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Pharmacy Data Integrity for Optimal Analytics

Pharmacy Data Integrity for Optimal Analytics

C. David Butler
Copyright: © 2014 |Volume: 4 |Issue: 2 |Pages: 25
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781466655393|DOI: 10.4018/IJKDB.2014070103
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MLA

Butler, C. David. "Pharmacy Data Integrity for Optimal Analytics." IJKDB vol.4, no.2 2014: pp.21-45. http://doi.org/10.4018/IJKDB.2014070103

APA

Butler, C. D. (2014). Pharmacy Data Integrity for Optimal Analytics. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 4(2), 21-45. http://doi.org/10.4018/IJKDB.2014070103

Chicago

Butler, C. David. "Pharmacy Data Integrity for Optimal Analytics," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 4, no.2: 21-45. http://doi.org/10.4018/IJKDB.2014070103

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Abstract

The use and creation of data continues to proliferate, with each year seeing further reliance on information systems to support practitioners and organizations. This surge is expected to continue at an even faster pace with the increased use of inexpensive storage methods of any type of data, plus greater reliance on computerized clinical decision-making tools. Yet data integrity remains essential to every organization and to every healthcare practitioner in order to ensure the correct use of patient information to optimize care. It provides the assurance that the data you see every day is the same as it was the day before. It promises that the drug dosage regimen “QID,” whether you define it as four times daily or four times daily with meals and at bedtime, is applied using the same parameters for every patient (as you define a patient), across every day (or any time period), as you define day in your health care setting. It also means that referential connections between data values must be consistent. When a specific patient takes a specific combination of drug products, referential integrity must be applied to ensure the correct products, drug ingredients and strengths are recognized as being received by that patient. Definitions about data and their referential relationships must be made by the business person (the practitioner), rather than by information technology (IT). Only by doing this can appropriate business rules by applied by a database, which manages the information used in electronic medical records. Once a decision is made about what a datum represents, and how it relates to other data, whether by an individual or a group, it is imperative that the decision remain consistent over time. Should the definition evolve, it is also imperative that that evolution be tracked. Thus, organizations must establish governance committees to maintain consistency both across an organization and across time. Governance committees must have the highest level of authority to ensure that rules are not overridden on a casual, intermittent basis. Once business rules for data have been established, use of a relational database provides one of the strongest tools for ensuring that data integrity is maintain. This paper explores the concepts serving as the foundation for today's relational database management systems. A top-down approach is described using an Entity-Relationship diagram that can be used to create a relational model for implementation in a relational database management system. A bottom-up approach is described using functional dependencies and normalization. A pharmacist should be able to apply these concepts in corporation with a database architect to ensure the appropriate, consistent use of drug data within an organization. A pharmacist must be able to validate all drug information being used across the organization in order to minimize medication errors and optimize patient care. Only by being the subject matter expert on governance committees and working closely with IT and quality assurance can pharmacy maintain appropriate control over the use of drug information by healthcare technology.

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