Perspectives on the Implementation of an Ethical Rapid Learning Health System

Background Interest in value-based healthcare, generally defined as providing better care at lower cost, has grown worldwide, and learning health systems (LHSs) have been proposed as a key strategy for improving value in healthcare. LHSs are emerging around the world and aim to leverage advancements in science, technology and practice to improve health system performance at lower cost. However, there remains much uncertainty around the implementation of LHSs and the distinctive features of these systems. This paper presents a conceptual framework that has been developed in Canada to support the implementation of value-creating LHSs. Methods The framework was developed by an interdisciplinary team at the Institut national d’excellence en santé et en services sociaux (INESSS). It was informed by a scoping review of the scientific and grey literature on LHSs, regular team discussions over a 14-month period, and consultations with Canadian and international experts. Results The framework describes four elements that characterise LHSs, namely (1) core values, (2) pillars and accelerators, (3) processes and (4) outcomes. LHSs embody certain core values, including an emphasis on participatory leadership, inclusiveness, scientific rigour and person-centredness. In addition, values such as equity and solidarity should also guide LHSs and are particularly relevant in countries like Canada. LHS pillars are the infrastructure and resources supporting the LHS, whereas accelerators are those specific structures that enable more rapid learning and improvement. For LHSs to create value, such infrastructures must not only exist within the ecosystem but also be connected and aligned with the LHSs’ strategic goals. These pillars support the execution, routinisation and acceleration of learning cycles, which are the fundamental processes of LHSs. The main outcome sought by executing learning cycles is the creation of value, which we define as the striking of a more optimal balance of impacts on patient and provider experience, population health and health system costs. Conclusions Our framework illustrates how the distinctive structures, processes and outcomes of LHSs tie together with the aim of optimising health system performance and delivering greater value in health systems. Electronic supplementary material The online version of this article (10.1186/s12961-019-0477-3) contains supplementary material, which is available to authorized users.

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“…• CancerLinQ regulatory framework and guiding principles for the ethical management and use of data [63, 68]…”

Section: Resultsmentioning

confidence: 99%

A framework for value-creating learning health systems

Menear

Blanchette

Demers‐Payette

et al. 2019

Health Res Policy Sys

187

271

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“…If we accept that the rapid-learning concept is still fundamentally attractive, which the volume of literature our keyword search initially returned (with an upwards year-on-year trend) suggests, then the remaining, practical, barriers merit closer exploration. In the sections below we examine challenges to the routine use of rapid-learning previously highlighted in the LHS literature [11,[95][96][97][98][99][100][101][102]93] and the lack of methodological consensus identified by our review.…”

Section: Discussionmentioning

confidence: 99%

Learning healthcare systems and rapid learning in radiation oncology: Where are we and where are we going?

Price

Mackay

Aznar

et al. 2021

Radiotherapy and Oncology

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CancerLinQ: Origins, Implementation, and Future Directions

Rubinstein

Warner

2018

JCO Clinical Cancer Informatics

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Rapid-learning health systems have been proposed as a potential solution to the problem of quality in medicine, by leveraging data generated from electronic health systems in near-real time to improve quality and reduce cost. Given the complex, dynamic nature of cancer care, a rapid-learning health system offers large potential benefits to oncology practice. In this article, we review the rationale for developing a rapid-learning health system for oncology and describe the sequence of events that led to the development of ASCO’s CancerLinQ (Cancer Learning Intelligence Network for Quality) initiative, as well as the current state of CancerLinQ, including its importance to efforts such as the Beau Biden Cancer Moonshot. We then review the considerable challenges facing optimal implementation of a rapid-learning health system such as CancerLinQ, including integration of rapidly expanding multiomic data, capturing big data from a variety of sources, an evolving competitive landscape, and implementing a rapid-learning health system in a way that satisfies many stakeholders, including patients, providers, researchers, and administrators.

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Perspectives on the Implementation of an Ethical Rapid Learning Health System

Cited by 3 publications

References 4 publications

A framework for value-creating learning health systems

A framework for value-creating learning health systems

Learning healthcare systems and rapid learning in radiation oncology: Where are we and where are we going?

CancerLinQ: Origins, Implementation, and Future Directions

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