Data Quality Management Model (Updated)
Editor's Note: This practice brief supersedes the March 1998 "Checklist to Assess Data Quality Management Efforts" and the June 1998 "Data Quality Management Model" practice briefs.
Healthcare leaders face many challenges, from the ICD-10-CM/PCS transition to achieving meaningful use, launching accountable care organizations (ACOs) and value-based purchasing programs, assuring the sustainability of health information exchanges (HIEs), and maintaining compliance with multiple health data reporting and clinical documentation requirements—among others. These initiatives impacting the health information management (HIM) and healthcare industries have a common theme: data.
As electronic health record (EHR) systems have become more widely implemented in all healthcare settings, these systems have developed and employed various methods of supporting documentation for electronic records. Complaints and concerns are often voiced—whether via blogs, online newsletters, or listservs—regarding the integrity, reliability, and compliance capabilities of automated documentation processes. Several articles in the Journal of AHIMA establish guidelines for preventing fraud in EHR systems. Documentation practices are within the domain of the HIM profession, for both paper and electronic records.1
As a result, the need for more rigorous data quality governance, stewardship, management, and measurement is greater than ever.
This practice brief will use the following definitions:
Data Quality Management: The business processes that ensure the integrity of an organization's data during collection, application (including aggregation), warehousing, and analysis.2 While the healthcare industry still has quite a journey ahead in order to reach the robust goal of national healthcare data standards, the following initiatives are a step in the right direction for data exchange and interoperability:
Data Quality Measurement: A quality measure is a mechanism to assign a quantity to quality of care by comparison to a criterion. Quality measurements typically focus on structures or processes of care that have a demonstrated relationship to positive health outcomes and are under the control of the healthcare system.3 This is evidenced by the many initiatives to capture quality/performance measurement data, including:
These data sets will be used within organizations for continuous quality improvement efforts and to improve outcomes. They draw on data as raw material for research and comparing providers and institutions with one another.
Payment reform and quality measure reporting initiatives increase a healthcare organization's data needs for determining achievement of program goals, as well as identifying areas in need of improvement. The introduction of new classification and terminology systems—with their increased specificity and granularity—reinforce the importance of consistency, completeness, and accuracy as key characteristics of data quality.4 The implementation of ICD-10 CM/PCS will impact anyone using diagnosis or inpatient procedure codes, which are pervasive throughout reimbursement systems, quality reporting, healthcare research and epidemiology, and public health reporting. SNOMED CT, RxNorm, and LOINC terminologies have detailed levels for a variety of healthcare needs, ranging from laboratory to pharmacy, and require awareness of the underlying quality from the data elements.
Healthcare data serves many purposes across many settings, primarily directed towards patient care. The industry is also moving towards an increased focus on ensuring that collected data is available for many other purposes. The use of new technologies such as telemedicine, remote monitoring, and mobile devices is also changing the nature of access to care and the manner in which patients and their families interact with caregivers. The rates of EHR adoption and development of HIEs continue to rise, which brings attention to assuring the integrity of the data regardless of the practice setting, collection method, or system used to capture, store, and transmit data across the healthcare continuum of care.
The main outcome of data quality management (DQM) is knowledge regarding the quality of healthcare data and its fitness for applicable use in the intended purposes. DQM functions involve continuous quality improvement for data quality throughout the enterprise (all healthcare settings) and include data application, collection, analysis, and warehousing. DQM skills and roles are not new to HIM professionals. As use of EHRs becomes widespread, however, data are shared and repurposed in new and innovative ways, thus making data quality more important than ever.
Data quality begins when EHR applications are planned. For example, data dictionaries for applications should utilize standards for definitions and acceptable values whenever possible. For additional information on this topic, please refer to the practice brief "Managing a Data Dictionary."5
The quality of collected data can be affected by both the software—in the form of value labels or other constraints around data entry—and the data entry mechanism whether it be automated or manual. Automated data entry originates from various sources, such as clinical lab machines and vital sign tools like blood pressure cuffs. All automated tools must be checked regularly to ensure appropriate operation. Likewise, any staff entering data manually should be trained to enter the data correctly and monitored for quality assurance.
For example, are measurements recorded in English or metric intervals? Does the organization use military time? What is the process if the system cannot accept what the person believes is the correct information?
