Medical Coding as a Crucial Form of Managing Information in Healthcare

Data management remains one of the key concerns in the healthcare environment. Specifically, condensing information about the properties of specific medications, including their purpose, expected effects, possible side effects, and proper use, as well as contents, volume, weight, and other vital information often represents a challenge. Herein lies the significance of medical coding as the tool for inscribing vital data that will point to the essential properties of a particular medication. According to the American Academy of Professional Coders (n.d.), in concise definition, medical coding is a process involving the transformation of a certain piece of information into a series of codes for effective data transfer. However, due to current issues with coding, the accuracy of the process may suffer, leading to incomplete or missing information on the labels. To improve the current labeling framework, stringent quality measures coupled with a system of tracking and tracing errors must be introduced.

To understand the scope and scale of the problem, one will need to take a look at some of the most common coding errors observed in the healthcare setting. For example, one should point to the high prevalence of missing data in the codes supplied by corresponding healthcare organizations (Hu et al., 2017). For instance, on the part of a healthcare organization, the absence of the diagnosis code should be mentioned as one of the most common occurrences in the coding process (Agency for Healthcare Research and Quality, n.d.). Consequently, the choice and delivery of appropriate medication to a patient will become highly complicated. Another common medical coding error observed in the clinical setting involves misunderstandings in the billing department. As a result of miscalculations, a case of upcoding, in which a patient is charged a higher price than needed for a specific medication may occur (Grant-Kels, Kim, & Graff, 2016). The specified situations are particularly unpleasant for the patients that come from disadvantaged backgrounds and struggle economically. Finally, coding errors taking place in the course of the Telehealth application must be mentioned. Resulting from technology glitches or incompatibility of certain technological devices, delays in the delivery of information, as well as the failure to provide crucial data, may be observed in the context of the Telehealth communication process (Arizona Health Care Cost Containment System, n.d.). Although the described medical coding errors are quite dangerous, they are still manageable and, in most cases, preventable once an appropriate risk management strategy and a well-built information management framework are integrated.

To improve coding accuracy, the approach toward using electronic medical records (EMR) properly must be designed. Specifically, all staff members must be carefully and meticulously instructed on the proper use of EMR and the management of data within the established EMR system. Namely, training and proper education must be made mandatory for all staff members so that they could operate the respective EMR tools flawlessly. As a result, staff members will acquire the skills for operating the EMR system so that the essential information could be coordinated and delivered accordingly.

Simultaneously, improvements must be made to the technological aspect of managing the data transfer occurring during labeling. Specifically, the synchronization between the key systems and devices must take place at all times. To ensure that no disruptions should happen, the delivery of the key information must be followed by a confirmation delivered from the recipient’s device to that one of the sender. Thus, errors and the cases of missing data will be tracked down and addressed properly.

Finally, the issue of communication as one of the primary factors shaping the efficacy and accuracy of medical labeling will need to be improved. Specifically, in situations that involve the absence of specific data or an instance of an obvious error, the issue needs to be identified and reported immediately so that proper amendments are made and the issue is resolved accordingly. Thus, a communication channel between healthcare institutions and their members must be created. While the current EMR tool appears to be quite efficient, an additional communication tool for reporting issues and misunderstandings so that they could be addressed must be created and incorporated into every healthcare setting homogenously. Thus, the problem of labeling will be finally resolved, and the threat of data mismanagement will be minimized.

By integrating the system of tracking and tracing errors in health services delivery and pharmaceutical labeling, as well as enhancing the quality management framework, one will improve the efficacy of the current labeling process and reduce the risks of mismanaging medication data. As a result, patients’ safety will be enhanced, and a drop in poor patient outcomes will be observed. Furthermore, the specified change will allow improving the reimbursement cycle for patients, allowing them to obtain the full extent of high-quality medical services that their insurance guarantees. Therefore, a shift in the quality management approach will occur, allowing the current coding framework to become more effective. With the described change, the outcomes of labeling and coding are expected to be improved significantly.


Agency for Healthcare Research and Quality. (n.d.). HCUP coding practices. Web.

Arizona Health Care Cost Containment System. (n.d.). Medical coding resources. Web.

American Academy of Professional Coders. (n.d.). What is medical coding? Web.

Grant-Kels, J. M., Kim, A., & Graff, J. (2016). Billing and upcoding: What’s a doctor-patient to do? International Journal of Women’s Dermatology, 2(4), 149. Web.

Hu, Z., Melton, G. B., Arsoniadis, E. G., Wang, Y., Kwaan, M. R., & Simon, G. J. (2017). Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record. Journal of Biomedical Informatics, 68, 112-120. Web.

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