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Collaborative Systems Analytics to Advance Clinical Care: Application to Congenital Cardiac Patients

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Healthcare Policy, Innovation and Digitalization

Abstract

This chapter reports the operations research advances in the Edelman finalist work on “Collaborative Systems Analytics: Establishing Effective Clinical Practice Guidelines for Advancing Congenital Cardiac Care.” The clinical advances and results of this project have been reported elsewhere. This paper highlights the OR-analytic advances and briefly summarizes the clinical implementations, results, and impacts. Specifically, we devised a customizable model and decision support framework that combines systems modeling, simulation-optimization decision analytics, clustering, and machine learning within a collaborative learning paradigm to help hospitals pinpoint key factors on practice variation, and design clinical practice guidelines (CPGs) for rapid implementation to improve the outcomes of congenital heart defects surgeries. The OR-analytic collaborative learning framework described herein is generalizable and is applicable for numerous domains. Within healthcare, it enables systems redesign, quality improvement, resource allocation, and clinical support and decision advances. The computational engine facilitates systems and process optimization. The results improve efficiency of healthcare delivery, reduce costs and wastes while improving quality of life of patients. A critical contribution is that the system offers an effective, flexible way to study adequate numbers of patients across multiple sites with uncommon diseases through a common infrastructure for recruiting, monitoring, and following patients whose conditions will be characterized in a standard fashion. The modeling-computational framework facilitates the design of a common CPG, and its successful implementation with documented and measurable clinical outcomes. Such a framework permits a flexible clinical transformative environment that can accommodate practice variance while enabling care teams to identify critical system pathways for multiple-site clinical care and process improvement. The hypothesis testing and dissemination of findings allows for rapid learning and adoption at multiple sites with a shortened duration and only a fraction of the budget as opposed to the conventional randomized clinical trials. Hence, the OR-analytic collaborative learning framework can serve as a blueprint for other clinical and process-improvement initiatives.

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Acknowledgements

The author thanks the collaborators from multiple clinical sites, including Children’s Healthcare of Atlanta, Atlanta, Georgia; C. S. Mott Children’s Hospital, Ann Arbor, Michigan; Cardiac Surgery Department, University of Michigan Medical School, Ann Arbor, Michigan; Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; Texas Children’s Hospital, Houston, Texas; Departments of Pediatrics, Baylor College of Medicine, Houston, Texas; Primary Children’s Hospital, Salt Lake City, Utah; Pediatric Critical Care Medicine, University of Utah School of Medicine, Salt Lake City, Utah; National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, and the Pediatric Heart Network Investigators, Bethesda, Maryland. We acknowledge Niquelle Brown, Cory Girard, Jinha Lee, Kevin Yee, TsungLin Wu, and Ruilin Zhou from Georgia Tech for performing some of the time-motion and system process observations. The study was supported by U01 grants from the National Heart, Lung, and Blood Institute, and the National Science Foundation (IIP- 0832390, IIP-1361532). The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of the National Heart, Lung, and Blood Institute or the National Science Foundation. We acknowledge that the clinical advances and results of this project have been reported elsewhere (Bates et al. 2018; Mahle et al. 2016a, 2016b; McHugh et al. 2019; Wolf et al. 2016; Witte et al. 2021), and we include some excerpts herein for completeness. We thank the editors and reviewers for their critical comments to improve the manuscript.

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Authors and Affiliations

  1. Center for Operations Research in Medicine and HealthCare, The Data and Analytics Innovation Institute, Atlanta, Georgia

    Eva K. Lee

  2. USA NSF I/UCRC Center for Health Organization Transformation, Washington, USA

    Eva K. Lee

  3. Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia

    Eva K. Lee

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Editors and Affiliations

  1. Department of Quantitative Methods, School of Business, Istanbul University, Istanbul, Türkiye

    Eyüp Çetin

  2. Department of Business Administration, Faculty of Business Administration and Economics, Trakya University, Edirne, Türkiye

    Hilal Özen

Appendix: Early Extubation Clinical Practice Guideline

Appendix: Early Extubation Clinical Practice Guideline

In Fig. 12.10, we show the consensus CPG developed and implemented at the active sites.

Fig. 12.10
A chart displays C P G for post-operative patients derived from an A P H N collaborative project. Left, it outlines the inclusion and exclusion criteria, a flow chart guiding subsequent steps. Right, it provides recommendations for early extubation, I C U system readiness. close monitoring, and timetables.

The early extubation clinical practice guidelines facilitate best practice dissemination

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Lee, E.K. (2023). Collaborative Systems Analytics to Advance Clinical Care: Application to Congenital Cardiac Patients. In: Çetin, E., Özen, H. (eds) Healthcare Policy, Innovation and Digitalization. Accounting, Finance, Sustainability, Governance & Fraud: Theory and Application. Springer, Singapore. https://doi.org/10.1007/978-981-99-5964-8_12

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