Building, scaling, and sustaining a learning health system for surgical quality improvement: A toolkit

Krapohl, Greta L.; Hemmila, Mark R.; Hendren, Samantha; Bishop, Kathy; Rogers, Rhonda; Rocker, Cheryl; Fasbinder, Laurie; Englesbe, Michael J.; Vu, Joceline V.; Campbell, Darrell A.

doi:10.1002/lrh2.10215

Cited by 14 publications

(15 citation statements)

References 19 publications

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“…For example, insofar as virtually all surgeons in this cohort had nearly identical reliability-adjusted complication rates but more variation in patient satisfaction, public reporting or wider use of patient satisfaction may be merited, at least for low-risk operations. Alternatively, the use of individual surgeon outcomes is likely most useful at an institutional or even departmental level—rather than at the public level—where surgeons can engage in root cause analysis and quality improvement within a learning health system 42. What’s more, these results highlight the importance of considering measurement reliability when engaging in quality improvement efforts at the institutional or regional level.…”

Section: Discussionmentioning

confidence: 99%

The Measurement Reliability of Complications and Patient Satisfaction After Common Surgical Procedures

2022

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Objective: To evaluate the reliability of surgeon outcomes. Background: Surgeons’ outcomes are now widely used in public reporting and value-based reimbursement, but the reliability of these measures continues to raise concerns. Methods: We performed a retrospective study of surgeons performing cholecystectomy, colectomy, and hernia repair on adult patients between January 1, 2017, and December 31, 2020. Outcomes were risk-adjusted rates of complications and high patient satisfaction. We estimated the reliability of each outcome, its relationship with case volume, and the number of surgeons who reached an acceptable level of reliability (≥0.70). Results: A total of 23,533 patients with a mean age of 56.8 (16.2) years and 10,191 (43.3%) females underwent operations by 333 surgeons. Risk-adjusted complication rate was 2.5% [95% confidence interval (CI): 2.2%–2.8%] and risk-adjusted high satisfaction rate was 79.9% (95% CI: 78.7%–81.0%). The reliability of the complication rate was 0.27 (95% CI: 0.25–0.29) and the reliability of the high satisfaction rate was 0.53 (95% CI: 0.50–0.55). Reliability increased with case volume; however, only 5 (1.5%) surgeons performed enough cases to reach acceptable reliability for their complication rate, while 86 (25.8%) surgeons reached acceptable reliability for their patient satisfaction rate. After adjustment for reliability, the range of complication rates decreased 29-fold from 0% to 14.3% to 2.4% to 2.9%, and the range of patient satisfaction decreased 2.6-fold from 25.3% to 100.0% to 64.9% to 92.4%. Conclusions: Among surgeons performing common operations, complications and patient satisfaction had relatively low reliability. Although reliability increased with volume, most surgeons had insufficient case volume to achieve acceptable reliability of their outcomes. As such, these measures likely offer little to no meaningful information to inform decision-making.

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Section: Discussionmentioning

confidence: 99%

The Measurement Reliability of Complications and Patient Satisfaction After Common Surgical Procedures

2022

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“…LHSs should engage partners to identify their priorities and help identify what the LHS's priorities should be. 79 As these factors may vary dynamically, the context and relevant partner priorities should be evaluated in depth at the beginning of a learning cycle and iteratively revisited throughout the cycle. 59,80 More broadly, engagement of partners across multiple levels of the socioecologic spectrum (patients, the community, clinicians, researchers, leaders, policy makers) is needed to align the LHS with the diverse priorities and practical considerations from each perspective, cultivate a shared understanding of the value of an LHS, and further the sense of investment in the mission and vision of an LHS.…”

Section: Multilevel Implementation Partner Engagement Solutionsmentioning

confidence: 99%

How Dissemination and Implementation Science Can Contribute to the Advancement of Learning Health Systems

Trinkley¹,

Ho²,

Glasgow³

et al. 2022

Academic Medicine

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Many health systems are working to become learning health systems (LHSs), which aim to improve the value of health care by rapidly, continuously generating evidence to apply to practice. However, challenges remain to advance toward the aspirational goal of becoming a fully mature LHS. While some important challenges have been well described (i.e., building system-level supporting infrastructure and the accessibility of inclusive, integrated, and actionable data), other key challenges are underrecognized, including balancing evaluation rapidity with rigor, applying principles of health equity and classic ethics, focusing on external validity and reproducibility (generalizability), and designing for sustainability. Many LHSs focus on continuous learning cycles, but with limited consideration of issues related to the rapidity of these learning cycles, as well as the sustainability or generalizability of solutions. Some types of data have been consistently underrepresented, including patient-reported outcomes and preferences, social determinants, and behavioral and environmental data, the absence of which can exacerbate health disparities. A promising approach to addressing many challenges that LHSs face may be found in dissemination and implementation (D&I) science. With an emphasis on multilevel dynamic contextual factors, representation of implementation partner engagement, pragmatic research, sustainability, and generalizability, D&I science methods can assist in overcoming many of the challenges facing LHSs. In this article, the authors describe the current state of LHSs and challenges to becoming a mature LHS, propose solutions to current challenges, focusing on the contributions of D&I science with other methods, and propose key components and characteristics of a mature LHS model that others can use to plan and develop their LHSs.

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“…What is a national learning system that supports QoC? Implementing partners have started to develop national learning systems to focus on topics such as QoC and QI in surgery, 23 child health 24 and SARS-CoV-2 exposure of healthcare personnel. 25 The purpose of a national learning system that supports QoC is to: (1) use a variety of programmatic and implementation research approaches to identify practices that have improved QoC at the patient and provider levels, thus contributing to a robust health system; and (2) scale up and sustain strategies that district-level and national-level stakeholders have successfully used to support QI at the patient and provider levels, thus achieving the agreed-upon quality standards.…”

Section: The Conceptual Frameworkmentioning

confidence: 99%

National learning systems to sustain and scale up delivery of quality healthcare: a conceptual framework

Lattof

Maliqi

Livesley³

et al. 2022

BMJ Glob Health

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All around the world, health systems fail to provide good quality of care (QoC). By developing learning systems, health systems are able to better identify good practices and to explain how to sustain and scale these good practices. To facilitate the operationalisation of national learning systems, the Network for Improving Quality of Care for Maternal Newborn and Child Health (the Network) developed a conceptual framework for national learning systems to support QoC at scale. The Network facilitated an iterative process to reach consensus on a conceptual framework for national learning systems to sustain and scale up delivery of quality healthcare. Following a landscape analysis, the Network Secretariat and WHO convened two consultative meetings with country partners, technical experts and stakeholders. Based on these inputs, we developed a conceptual framework for national learning systems to support QoC at scale. National learning systems use a variety of approaches to identify practices that have improved QoC at the patient and provider levels. They also facilitate scale up and sustain strategies used successfully to support quality improvement. Despite growing consensus on the importance of learning for QoC, no one has yet detailed how this learning should be operationalised nationally. Our conceptual framework is the first to facilitate the operationalisation of national learning systems so that health systems can begin to develop, adapt and implement mechanisms to learn about what works or fails and to scale up and sustain this learning for QoC.

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Building, scaling, and sustaining a learning health system for surgical quality improvement: A toolkit

Cited by 14 publications

References 19 publications

The Measurement Reliability of Complications and Patient Satisfaction After Common Surgical Procedures

The Measurement Reliability of Complications and Patient Satisfaction After Common Surgical Procedures

How Dissemination and Implementation Science Can Contribute to the Advancement of Learning Health Systems

National learning systems to sustain and scale up delivery of quality healthcare: a conceptual framework

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