Data integrity is an area of significant concern to those who implement database solutions in virtually any setting. Organizations across all industries increasingly generate and use data to support the operations and decision-making necessary to be competitive. Therefore, data at the heart of all this activity must be valid. Yet, given the high stakes of data involving patient care, data integrity provides even more than a competitive advantage. Data integrity can be a matter of legal compliance or even a matter of life and death.
In this Assignment, you consider strategies for preventing unintended consequences related to data integrity. Specifically, you develop a plan to prevent medical record errors related to data quality and integrity in an emergency department.
Review this scenario:
A patient named A. Watsoncox arrives alone in the emergency department with altered mental status and trauma due to a motor vehicle accident (MVA). She is registered in the emergency medical record (EMR) from a previous visit. A new encounter and record is started for A. Watsoncox.
According to the EMR, she has no allergies and is taking no medications daily.
Before being taken to surgery, she is given Rocephin, 1 gram. She has difficulty breathing and a severe rash after the medication infuses.
It is discovered after the patient is stabilized that an error was made during her last registration. Her name is A. Watson-Cox. There are two records for her in the EMR database.
To prepare:
Review the scenario.
Identify the electronic health records (EHR) data integrity and security issues that may be present in the scenario.
Consider ways to avoid duplication of records that may apply to the scenario.
Review the Learning Resources, focusing on the concepts related to data quality.
Research the Walden Library for literature related to data quality and select two articles to use in your Assignment.
Assignment (3- to 5-page paper):
Develop a plan to prevent emergency medical record (EMR) errors related to data quality and integrity based on the scenario.
Your plan should contain an introduction that clearly identifies and explains data integrity and security issues relevant to the electronic medical records (EMR) in the scenario.
State your proposed plan, which should contain three strategies to ensure data integrity.
Explain your rationale for each strategy in the plan.
Include a brief summary that highlights how your plan can improve data handling and integrity.
Be specific and provide examples. Support your plan with your peer reviewed research from the Walden Library.
References
#1
Harrington, J. (2016). Relational database design and implementation (4th ed.). Cambridge, MA: Morgan Kaufmann.
Chapter 25, “Data Quality†(pp. 509–521)
#2
Mulissa, Z., Wendrad, N., Bitewulign, B., Biadgo, A., Abate, M., Alemu, H., Abate, B., Kiflie, A., Magge, H., Parry, G., & Kabir, R. (2020). Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis. PLoS ONE, 15(8), e0237703. https://doi.org/10.1371/journal.pone.0237703
#3
Knauer, T., Nikiforow, N., & Wagener, S. (2020). Determinants of information system quality and data quality in management accounting. Journal of Management Control, 31(1–2), 97. https://doi.org/10.1007/s00187-020-00296-y
#4
Bai, X. (2012). A mathematical framework for data quality management in enterprise systems. INFORMS Journal on Computing, 24(4), 648.
Database Integrity Plan
Today’s business information systems including hospitals largely use databases to store crucial data. These systems commonly use the relational database model that provides multiple access points to the stored data (Harrington, 2016). The electronic medical records (EMR) is a good example of a database that utilizes the relational database model. Allowing access to the stored data from multiple access points on the EMR allows the healthcare providers involved in the care of a patient to easily view and use the data from their various locations to care for the patient. The fact that data from the EMR is used by several healthcare providers and the need to enhance the patient’s safety call for the need to maintain data integrity by ensuring that the information that is entered into the database is accurate (Harrington, 2016). The data integrity and security issues relevant to the EMR scenario concern duplication of patient data. The patient’s details were entered twice at the time of registration. The availability of two medical records for the same patient poses safety risks for her in that it increases the likelihood of performing double medical tests on the same patient (Mulissa et al., 2020). For example, another healthcare provider can administer the same medications that have already been issued by another provider leading to a drug overdose. Additionally, surgery can be performed on the same site on the patient’s body twice on the same day. To avoid such issues in the future, the organization should have a proper plan to enhance data integrity and security on the EMR database.
