Data Mining and Improving Patient Outcomes
Welcome to week 4 of the blog 💚we are half way through our course and going to be exploring the potentials of data mining and how it can improve nursing care which improves patient outcomes in the longrun. I am also going to review the article Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks and discussing how data mining is beneficial in improving patient care.
What is clinical data mining and how as future FNPs can we use it to benefit our patients? Clinical data mining is used to analyze, synthesize, and sort through the plethora of big data the medical world has to assist with tasks such as risk stratification, diagnosis, classification, survival prediction, and predict trends (Qiao et al., 2024). Data mining is a form of artificial intelligence that is becoming increasingly important in the medical field and has been used to not only make advancements in diagnostics and disease predictions but also to help monitor and manage healthcare systems (Kolling et al., 2021). Data mining is not a modern concept, but what was once done on pen and paper is now done with the most advanced software and computers in modern technology to handle the vast amount of data that is available in today's world. We are constantly collecting data in healthcare and utilizing it to make advancements, but without sorting it, analyzing it and being able to make sense of the trends within the data we simply just have lots of information without any application. This is why there is such a need for and such a strong push towards advancements in data management. Utilizing the different data mining software available today, you can obtain whatever trend or analysis you are looking for from the larger set of data you originally start with.
The article
Automated data mining of the electronic health record for investigation of
healthcare-associated outbreaks examines how data mining can be used to
investigate the outbreak of an infection in a healthcare system by using data
mining to investigate patient records. 9 hospital outbreaks between 2011-2016
were examined, and data mining was used by utilizing EHRs of infected patients
to then see where they had been, who they had been in contact with, and how
they could prevent the spread to other patients (Sundermann et al., 2019). The rationale
for using data mining in outbreaks is not only to slow the spread but also to hopefully
identify the source. Large data sets are analyzed, such as patient location,
interaction, treatments, therapies, and procedures received, and provider interaction,
to help identify the source (Sundermann et al., 2019). This example of data
mining can be so crucial to hospital outbreaks, especially when it comes to
infections that are especially rare, deadly, or rapidly transmitted, as it is essential
to try to stop the outbreak as quickly as possible.
As a future
FNP, I can undoubtedly see utilizing data mining in many different areas of
patient care. Especially with managing chronic health conditions and trying to
reduce the negative comorbidities associated with them. Utilizing predictivesoftware to determine what other negative consequences patients may be at risk
for could certainly be beneficial in using preventive medicine to help not only
significantly improve patient outcomes but also potentially reduce their
healthcare costs in the long run. I could also see utilizing data mining to see
where patients are happy with their healthcare and where they want improvement.
As a future FNP, I could also see regularly utilizing data mining to help with
medication prescribing and administration, as well as diagnostics. If there is
a way to help diagnose patients faster and with more efficacy, provide them
with better quality of care, run more accurate risk assessments, and improve
their quality of life and overall outcomes, then I believe in using whatever
technology or software is necessary. Our goals as healthcare workers and future
providers are to care for our patients and to help them obtain the best goal
achievable for them, and utilizing data mining is a powerful tool for us.
References
Kolling, M. L., Furstenau, L. B., Sott, M. K., Rabaioli, B., Ulmi, P. H., Bragazzi, N. L., & Tedesco, L. P. C. (2021). Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development. International Journal of Environmental Research and Public Health, 18(6), 3099. https://doi.org/10.3390/ijerph18063099
Qiao, H., Chen, Y., Qian, C., & Guo, Y. (2024). Clinical data mining: challenges, opportunities, and recommendations for translational applications. Journal of Translational Medicine, 22(1). https://doi.org/10.1186/s12967-024-05005-0
Sundermann, A. J., Miller, J. K., Marsh, J. W., Saul, M. I., Shutt, K. A., Pacey, M., Mustapha, M. M., Ayres, A., Pasculle, A. W., Chen, J., Snyder, G. M., Dubrawski, A. W., & Harrison, L. H. (2019). Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks. Infection Control & Hospital Epidemiology, 40(3), 314–319. https://doi.org/10.1017/ice.2018.343
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