Observational Studies in a Learning Health System

2013 ◽  
Keyword(s):  
Health System ◽  
Observational Studies ◽  
Learning Health System

scholarly journals Analytical Methods for a Learning Health System: 3. Analysis of Observational Studies

2017 ◽  
Vol 5 (1) ◽  
pp. 30
Author(s):  
Michael Stoto ◽  
Michael Oakes ◽  
Elizabeth Stuart ◽  
Randall Brown ◽  
Jelena Zurovac ◽  
...  
Keyword(s):  
Health System ◽  
Analytical Methods ◽  
Observational Studies ◽  
Learning Health System

scholarly journals Analytical Methods for a Learning Health System: 2. Design of Observational Studies

2017 ◽  
Vol 5 (1) ◽  
pp. 29 ◽  
Author(s):  
Michael Stoto ◽  
Michael Oakes ◽  
Elizabeth Stuart ◽  
Elisa L. Priest ◽  
Lucy Savitz
Keyword(s):  
Health System ◽  
Analytical Methods ◽  
Observational Studies ◽  
Learning Health System ◽  
System 2

Generating Knowledge from Best Care: Advancing the Continuously Learning Health System

2016 ◽  
Vol 6 (9) ◽  
Author(s):  
Edward Abraham ◽  
◽  
Carlos Blanco ◽  
Celeste Castillo Lee ◽  
Jennifer B. Christian ◽  
...  
Keyword(s):  
Health System ◽  
Learning Health System

scholarly journals Sustainable generation of patient‐led resources in a learning health system

2021 ◽  
Author(s):  
Jennie David ◽  
Catalina Berenblum Tobi ◽  
Samantha Kennedy ◽  
Alexander Jofriet ◽  
Madeleine Huwe ◽  
...  
Keyword(s):  
Health System ◽  
Learning Health System

scholarly journals Analytical Methods for a Learning Health System: 1. Framing the Research Question

2017 ◽  
Vol 5 (1) ◽  
pp. 28
Author(s):  
Michael Stoto ◽  
Michael Oakes ◽  
Elizabeth Stuart ◽  
Lucy Savitz ◽  
Elisa L. Priest ◽  
...  
Keyword(s):  
Health System ◽  
Analytical Methods ◽  
Research Question ◽  
Learning Health System ◽  
System 1

scholarly journals An Analysis of the Learning Health System in Its First Decade in Practice: Scoping Review (Preprint)

2019 ◽  
Author(s):  
Jodyn E Platt ◽  
Minakshi Raj ◽  
Matthias Wienroth
Keyword(s):  
Health Care ◽  
Health System ◽  
Thematic Analysis ◽  
Scoping Review ◽  
Rapid Learning ◽  
The Past ◽  
Learning From Data ◽  
Learning Health System ◽  
Over Time ◽  
Value Of Health

BACKGROUND In the past decade, Lynn Etheredge presented a vision for the Learning Health System (LHS) as an opportunity for increasing the value of health care via rapid learning from data and immediate translation to practice and policy. An LHS is defined in the literature as a system that seeks to continuously generate and apply evidence, innovation, quality, and value in health care. OBJECTIVE This review aimed to examine themes in the literature and rhetoric on the LHS in the past decade to understand efforts to realize the LHS in practice and to identify gaps and opportunities to continue to take the LHS forward. METHODS We conducted a thematic analysis in 2018 to analyze progress and opportunities over time as compared with the initial <i>Knowledge Gaps and Uncertainties</i> proposed in 2007. RESULTS We found that the literature on the LHS has increased over the past decade, with most articles focused on theory and implementation; articles have been increasingly concerned with policy. CONCLUSIONS There is a need for attention to understanding the ethical and social implications of the LHS and for exploring opportunities to ensure that these implications are salient in implementation, practice, and policy efforts.

scholarly journals Designing for Accelerated Translation (DART) of emerging innovations in health

2019 ◽  
Vol 3 (2-3) ◽  
pp. 53-58 ◽  
Author(s):  
Alex T. Ramsey ◽  
Enola K. Proctor ◽  
David A. Chambers ◽  
Jane M. Garbutt ◽  
Sara Malone ◽  
...  
Keyword(s):  
Decision Making ◽  
Health System ◽  
Dynamic Process ◽  
Evidence Base ◽  
Dynamic Decision Making ◽  
Dissemination And Implementation ◽  
The Past ◽  
Learning Health System ◽  
Stakeholder Partnerships ◽  
Over Time

AbstractAccelerating innovation translation is a priority for improving healthcare and health. Although dissemination and implementation (D&I) research has made significant advances over the past decade, it has attended primarily to the implementation of long-standing, well-established practices and policies. We present a conceptual architecture for speeding translation of promising innovations as candidates for iterative testing in practice. Our framework to Design for Accelerated Translation (DART) aims to clarify whether, when, and how to act on evolving evidence to improve healthcare. We view translation of evidence to practice as a dynamic process and argue that much evidence can be acted upon even when uncertainty is moderately high, recognizing that this evidence is evolving and subject to frequent reevaluation. The DART framework proposes that additional factors – demand, risk, and cost, in addition to the evolving evidence base – should influence the pace of translation over time. Attention to these underemphasized factors may lead to more dynamic decision-making about whether or not to adopt an emerging innovation or de-implement a suboptimal intervention. Finally, the DART framework outlines key actions that will speed movement from evidence to practice, including forming meaningful stakeholder partnerships, designing innovations for D&I, and engaging in a learning health system.

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