Meaningful data analysis must be built upon high-quality data. Provided that underlying data is correct, the analysis must use data in the correct context. For example, many organizations do not collect external cause data if it is not required. Gunshot wounds would require external cause data, whereas slipping on a rug would not. Developing an analysis around external causes and representing it as complete would be misleading in many facilities. Additionally, the copy capabilities available as a result of electronic health data are likely to proliferate as EHR utilization expands. Readers can refer to AHIMA's Copy Functionality Toolkit for more information on this topic.6 Finally, with many terabytes of data generated by EHRs, the quality of the data in warehouses will be paramount. The following are just some of the determinations that need to be addressed to ensure a high-quality data warehouse:
Consequently, the healthcare industry needs data governance programs to help manage the growing amount of electronic data.
Data governance is the high-level, corporate, or enterprise policies and strategies that define the purpose for collecting data, the ownership of data, and the intended use of data. Accountability and responsibility flow from data governance, and the data governance plan is the framework for overall organizational approach to data governance.7
Information Governance and Stewardship
Information governance provides a foundation for the other
The DQM model was developed to illustrate the different data quality challenges. The table "Data Quality Management Model" includes a graphic of the DQM domains as they relate to the characteristics of data integrity, and "Appendix A" includes examples of each characteristic within each domain. The model is generic and adaptable to any care setting and for any application. It is a tool or a model for HIM professionals to transition into enterprise-wide DQM roles.
Assessing Data Quality Management Efforts
Traditionally, healthcare data quality practices were coordinated by HIM professionals using paper records and department-based systems. These practices have evolved and now utilize data elements, electronic searches, comparative and shared databases, data repositories, and continuous quality improvement. As custodians of health records, HIM professionals have historically performed warehousing functions such as purging, indexing, and editing data on all types of media: paper, images, optical disk, computer disk, microfilm, and CD-ROM. In addition, HIM professionals are experts in collecting and classifying data to support a variety of needs. Some examples include severity of illness, meaningful use, pay for performance, data mapping, and registries. Further, HIM professionals have encouraged and fostered the use of data by ensuring its timely availability, coordinating its collection, and analyzing and reporting collected data. To support these efforts, "Checklist to Assess Data Quality Management Efforts" on page 67 outlines basic tenets in data quality management for healthcare professionals to follow.
With AHIMA members fulfilling a wide variety of roles within the healthcare industry, HIM professionals are expanding their responsibilities in data governance and stewardship. Leadership, management skills, and IT knowledge are all required for effective expansion into these areas.
Roles such as clinical data manager, terminology asset manager, and health data analyst positions will continue to evolve into opportunities for those HIM professionals ready to upgrade their expertise to keep pace with changing practice.
Direct link between HIM and Patient Outcomes
The quality of healthcare across the continuum rests on the integrity, reliability, and accuracy of health information. The various methods of documentation in electronic health records can be unreliable for patient care if documentation guidelines and best practices are not followed. HIM professionals have intimate knowledge of these documentation guidelines and are invaluable resources when it comes to helping providers determine how they will create templates, formats, notes, and other data elements in the EHR.
For example, a study of 60 randomly selected patient records with 1,891 notes from the Veterans Health Administration's computerized patient record system found that 84 percent of notes contained at least one documentation error, with an average of 7.8 documentation mistakes per patient.10 Data integrity and quality are important, but new technology can—and does—produce new, real challenges.
EHRs consist of both structured and unstructured data, leaving a variety of opportunities for error. With recent government initiatives, such as meaningful use, there is increasing pressure for healthcare entities and providers to attest to quality healthcare data. In addition, the data should be trusted to support clinical, financial, and administrative decisions.
Data quality is dependent upon secure housing as well as efficient and effective accessibility when needed. These critical factors impact the overall data quality process that enables continuous improvements toward quality patient care.
Integrity of health information is an obligation of HIM. Findings from the HIM Core Model 12 work identified several near-future and future roles (including chief knowledge officer and health record advocate) for health information managers. HIM professionals must assume a leadership role in transforming these functions. Now is the time to analyze and visualize documented and undocumented intra- and interdepartmental HIM functions to understand the current and future state of the HIM department while ensuring HIM best practices and standards are consistently maintained.
Prepared by (Updated)
June Bronnert, RHIA, CCS, CCS-P
Jan-Marie Barsophy, RHIT
Original Data Quality Management Model and Checklist to Assess Data Quality Management Efforts: Prepared by
Bonnie Cassidy, MPA, RRA, Chair
Original Data Quality Management Model: Acknowledgements
Jennifer Carpenter, RRA
Checklist to Assess Data Quality Management Efforts
Use the checklist below to assess overall data quality management efforts within an organization or for an application.
The purpose for data are collection.
The process by which data elements are accumulated.
Warehousing and Interoperability
Processes and systems used to archive data and data journals.
The process of translating data into information that can be utilized in an application.