Quantifying Dynamic Risk with Predictive Analytics

Predictive analytics enables innovative approaches for leveraging real-time clinical data to assess risk. Predictive methodologies and applications that use dynamic patient information to update health risk assessments over time are especially valuable. For this purpose, two healthcare applications have been developed that assess either risk of hospital readmission or probability of elective, surgical procedures. Both techniques use electronic health record information as a source of features for predictive modeling. These applications are rooted in health system operations and may provide innovative value for monitoring and managing clinical operation risk. INTRODUCTION This paper provides readers with a walkthrough for using predictive analytics to assess risk by describing the general workflow and providing two real-world examples. The predictive analytics methods discussed have broad applicability to numerous applications and industries. The authors intend to present a stepping off point for readers looking to use predictive analytics to assess and manage clinical operation risk in a forward-looking way. While these examples are not specific to clinical trial execution, they provide process templates for risk identification in a dynamic data environment. To describe the scope of the paper, the first portion of the paper called “Predictive Modeling Workflow” provides a primer from data selection to predictive modeling. The second portion of the paper, which is broken into two sections called “Healthcare Application 1” and “Healthcare Application 2,” provides two detailed, practical examples of predictive modeling related to risk management that the authors encountered in a real-world, healthcare setting. PREDICTIVE MODELING WORKFLOW The workflow of predictive modeling can be characterized by a series of processes. These processes are described within the sub-sections that follow. LOCATING AND SELECTING DATA The key to navigating this step of the workflow successfully is flexibility. First the modeler needs to identify and locate the data sources that will be used for the analysis. Typically, the modeler will identify historical data and outcomes to use as the basis of the predictive model. To identify the data sources appropriately, the modeler needs to build a working relationship with the end user(s) of the model. These users provide critical subject matter expertise, including establishing the goals and objectives of the model and identifying prospective data sources for modeling. To benefit the most from predictive modeling, the modeler needs to maintain objectivity in selecting the data sources for inclusion in the predictive model. The end user may, intentionally or unintentionally, introduce their own biases or other limitations on the data sources the modeler should consider. For example, the end user seeking to manage risk in a clinical trial might only present health record metrics and clinical history, whereas the modeler may see benefit in expanding the scope to include social determinants of health for a more advantageous model. The modeler should be wary, though, that the availability and connectivity of the data in the implementation setting will ultimately dictate what may be used. The next step is to locate the data sources and the stewards of those data sources. End users can often be helpful in making those connections. When working with the data of another data steward, the modeler is stepping into the steward’s domain. The infrastructure for how data is stored within each business unit can be vastly different even within the same organization. The modeler needs to work with each data steward to determine the best approach for both practical access and data protection. Some data may be sensitive as is the case with the healthcare field, which typically contains data that can be classified as personally identifiable information (PII) or protected health information (PHI). PII is any information that can be used to identify, locate, or contact a specific individual. PHI is any health information belonging to a specific individual that was created by or delivered to a health provider. The modeler needs to maintain awareness and protocols for handling protected and sensitive information detailed in business associate agreements or federal acts such as the Health Insurance Portability and Accountability Act of 1996 (HIPAA).