Transformation Reply
Read a selection of your colleagues’ responses and respond to at least two of your colleagues on two different days. Expand upon your colleague’s posting or offer an alternative perspective.
1.
Main post
Data Concepts
Big Data: Due to the widespread use of the Internet and the digitization of numerous types of information, including medical records, the term “big data” in the healthcare industry refers to enormous volumes of data that are too complicated or enormous for traditional technology to handle. It can have a significant impact on nurses’ evidence-based practice and the standard of clinical care they provide for patients by assisting them in making accurate decisions, getting the right information at the right time to support the best clinical decisions, and providing timely and accurate care to patients (Favaretto et al., 2020).
Data science: With the application of data science, enormous volumes of disorganized, organized, and unstructured data can be processed, managed, examined, and incorporated into healthcare systems. For these data to yield reliable results, appropriate administration and analysis are required (Grossi et al., 2021).
Data mining: Data mining combines computer techniques from statistics, machine learning, and pattern recognition to comprehend and analyze data in an attempt to forecast more variables or uncover correlations within the data. Healthcare organizations can use data mining to select CRM strategies; insurers can use it to spot fraud and abuse; physicians can use it to find best practices and therapies that work; and patients can use it to get better, cheaper healthcare (Wu et al., 2021).
Data analytics is the process of looking at raw data to identify trends, make inferences, and recommend areas for development. Healthcare analytics generates macro and micro insights and supports business and patient decision making by utilizing both recent and historical data (Batko & Ślęzak, 2022).
Machine learning: The goal of artificial intelligence (AI) and computer science is to replicate human learning with data and algorithms, to improve accuracy over time. Machine learning, which leverages cognitive technology to evaluate a vast number of medical records and carry out any required diagnostics, is revolutionizing the healthcare sector (Sarker, 2021).
By assisting them in making accurate decisions, receiving accurate information at the appropriate time to support the best clinical decision-making, and providing timely and accurate patient care, data concepts have had a significant impact on nurses’ use of evidence-based practice and the improvement of patient clinical care. The concepts of data collection, measurement, representation, and visualization are examples of fundamental principles that guide the organization, analysis, and interpretation of data. These ideas may align with my current practice if I think back on how I collect, arrange, and utilize patient data to inform decisions and deliver care.
Use of Data Concepts in Nursing Practice
I require a strong foundation in nursing theory and practice in addition to a basic comprehension of data collection and organization techniques. I also need to know how to use statistical software and other tools for data analysis and visualization. It takes all of these abilities to apply data concepts in an effective nursing practice. In addition to working with other medical professionals to collect and evaluate data, discuss findings, and make recommendations, I may also need to be well-versed in teamwork and communication.
Not Use of Data Concept in Nursing Practice
I could describe what it would take to implement data principles or predictive analytics in my nursing practice if I don’t already. This may mean collaborating with other medical specialists, obtaining support from the leadership, and obtaining the necessary data and technical know-how. The benefits of using data concepts and predictive analytics in clinical settings are advantageous to us because they enable us to identify patterns and trends in patient data, improve the efficiency of healthcare delivery, and make more informed decisions regarding patient care. How these techniques can help me have a better grasp of my patients’ requirements and provide more specialized, effective care.
Predictive Analytics and Challenges: Issues and Solutions
Healthcare predictive analytics solutions make use of big data and artificial intelligence. Predictive analytics is used to combine and process patient data from various sources, including medical imaging, insurance claims, administrative paperwork, electronic health records (EHR), and other sources, to identify patterns (Zhang, 2020). Using predictive analytics, medical practitioners can ascertain which diseases patients are most likely to develop. Additionally, will patients return to the hospital within 30 days after being released? Psychotic patients, for instance, are more inclined to hurt themselves or act aggressively against others. As a result, they are typically placed on DTO or DTS hold. Within 30 days of discharge, the patient is likely to be readmitted to the hospital if they refuse their psychotic meds.
