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 decad...
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| Format: | Article |
| Language: | English |
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BMC
2020-05-01
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| Series: | BMC Medicine |
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| Online Access: | http://link.springer.com/article/10.1186/s12916-020-01563-4 |
| _version_ | 1828878914994307072 |
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| author | Jeffrey Braithwaite Paul Glasziou Johanna Westbrook |
| author_facet | Jeffrey Braithwaite Paul Glasziou Johanna Westbrook |
| author_sort | Jeffrey Braithwaite |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T09:17:00Z |
| format | Article |
| id | doaj.art-8753d1f760d441d09008dc245096f50d |
| institution | Directory Open Access Journal |
| issn | 1741-7015 |
| language | English |
| last_indexed | 2024-12-13T09:17:00Z |
| publishDate | 2020-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medicine |
| spelling | doaj.art-8753d1f760d441d09008dc245096f50d2022-12-21T23:52:49ZengBMCBMC Medicine1741-70152020-05-011811810.1186/s12916-020-01563-4The three numbers you need to know about healthcare: the 60-30-10 ChallengeJeffrey Braithwaite0Paul Glasziou1Johanna Westbrook2Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie UniversityInstitute for Evidence-Based Health Care, Faculty of Health Sciences and Medicine, Bond UniversityCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie UniversityAbstract 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.http://link.springer.com/article/10.1186/s12916-020-01563-4Learning health systemComplexityComplexity scienceChangeEvidence-based careClinical networks |
| spellingShingle | Jeffrey Braithwaite Paul Glasziou Johanna Westbrook The three numbers you need to know about healthcare: the 60-30-10 Challenge BMC Medicine Learning health system Complexity Complexity science Change Evidence-based care Clinical networks |
| title | The three numbers you need to know about healthcare: the 60-30-10 Challenge |
| title_full | The three numbers you need to know about healthcare: the 60-30-10 Challenge |
| title_fullStr | The three numbers you need to know about healthcare: the 60-30-10 Challenge |
| title_full_unstemmed | The three numbers you need to know about healthcare: the 60-30-10 Challenge |
| title_short | The three numbers you need to know about healthcare: the 60-30-10 Challenge |
| title_sort | three numbers you need to know about healthcare the 60 30 10 challenge |
| topic | Learning health system Complexity Complexity science Change Evidence-based care Clinical networks |
| url | http://link.springer.com/article/10.1186/s12916-020-01563-4 |
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