AHIMA Medical Association’s Data in Reports

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Table of Contents

Introduction
Main body
Conclusion
References

Introduction

Information is a tremendous part of the healthcare system today. Considering the technology improvements, the essence of efficient electronic health record obtaining methods is rising. Consequently, keeping data accurate, accessible, and well organized is one of the critical priorities for the Health Information Manager Director. I have selected three data elements from AHIMA’s Data Quality Model: Data Accuracy, Data Accessibility, and Data Timeliness. The role of these elements is essential when running reports for data analysis for various reasons.

Main body

The data in the electronic health records should be accurate, which means that there should be no mistakes. Inaccuracies in electronic health record data might negatively affect the secondary analysis results (Diaz-Garelli et al., 2020). Moreover, keeping healthcare data accurate is crucial because it could be shared with other organizations. In order to eliminate inconsistencies in the secondary analysis of the data results, the HIM director should factor the data accuracy. For that, the identification of the purpose of the application is required. There should be a particular reason and goal for collecting the data. In addition, it is vital to ensure that the collected data is current. It is crucial to imply an effective management policy to track the changes in the information warehouse (Davoudi et al., 2015). Using these recommendations, the HIM director would maintain the accuracy of the data.

The next element is the accessibility of the data accessibility. When running reports for data analysis, the HIM director should take into consideration the legal terms regarding the data collection process. Some boundaries as legal and financial factors determine the data to be collected (Davoudi et al., 2015). For instance, the concerns regarding privacy should be emphasized when accessing healthcare data. Such information as the birth date should be collected with the verification. Some information could be accessed by using already existing data systems. The instrument for data collection should be carried out with consideration of the mentioned factors. In order to keep the information accessible, creating an efficient instrument for data collection is required. Such an instrument should assess the most effective methods of collecting health records data.

The third element important to consider when running a report is the timeliness of data. : The National Healthcare Safety Network Hemovigilance Module represented improvements in the reporting timeliness (Edens et al., 2018). When running a report HIM director should check if a timely analysis of the data was provided. In order to ensure timely data collection, the process and collection instrument functions should be assessed (Davoudi et al., 2015). In addition, it is worthy to consider how often the particular information should be updated. For example, some information should be updated more often than others. Considering the factors mentioned above would help to avoid the undesirable consequences in the report results.

Conclusion

In conclusion, the HIM director should factor in data elements such as Data Accuracy, Data Accessibility, and Data Timeliness to avoid mistakes in the reports for data analysis. Such mistakes might have adverse consequences for the patients and other organizations with which the data is shared. In order to maintain the accuracy, accessibility, and timeliness of the data, the HIM director is required to ensure that there is an efficient instrument for processing and collecting healthcare data. The director should take into consideration legal and financial boundaries and implement the most relevant way to keep data quality high.

References

Davoudi, S., Dooling, J. A., Glondys, B., Jones, T. D., Kadlec, L., Overgaard, S. M., Ruben, K., Wendicke, A. (2015). Data Quality Management Model. Journal of AHIMA, 86(10). Web.

Diaz-Garelli, F., Strowd, R., Lawson, V. L., Mayorga, M. E., Wells, B. J., Lycan Jr, T. W., & Topaloglu, U. (2020). Workflow differences affect data accuracy in oncologic EHRs: A first step toward detangling the diagnosis data Babel. JCO Clinical Cancer Informatics, 4, 529-538. Web.

Edens, C., Haass, K. A., Cumming, M., Osinski, A., O’Hearn, L., Passanisi, K., … Andrzejewski, C. (2018). Evaluation of the National Healthcare Safety Network Hemovigilance Module for transfusion-related adverse reactions in the United States. Web.

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AHIMA Medical Association’s Data in Reports

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