The Proposed Plan and Rationale
The organization can consider implementing a number of evidence-based strategies to avoid duplication of records on the EMR. The three strategies that the facility should include in the proposed plan entail improving information system designs through system sophistication and flexibility, employing an integrated framework for effective data quality and controls, and the use of unit-level data management initiatives focusing on specific groups of patients. A description of the components of the three strategies and their rationales are provided below;
Improving information System Design Through System Sophistication and Flexibility
Information system design defines the structure of the EMR database. The quality of data stored in the database is largely determined by the functionality and the quality of the information system design. High-quality patient data is that which adequately meets the objectives and goals of the EMR. Therefore, the organization should design the EMR system in a manner that can detect duplication of data as a way to ensure the accuracy of the stored data. The two strategies that the organization can apply to achieve this goal include system sophistication and system flexibility (Knauer et al., 2020). With the support of qualified information technology professionals, the organization should promote system sophistication by making the EMR database user-friendly, minimizing access and computation time, and enhancing integration. System flexibility entails creating an EMR database that can be adjusted, redesigned, and customized to meet user needs (Knauer et al., 2020). The rationale for improving information system design is to create an EMR system that facilitates the combination of data from various systems and filtering it to detect data duplication.
Employing an Integrated Framework for Effective Data Quality and Controls
Data entry errors affect the flow of electronic information leading to poor-quality data and unintended outcomes. Data duplication is an error that is commonly reported among users of health information systems. To avoid such errors, the organization should implement a framework that ensures data quality controls leading to speedy error detection (Bai, 2012). Bai (2012) confirmed the effectiveness of an integrated mathematical framework in creating effective controls that limit error propagation. The rationale for using an integrated mathematical framework is that it will enable the organization to use mathematical formulations that prevent data duplication.
The Use of Unit-Level Data Management Initiatives Focusing on Specific Groups of Patients
Data that is stored in a database is easier to manage when it is segregated into different forms. For example, data in an EMR database can be divided into groups to capture information for specific patient populations. The organization should implement unit-level data management initiatives based on patients’ needs. Data quality improvement initiatives that are embedded within unit-level improvement strategies have been found to enhance data accuracy in health information systems (Mulissa et al., 2020). The rationale for employing unit-level data management initiatives is to have data that is easy to manage and verify, thereby minimizing errors, preventing duplication, and identifying double entries.
Summary and Conclusion
As they use database systems to drive their operations, contemporary healthcare organizations should strive to create database designs and employ management schemes that ensure data accuracy and integrity. Duplication of patients’ data is a big threat to patient safety which can also affect a hospital’s reputation. The plan described above will improve data handling and integrity in the organization in various ways. For example, improving information system design will create an EMR system that facilitates the combination of data from various systems and filtering it to detect data duplication. Additionally, the use of an integrated mathematic framework will enable the organization to use mathematical formulations that prevent data duplication. Besides, employing unit-level data management initiatives will enable the organization to have data that is easy to manage and verify, thereby minimizing errors, preventing duplication, and identifying double entries.
References
Bai, X. (2012). A mathematical framework for data quality management in enterprise systems. INFORMS Journal on Computing, 24(4), 648.
Harrington, J. (2016). Relational database design and implementation (4th ed.). Cambridge, MA: Morgan Kaufmann.
Chapter 25, “Data Quality” (pp. 509–521)
Knauer, T., Nikiforow, N., & Wagener, S. (2020). Determinants of information system quality and data quality in management accounting. Journal of Management Control, 31(1–2), 97. https://doi.org/10.1007/s00187-020-00296-y
Mulissa, Z., Wendrad, N., Bitewulign, B., Biadgo, A., Abate, M., Alemu, H., Abate, B., Kiflie, A., Magge, H., Parry, G., & Kabir, R. (2020). Effect of data quality improvement intervention on health management information system data accuracy: An interrupted time series analysis. PLoS ONE, 15(8), e0237703. https://doi.org/10.1371/journal.pone.0237703