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

Abstract

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.

Keywords: Change; Clinical networks; Complexity; Complexity science; Evidence-based care; Healthcare systems; Learning health system; Patient safety; Policy; Quality of care.

Fig. 1

Social-professional network changes measured via a social network analysis of the Translational Cancer Research Network (TCRN) in Eastern Sydney, Australia. Each dot (node) represents a TCRN member, and each line (vector) a collaborative tie (adapted from Long et al. [33]). Permission is provided under Creative Commons Attribution License 4.0

Fig. 2

Phases of implementation as Formative Evaluation Feedback Loops (FEFL) (adapted from Braithwaite et al. [56] and Braithwaite et al. [57]). Used with permission from Oxford University Press

Fig. 3

Cycles of advancement in the deep learning health system (adapted from Norgeot et al. [63]). Used with permission from Springer Nature