Any successful business largely relies on data to make informed decisions. Such decisions can result in high-value outcomes or dire consequences depending on the accuracy and quality of data used. Traditionally, data management techniques heavily relied on manual data collection methods, such as face-to-face communication with customers. With time, however, companies have adopted sophisticated data management and analytics tools that can perform data-related tasks seamlessly. Due to the impact that data quality and accuracy have on business decision-making processes, companies have strived to standardize data entry as well as ensuring that the information gathered is measurable and useful. However, errors still do not occur. Organizations, including the healthcare facility in which I worked, have suffered immensely as a result of pegging business decisions on inaccurate data that results from mistakes in collecting, compiling, and utilizing data.
Example of Data Error in Place of Work and its Consequences
During my work as a registered nurse (RN) working in the emergency department (ED) at a private healthcare facility, errors in data collection and utilization resulted in costly misjudgment. EDs are particularly susceptible to data errors due to time constraints as many of their patients have to be attended to within the shortest time possible (Ward, Self, & Froehle, 2015). In this particular event, a patient was brought in with severe injuries sustained from a fatal accident. a nurse failed to accurately capture the patient’s medical history records. the nurse failed to note the patient’s penicillin allergy. After the patient was stabilized, a hospital intern unknowingly administered what turned to be a lethal drug to the patient (Amoxicillin). the patient suffered from irreversible brain damage and later died.
Just like any business, healthcare facilities suffer immensely from data errors. While some of the errors may appear simple, others are subtle and their consequences are far-reaching. In some cases, such mistakes have resulted in irreversible consequences such as jail terms and the closing down of businesses (Brown, Kaiser, & Allison, 2018). In healthcare, for instance, doctors or nurses who give wrong medication prescriptions may end up suffering from self-doubt, shame, and guilt throughout their career and even lose their jobs. In the mentioned case, after a series of court cases, the admission nurse was found guilty of negligence and lost her job. To the business entity, the results of such mistakes may be far-reaching. For instance, in the mentioned case, the healthcare institution faced huge financial costs associated with many court hearings not to mention the hefty fines and damages that the healthcare facility had to pay to affect the business operations directly. Further, a bad reputation may lead to lost clients, leading to reduced revenues (Haug, Zachariassen, & Van Liempd, 2011). All this affects the business’s profitability, to some hindering its ability to continue operations. The lack of confidence of affected medical professionals may lead to lost productivity. Additionally, such misjudgments may attract government interventions, thus creating compliance bottlenecks that can significantly affect business operations.
What Might Have Been Done Differently to Avoid the Problem?
Businesses can implement a number of strategies to avoid errors resulting from poor data quality and management practices. For instance, errors can be prevented through employee management strategies such as training them on the importance of data, providing a good working environment, hiring sufficient and competent staff, prioritizing accuracy over speed, and avoiding overloading staff (Rodziewicz & Hipskind, 2020). Any organization that seeks to avoid errors emanating from bad data must first educate and train its staff on how valuable the information is. Before employees are tasked with the responsibility of entering compiling or analyzing data, they must be trained on the consequences of inaccurate data. The nurses working in the ED department ought should have been educated that capturing any relevant medical history record can go a long way in avoiding misjudgments that can cost not only the hospital and fellow staff but also the lives of patients.
Organizations must also encourage accuracy over speed as a way of encouraging staff to accurately input and analyze data. While speed is a good attribute, employees should never be pushed to achieve bulk work at the expense of data accuracy. Nurses working in EDs should be given enough time to double-check their records before proceeding patients to medical administration centers (Ward et al., 2015). One way of ensuring this is achieved is by reducing work overload and employing enough staff. Similarly, healthcare facilities can require all data to be double-checked by either peers or supervisors. data accuracy can also be improved through the use of data profiling techniques, whereby such data is analyzed to ensure it is complete, correct, consistent, and reasonable. Reducing data redundancy by ensuring that only required data is collected and entered into healthcare records can save time, thus allowing ED nurses to focus on the most important aspects of medical data records. Further, standardizing processes by acquiring data collection software and related technologies to prompt and guide staff on mandatory data could have avoided the error.
Accurate data is a prerequisite in making high-value and informed decisions. Just as accuracy in data management is important in making good decisions, so it is when bad data management skills are employed. Errors when collecting, compiling, and utilizing data can lead to irreversible consequences for organizations. In healthcare, errors when recording patient medical history can result in wrong charting information, thus, resulting in misdiagnosis or wrong medication. In some cases, such mistakes can lead to death, hefty financial losses, and erosion of a good reputation. Mistakes leading to bad data can be prevented through employee management strategies such as training them on the importance of data, providing a good working environment, hiring sufficient and competent staff, prioritizing accuracy over speed, and avoiding overloading staff.
Brown, A. W., Kaiser, K. A., & Allison, D. B. (2018). Issues with data and analyses: Errors, underlying themes, and potential solutions. Proceedings of the National Academy of Sciences, 115(11), 2563-2570. https://www.pnas.org/content/115/11/2563
Haug, A., Zachariassen, F., & Van Liempd, D. (2011). The costs of poor data quality. Journal of Industrial Engineering and Management (JIEM), 4(2), 168-193. https://jiem.org/index.php/jiem/article/view/232/130
Rodziewicz, T. L., & Hipskind, J. E. (2020). Medical error prevention. In StatPearls [Internet]. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK499956/
Ward, M. J., Self, W. H., & Froehle, C. M. (2015). Effects of common data errors in electronic health records on emergency department operational performance metrics: A Monte Carlo simulation. Academic Emergency Medicine, 22(9), 1085-1092. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4560638/