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Data Quality Management Model” Practice Briefs. Healthcare leaders face many challenges today, including payment reform, the transition to ICD- 1. CM/PCS, health information exchange, and value- based purchasing programs. The common thread in these challenges is ensuring that data are a trusted source that can be easily accessed, shared, and exchanged. As electronic health record (EHR) systems have become more widely implemented in all healthcare settings, the need for information governance (IG) is greater than ever. To meet these advanced challenges, rigorous information and data governance, stewardship, management, and measurement is fundamental. The AHIMA Information Governance Principles for Healthcare (IGPHC).
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For this purpose, data quality management and data quality measurement are defined in the following sections. Data Quality Management Definition Data quality management is defined as the business processes that ensure the integrity of an organization’s data during collection, application (including aggregation), warehousing, and analysis. While the healthcare industry still has quite a journey ahead in order to reach the robust goal of national healthcare data standards, the following are a few sample initiatives that are a step in the right direction for data exchange and interoperability: C- CDA: Consolidated Clinical Document Architecture DEEDS: Data Elements for Emergency Department Systems UHDDS: Uniform Hospital Discharge Data Set MDS: Minimum Data Set (long- term care)ICD- 1. CM/PCS: International Classification of Diseases, Clinical Modification/Procedure Coding Systems SNOMED CT: Systemized Nomenclature of Medicine—Clinical Terms LOINC: Logical Observation Identifiers Names and Codes Rx. Norm: Standardized nomenclature for clinical drugs. DSM- 5: Diagnostic and Statistical Manual of Mental Disorders Data Quality Measurement Definition A quality measure is a mechanism to assign a quantitative figure 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.
This is evidenced by the many initiatives to capture quality/performance measurement data, including: The Joint Commission Core Measure Sets. Outcome and Assessment Information Set (OASIS) for home healthcare. National Quality Forum (NQF)National Committee for Quality Assurance (NCQA) The Healthcare Effectiveness Data and Information Set (HEDIS)“Meaningful Use” EHR Incentive Program (defined core and menu sets)Establishing Information Value through Data Quality Management.
Information is a fundamental resource that must be safeguarded, verified, and appropriately interpreted in healthcare to ensure the provision of safe, effective, and high quality care. With the current incentives for the adoption of health information technology, there is a need to ensure that the collected information is trustworthy. There must be integrity of all information generated or used in a healthcare organization, regardless of its source. All data must be accurate, timely, relevant, valid, and complete to ensure the reliability of the information. In healthcare, data are ubiquitous.
Data elements will be used within organizations for continuous quality development efforts and to strategically advance patient care, in addition to benchmarking population health initiatives. Within a healthcare organization, data elements are a measure by which progress is measured and the future is calculated. Indeed, the central initiatives of payment reform and quality measure reporting intensify an organization’s data needs. 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. The implementation of ICD- 1. CM/PCS impacts anyone using diagnosis or inpatient procedure codes, which are pervasive throughout reimbursement systems, healthcare research and epidemiology, and public health reporting. SNOMED CT, Rx. Norm, and LOINC terminologies have detailed levels for a variety of healthcare needs, ranging from laboratory to pharmacy, and require a ready awareness of the underlying quality of the derived data elements.
Healthcare data serves countless purposes across numerous settings. The primary use of data continues to be the support of bedside care. New technologies such as telemedicine, remote monitoring, and mobile devices are also changing the nature of access to care and the manner in which patients and their families are interacting with caregivers.
The rates of EHR adoption and development of health information exchanges (HIEs) continue to rise, which brings attention to ensuring the integrity of the data regardless of the practice setting, collection method, or system used to capture, store, and transmit data across the 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 all of its intended purposes. DQM functions involve continuous quality improvement for data quality throughout the enterprise (all data in all healthcare settings) and include data application, collection, analysis, and warehousing.
DQM skills and roles are not new to HIM professionals. As use of health information technology becomes widespread, however, data are shared and repurposed in new and innovative ways, thus making data quality more important than ever.
Data quality protocols must be implemented in the early stages of technological application planning. 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 entitled “Managing a Data Dictionary.”3. The quality of collected data can be affected by software design and the mechanisms for data population (automated or manual entry).
Automated population of data originates from various sources—systems such as clinical lab machines and vital sign tools like blood pressure cuffs. All automated sources must be checked regularly to ensure appropriate calibration. Likewise, any staff entering data manually should be trained to enter the data correctly and monitored for quality assurance such as registrars entering patient demographic data at the point of care. Meaningful data analysis must be built upon high quality data.
Provided that underlying data are correct, the analysis must use data in the correct context, and inferences must be limited to a comparable population. 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. Finally, with many terabytes of data generated by health information technology applications, 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: Static data (date of birth, once entered correctly, should not change)Dynamic data (patient temperature may fluctuate throughout the day)Maintenance scheduling (when and how data updates)Versioning (DRGs and EHR systems change over time; it is important to know which DRG grouper or EHR version was used) Consequently, the healthcare industry needs information and data governance programs to help manage the growing amount of electronic data and information. Furthermore, the collection of meaningful metrics such as offshore data transmission requires governance and procedural compliance.
Information Governance and Data Stewardship. Many healthcare professionals view data governance (DG) and information governance (IG) as the same concept. Sometimes the terms are used interchangeably. But DG and IG are not the same. There are distinctions between them in both application and scope. Data represents the facts or measurements that, when put into context, become information. Information, therefore, is data in context.
Information governance cannot occur without data governance—the two are inextricably linked. Information governance provides the enterprise- wide structure and framework that is essential to support data governance. Despite the diversity in the healthcare industry, information across the various types of organizations can be governed using the eight aforementioned common principles of accountability, transparency, integrity, protection, compliance, availability, retention, and disposition. These IGPHC principles can be adopted in any organization within the healthcare industry regardless of size or type and are grounded in the following data quality management functions and characteristics of data quality (which are discussed below). Information governance provides the foundation for the other data- driven functions in AHIMA’s HIM Core Model by providing parameters based on organizational and compliance policies, processes, decision rights, and responsibilities. Governance functions and stewardship ensure that the use and management of health information is compliant with jurisdictional law, regulations, standards, and organizational policies. To ensure data quality management, data should employ security controls to provide protection for data.