Data Mining

 

   Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks

By: 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. (2021). Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks. Infection Control & Hospital Epidemiology, 42(6), 1-7.

Data Mining  what is It?

Data mining in healthcare identifies useful and trending patterns by analyzation of large data sets. This data is used to predict trends and determine interventions (University of South Florida Health. (n.d.).

In the article by Sundermann et al. (2021). The power and benefits of data mining are expressed when detecting healthcare associated outbreaks. The importance of data analytics can improve monitoring and trends, infection control measures, and interventions.

Automated data mining gives APRNs real-time analytics which guide evidence-based decisions and personalized care. Clinical data mining enhances clinical concerns and APRN interventions by:

 Improving patient safety by identifying emerging concerns such as sepsis, infections, or drug reactions. Identifying concerns allows APRNs the ability to intervene in a timely manner and reduce hospital readmissions.

  Predictive analytics assist to identify complications, allowing predictive and personalized care. Healthcare assessments can be made on need for further education, interventions and monitoring. Enhanced health records (EHR) increase personalization of care with patient risk factors and interventions.

Data mining allows for smooth and efficient care, improving relationships between healthcare professionals and their patients. Data mining reduces administrative burdens and enables APRNs to focus and personalize care. 

Data mining is a contributing analytic supporting population health management by identifying trends, disparities in healthcare, and effective protocols. The information collected encourages public health initiatives and policy advocacy.

Benefits of Data Mining:

·         Detecting Fraud & Abuse

·         Management of Healthcare

·         Enabling Predictive Analytics

·         Measure Treatment Effectiveness

·         Enhanced Clinical Decision Making

·         Increased Diagnosis Accuracy

·         Improved/Measure Treatment Efficiency

·         Detect Harmful Interactions

·         Improve Relationships

·         Cost Effective

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Implications of clinical data mining for illuminating and enhancing clinical concerns and advanced practice nursing interventions.

 

 

Clinical Data Mining in a Public-School Setting:

Clinical data mining is a tool that can transform school nursing by enabling data-driven decision-making to track and manage infectious diseases such as COVID-19, influenza, and measles most recently. Through the use of data analytics, I can identify outbreak patterns, (classrooms, grade levels, families, school elementary, middle, high) monitor vaccination rates, (district wide) and assess the effectiveness of health interventions within the school setting (what areas on experiencing less, and why). Early detection through real-time data monitoring allows for efficiency, minimizing disease spread and ensuring student safety.

As a school nurse, I would use clinical data mining to enhance disease surveillance, coordinate timely interventions, and improve communication with public health officials, parents, and staff. Predictive analytics can help identify at-risk populations, allowing for targeted education and vaccination efforts. By integrating electronic health records and automated reporting systems, I can streamline case tracking and ensure compliance with immunization requirements.

Influenza, in particular, remains a recurring public health concern in schools, often leading to widespread absenteeism. For comprehensive and current influenza data, the NYS Department of Health provides weekly reports detailing activity (see graph below). With clinical data mining, I can track flu trends, assess the impact of vaccination campaigns, and implement timely prevention strategies such as hand hygiene education and early symptom identification.

While measles recently is making headlines, globally, measles cases surged to an estimated 10.3 million in 2023, a 20% increase from the previous year, according to the World Health Organization (WHO) underscoring the critical need for robust vaccination programs and vigilant monitoring.

Ultimately, clinical data mining will empower me to take a proactive approach to public health in schools, fostering a safer environment where students can learn and thrive without the disruption of preventable infectious diseases.

                                  

Healthy.ny.gov

 

Measles

                                         

CDC.gov

                   

References:

Clack, L. (2023). Using data analytics to predict outcomes in healthcare. AHIMA Journal. Retrieved from AHIMA Using Data Analytics to Predict Outcomes in Healthcare

Kolling ML, Furstenau LB, Sott MK, Rabaioli B, Ulmi PH, Bragazzi NL, Tedesco P. (2021). Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development. Int J Environ Res Public Health 17;18(6):3099. doi:10.3390/ijerph18063099. 

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. (2021). Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks. Infection Control & Hospital Epidemiology, 42(6), 1-7

TEDx Talks. (2020). How data is driving the future of healthcare | Prashant Natarajan | TEDxSJSU [Video]. YouTube. https://youtu.be/oFVlzuoRHcw

University of South Florida Health. (n.d.). Data mining in healthcare: Purpose, benefits, and examples. USF Health Online. Retrieved from https://www.usfhealthonline.com/resources/healthcare-analytics/data-mining-in-healthcare/

6 Benefits of using Data Mining in Healthcare | Analytics Steps

World Health Organization (WHO). (2024, November 14). Measles cases surge worldwide, infecting 10.3 million people in 2023. Retrieved from https://www.who.int/news/item/14-11-2024-measles-cases-surge-worldwide--infecting-10.3-million-people-in-2023.