Healthcare is focused on catching up with other industries when it comes to data management maturity and building reliable health record solutions. Many other industries, such as finance and insurance, invested in technology and data management practices decades before healthcare. Fortunately, with the help of government agencies and industry demand, healthcare has adapted and increased investment in information technology in recent years. It’s now necessary to have an Electronic Health Record (EHR) system at every hospital, ambulatory clinic and even dentist’s offices. Having EHR systems implemented provides the ability to report data from them. This data can be used for improving quality of care, treating patients proactively, and even preventing certain diseases. Enabled by different systems and technologies adapted at hospital organizations, clinical data captured from these systems has become a valuable information asset. This is true only if you and the organization trust the data. Hospital organizations spend a lot of time and money validating data quality for various operational reasons including patient safety risk. When organizations are unable to trust patient data, it can result in many issues such as skewed reports and analysis and ultimately may cause harm to patients. In data reporting, people often refer to two terms: Data Integrity and Data Quality. These two terms are often used interchangeably but there are important distinctions. While data quality refers to whether data is reliable and accurate, data integrity goes beyond data quality. Data integrity requires that data be complete, accurate, consistent, and in context. Data integrity is what makes the data insightful and actionable. Data Quality When looking at data quality, it's important to understand the relationship between data quality and data integrity. Data quality mainly refers to the reliability of the data. To understand the quality of data one must ask the following questions: Is the data complete? To be complete data must be a representation of the total amount of data needed to meet report requirements. Is the data valid? This means the data must come from the appropriate source and all requirements for that source are met upon data entry. Is the data unique? This confirms there is no data redundancy in the system of use. Was this data captured timely? Timeliness ensures the data set is up to date for meeting the report requirements. How consistent is the data? Data consistency ensures all data elements meets standards set throughout a database or data set. Data Integrity Data integrity defines the accuracy and consistency of data, but one must go through additional queries to confirm data gathered or extracted has the quality and integrity necessary to trust data for the intended use case. This is extremely important in healthcare given most use cases involve patient data. To understand the integrity of data one must ask the following questions: Does the data have quality? All steps mentioned above must be followed to consider the data meets its quality requirements. Is the data accurate? This means the data being used is complete and the source can be verified as trusted. Does the data have logical integrity? This means the data retains accuracy and completeness even if used in different contexts. Does the data have physical integrity? This ensures the physical data storage location is intact from malware, outages, disasters, etc. Bottom Line It is important the healthcare industry can rely upon accurate and high-qualitydata to serve the needs of patients and the organizations which serve them. Often data consumers are healthcare providers and departments that make critical decisions with this data. Both data quality and integrity are essential for a healthcare organization to make clinical and financial decisions. Most organizations set data quality standards and should continue to strive for improved data integrity. To provide the most meaningful and actionable data for your organization, you are encouraged to implement measures to ensure reported data meets established data quality and integrity standards. | 