References
Batko, K., & Ślęzak, A. (2022). The use of big data analytics in healthcare. Journal of Big Data, 9(1). https://doi.org/10.1186/s40537-021-00553-4
Favaretto, M., De Clercq, E., Schneble, C., & Elger, B. (2020). What is your definition of big data? researchers’ understanding of the phenomenon of the decade. PLOS ONE, 15(2), e0228987. https://doi.org/10.1371/journal.pone.0228987
Grossi, V., Giannotti, F., Pedreschi, D., Manghi, P., Pagano, P., & Assante, M. (2021). Data science: A game changer for science and innovation. International Journal of Data Science and Analytics, 11(4), 263–278. https://doi.org/10.1007/s41060-020-00240-2
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3). https://doi.org/10.1007/s42979-021-00592-x
Wu, W.-T., Li, Y.-J., Feng, A.-Z., Li, L., Huang, T., Xu, A.-D., & Lyu, J. (2021). Data mining in clinical big data: The frequently used databases, steps, and methodological models. Military Medical Research, 8(1). https://doi.org/10.1186/s40779-021-00338-z
2.
Data Science Applications and Processes
In the past decade, healthcare has witnessed a remarkable revolution attributed to advanced technologies and digital information. Advanced computing technologies are being used to mine massive amounts of data and give valuable information that can be used in healthcare to make decisions and improve patient care (Parikh et al., 2019). Therefore, it is vital to investigate the use and impact of predictive analytics in healthcare, including the challenges and opportunities.
According to Zhu et al. (2019), predictive analytics are machine learning, predictive modeling, and data mining used to analyze historical and current data to predict the future. Healthcare organizations have embraced predictive analytics to use historical and current data to predict patterns or trends in the future. Healthcare organizations are using predictive analytics in disease prediction and prevention. For example, predictive analytics will look at relevant data such as a patient’s medical history, genetic predisposition, and lifestyle factors to predict the likelihood of the individual developing certain diseases (Parikh et al., 2019). With such information, healthcare providers can intervene early to prevent the disease.
Predictive analytics have enabled healthcare organizations to manage patient flow effectively, enhance operational effectiveness, and allocate resources optimally, thus improving care quality and shortening wait times. Healthcare organizations can use predictive analytics to plan for available resources (Pianykh et al., 2020). For example, predictive analytics can predict patient peak times and volumes; hence, the organization can anticipate the needed resources, such as equipment and staff.
Practical Application
A practical application for predictive analytics in nursing practice would be using predictive analytics to predict medication adherence and readmission risk of patients coming in at the practice. Predictive analytics will analyze the massive amount of data comprising patient demographics, current medications, medical history, and other relevant data to determine non-adherence and readmission risk predictors. Predictive analytics can be used to analyze each patient’s information and provide their risk for readmission and non-adherence (Ashfaq et al., 2019). Predictive analytics can also put the patients in high, moderate, and low groups so that healthcare providers can tailor interventions applicable to each group and personalize the interventions according to each patient’s non-adherence and readmission scores.
Challenges and Opportunities
The future of predictive analytics in healthcare has great potential, but it also comes with challenges. For it to be adopted and integrated into the working healthcare systems, it is bound to face resistance to change among healthcare providers and overcome the disruption of the current workflow in healthcare organizations. The other challenge it might face is its validity since it must be generalized in diverse healthcare organizations and patient populations. So, it has to be reliable and accurate (Prakash & Das, 2021). On the other hand, predictive analytics offers excellent opportunities for research and innovation. Predictive analytics can be used in research and innovation in drug development, healthcare trends, and disease mechanisms.
References
Ashfaq, A., Sant’Anna, A., Lingman, M., & Nowaczyk, S. (2019). Readmission prediction using deep learning on electronic health records. Journal of Biomedical Informatics, 97, 103256. https://doi.org/10.1016/j.jbi.2019.103256
Medical practitioner’s adoption of intelligent clinical diagnostic decision support systems: A mixed-methods study. Information & management, 58(7), 103524. https://doi.org/10.1016/j.im.2021.103524