Overview, Learning Objectives, Reading Assignments, and Resources
Module 6: Measurement, data and trust: Supporting change with data
This module discusses common challenges and a range of solutions for collecting and reporting overuse data when taking action on overuse.
To build a culture of trust, innovation, and improvement; it is essential to share transparent, meaningful and actionable data about the targeted overused service with stakeholders. Pre-defined measures of overuse are often not readily available and reliable sources of data can take time to identify and even longer to extract into actionable measures within a report or dashboard for providers. Reliable and valid data translated into measures that are clinically meaningful are often critical to getting the provider to the table for a discussion about your targeted area of overuse. In addition, it is also extremely important to confirm impressions about the rate of overuse in your setting when selecting your overuse topic/service for your project. Some projects have been derailed because initial anecdotal impressions about a service being frequently overused are not supported by the data.
- Describe the differences among outcome, process and balancing measures for an overuse initiative.
- Understand the steps needed to identify your data source, method of collection, measure specification and how these measures will be reported.
- Discuss how to effectively present data that reveals variation in rates of overuse across settings and providers.
Although not focused on de-implementing a low-value care service, the explanation of a ‘balancing measure’ should clearly translate to addressing overused care.
- Citation: Toma M, Dreischulte T, Gray NM, et al Balancing measures or a balanced accounting of improvement impact: a qualitative analysis of individual and focus group interviews with improvement experts in Scotland BMJ Quality & Safety 2018;27:547-556
Value of Small Sample Sizes in Rapid-cycle Quality Improvement Projects
A surprising explanation of why large numbers of observations on rates of use of an overused service may not be necessary, and the implications of that for tackling low-value care.
- Citation: Etchells E, Ho M, Shojania KG Value of small sample sizes in rapid-cycle quality improvement projects BMJ Quality & Safety 2016;25:202-206.
Evaluation of an Intervention to Reduce Low-Value Preoperative Care for Patients Undergoing Cataract Surgery at a Safety-Net Health System
A great example of efforts to reduce low-value pre-operative care, how data and measures were obtained and used, along with an important example of a balancing measure.
- Citation: Mafi JN, Godoy-Travieso P, Wei E, et al. Evaluation of an Intervention to Reduce Low-Value Preoperative Care for Patients Undergoing Cataract Surgery at a Safety-Net Health System. JAMA Intern Med. 2019;179(5):648-657. PMC6503569
Science of Improvement: Establishing Measures
This resource from the Institute for Healthcare Improvement describes 3 types of measures in health care, where to look for data to create these measures, and how to present data when doing an improvement project.
IHI Open School
Working in Concert: A How-To Guide to Reducing Unwanted Variation in Care
This resource provides remarkable insights in how to use data when talking with clinicians and provides case examples of how to do so.
- Citation: “Working in Concert: A How-To Guide to Reducing Unwanted Variation in Care.” California Health Care Foundation. September 2014