Emilie Francis-Auton, Janet C Long, Mitchell Sarkies, Natalie Roberts, Johanna Westbrook, Jean-Frederic Levesque, Diane E Watson, Rebecca Hardwick, Peter Hibbert, Chiara Pomare, Jeffrey Braithwaite
{"title":"医疗保健大型系统转型的四个系统促进因素:混合方法现实主义评估》。","authors":"Emilie Francis-Auton, Janet C Long, Mitchell Sarkies, Natalie Roberts, Johanna Westbrook, Jean-Frederic Levesque, Diane E Watson, Rebecca Hardwick, Peter Hibbert, Chiara Pomare, Jeffrey Braithwaite","doi":"10.1111/1468-0009.12684","DOIUrl":null,"url":null,"abstract":"<p><p>Policy Points The implementation of large-scale health care interventions relies on a shared vision, commitment to change, coordination across sites, and a spanning of siloed knowledge. Enablers of the system should include building an authorizing environment; providing relevant, meaningful, transparent, and timely data; designating and distributing leadership and decision making; and fostering the emergence of a learning culture. Attention to these four enablers can set up a positive feedback loop to foster positive change that can protect against the loss of key staff, the presence of lone disruptors, and the enervating effects of uncertainty.</p><p><strong>Context: </strong>Large-scale transformative initiatives have the potential to improve the quality, efficiency, and safety of health care. However, change is expensive, complex, and difficult to implement and sustain. This paper advances system enablers, which will help to guide large-scale transformation in health care systems.</p><p><strong>Methods: </strong>A realist study of the implementation of a value-based health care program between 2017 and 2021 was undertaken in every public hospital (n = 221) in New South Wales (NSW), Australia. Four data sources were used to elucidate initial program theories beginning with a set of literature reviews, a program document review, and informal discussions with key stakeholders. Semistructured interviews were then conducted with 56 stakeholders to confirm, refute, or refine the theories. A retroductive analysis produced a series of context-mechanism-outcome (CMO) statements. Next, the CMOs were validated with three health care quality expert panels (n = 51). Synthesized data were interrogated to distill the overarching system enablers.</p><p><strong>Findings: </strong>Forty-two CMO statements from the eight initial program theory areas were developed, refined, and validated. Four system enablers were identified: (1) build an authorizing environment; (2) provide relevant, authentic, timely, and meaningful data; (3) designate and distribute leadership and decision making; and (4) support the emergence of a learning culture. The system enablers provide a nuanced understanding of large-system transformation that illustrates when, for whom, and in what circumstances large-system transformation worked well or worked poorly.</p><p><strong>Conclusions: </strong>System enablers offer nuanced guidance for the implementation of large-scale health care interventions. The four enablers may be portable to similar contexts and provide the empirical basis for an implementation model of large-system value-based health care initiatives. With concerted application, these findings can pave the way not just for a better understanding of greater or lesser success in intervening in health care settings but ultimately to contribute higher quality, higher value, and safer care.</p>","PeriodicalId":49810,"journal":{"name":"Milbank Quarterly","volume":" ","pages":"183-211"},"PeriodicalIF":4.8000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10938932/pdf/","citationCount":"0","resultStr":"{\"title\":\"Four System Enablers of Large-System Transformation in Health Care: A Mixed Methods Realist Evaluation.\",\"authors\":\"Emilie Francis-Auton, Janet C Long, Mitchell Sarkies, Natalie Roberts, Johanna Westbrook, Jean-Frederic Levesque, Diane E Watson, Rebecca Hardwick, Peter Hibbert, Chiara Pomare, Jeffrey Braithwaite\",\"doi\":\"10.1111/1468-0009.12684\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Policy Points The implementation of large-scale health care interventions relies on a shared vision, commitment to change, coordination across sites, and a spanning of siloed knowledge. Enablers of the system should include building an authorizing environment; providing relevant, meaningful, transparent, and timely data; designating and distributing leadership and decision making; and fostering the emergence of a learning culture. Attention to these four enablers can set up a positive feedback loop to foster positive change that can protect against the loss of key staff, the presence of lone disruptors, and the enervating effects of uncertainty.</p><p><strong>Context: </strong>Large-scale transformative initiatives have the potential to improve the quality, efficiency, and safety of health care. However, change is expensive, complex, and difficult to implement and sustain. This paper advances system enablers, which will help to guide large-scale transformation in health care systems.</p><p><strong>Methods: </strong>A realist study of the implementation of a value-based health care program between 2017 and 2021 was undertaken in every public hospital (n = 221) in New South Wales (NSW), Australia. Four data sources were used to elucidate initial program theories beginning with a set of literature reviews, a program document review, and informal discussions with key stakeholders. Semistructured interviews were then conducted with 56 stakeholders to confirm, refute, or refine the theories. A retroductive analysis produced a series of context-mechanism-outcome (CMO) statements. Next, the CMOs were validated with three health care quality expert panels (n = 51). Synthesized data were interrogated to distill the overarching system enablers.