The Monash Learning Health System Maturity Matrix: Codesign of a Tool to Measure and Guide Improvement in Complex Health System Behaviour

The Monash Learning Health System Maturity Matrix: Codesign of a Tool to Measure and Guide Improvement in Complex Health System Behaviour | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search The Monash Learning Health System Maturity Matrix: Codesign of a Tool to Measure and Guide Improvement in Complex Health System Behaviour View ORCID Profile Darren Rajit , View ORCID Profile Alison Johnson , View ORCID Profile Sandy Reeder , View ORCID Profile Dominique Cadilhac , View ORCID Profile Joanne Enticott , View ORCID Profile Helena Teede doi: https://doi.org/10.1101/2025.04.09.25325486 Darren Rajit 1 Monash Centre for Health Research and Implementation, Faculty of Medicine , Nursing, and Health Sciences, Monash University , Clayton, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Darren Rajit Alison Johnson 1 Monash Centre for Health Research and Implementation, Faculty of Medicine , Nursing, and Health Sciences, Monash University , Clayton, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alison Johnson Sandy Reeder 1 Monash Centre for Health Research and Implementation, Faculty of Medicine , Nursing, and Health Sciences, Monash University , Clayton, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sandy Reeder Dominique Cadilhac 4 Stroke and Ageing Research, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University , Clayton, Victoria, Australia 5 Stroke Division, The Florey Institute of Neuroscience and Mental Health Heidelberg , Victoria, Australia 6 Centre of Research Excellence to accelerate Stroke Trial innovation and Translation, Monash University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dominique Cadilhac Joanne Enticott 1 Monash Centre for Health Research and Implementation, Faculty of Medicine , Nursing, and Health Sciences, Monash University , Clayton, Victoria, Australia 2 Monash Partners Academic Health Sciences Centre , Clayton, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joanne Enticott For correspondence: joanne.enticott{at}monash.edu Helena Teede 1 Monash Centre for Health Research and Implementation, Faculty of Medicine , Nursing, and Health Sciences, Monash University , Clayton, Victoria, Australia 2 Monash Partners Academic Health Sciences Centre , Clayton, Victoria, Australia 3 Monash Health Endocrinology and Diabetes Departments Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Helena Teede Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Importance Learning Health Systems (LHS) have proven efficacy in catalysing healthcare improvement, but adoption and scale-up in complex healthcare systems remains challenging, with limited implementation guidance. Objective To measure alignment with LHS principles and guide LHS implementation, we aimed to codesign, refine and apply an LHS Maturity Matrix (LHS-MM) based on the Monash LHS framework. Design In this mixed methods study, our scoping review identified existing tools. We then applied the Double Diamond design and innovation model (discover, define, develop, deliver) in the development of the LHS-MM. Insights from engineering, prior tools, and the Monash LHS Framework were leveraged to adapt the LHS-MM. This was refined through codesign, and triangulation with evidence-based implementation frameworks. The LHS-MM was then delivered in a test case on stroke. Participants Codesign was conducted with subject matter experts (n=18), and end users of the LHS-MM (n=11). Setting Wbithin a high-income high quality national health system (Australia), across regional and urban settings. Outcomes A tool to measure implementation fidelity and alignment of healthcare system behaviours and processes with LHS principles, and guide organisations in effective LHS implementation for healthcare improvement. Results Tools uncovered in the discover and define phase emerged from the scoping review included the Cincinnati Network Maturity Grid. We adapted this tool to align to the Monash LHS framework. Codesign elevated the tool to focus on assessing complex systems behaviours aligned to LHS principles, with modification of assessment criteria, rating scales and scenarios for use. The LHS-MM assesses system-level behaviours across eight components on a numerical, five-point scale (1-5), visualised as a radar chart. Components include stakeholder engagement, priority identification, evidence-based information, evidence synthesis and guidelines, data systems, benchmarking, implementation, and healthcare improvement. Finally, in the deliver phase, a test case in stroke care revealed ratings from 4/5 (Established) to 5/ 5 (Transformative). Conclusion Through an iterative and