Predictive Analytics in Healthcare

Question
Review this week related topics: Big Data, Data Science, Data Mining, Data Analytics, and Machine Learning. 
Consider the process and application of each topic.
Reflect on how each topic relates to nursing practice. 
The assignment:
Post a summary on how predictive analytics might be used to support healthcare. Note: These topics may overlap as you will find in the readings (e.g., some processes require both Data Mining and Analytics).
In your post include the following:
Describe a practical application for predictive analytics in your nursing practice (you can do behavioral health or med surg). What challenges and opportunities do you envision for the future of predictive analytics in healthcare? INCLUDE 3 REFERENCES

Answer
1. Introduction
Predictive analytics is rapidly emerging as a valuable tool for the identification and management of high-risk populations in today’s leading healthcare organizations. In its broadest sense, predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. The predictive model has been widely used to identify those who have higher probabilities of certain outcomes, more complex and severe medical conditions, and higher utilization of health services. In these cases, a small number of individuals are identified as having a much higher probability of incurring the outcome. The primary goal in these cases is to improve quality of care for the high-risk individuals and reduce overall costs by identifying them before the adverse outcomes occur. Predictive analytics is a process in which the predictions work in a feedback loop by identifying the probability of an outcome and identifying the factors that can be modified to change the outcome. In these scenarios, prediction is used to target intervention, by clarifying the relative risk of different outcomes and by discovering which factors and interventions have the greatest potential to alter those outcomes. This can be particularly important for healthcare providers working to improve health of populations and reduce the per capita cost. High-risk, high-cost individuals can often be identified with complex or chronic conditions that can be improved if the right interventions can be targeted. In the case of disease management, predictive models using patient specific data can identify those in the early stages of a disease for which effective intervention can prevent progression to more severe outcomes. By identifying the different factors that can influence the disease progression, the provider can discern which patients would benefit the most from the available interventions. This allows for targeting the right treatment to the right patient.
1.1 Definition of Predictive Analytics
Predictive analytics is different from other statistical analysis as it is more futuristic and often uses statistical techniques and data mining concepts to analyze the data. This concept focuses on prediction and not description. For example, in database marketing, it’s a common use of predictive analysis. Various companies use this data to make future predictions on customer behavior, customer trends, and to develop customer relationship management systems. This concept is also used in various other fields, including but not limited to financial services, insurance, healthcare, travel, telecommunications, etc.
Predictive analytics is the technique used to determine the outcome of a situation and is done through data collection, data analysis, statistics, and machine learning. This process is beneficial in solving complex issues and also to identify future opportunities. The data from which the analysis is done can exist in various forms, for example, it can be structured or unstructured and can be internal or external to the system.
1.2 Importance of Predictive Analytics in Healthcare
With today’s increasing demand of healthcare worldwide, it is in the best interest of any healthcare provider to utilize every ability to provide the best care possible. However, situational awareness and the ability to predict an outcome in a patient’s case has not been healthcare’s strong suit. In the past, healthcare has reacted to critical situations and have controlled the damage, but what if that damage could have been prevented with a higher quality of care? Predictive analytics can answer that question. An important and sometimes life-saving tool, predictive analytics is instrumental in providing the best possible care for any patient. One of the most important reasons to utilize predictive analytics in healthcare is its ability to determine a possible outcome in a patient’s case. By utilizing the patient’s history and existing knowledge, predictive analytics can suggest a future outcome or the probability of a particular illness/injury occurring. This can be extremely useful in cases of organ failure, as data can be collected to determine whether or not the patient is in need of an organ transplant in the near future. Simulation models can then determine the best treatment for that patient and the probability of survival with and without the transplant. Cost is always a factor with medical treatment and the ability to predict an outcome based on particular treatments can help to determine the best and most cost-effective treatment for any patient.
2. Practical Application of Predictive Analytics in Nursing Practice
2.1 Identifying High-Risk Patients
2.2 Early Detection of Complications
2.3 Personalized Treatment Plans
3. Challenges of Predictive Analytics in Healthcare
3.1 Data Quality and Accessibility
3.2 Privacy and Security Concerns
3.3 Integration with Existing Systems
4. Opportunities of Predictive Analytics in Healthcare
4.1 Improved Patient Outcomes
4.2 Cost Reduction
4.3 Enhanced Resource Allocation
5. Future of Predictive Analytics in Healthcare
5.1 Advancements in Machine Learning Algorithms
5.2 Integration of Wearable Devices and IoT
5.3 Collaboration between Healthcare Providers and Data Scientists
6. References