Uacds data elements

Chapter 4: Healthcare Data Sets and Standards

Kathy Giannangelo, MA, RHIA, CCS, CPHIMS, FAHIMA

Learning Objectives

Describe the purpose of healthcare data sets and standards

Explain the importance of healthcare data sets and standards

Identify the common health information standardized data sets

Explain the need for electronic data interchange standards

Explain the healthcare data needs in an electronic environment

Discuss how data standards are developed

Identify well-known standards that support electronic health record (EHR) systems

Discuss how data standards support the development of EHR systems

Identify prominent health informatics standards development organizations (SDOs)

Recognize the impact of the Health Insurance Portability and Accountability Act of 1996 (HIPAA) on the development of health informatics standards

Explain the relationship of core data elements to healthcare informatics standards in electronic environments

Describe the role of government agencies, such as the ONC, in healthcare informatics standards development, testing, coordination, and harmonization

Key Terms

American College of Radiology and the National Electrical Manufacturers Association (ACR-NEMA)

American National Standards Institute (ANSI)

ASTM International

Clinical Document Architecture (CDA)

Common Formats Version 1.1

Continuity of Care Document (CCD)

Continuity of Care Record (CCR)

Core data elements

Core measure

Data dictionary

Data element

Data Elements for Emergency Department Systems (DEEDS) 1.0

Data set

Data standard

Department of Health and Human Services (HHS)

Digital Imaging and Communication in Medicine (DICOM)

Electronic data interchange (EDI)

Extensible markup language (XML)

Health Information Technology Expert Panel (HITEP)

Healthcare Effectiveness Data and Information Set (HEDIS)

Healthcare informatics standards

HIT Policy Committee (HITPC)

HIT Standards Committee (HITSC)

Hospital discharge abstract system

Inpatient

Institute of Electrical and Electronics Engineers (IEEE)

Metadata

Minimum Data Set (MDS) Version 3.0

National Center for Health Statistics (NCHS)

National Committee on Vital and Health Statistics (NCVHS)

National Institute for Standards and Technology (NIST)

Nationwide Health Information Network (NHIN)

Office of the National Coordinator of Health Information Technology (ONC)

ORYX initiative

Outcomes and Assessment Information Set (OASIS-C)

Outpatients

Prospective payment system (PPS)

Quality Data Model (QDM)

Standard

Standards and Interoperability (S&I) Framework

Standards development organizations (SDOs)

Structure and content standards

Transaction standards

Uniform Ambulatory Care Data Set (UACDS)

Uniform Hospital Discharge Data Set (UHDDS)

Introduction

Data and information pertaining to individuals who use healthcare services are collected in virtually every setting where healthcare is delivered. As noted in other chapters, data represent basic facts and measurements. In healthcare, these facts usually describe specific characteristics of individual patients. The term data is plural. Although the singular form is datum, the term that is frequently used to describe a single fact or measurement is data element. For example, age, gender, insurance company, and blood pressure are all data elements concerning a patient. The term information refers to data that have been collected, combined, analyzed, interpreted, and/or converted into a form that can be used for specific purposes. In other words, data represent facts; information represents meaning.

In healthcare settings, data are stored in the individual’s health record whether that record is in paper or electronic format. The numerous data elements in the health record are then combined, analyzed, and interpreted by the patient’s physician and other clinicians. For example, test results are combined with the physician’s observations and the patient’s description of his or her symptoms to form information about the disease or condition that is affecting the patient. Physicians use both data and information to diagnose diseases, develop treatment plans, assess the effectiveness of care, and determine the patient’s prognosis.

Data about patients can be extracted from individual health records and combined as aggregate data. Aggregate data are used to develop information about groups of patients. For example, data about all of the patients who suffered an acute myocardial infarction during a specific time period could be collected in a database. From the aggregate data, it would be possible to identify common characteristics that might predict the course of the disease or provide information about the most effective way to treat it. Ultimately, research using aggregated data might be used for disease prevention. For example, researchers identified the link between smoking and lung cancer by analyzing aggregate data about patients with a diagnosis of lung cancer; smoking cessation programs grew from the identification of the causal effect of smoking on lung cancer and a variety of other conditions.

History of Healthcare Data Collection

The first known efforts to collect and use healthcare data to produce meaningful statistical profiles date back to the seventeenth century. In the early 1600s, Captain John Graunt gathered data on the common causes of death in London. He called his study the Bills of Mortality. However, few systematic efforts were undertaken to collect statistical data about the incidence and prevalence of disease until the mid-20th century, when technological developments made it possible to collect and analyze large amounts of healthcare data.

Modern efforts at standardizing healthcare data began in the 1960s. At that time, healthcare facilities began to use computers to process larger amounts of data than could be handled manually. The goal was to make comparisons among data from multiple providers. It soon became evident that healthcare organizations needed to use standardized, uniform data definitions in order to arrive at meaningful data comparisons.

The first data standardization efforts focused generally on hospitals and specifically on hospital discharge data. The intent of the efforts was to standardize definitions of key data elements commonly collected in hospitals. Discharge data were collected in hospital discharge abstract systems. These systems used databases compiled from aggregate data on all the patients discharged from a particular facility. The need to compare uniform discharge data from one hospital to the next led to the development of data sets or lists of recommended data elements with uniform definitions.

Today, hospitals and other healthcare organizations collect more data and develop more information than ever before. Moreover, data and information from the health records of individual patients are used for more purposes than ever before. The demand for information is coming from users within the organizations as well as from external users such as third-party payers, government agencies, accreditation organizations, and others. The extensive use of information within and across organizational boundaries demands standards that promote interoperable electronic interchange of data and information. Information and informatics standards are critical in the migration to electronic health records (EHRs), as described in chapter 16.

Data Sets in the Electronic Environment

The data sets originally developed to support uniform data collection are inadequate for an electronic environment, and many public and private organizations have been actively engaged in the process of developing healthcare informatics standards to support EHR development and information interchange. Standards development is a dynamic process as key players in the standards development community negotiate, refine, and revise standards. The critical importance of healthcare information and informatics standards has been recognized in recent federal initiatives including those of the Office of the National Coordinator of Health Information Technology (ONC).

According to Toward a National Health Information Infrastructure by the National Committee on Vital and Health Statistics (NCVHS), “if information in multiple locations is to be searched, shared, and synthesized when needed, we will need agreed-upon information guardians that can exchange data with each other … we will need equitable rules of data exchange so that competitors (within or between healthcare provider systems, health information management companies, or health web services) will be willing to connect and share data” (NCVHS 2000).

Developing Standardized Data Sets and Standards

This chapter describes the initial efforts at developing standardized data sets for use in different types of healthcare settings, including acute care, ambulatory care, long-term care, and home care. It explores the recent national initiatives related to interoperability and connectivity of healthcare information systems that will support widespread implementation of EHRs. It also explains the work of developing a Nationwide Health Information Network (NHIN) that will improve patient care, increase safety, and assist clinical and administrative decision making.

It is essential that the HIM professional understand the purpose, content, and importance of healthcare data sets and standards. HIM professionals work with many of these data sets and data standards on the job. In the years to come, the roles of the HIM professional will be influenced and likely change as standards continue to develop, be adopted, and ultimately implemented.

Theory into Practice

In a large Midwestern health system, the director of health information services leads the system’s clinical data standards committee. The committee recently decided to develop a data dictionary as a first step toward implementing an EHR system. To assist in this effort, the group used the following guidelines (AHIMA e-HIM Workgroup on EHR Data Content 2006):