evidence-informed codesign process, we have generated the Monash LHS-MM. Further research and government implementation is underway to operationalise the Monash LHS-MM to measure fidelity and guide LHS implementation, advancing the field both within and beyond the Australian healthcare system and globally. As an implementation guide and monitoring tool, it will be a pivotal ingredient inside implementation toolkits currently being developed worldwide, supporting LHSs to fulfil their promise and enable the next frontier of healthcare innovation. Introduction Effective, sustained improvement in complex health systems has been elusive 1 , 2 . Theory driven Implementation science frameworks such as the Consolidated Framework for Implementation Research (CFIR) 3 are often used as static determinant frameworks with limitations in operationalising delivery of improvement or catalysing iterative learning in complex systems over time 4 , 5 . Learning Health Systems (LHS), 6 have emerged in this context as an evidence-based approach, aligned to the process domain of the CFIR, positing that complex systems change requires aligning people, data and culture 7 – 10 . LHS approaches seek to close the evidence to practice gap, by moving beyond the ‘what’, to a focus on the ‘how’: that is how an improvement is contextualised, implemented, assessed, optimised and sustained for impact 11 . Despite increasing interest, LHS research has been mainly theoretical, with relatively few high-quality empirical implementation studies, as captured in our recent scoping review 12 . Additionally, LHS research often lacks codesign 10 with limited involvement of stakeholders or end-users in prioritising problems and designing solutions 13 and most are academically focused on technological 14 or data 15 aspects of the LHS, with limited investigation of complex system, or socio-technical factors 12 . The evidence-based Monash LHS framework underpinning this work was developed through codesign with health system stakeholders and community end-users ( Figure 1 ) to address these limitations. This involved engaging with national and international experts, a systematic literature review analysing successful LHS case studies 10 , followed by stakeholder engagement 16 and codesign 9 . The Monash LHS framework recognised health systems as complex adaptive systems and positions iterative improvement as emergent system level behaviours with healthcare improvement arising from the accumulation of complex non-linear processes and interactions 17 . It recognised these interactions across four evidence domains with eight LHS components ( Table 1 ). Components are operationalised at the systems level through processes and tools that are integrated to elicit key emergent systems behaviours, aiming for iterative healthcare improvement ( Table 1 ), referred here as the “LHS Cycle”. Download figure Open in new tab Figure 1: The evidence-based Monash Learning Health System (LHS) Framework 9 . LHS Domains: Evidence from Stakeholders (orange), Evidence from Research (green), Evidence from Practice and Data (light blue), and Evidence from Implementation (dark blue). LHS Components are numbered. View this table: View inline View popup Table 1: Examples of underlying processes or tools that are integrated and implemented in a Learning Health System to drive direct system level behavioural outcomes. Collectively, these direct behavioural outcomes lead to the higher level, indirect emergent behaviour of cyclical healthcare improvement. The Monash LHS framework has been applied in Australia by government, health services and nationally funded implementation research and health system partnerships at urban (MRFF2023389 18 ), regional (RARUR000072 19 ) and national levels 7 . Ongoing LHS implementation and scale-up highlighted limited to monitor fidelity, benchmark or guide implementation. 19 We aimed to engage stakeholders to iteratively codesign an LHS Maturity Matrix (LHS-MM) within the Australian context, based on the Monash LHS framework. Method This mixed methods study was underpinned by the validated codesign and innovation Double Diamond method 20 , composed of four iteratively applied phases: Discover (Scoping Review, Expert and User Group Codesign), Define (Expert and User Group Codesign), Develop (triangulation with implementation science frameworks), and Deliver (Small-scale testing and refinement in the Australian Stroke LHS 21 , 22 ) The work was led by our interdisciplinary team including a researcher-in-residence biomedical engineer (DR), clinicians (HT, DC, AJ) and implementation and LHS experts (HT, AJ, DC, SR, and JE). Ethics approval (ID:19969), Reporting and design of this study align with the Standards for Reporting Qualitative Research (SRQR) Checklist 23 (S6). Discover, & Define: Scoping Review, and Codesign Involving Experts and User Groups An initial scoping review aiming to capture existing LHS implementation and evaluation research, including case studies and tools, was conducted and is published elsewhere 24 . The US based Network Maturity Grid tool 25 emerged as the most advanced 24 . It was developed over several years through evidence from stakeholders, a literature review, and multiple case examples 25 , 26 . Here, we built on this tool in codesigning the LHS-MM, with permission for adaption provided from the creators. A purposive sample of experts with experience in health systems research, LHS, evaluation, implementation science, health service delivery and complex research, as well as consumer and community members were engaged. The network established during the Monash LHS development (AJ, HT, JE) was also leveraged 16 The interview schedule was informed by our systematic reviews on effective LHS models 10 and scoping review on implementation tools 24 and was designed to discover the need, purpose, perceived usefulness, components and refinements of the tool, including exploring the skeleton prototype LHS-MM. The interviewer (AJ) introduced the Monash LHS framework and context, before exploring the outlined topics and then the prototype LHS-MM. Input was solicited verbally and confirmed post interview via email and inline comments directly on prototype documents. The LHS-MM was revised iteratively, including around assessment criteria and maturity levels, evaluation matrix format, potential scenarios of use and target audiences, and possible integration into existing workflows. Successive dated versions of the tool stored on secure file servers. Interviews were continued to saturation, where no further actionable changes were added. A workshop was conducted with identified stakeholders/ end-users, including embedded researchers and healthcare improvement project leads who were actively implementing the Monash LHS framework in a regional health system transformation program (MRFF RARUR000072) involving a diverse array of improvement projects. Participants were invited to apply the LHS-MM to assess LHS maturity in a group setting, with individual participants assessing each LHS component. Feedback and reflections were captured on experience using the LHS-MM, including potential usefulness in their broader work. Recommended refinements were captured via participatory engagement, with inputs captured verbally, in writing via email and directly on tool documents during and after the workshop. All feedback from expert and end user engagement was considered of equal importance, aligned to sharing of power in co-design 27 . Overall, multiple perspectives were captured and integrated to further define the tool. Throughout the codesign process, the co-authors met regularly and communicated via email to iteratively discuss and integrate evidence sources and build on the tool with successive dated versions stored on secure file servers. Develop: Triangulation with Theory Driven Frameworks The LHS-MM generated via integrating results from the scoping review, feedback from experts and insights from the workshop, was then triangulated with evidence-based implementation frameworks (CFIR) 3 , evaluation frameworks (Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM)) 28 , and the adherence construct of implementation fidelity 29 . These frameworks were selected a-priori for their robust evidence base and extensive utilisation in implementation science 3 , 30 – 32 . Deliver: Small Scale Test Case – Australian Stroke LHS The LHS-MM was then tested with the Australian Stroke LHS Program 21 based on publicly available evidence. This program 21 is a national network of consumer-clinician alliances 22 , data monitoring systems 33 , 34 , evidence synthesis bodies 35 and research centres 36 that have collectively led improvements in evidence-based stroke care in Australia. It was recently showcased as an exemplar for LHS approaches 21 aligning to the Monash LHS Framework 9 , 22 . The test case was conducted independently by DR, with DC, a leader for the Australian Stroke LHS providing additional context, clarification, and evidence. Results The multi-method results from each phase are summarised in Table 2 . In summary, the scoping review generated a skeleton prototype that was adapted to the Monash LHS, with iteratively refinement during discovery, defining and developing the LHS-MM. View this table: View inline View popup Table 2: Summarised results and contributions of each step in the multi method study to Monash LHS-MM development Collectively this codesign processes involved fundamental changes to structure, wording, rating