Predictive analytics is a rapidly growing area of data science. It uses various statistical techniques, such as predictive modeling and machine learning, to make predictions about unknown future events. In healthcare, predictive analytics analyzes historical and real-time data to identify patient behavior, manage chronic diseases, and improve patient care. The tool allows clinicians, administrative staff, and financial experts to receive alerts about potential events before they happen. This, in turn, helps them to make more informed decisions revolving around patient care.
Today, numerous hospitals across the nation are using real-time predictive analytics. One area in particular where the integration of predictor and intervention are showing efficacy is in preventing infections before they happen.
Prediction and prevention go hand-in-hand
According to studies, central line days is an important predictor of infection. A central line is needed to give fluids and medications, to withdraw blood, as well as to monitor a patient’s condition. Central line days are the total number of days a central line is in place for each patient in the ICU. With predictive analytics, clinicians can more easily track how long a central line has been in a patient. Additionally, this tool helps them to identify if the patient is at an elevated risk of developing a chronic infection. Doing so allows them to intervene early on.
Integrating predictive analytics into workflows can be rather difficult. Clinicians should performsmall tests of change to see what works best in regards to implementation. Electronic health records, when implemented well, become a valuable hub for information and a key tool for communication. By applying predictive analytics to EHR data, clinicians harness the power of clinical decision support. This type of support can help to avoid medical errors and long-term health problems that are costly and difficult to treat
To create a sustainable patient safety analytics initiative, healthcare organizations should consider creating a roadmap that follows these steps:
- Recruiting the right team and creating a culture of change – Recruiting a coordinated team of clinicians that can communicate clearly and openly is vital. Together, they ensure that risk scores and clinical alerts are received in a timely manner. In turn, they can help to implement approaches to patient safety prevention and reporting.
- Identifying a well-defined use case – There is no ‘one-size fits all’ approach feasible for most healthcare organizations. In order to target their efforts with the most efficiency, clinicians should choose a well-defined use case. One with a financial and quality component.
- Balancing big data with velocity – Reporting and identifying data as close to real-time as possible is crucial in helping clinicians to rapidly identify the underlying medical case. With a high-velocity program, care teams can immediately act based on best practices to mitigate the problem in question. Understanding how to balance big data with velocity to specific use cases will help healthcare organizations move from volume to value.
- Integrating analytics into a patient-centered workflow – When it comes to patient safety, focusing on smart data allows organizations to create meaningful alerts and alarms. Furthermore, conducting comprehensive usability analysis helps to ensure that clinicians receive the right alerts at the right time with the right information.
Tracing patients along their journey has helped the healthcare system to better understand patterns of avoidable incidents, such as infections. As a result of combining multiple sets of data, healthcare organizations are becoming better equipped to implement infection control protocols. That is, protocols that are tailored to the real-world obstacles of controlling disease spread in a complex environment.
The use of predictive analytics to solve cost, quality, and resource-related problems is driving a culture of accountability and safety. Yet, experts caution that this tool is only as good as the underlying data available to predict outcomes. Additionally, hospitals require the proper resources to respond to and to change these outcomes. This is where leveraging predictive analytics comes in. It has the potential to prevent negative outcomes, as well as to keep patient safety at the core of quality improvement goals.