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Data Utilization HCSS
Version 2010A1

Data Utilization


Improving the delivery of health care and the patient outcomes related to that care has always been a goal for health care providers. The last several decades have seen exciting advances in the methods employed toward this end. Through the study of work conducted in the industrial community and improvement methods with demonstrated effectiveness, tools and techniques of quality improvement have been applied to health care settings. However, the concept of collecting health care data to study care delivery and outcomes is not really new. Historical examples include the work of Florence Nightingale and Dr. Ernest Codman. Miss Nightingale is generally remembered as the founder of modern nursing, however her groundbreaking-work involving the collection of standardized data, the use of statistical analysis and graphical display is equally important. It was during the Crimean war that she invented the polar-area diagram to display various causes of death among soldiers as proportions of a wedge in a circle. Each wedge represented a month, thereby providing comparisons over time. Ernest Codman, a surgeon, was another pioneer in the use of data to track outcomes in healthcare. Early in the twentieth century he developed a system for collecting a set of standardized data on his surgical patients that included diagnosis, treatment, hospital complications and the result one year later. Called the End Result Idea, many of Codman’s principles are now captured in current outcomes measurement. Both of these pioneers used data and scientific methods to evaluate performance and improve the quality of care. While advances in the science and technology of quality improvement have introduced additional tools and sophisticated types of data analysis, it is important to remember that measurement need not be difficult and complex to be effective, as illustrated by these historical examples.

For many readers of this guide, the methods and terminology associated with the use of performance measures, data collection, analysis and interpretation are very familiar and no introduction is needed. For others, this material may be less familiar. This section provides a brief review of some basic data analysis options as well as references for publications that address this topic in more detail.

The greater the understanding of the measurement process, the more effectively opportunities for improvement can be identified and changes implemented. Deciding what to measure, how to measure and how to analyze your data are important keys to success. The performance measures in this booklet are quantitative tools (for example, rate ratio, index, percentage) developed to provide an indication of your organization’s performance on a selected process or outcome related to a specific disease or topic. The individual measure data provide the critical pieces that will be used in various analyses to identify patterns, trends, and opportunities for improvement, and to document performance and results. By using the standardized data definitions and calculation formulas (flowcharts) provided for each measure in this disease specific set, performance within your organization can be tracked over time. In addition to these measures your organization may find it important to examine other processes and outcomes. The performance measurement tools and analysis approaches reviewed in this section may assist your organization to understand variations in processes, to identify improvement opportunities, and to document and sustain improved performance.


Data are the critical components used for analysis. As such, a few words about the data themselves are warranted. Data include facts, observations, and measurements. As collected and recorded, they are often referred to as “raw data”. Through the application of appropriate statistical techniques and analysis tools, data can be interpreted and translated into information. Because analyses and ultimately conclusions are driven by data, the quality of the data is critical. The old adage “garbage in – garbage out” definitely applies here. Time spent up front to ensure that data are accurate, complete and consistent will support the integrity of the results. Data definitions and suggested sources have been provided for each measure in this set. It will be important to apply the definitions exactly as written and identify a consistent source for each data element within your organization’s documentation system. In some cases it may be necessary or more efficient to add a data element or a place to document observations/measurements to existing forms. These steps will help to streamline the collection process, minimize missing entries and ensure the credibility of your data.

There are a variety of tools used to facilitate the performance improvement process and analysis of performance measure data. Some are designed to support activities conducted by a team as part of a systematic approach to quality improvement. Many approaches are available but they share the use of methodology designed to systematically guide people through the stages of an improvement initiative. One example of a well known method is the Plan-Do-Study-Act (PDSA) cycle developed by Walter Shewart (1891-1967). Examples of performance measurement tools designed for group processes include brainstorming and multi-voting. Use of an organized approach to performance measurement is one of the expectations for health care staffing services certification. For an in-depth review of performance measurement methods and analysis tools/techniques several references are included at the end of this section. The overview of some of the common tools used for data analysis and display provided here may assist participants beginning the process of translating data into information. The tools described below are divided into two categories; those for understanding root causes for problems and those for analyzing/displaying data. For additional information on the methods and tools presented here, as well as others, see Tools for Performance Measurement in Health Care: A Quick Reference Guide and other suggested references for additional reading at the end of this section.

Root Cause Analysis (Identifying relationships and possible causes):
These tools are designed to assist your organization in examining the relationship of various factors to the targeted performance as well as identifying possible causes for unsatisfactory performance/outcomes.

Root Cause Analysis Tools (pdf)

Suggested References for Additional Reading

Framework for Improving Performance: From Principle to Practice. Joint Commission on Accreditation of Healthcare Organizations, Oakbrook Terrace, Il. 1994.

Managing Performance Measurement Data in Health Care. Joint Commission Resources, Inc, Oakbrook Terrace, Il. Joint Commission on Accreditation of Healthcare Organizations, 2001.

Measuring Quality Improvement in Healthcare: a guide to statistical process control applications. In: Quality Resources. Carey RG, Lloyd RC. ASQ Press, Milwaukee, WI, 2001

Overcoming Performance Measurement Challenges For Hospitals. Joint Commission Resources, Oakbrook Terrace, Il. 2005.

The Team Handbook. PR Scholtes, BL Joiner, BJ Streibel. Oriel, Madison, WI, 2003.

Tools for Performance Measurement in Health Care: A Quick Reference Guide. Joint Commission on Accreditation of Healthcare Organizations, Oakbrook Terrace, Il. 2002.

Using Performance Improvement Tools in HEALTH CARE Settings. Joint Commission on Accreditation of Healthcare Organizations, Oakbrook Terrace, Il. 2006.


Florence Nightingale. Agnes Scott College. Available at: http://www.agnesscott.edu/lriddle/women/nitegale.htm. Accessed December 15, 2003.

Tools for Performance Measurement in Health Care: A Quick Reference Guide. Joint Commission on Accreditation of Healthcare Organizations, Oakbrook Terrace, Il. 2002

Related Topics

Related Topics
a. Table of Contents for HCSS Manual

Data Utilization HCSS
Specifications Manual for Joint Commission National Quality Core Measures (2010A1)
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