criteria and conceptualisation of the LHS and its maturity (Summarised in S7). Monash LHS Maturity Matrix (LHS-MM) The Monash LHS-MM tool includes a worksheet (S1) and report template (S2) and requires users to self-assess systems behaviours across four LHS domains and eight LHS components ( Table 1 ), each with five maturity levels and associated quantitative scores: Not Started (1), Beginning (2), Developing (3), Established (4) and Transformative (5). These criteria (full wording in S1) evaluate system level behaviours generated through underlying processes that are characteristic of an increasingly mature LHS. At higher maturity levels, fulfilment of assessment criteria in a LHS domain is sequential and requires prerequisites from earlier domains. Complexity in assessment criteria increases with maturity, reflecting how LHS systems behaviours builds successively across components. This is especially apparent later in the Monash LHS cycle in the Implementation and Healthcare Improvement components. The result is a graphical view of LHS maturity (implementation fidelity) ( Figure 2 ), highlighting strengths, deficiencies, and routes for improvement or investment. Download figure Open in new tab Figure 2: Sample Radar Chart that is produced by the Monash LHS Maturity Matrix (LHS-MM), highlighting ability to track maturity over time (Start of Project in Blue vs December 23 in Orange) In the following sections, we provide further detail on assessment of LHS domains and components in the LHS-MM. Stakeholder Evidence Domain Stakeholders derived evidence is generated from individuals or groups who have a stake in both the problems uncovered and the solutions that are proposed within a system 37 . Measurement of maturity in this LHS domain focuses on how stakeholders are engaged (LHS Component: Engagement of People ) for problem ideation, and co-design of interventions; and how stakeholder priorities are integrated into decision making and project planning (LHS Component: Identifying Priorities) . The Engagement of People scale is informed by the National Medical and Health Research Council framework for consumer involvement 38 and the International Association for Public Participation Spectrum of Public Participation model 39 . The Identifying Priorities scale is informed by the James Lind Alliance approach to priority setting 40 and Delphi processes 41 . Research Evidence Domain Research derived evidence is generated by embedding high quality, peer-reviewed research. Measurement of maturity in this LHS domain focuses on how existing peer-reviewed research ( LHS Component: Evidence Based Information ) is being accessed, generated, incorporated and synthesised ( LHS Component: Evidence Synthesis and Guidelines ). At higher levels of maturity, integration of stakeholder priorities from the Stakeholder LHS domain is expected to guide evidence generation and synthesis, as well as new research evidence being integrated and synthesised as it is produced. Data and Practice Derived Evidence Domain Data derived evidence refers to facts, circumstances or perceptions that can be analysed to inform decisions. This can be both qualitative and quantitative data that is generated through routine health system functioning. Measurement in this LHS domain focuses on how relevant data is being captured and accessed ( LHS Component: Data and Information Systems ); before being analysed, reported, and used to inform healthcare improvement activities ( LHS Component: Benchmarking ). Relevant data is determined by alignment with stakeholder priorities and research evidence, as elicited from the prior LHS domains. At higher maturity levels, there is clear evidence that stakeholder priority and research evidence is being used to drive the way data is being accessed, analysed and benchmarked to inform improvement activity in an ongoing cycle. Implementation Derived Evidence Implementation derived evidence refers to i) how implementation science and practice-based evidence is being used to inform and create the conditions necessary for sustainable change and innovation ( LHS Component: implementation ), and ii) how evidence from the other three LHS domains are being integrated and evaluated ( LHS Component: Healthcare Improvement ) to underpin ongoing healthcare improvement. The Implementation component scale is informed by the CFIR framework, whereas the Healthcare Improvement component scale is informed by the RE-AIM framework. At higher maturity levels, implementation science and evidence from all LHS domains is being used at scale, to i) select and adapt novel innovations (Innovation Domain of CFIR), and ii) co-design and operationalise implementation or de-implementation strategies for these innovations (Process Domain