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

10.2196/17026 ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. e17026 ◽  
Author(s):  
Jodyn E Platt ◽  
Minakshi Raj ◽  
Matthias Wienroth
Keyword(s):  
Health Care ◽  
Health System ◽  
Thematic Analysis ◽  
Rapid Learning ◽  
The Past ◽  
Learning From Data ◽  
Learning Health System ◽  
Initial Knowledge ◽  
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 Knowledge Gaps and Uncertainties 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 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.

scholarly journals Value-Based Health Care Supported by Data Science

2018 ◽  
pp. 193-212 ◽  
Author(s):  
Tiffany I. Leung ◽  
G. G. van Merode
Keyword(s):  
Health Care ◽  
Service Delivery ◽  
Health System ◽  
Healthcare System ◽  
Patient Outcomes ◽  
Data Science ◽  
Costs Of Care ◽  
Learning Health System ◽  
Measuring Outcomes ◽  
Cost Measurement

AbstractThe value agenda involves measuring outcomes that matter and costs of care to optimize patient outcomes per dollar spent. Outcome and cost measurement in the value-based health care framework, centered around a patient condition or segment of the population, depends on data in every step towards healthcare system redesign. Technological and service delivery innovations are key components of driving transformation towards high-value health care. The learning health system and network-based thinking are complementary frameworks to the value agenda. Health care and medicine exist in a data-rich environment, and learning about how data can be used to measure and improve value of care for patients is and increasingly essential skill for current and future clinicians.

A Rapid-Learning Health System

2007 ◽  
Vol 26 (Suppl1) ◽  
pp. w107-w118 ◽  
Author(s):  
Lynn M. Etheredge
Keyword(s):  
Health System ◽  
Rapid Learning ◽  
Learning Health System

Advancing health care quality and safety through action learning

2017 ◽  
Vol 30 (2) ◽  
pp. 148-158 ◽  
Author(s):  
Simon Mathews ◽  
Sherita Golden ◽  
Renee Demski ◽  
Peter Pronovost ◽  
Lisa Ishii
Keyword(s):  
Health Care ◽  
Health System ◽  
Health Care Quality ◽  
Design Methodology ◽  
Action Learning ◽  
Care Quality ◽  
Quality And Safety ◽  
Content Type ◽  
Learning Health System ◽  
Academic Medical

Purpose The purpose of this study is to demonstrate how action learning can be practically applied to quality and safety challenges at a large academic medical health system and become fundamentally integrated with an institution’s broader approach to quality and safety. Design/methodology/approach The authors describe how the fundamental principles of action learning have been applied to advancing quality and safety in health care at a large academic medical institution. The authors provide an academic contextualization of action learning in health care and then transition to how this concept can be practically applied to quality and safety by providing detailing examples at the unit, cross-functional and executive levels. Findings The authors describe three unique approaches to applying action learning in the comprehensive unit-based safety program, clinical communities and the quality management infrastructure. These examples, individually, provide discrete ways to integrate action learning in the advancement of quality and safety. However, more importantly when combined, they represent how action learning can form the basis of a learning health system around quality and safety. Originality/value This study represents the broadest description of action learning applied to the quality and safety literature in health care and provides detailed examples of its use in a real-world context.

Mental Health System Historians: Adults with Schizophrenia Describe Changes in Community Mental Health Care Over Time

2014 ◽  
Vol 86 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Catherine H. Stein ◽  
Jaclyn E. Leith ◽  
Lawrence A. Osborn ◽  
Sarah Greenberg ◽  
Catherine E. Petrowski ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Community Mental Health ◽  
Mental Health Care ◽  
Health System ◽  
Mental Health System ◽  
Community Mental Health Care ◽  
Over Time

scholarly journals Patients as Agents in Behavioral Health Research and Service Provision: Recommendations to Support the Learning Health System

2019 ◽  
Vol 10 (05) ◽  
pp. 841-848 ◽  
Author(s):  
Hannah K. Galvin ◽  
Carolyn Petersen ◽  
Vignesh Subbian ◽  
Anthony Solomonides
Keyword(s):  
Health Care ◽  
Health System ◽  
Behavioral Health ◽  
Health Care System ◽  
Health Research ◽  
Information Technologies ◽  
Care Delivery ◽  
Learning Health System ◽  
Behavioral Health Research ◽  
Care System

AbstractAuthentic inclusion and engagement of behavioral health patients in their care delivery and in the process of scientific discovery are often challenged in the health care system. Consequently, there is a growing need to engage with and better serve the needs of behavioral health patients, particularly by leveraging health information technologies. In this work, we present rationale and strategies for improving patient engagement in this population in research and clinical care. First, we describe the potential for creating meaningful patient–investigator partnerships in behavioral health research to allow for cocreation of knowledge with patients. Second, in the context of behavioral health services, we explore the utility of sharing clinical notes to promote patients' agency in care delivery. Both lines of inquiry are centered in a Learning Health System model for behavioral health, where patients are agents in enhancing the therapeutic alliance and advancing the process of knowledge generation. Recommendations include genuinely democratizing the health care system and biomedical research enterprise through patient-centered information technologies such as patient portals. In research and technology development, we recommend seeking and tailoring behavioral health patients' involvement to their abilities, promoting patient input in data analysis plans, evaluating research and informatics initiatives for patients and clinicians, and sharing success and research findings with patients. In clinical practice, we recommend encouraging patients to read behavioral health notes on portals, engaging in proactive communication regarding note content, assessing outcomes including stress and anxiety in response to note content, and working with technology providers to support note-sharing governance and deployment.

scholarly journals Status Update on Data Required to Build a Learning Health System

2020 ◽  
Vol 38 (14) ◽  
pp. 1602-1607 ◽  
Author(s):  
Monica M. Bertagnolli ◽  
Brian Anderson ◽  
Kelly Norsworthy ◽  
Steven Piantadosi ◽  
Andre Quina ◽  
...  
Keyword(s):  
Health Care ◽  
Health System ◽  
Large Scale ◽  
Point Of Care ◽  
Health Care Systems ◽  
Clinical Care ◽  
Real World Data ◽  
Learning Health System ◽  
Care Systems ◽  
Data Elements

Wide adoption of electronic health records (EHRs) has raised the expectation that data obtained during routine clinical care, termed “real-world” data, will be accumulated across health care systems and analyzed on a large scale to produce improvements in patient outcomes and the use of health care resources. To facilitate a learning health system, EHRs must contain clinically meaningful structured data elements that can be readily exchanged, and the data must be of adequate quality to draw valid inferences. At the present time, the majority of EHR content is unstructured and locked into proprietary systems that pose significant challenges to conducting accurate analyses of many clinical outcomes. This article details the current state of data obtained at the point of care and describes the changes necessary to use the EHR to build a learning health system.

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