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The need for correct information in healthcare is vital, but the ability to maintain – and provide – correct information lags behind our need today. The need for better use of data in healthcare is well-supported (Troiano, D., Jones, M.A., Smith, A.H., 2015). Correct information is only as good as the integrity of the data. This means not only must correct values – and only correct, consistent values - always exist, but correct, consistent relationships between values must always exist as well. Pharmacy has long operated under the axiom “the right drug to the right patient at the right time.” Today’s application of technology within healthcare leads to a rephrasing of this to: “the right information to the right person at the right time.” Information systems throughout an organization require careful review and assessment in order to ensure that this occurs. (Lapinsky, 2008; Ward, 2004) A lack of data integrity has been cited as a potential underlying cause of missed clinical identification of adverse drug reactions. (Durrieu, G., Batz, A., Rousseau, V., et al., 2014) It has also been recognized in analysis of e-prescriptions for correct national drug codes for products that significant deficiencies in data quality exists. (Dhavle, A.A., Ward, S., Rupp, M.T., et al., 2015) The authors noted that more than 2 out of every 1,000 e-prescriptions contained a free-text drug description that described a completely different drug concept than that associated with its national drug code (NDC) value. This represented a subset of the 42,602 e-prescriptions that were evaluated, of which only 67.70% (28,172) were found to contain a representative NDC number.
Further, as noted above, there is a call for increasing the use of data in electronic health records and code sets for caring for patients, as well as drug databases for clinical decision support (Troiano, D., Jones, M.A., Smith, A.H., 2015). In addition, there is an increased demand for integration of pharmacy data sets, other patient data sets and even data from collectives of research networks for such purposes as population-based monitoring of disease management, post-approval drug safety and efficacy monitoring, patient care and fraud, waste and abuse detection across organizations and regions. (Green, S.L., Pfenning S., 2015; Belperio, P.S., Backus, L.I., Ross, D., 2014; Griesbach, S.A., Lustig, A., Malsin, L., et al., 2015; AMCP, 2015).
Each of these initiatives underlies the need for high data integrity; the combination of any of these makes the need paramount. But data integrity can become mind-boggling. Understanding the simple phrase “Data integrity is ‘keeping data ‘whole’” becomes an etymological adventure. The more an individual uses data over time, or the more data is used by a wider audience with broader needs, the greater the risk for different interpretations – thus, the need for stronger rules to ensure data integrity. Data integrity must be applied across a system. Not a computer system; rather, an organizational system. Maintenance of data integrity requires agreement on the definition and purpose of all data by all individuals within an organization. Today’s widespread use of electronic storage and sharing of data highlights our limited ability to control quality and consistency of data. Thus, it is imperative that we utilize processes and tools to ensure data integrity.
Appropriate enterprise governance and data governance must be assigned from the top authority in an organization down in order to improve odds that no data mistakes are made. Data governance is the task of setting standards for what type of data can be entered in a record of a column. It is commonly the authority of a team of technical and business users. Enterprise governance is the corporate assignment of authority to the person who has the right to decide data governance rules. If a database is used by only one person, that person has the authority. If a database is used by multiple departments in an organization, it is wise to establish an enterprise governance committee to identify and enforce lines of authority across decision makers throughout the organization. This ensures that the person responsible for data in a column is not overridden inappropriately.