Data in Clinical Decision Making

 

Connecting data to clinical decision making

All available patient and medical information drive clinical decision making.  This data has the ability to improve quality of care, reduce costs, and achieve overall better health. 

What is Big Data?  Big data is a large amount of data that is a combination of nursing science and informatics that is analyzed to provide insight, information, and knowledge.   Some examples of big data sources are electronic health records and mobile applications.  Big data is changing nursing in a variety of ways such as documentation, staffing, workflow, evidence-based practices, new nursing roles, enhancing patient engagement, telemedicine, and learning and development.  Nurses can analyze collected data to determine how to efficiently treat a patient, organize an operation, or staff a unit.  This data helps nurses make decisions that are streamlined and effective.  Big data refers to a massive amount of information that has been created and collected using technology for the purpose of making improvements in healthcare using accurate and real-time information.

What is Actionable Data?       Actionable data is the action put forth on a collection of big data. Actionable data is accurate, ubiquitous, accessible, organization and of high-quality.  Data becomes actionable when it is standardized and organized to create insights into common themes.  Actionable data can provide the direction needed to create quality improvement strategies.

What is Predictive Analytics? Predictive analytics takes the collected data to predict a likely outcome based on the patterns of the data.  This allows healthcare providers the opportunity to prevent adverse events and improve health issues based on predictive analytics of information.  This also allows them to make an informed decision based on the data trends.  This data may or may not be real-time data.  Real-time data predictive analytics can assist healthcare providers in predicting when something could go wrong and identify patients with a high risk of a poor health outcome so they can be a step ahead and improve patient outcomes.  Predictive analytics tools can also be used to predict financial situations, efficiency and patient satisfaction. 

Predictive Analytics Healthcare

This video was created by Imaginovation to review how predictive analytics can improve healthcare.  Predicative analytics can predict progression of diseases as well as predict and prevent suicides.  Analyzing data and making predictions can also fight diseases, such as cancer.  There are many healthcare organizations who are now using available technology to improve patient care and improve efficiency of healthcare organizations.    

Big Data Healthcare Article

This article reviews all the different and available health data used for decision making in healthcare.  Some examples are electronic health records, smart devices, and genetic databases.  Technology has allowed big data to be converted to actionable data.  This actionable data can be analyzed to gain better understanding and implement evidence-based practices.  The goal is to improve healthcare and patient outcomes using the data obtained from technological advances.  This data can be applied to research, population health, it can reduce costs, and also can be used for preventive measures. 

 Data Analytics in Healthcare

This article discusses how data analytics is evolving and improving patient outcomes based on the gathered data.  Technology has also evolved and healthcare organizations have adopted new technologies.  Electronic health records provide more organized data and the Cloud allows large amounts of information to be processed in real time.  Data analytics provide the information for which predictions can be made. 

Watch this video to learn how big data is converted and used for high confidence decision making.  Data needs to be organized and collecting and analyzing data can take time and resources and can be expensive.  For this reason, it is important to streamline information to make it usable for decision making.