</p><p><strong>Findings: </strong>Forty-two CMO statements from the eight initial program theory areas were developed, refined, and validated. Four system enablers were identified: (1) build an authorizing environment; (2) provide relevant, authentic, timely, and meaningful data; (3) designate and distribute leadership and decision making; and (4) support the emergence of a learning culture. The system enablers provide a nuanced understanding of large-system transformation that illustrates when, for whom, and in what circumstances large-system transformation worked well or worked poorly.</p><p><strong>Conclusions: </strong>System enablers offer nuanced guidance for the implementation of large-scale health care interventions. The four enablers may be portable to similar contexts and provide the empirical basis for an implementation model of large-system value-based health care initiatives. With concerted application, these findings can pave the way not just for a better understanding of greater or lesser success in intervening in health care settings but ultimately to contribute higher quality, higher value, and safer care.</p>\",\"PeriodicalId\":49810,\"journal\":{\"name\":\"Milbank Quarterly\",\"volume\":\" \",\"pages\":\"183-211\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10938932/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Milbank Quarterly\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/1468-0009.12684\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/12/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Milbank Quarterly","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/1468-0009.12684","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/12/25 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Four System Enablers of Large-System Transformation in Health Care: A Mixed Methods Realist Evaluation.
Policy Points The implementation of large-scale health care interventions relies on a shared vision, commitment to change, coordination across sites, and a spanning of siloed knowledge. Enablers of the system should include building an authorizing environment; providing relevant, meaningful, transparent, and timely data; designating and distributing leadership and decision making; and fostering the emergence of a learning culture. Attention to these four enablers can set up a positive feedback loop to foster positive change that can protect against the loss of key staff, the presence of lone disruptors, and the enervating effects of uncertainty.
Context: Large-scale transformative initiatives have the potential to improve the quality, efficiency, and safety of health care. However, change is expensive, complex, and difficult to implement and sustain. This paper advances system enablers, which will help to guide large-scale transformation in health care systems.
Methods: A realist study of the implementation of a value-based health care program between 2017 and 2021 was undertaken in every public hospital (n = 221) in New South Wales (NSW), Australia. Four data sources were used to elucidate initial program theories beginning with a set of literature reviews, a program document review, and informal discussions with key stakeholders. Semistructured interviews were then conducted with 56 stakeholders to confirm, refute, or refine the theories. A retroductive analysis produced a series of context-mechanism-outcome (CMO) statements. Next, the CMOs were validated with three health care quality expert panels (n = 51). Synthesized data were interrogated to distill the overarching system enablers.
Findings: Forty-two CMO statements from the eight initial program theory areas were developed, refined, and validated. Four system enablers were identified: (1) build an authorizing environment; (2) provide relevant, authentic, timely, and meaningful data; (3) designate and distribute leadership and decision making; and (4) support the emergence of a learning culture. The system enablers provide a nuanced understanding of large-system transformation that illustrates when, for whom, and in what circumstances large-system transformation worked well or worked poorly.
Conclusions: System enablers offer nuanced guidance for the implementation of large-scale health care interventions. The four enablers may be portable to similar contexts and provide the empirical basis for an implementation model of large-system value-based health care initiatives. With concerted application, these findings can pave the way not just for a better understanding of greater or lesser success in intervening in health care settings but ultimately to contribute higher quality, higher value, and safer care.
期刊介绍:
The Milbank Quarterly is devoted to scholarly analysis of significant issues in health and health care policy. It presents original research, policy analysis, and commentary from academics, clinicians, and policymakers. The in-depth, multidisciplinary approach of the journal permits contributors to explore fully the social origins of health in our society and to examine in detail the implications of different health policies. Topics addressed in The Milbank Quarterly include the impact of social factors on health, prevention, allocation of health care resources, legal and ethical issues in health policy, health and health care administration, and the organization and financing of health care.