Informatics and technology enable us to learn from every patient: Pharmacists' many roles in learning health systems

Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature

The "learning health system" (LHS) concept has been defined in broad terms, which makes it challenging for health system leaders to determine exactly what is required to transform their organization into an LHS. This study provides a conceptual map of the LHS landscape by identifying the activities, principles, tools, and conditions that LHS researchers have associated with the concept. Through a multi-step screening process, two researchers identified 79 publications from PubMed (published before January 2020) that contained information relevant to the question, "What work is required of a healthcare organization that is operating as an LHS?" Those publications were coded as to whether or not they referenced each of 94 LHS elements in the taxonomy developed by the study team. This taxonomy, named the Learning Health Systems Consolidated Framework (LHS-CF), organizes the elements into five "bodies of work" (organizational learning, translation of evidence into practice, building knowledge, analyzing clinical data, and engaging stakeholders) and four "enabling conditions" (workforce skilled for LHS work, data systems and informatics technology in place, organization invests resources in LHS work, and supportive organizational culture). We report the frequency that each of the 94 elements was referenced across the 79 publications. The four most referenced elements were: "organization builds knowledge or evidence," "quality improvement practices are standard practice," "patients and family members are actively engaged," and "organizational culture emphasizes and supports learning." By dissecting the LHS construct into its component elements, the LHS-CF taxonomy can serve as a useful tool for LHS researchers and practitioners in defining the aspects of LHS they are addressing. By assessing how often each element is referenced in the literature, the study provides guidance to health system leaders as to how their organization needs to evolve in order to become an LHS - while also recognizing that each organization should emphasize elements that are most aligned with their mission and goals.

A Learning Health System Framework to Operationalize Health Data to Improve Quality Care: An Australian Perspective

Our healthcare system faces a burgeoning aging population, rising complexity, and escalating costs. Around 10% of healthcare is harmful, and evidence is slow to implement. Innovation to deliver quality and sustainable health systems is vital, and the methods are challenging. The aim of this study is to describe the process and present a perspective on a coproduced Learning Health System framework. The development of the Framework was led by publicly funded, collaborative, Academic Health Research Translation Centres, with a mandate to integrate research into healthcare to deliver impact. The focus of the framework is “learning together for better health,” with coproduction involving leadership by an expert panel, a systematic review, qualitative research, a stakeholder workshop, and iterative online feedback. The coproduced framework incorporates evidence from stakeholders, from research, from data (practice to data and data to new knowledge), and from implementation, to take new knowledge to practice. This continuous learning approach aims to deliver evidence-based healthcare improvement and is currently being implemented and evaluated.

Learning health systems using data to drive healthcare improvement and impact: a systematic review

Background

The transition to electronic health records offers the potential for big data to drive the next frontier in healthcare improvement. Yet there are multiple barriers to harnessing the power of data. The Learning Health System (LHS) has emerged as a model to overcome these barriers, yet there remains limited evidence of impact on delivery or outcomes of healthcare.

Objective

To gather evidence on the effects of LHS data hubs or aligned models that use data to deliver healthcare improvement and impact. Any reported impact on the process, delivery or outcomes of healthcare was captured.

Methods

Systematic review from CINAHL, EMBASE, MEDLINE, Medline in-process and Web of Science PubMed databases, using learning health system, data hub, data-driven, ehealth, informatics, collaborations, partnerships, and translation terms. English-language, peer-reviewed literature published between January 2014 and Sept 2019 was captured, supplemented by a grey literature search. Eligibility criteria included studies of LHS data hubs that reported research translation leading to health impact.

Results

Overall, 1076 titles were identified, with 43 eligible studies, across 23 LHS environments. Most LHS environments were in the United States (n = 18) with others in Canada, UK, Sweden and Australia/NZ. Five (21.7%) produced medium-high level of evidence, which were peer-reviewed publications.

Conclusions

LHS environments are producing impact across multiple continents and settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06215-8.

Leaders' perspectives on learning health systems: a qualitative study

Background

Integrated utilisation of digital health data has the power to transform healthcare to deliver more efficient and effective services, and the learning health system (LHS) is emerging as a model to achieve this. The LHS uses routine data from service delivery and patient care to generate knowledge to continuously improve healthcare. The aim of this project was to explore key features of a successful and sustainable LHS to inform implementation in an Academic Health Science Centre context.

Methods

We purposively identified and conducted semi-structured qualitative interviews with leaders, experienced in supporting or developing data driven innovations in healthcare. A thematic analysis using NVivo was undertaken.

Results

Analysis of 26 interviews revealed five themes thought to be integral in an effective, sustainable LHS: (1) Systematic approaches and iterative, continuous learning with implementation into healthcare contributing to new best-practice care; (2) Broad stakeholder, clinician and academic engagement, with collective vision, leadership, governance and a culture of trust, transparency and co-design; (3) Skilled workforce, capability and capacity building; (4) Resources with sustained investment over time and; (5) Data access, systems and processes being integral to a sustainable LHS.

Conclusions

This qualitative study provides insights into the elements of a sustainable LHS across a range of leaders in data-driven healthcare improvement. Fundamentally, an LHS requires continuous learning with implementation of new evidence back into frontline care to improve outcomes. Structure, governance, trust, culture, vision and leadership were all seen as important along with a skilled workforce and sustained investment. Processes and systems to optimise access to quality data were also seen as vital in an effective, sustainable LHS. These findings will inform a co-designed framework for implementing a sustainable LHS within the Australian healthcare and Academic Health Science Centre context. It is anticipated that application of these findings will assist to embed and accelerate the use of routine health data to continuously generate new knowledge and ongoing improvement in healthcare delivery and health outcomes.