A maturity grid assessment tool for learning networks. Issue 2 (26th June 2020)

• Record Type: Journal Article
• Authors: Lannon, Carole; Schuler, Christine L.; Seid, Michael; Provost, Lloyd P.; Fuller, Sandra; Purcell, David; Forrest, Christopher B.; Margolis, Peter A.
• Is Part Of: Learning health systems. Volume 5:Issue 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-26
• Subjects: learning health system -- learning network -- maturity grid -- quality improvement; Medical care -- Research -- Periodicals; Medical informatics -- Periodicals; Health planning -- Periodicals; 362.1068
• DOI: 10.1002/lrh2.10232
• Languages: English
• ISSNs: 2379-6146

Abstract

Abstract: Background: The vision of learning healthcare systems (LHSs) is attractive as a more effective model for health care services, but achieving the vision is complex. There is limited literature describing the processes needed to construct such multicomponent systems or to assess development. Methods: We used the concept of a capability maturity matrix to describe the maturation of necessary infrastructure and processes to create learning networks (LNs), multisite collaborative LHSs that use an actor‐oriented network organizational architecture. We developed a network maturity grid (NMG) assessment tool by incorporating information from literature review, content theory from existing networks, and expert opinion to establish domains and components. We refined the maturity grid in response to feedback from network leadership teams. We followed NMG scores over time for nine LNs and plotted scores for each domain component with respect to SD for one participating network. We sought subjective feedback on the experience of applying the NMG to individual networks. Results: LN leaders evaluated the scope, depth, and applicability of the NMG to their networks. Qualitative feedback from network leaders indicated that changes in NMG scores over time aligned with leaders' reports about growth in specific domains; changes in scores were consistent with network efforts to improve in various areas. Scores over time showed differences in maturation in the individual domains of each network. Scoring patterns, and SD for domain component scores, indicated consistency among LN leaders in some but not all aspects of network maturity. A case example from a participating network highlighted the value of the NMG in prompting strategic discussions about network development and demonstrated that the process of using the tool was itself valuable. Conclusions: The capability maturity grid proposed here provides a framework to help those interested in creating Learning Health Networks plan and develop them over time.