Year: 2014
Type: article
Abstract: A patient-centered, continuously learning healthcare system is a compelling vision for the future of healthcare, introduced by the Institute of Medicine. A key part of this vision is the creation of feedback loops to support continuous clinical learning and behavior change. Opportunities to generate clinical performance feedback are increasing, due to globally unprecedented growth in the adoption of eHealth. These opportunities are especially promising in low-income countries where a critical problem is poor performance of healthcare providers that lowers the quality of care. Clinical audit and feedback, defined as the provision of performance summaries to healthcare providers, teams, and organizations, is widely used for quality improvement and the implementation of evidence-based practice. Evidence shows that clinical audit and feedback can significantly improve compliance with desired practice, but it is unclear when and how it is most effective. Psychological theories offer rigorously evaluated theoretical causal mechanisms that may explain when feedback is likely to be effective for clinical learning and behavior change, but these have rarely been used to inform the design of feedback interventions. In addition to uncertainty regarding the effect of feedback on clinical performance, a critical challenge for using eHealth data to automate the delivery of feedback is understanding data quality for the purpose of performance measurement. To overcome the dual challenges of variable data quality and performance feedback effectiveness, I propose a novel, theory-informed approach for generating clinical performance feedback: automated feedback message tailoring. This research explores evidence, theories, methods, and clinical settings that establish a foundation of knowledge for the automated tailoring of feedback messages. I developed and applied this knowledge within antiretroviral therapy clinics in Malawi, Africa, where an electronic medical record system is routinely used, to understand the potential impact of feedback message tailoring in low-resource settings. This work introduces a novel information tool that may enable clinical supervisors to use existing eHealth data to provide more effective performance feedback, and which may support the testing of hypotheses about the effect of tailored feedback messages on clinical performance.
Authors: Landis Lewis, J Zachary
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W132768293