Identifying Chronic Conditions

How Sick is my Cohort of Patients? A General Approach to Identifying Chronic Conditions

Patricia Ferido
Tuesday, June 22, 2021, 10:00am-2:00pm Pacific Daylight Time

Register Now

Class Description

With the COVID-19 pandemic, the need for evidence-based healthcare research has become increasingly apparent. Even before the recent health crisis, the volume of available data on healthcare had been growing exponentially. Claims data and electronic health records provide rich insight into the health status of patients and the care provided by health care systems. Successfully uncovering these insights, however, requires an understanding of the data, as well as standardized and validated methods of analysis. This class will provide an overview of best practices when working with claims data, specifically Medicare claims data. Topics covered will include: the general structure of claims data, how best to use that information to identify disease cohorts, different approaches for measuring the health status of patients (e.g., Charlson Comorbidity Index, the Elixhauser Comorbidity Index, Hierarchical Condition Category Coding, etc.), and a deep dive into the Chronic Condition Warehouse (CCW) algorithms. Finally, the class will conclude with the workshopping of a SAS Macro that applies CCW-like rules to any dataset that resembles insurance claims or electronic health records with a full-picture of diagnoses and procedures from patient medical visits. The macro package includes the CCW validated algorithms (the default option), but also has the flexibility for the user to apply the algorithm to a different set of diagnoses and procedures. The user can either implement variations of the CCW-definitions or identify entirely new conditions, so long as they can be implemented using diagnosis or procedure codes, claim types, and CCW-like rules. After taking the class, students will have an understanding of key factors to consider in disease cohort analysis and will have direct experience using this package to identify diseases in simulated data.

Meet the Instructor

Patricia Ferido is a Senior Research Programmer at the Leonard D. Schaeffer Center for Health Policy and Economics where she analyzes medical data for research on dementia care and treatment. Prior to joining the Schaeffer Center, she worked as an economics litigation consultant specializing in the analysis of labor data for wage and hour litigation. She holds a BA in both Economics and International Development Studies from UCLA and is pursuing a Masters in Public Policy Data Science at USC.