Regional Process Redesign of Lung Cancer Care: A Learning Health System Pilot Project

Fung-Kee-Fung, Michael; Maziak, Donna E.; Pantarotto, Jason; Smylie, Jennifer; Taylor, Leanne; Timlin, Thomas; Cacciotti, T.; Villeneuve, Patrick J.; Dennie, Carole; Bornais, C.; Madore, Suzanne; Aquino, José Henrique W.; Wheatley‐Price, Paul; Ozer, Rachel; Stewart, David J.

doi:10.3747/co.25.3719

Cited by 38 publications

(77 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples of such learning systems, or those attempting to emulate the characteristics of a learning health system, are emerging at a rapid pace. Cases in point are studies redesigning the care of lung cancer patients (The Ottawa Health Transformation Model) [64], the changing roles of researchers in different US settings using learning health system principles to reduce diagnostic errors and near misses [65], UK policy initiatives to build the infrastructure and data backbone on which the progress of learning health [56] and Braithwaite et al [57]). Used with permission from Oxford University Press systems will be based [66], and data collaborations to reduce mortality associated with sepsis amongst 21 hospitals of the Kaiser Permanente North California system [67] following earlier Kaiser Permanente examples, such as the work on Vioxx, and the early detection of its long-term side effects [68].…”

Section: The Deep Learning Health Systemmentioning

confidence: 99%

The three numbers you need to know about healthcare: the 60-30-10 Challenge

Braithwaite

Glasziou

Westbrook

2020

BMC Med

144

129

View full text Add to dashboard Cite

Background: Healthcare represents a paradox. While change is everywhere, performance has flatlined: 60% of care on average is in line with evidence-or consensus-based guidelines, 30% is some form of waste or of low value, and 10% is harm. The 60-30-10 Challenge has persisted for three decades. Main body: Current top-down or chain-logic strategies to address this problem, based essentially on linear models of change and relying on policies, hierarchies, and standardisation, have proven insufficient. Instead, we need to marry ideas drawn from complexity science and continuous improvement with proposals for creating a deep learning health system. This dynamic learning model has the potential to assemble relevant information including patients' histories, and clinical, patient, laboratory, and cost data for improved decision-making in real time, or close to real time. If we get it right, the learning health system will contribute to care being more evidence-based and less wasteful and harmful. It will need a purpose-designed digital backbone and infrastructure, apply artificial intelligence to support diagnosis and treatment options, harness genomic and other new data types, and create informed discussions of options between patients, families, and clinicians. While there will be many variants of the model, learning health systems will need to spread, and be encouraged to do so, principally through diffusion of innovation models and local adaptations. Conclusion: Deep learning systems can enable us to better exploit expanding health datasets including traditional and newer forms of big and smaller-scale data, e.g. genomics and cost information, and incorporate patient preferences into decision-making. As we envisage it, a deep learning system will support healthcare's desire to continually improve, and make gains on the 60-30-10 dimensions. All modern health systems are awash with data, but it is only recently that we have been able to bring this together, operationalised, and turned into useful information by which to make more intelligent, timely decisions than in the past.

show abstract

Section: The Deep Learning Health Systemmentioning

confidence: 99%

The three numbers you need to know about healthcare: the 60-30-10 Challenge

Braithwaite

Glasziou

Westbrook

2020

BMC Med

144

129

View full text Add to dashboard Cite

show abstract

“…Some of the studies in this review have utilized quality indicators that were used by other centres for research on lung cancer pathways [4,[27][28][29]. These quality indicators can be used to identify problems in the local lung cancer pathway, such as delays and non-adherence to clinical guidelines [4].…”

Section: Discussionmentioning

confidence: 99%

“…One of the first steps in implementing a lung cancer pathway is 'process mapping', in which the existing lung cancer pathway in the region is mapped and weak points identified [28]. Streamlining administration and use of the best information technology tools are also an important part of implementing the pathway [29].…”

Section: Development and Implementation Of Lung Cancer Care Pathwaysmentioning

confidence: 99%

“…Representatives from primary care, public health, occupational medicine, oncology, patients and their care givers participated in formulating this pathway [32]. Some centres addressed the delays in lung cancer care by redesigning the regional model of care [29]. The Ottawa Hospital Model operationalised the lung cancer diagnostic pathway and optimised patient flow from referral to initiation of treatment.…”

Section: Development and Implementation Of Lung Cancer Care Pathwaysmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Care Pathways for People with Lung Cancer- a Scoping Review of the Literature

Otty¹,

Brown²,

Sabesan³

et al. 2020

Int J Integr Care

View full text Add to dashboard Cite

Introduction: Much of the existing work around implementation of cancer optimal care pathways (OCP) has either focused exclusively on the clinical elements of care or has targeted individual stages in the cancer trajectory, rather than using a patient-centred or service delivery lens to inform the integration of care across the continuum. This review aimed to identify and summarise the available literature on lung cancer OCP. Methods: A scoping review was conducted, with literature across multiple databases and grey literature searched. Articles were included if the OCP was being used to manage adult patients with lung cancer and reported on either the development process and outcomes and/or barriers and facilitators associated with optimal care pathway development and/or uptake. Results: Of the 381 references screened, 32 articles were included. The lung cancer pathways reviewed varied significantly. A number of themes were identified including the development and implementation of the OCP; the use of quality indicators to audit the OCP; and studies on outcomes of the OCP incorporating timeliness of care delivery, patient experiences and health care utilisation and costs. Conclusions: The limited number of relevant articles found in this review may suggest that an OCP for lung cancer is still in its preliminary stages across the broader health systems.

show abstract

“…An essential component of a LHS is a collaborative platform that provides connectivity across silos, organizations, and professions. In the literature there is evidence that automated reports using the data from the entire LHS has led to the e cient identi cation of patients for standardised care, specialised care, follow-up or clinical trials [29][30][31]. Collection of information directly from patients before the clinical encounter has also been reported as bene cial due to time e ciencies [32], as well as creating PROMs (patient reported outcome measures) that are saved within the electronic medical record and can enable longitudinal tracking of individual patient outcomes and are used in aggregated research [4,5,32].…”

Section: Discussionmentioning

confidence: 99%

Leadership Perspectives on Learning Health Systems: a qualitative interview study

Enticott¹,

Braaf

Johnson³

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Regional Process Redesign of Lung Cancer Care: A Learning Health System Pilot Project

Abstract: BackgroundThe Ottawa Hospital (toh) defined delay to timely lung cancer care as a system design problem.

Cited by 38 publications

References 37 publications

The three numbers you need to know about healthcare: the 60-30-10 Challenge

The three numbers you need to know about healthcare: the 60-30-10 Challenge

Optimal Care Pathways for People with Lung Cancer- a Scoping Review of the Literature

Leadership Perspectives on Learning Health Systems: a qualitative interview study

Contact Info

Product

Resources

About