of CFIR). Thereafter, evaluation from implementation activities is continuously used to inform cycles of healthcare improvement, allowing evidence from all LHS domains to be linked towards outcomes. This marks the end of one LHS cycle and the start of another. Monash LHS-MM as an Implementation Fidelity Assessment Tool The LHS-MM is designed to assess the four subcategories (Content, Frequency, Duration and Coverage) of the “Adherence” construct of implementation fidelity in relation to the Monash LHS framework ( Table 3 ). Implementation fidelity refers to the extent to which a complex intervention (the Monash LHS framework) has been implemented as intended by the developers, whereas “Adherence” is the “bottom line” of Implementation fidelity, i.e., extent to which those wishing to implement LHS principles have “adhered” to the Monash LHS framework as planned. Adherence is decomposed into four subcategories: Content, Frequency, Duration, and Coverage. “Content” refers to “what” is being assessed, in this case how the Monash LHS framework has been conceptualised into measurable attributes as detailed above. Frequency, Duration and Coverage collectively refer to the “dose”, or the extent to which the Monash LHS framework is being delivered. View this table: View inline View popup Download powerpoint Table 3: The Monash LHS-MM approach to measure the Adherence construct of Implementation Fidelity, as related to the Monash LHS Framework Test Case – The Australian Stroke Learning Health System Program Figure 3 summaries the overall LHS maturity level of the Australian Stroke LHS. A completed LHS-MM, accompanying report, and assessment rationale with evidence for each component is available (S5a-SSc). Notably, the Australian Stroke LHS was rated as “Transformative (5/5)” in terms of Stakeholder engagement, Evidence Based Information, Evidence Synthesis, Data Information Systems, Benchmarking and Implementation. However, “Identifying Priorities” was an area for improvement (Developing (3/5)) due to limited evidence for how stakeholder priorities were being ranked. Additionally, given that implementation of tools and processes associated with the LHS principles was still relatively nascent, evidence of evaluation of ongoing LHS cycle is still nascent, thus a rating beyond Established (4/5) could not be assigned. Download figure Open in new tab Figure 3: Radar chart summarising results of assessment of the Monash LHS Maturity Matrix (LHS-MM) of the Australian Stroke Program Application The LHS-MM can be used as a brief reflexive, analysis with documentation to support maturity scores as done in our test case (S5a-c), or a deep analysis using mixed methods data collection such as interviews; for example with a realist evaluation 42 . Following the instructions in the LHS-MM worksheet (S1), the scores are completed and a summary sheet with a radar chart quantitatively visually displays maturity scores across all eight components ( Figure 2 ) accompanied by a report template (S2) to record results. The tool can be applied using simple word and excel outputs and has also been incorporated into an online Monash LHS implementation toolkit, with further instructions and support information. The maturity assessment using the LHS-MM is best conducted iteratively with benchmarking over time and across organisations to guide targeted efforts into improving health systems behaviours towards a mature LHS. Discussion Building on the LHS, we have generated a Monash LHS-MM codesigned from i) evidence from a scoping review of existing frameworks and tools 24 ii) codesign with expert stakeholders and end-users through the discovery, defining and development phases including integration of evidence-based, theory driven frameworks, (CFIR 3 , RE-AIM 28 and the conceptual framework of Implementation Fidelity 29 ). The resultant LHS-MM, designed for measuring fidelity and guiding implementation of an evidence-based LHS framework to enhance healthcare improvement, was then delivered in a test case in the Australian LHS Stroke program 21 . As such, it is both an implementation guide and monitoring tool, and can assist health services globally to establish a LHS to enhance healthcare improvements and deliver impact. This codesign process for the LHS-MM was underpinned by the conceptualisation of the LHS as a series of measurable, system level behaviours. These behaviours are generalisable across contexts but delivered through flexible processes unique to context. To address the need to guide and measure LHS implementation, we build upon work originating in the United States on maturity matrices usage in LHSs (Network Maturity Grid) 25 43 . These have emerged from the engineering sector 44 , 45 to assess and optimise behavioural