs should emphasize the horizontal and be greater in length than height. A general rule is that the y-axis (height) be three-quarters the x-axis (length) of the graph.

Abbreviations—Any abbreviations used should be spelled out for clarity.

Color —Color should be used as appropriate to the use of the graph. If the chart is going to be printed will it be printed in black and white or color?

Text—The font and use of capitalization needs to be considered carefully. The use of all capital letters can sometimes be difficult to read (Horton 2013, 510)

Data Visualization

See shape of data

Find skewed data and outliers

Explore trends

See missing data

Summary

Data is used to create information

Both are essential to an effective health care industry

Health informatics professionals need to be able to perform accurate data analyses

AB120015_Ch05.pptx

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