HAS

The countdown is on!

Register for HAS 21

Kathleen Clary, BSN, MSN, DNP

Vice President of Care Management & Patient Engagement

Kathleen Clary joined Health Catalyst in May 2017 as VP of Care Management & Patient Engagement. Prior to coming to Catalyst, Kathleen worked for MultiCare Health System as the Administrator of Patient Navigation & Care Coordination. Kathleen has a degree in Bachelors of Nursing from Mount Marty College, a Masters of Nursing from University of Washington, Tacoma, a Doctorate of Nursing Practice in Systems Leadership from Rush University, Chicago and currently enrolled (graduate 2018) at the Army War College to earn a Master’s in Strategic Studies.

See content from Kathleen Clary, BSN, MSN, DNP

Measuring the Value of Care Management: Five Tools to Show Impact

To earn legitimacy and resources within a healthcare organization, care management programs need objective, data-driven ways to demonstrate their success. The value of care management isn’t always obvious; while these programs may, in fact, be responsible for improvements in critical metrics, such as reducing readmissions, C-suite leaders need visibility into care management’s impact and processes to understand precisely how they’re improving care and lowering costs at their organizations.

Five analytics-driven technologies give healthcare leaders a comprehensive understanding of care management performance:

1. The Patient Stratification Application
2. The Patient Intake Tool
3. The Care Coordination Application
4. The Care Companion Application
5. The Care Team Insights Tool

Custom Care Management Algorithms that Actually Reveal Risk

Care management is a tool for population health that focuses scarce healthcare resources on the sickest patients. Care management leaders need to know who those sickest patients are (or may be). The static risk models typically used for stratifying patients into risk categories only paint a partial picture of health and are ineffective for modern care management programs. Custom algorithms are now capable of predicting risk based on multiple risk models and multiple data sources. They help care management teams confidently stratify patient populations to paint a complete picture of care needs and efficiently deliver care to those who need it most.

This article explains how custom algorithms work on static risk models to normalize risk scores and improve patient stratification, care management, and, ultimately, population health management.