Impact of Predictive Analytics in Nursing

Data Science Applications and Processes

In nursing, big data refers to the large amount of patient care and health data. Nurses can use data analysis to determine the best and most efficient treatment methods. Big data will allow you to analyze gazillions of data elements, which is beneficial for evidence-based best practices for nursing. Using big data, nurses can streamline their workflows by analyzing the data to determine the best way to treat patients efficiently (Elsaleh et al., 2020). Due to the frequent nursing shift changes and schedules, big data can be used to ensure enough nursing staff are available during peak hours. Big data can enhance the nursing leader’s efficiency to determine how many nurses they need at any given time. Predictive models can be developed to ensure accurate planning and staffing.

Nurses can use data science to develop, evaluate, and assess patient outcomes using clinical decision support tools, care coordination activities, and health portals. Data science is the process of analyzing, asking questions, and manipulating large datasets to uncover patterns and derive knowledge. Data mining is the understanding and interpreting of data through computation techniques from statistics, pattern recognition, and machine learning to identify relationships within the information. Nurses can use data mining to make real-time decisions on patient care based on the patterns they uncover for a disease progression or treatment.

Data analytics is the procedure of examining raw datasets to discover trends, draw conclusions, and identify the potential for improvement (Spachos et al., 2020). Nurses can use data analytics to determine the best treatment methodology for treating patients. Machine learning can assist nurses in gaining a better understanding of outcomes and processes of care. Using machine learning, nurses can also tailor interventions to support patients with complex health trajectories.

Knowledge of nursing informatics is vital for implementing these data concepts in nursing. Nurses should learn more about data analytics and management to become invaluable to employers and patients. Data concepts will empower a nurse to implement evidence-based practices in their nursing career, and they can use big data to make informed decisions on treatment selection. Data analytics can make predictions, allowing nurses to know how the patient will progress based on the implemented disease or treatment. Currently, the organization is using data analytics where predictive analytics is implemented. We can make data-driven decisions in critical care situations, ensuring patients receive appropriate care with predictive analytics. Predictive analytics helps nurses identify when the patient vitals are declining and when there is a need for intervention change. Patients recovering and improving can be moved from critical care based on predictive analytics of their recovery progression. Decision making is made easy since nurses can use data to determine if they are making the right decision regarding treatment.

Predictive analytics allows health care providers to determine patients at risk for developing chronic diseases or severe infections. Identifying patients at risk gives health care professionals an opportunity for early intervention and prevention of chronic diseases. Predictive analytics has been used in the medical decision-making process. Patients will respond differently to treatment, and using predictive analytics, health care facilities can determine patients who are likely to respond positively to treatment. For example, ongoing research aims to predict if a blood test can predict if the treatment for HPV-positive throat cancer is working months earlier. Currently, doctors have to rely on imaging scans every few months to determine if the patient’s tumors are shrinking (Haring et al., 2022). Therefore, the predictive blood test can benefit patients and reduce their health care costs. Early determination of treatment effectiveness allows providers to switch their treatment course if the current one is not working. Quality of care is improved because patient satisfaction is high and costs are lowered. Patients benefit from recovering sooner than if they had to go through treatment for long periods, and it is not working.