A Data Driven Approach to Achieving High Value Healthcare. Issue 3 (15th December 2017)

• Record Type: Journal Article
• Authors: Savitz, Lucy A.; Weiss, Lisa T.
• Is Part Of: EGEMS. Volume 5:Issue 3(2017)
• Publication Date: 2017-12-15
• Subjects: high-value care -- collaboration -- data sharing -- evidence generation; Medical records -- Data processing -- Periodicals; Medical care -- Data processing -- Periodicals; Medical Records; Automatic Data Processing; Medical care -- Data processing; Medical records -- Data processing; Periodicals; Periodicals; 651.504261
• DOI: 10.5334/egems.241
• Languages: English
• ISSNs: 2327-9214

Abstract

The purpose of this special issue is to disseminate learning from the High Value Healthcare Collaborative (HVHC). The HVHC is a voluntary, member-led organization based on trusted, working relationships among delivery system leaders. HVHC's mission is to be a provider-based learning health system committed to improving healthcare value through data, evidence, and collaboration. We begin by describing the organization and structure of HVHC in order to lay the context for a series of papers that feature work from this learning health system. HVHC was awarded a grant from the John and Laura Arnold Foundation to develop a generalizable model for dissemination and implementation. Implementation of the 3-hour sepsis bundle was used as a prototypic, complex intervention with an in-depth mixed methods evaluation across 16 member sites. The first four articles in this issue describe, in detail, various data and methodological challenges encountered together with strategies for overcoming these (see Knowlton et al., von Recklinghausen et al., Welch et al., and Taenzer et al.). Next, we illustrate how the Data Trust can support emerging questions relevant to member organizations. The paper by Albritton et al., explores the impact of observation stays on readmission rates. Knighton et al., explore the use of an area-based measure for health literacy to assess risk in disadvantaged populations. Two final papers illustrate the importance of fundamental data sources needed to support advanced data science.