Open AccessA maturity grid assessment tool for learning networks
Author(s) -
Lan Carole,
Schuler Christine L.,
Seid Michael,
Provost Lloyd P.,
Fuller Sandra,
Purcell David,
Forrest Christopher B.,
Margolis Peter A.
Publication year - 2021
Publication title -
learning health systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.501
H-Index - 9
ISSN - 2379-6146
DOI - 10.1002/lrh2.10232
Subject(s) - maturity (psychological) , computer science , grid , scope (computer science) , consistency (knowledge bases) , knowledge management , domain (mathematical analysis) , construct (python library) , artificial intelligence , process management , psychology , mathematics , engineering , computer network , developmental psychology , programming language , mathematical analysis , geometry
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.