outcomes from a systems perspective (key divergences in S7), and have also been used in public health settings with similar large scale health transformation projects 46 – 48 . Maturity matrices rely on behaviourally anchored rating scales 49 where each level on the scale depicts model behaviour to which assessors can compare their own context. The LHS-MM includes quantitative rating scales to assess LHS system behaviours by maturity from 1 to 5, in all eight LHS components ( Table 3 ). Here, we focus on assessing behaviours, without prescribing specific processes, allowing these to be employed in a bespoke manner, depending on context. This allows the flexible application of the LHS-MM across a range of settings and contexts in healthcare. Throughout the codesign process, the matrix was perceived to be useful in understanding, planning, implementing, refining and evaluating LHS implementation fidelity and adherence to the LHS framework. Next steps in development include implementation and evaluation in large scale national programs (APP1198561, APP2018718), which are underway across a range of LHS initiatives, with embedded research and evaluation 50 . The Monash LHS-MM builds on contemporary maturity matrices 25 , 46 , 48 , 51 and the LHS to integrate evidence-based implementation frameworks (CFIR 3 and RE-AIM 28 ) to improve system level capability in integrating implementation science at scale. For example, where CFIR excels at helping implementors identify “what” contextual, multi-level factors are salient to implementation, the LHS-MM helps implementors to quantitatively assess and chart a course to developing system level capabilities and behaviours that enable successful implementation and improvement activity; in this case both the “how” and “how well”. For example, in our Australian stroke test case, a lack of maturity in engaging stakeholders and enhancing priority setting was identified, and future investment could be targeted towards improving how stakeholder priorities are elicited via consensus, ranked, and shortlisted to inform downstream research, data and benchmarking, and implementation activity. The major strength of the Monash LHS-MM is that it is a codesigned resource and can function as an implementation guide. As both implementation guide and monitoring tool, it will assist health services globally to establish a LHS to enhance healthcare improvements and deliver impact. It will be an important component in implementation toolkits currently being developed around the world to enable the implementation of learning health systems to continuously learn from practice, adapt interventions, and optimize outcomes. This will ensure that LHSs are move beyond conceptual models into practical, scalable systems that drive meaningful change. As part of such implementation toolkits, it will help bridge the gap between research and practice, allowing health services worldwide to achieve sustained improvements in patient care, operational efficiency, and health equity. Ultimately, the LHS-MM will contribute to transforming healthcare systems into dynamic, learning organizations capable of meeting evolving challenges and improving population health outcomes. There are several limitations to this study. The LHS-MM was codesigned to align with the Monash LHS Framework and the Australian context. Therefore, it may require adaptation for other LHS models and settings. In refining the LHS-MM it was delivered through an Excel worksheet and Word template, which may lack sophistication, but enhances accessibility. It is now being integrated in an online implementation toolkit, with codesigned embedded guidance and resources. Workshops with end-users were conducted in group settings. As such, in-group dynamics may have influenced the results. Further, there is an element of subjectivity to self-assessment. Whilst the LHS-MM aims to enable a reflexive and self-assessment approach, this does allow potential bias, with the need for more prescriptive description of maturity that evolved over the codesign process. Further in-depth case studies are needed using mixed methods and incorporating multiple perspectives across a range of settings, to assess interrater reliability and construct validity. This is underway across multiple large-scale complex funded initiatives underpinned by the CFIR, LHS and the Monash LHS-MM. Conclusion Learning Health Systems (LHS) aim to deliver healthcare improvement through eliciting and integrating evidence-based systems behaviours that are operationalised by a series of processes unique to context. Given their complexity, implementation can be challenging and tools to support LHS implementation and evaluate implementation fidelity are scarce. In response, we have codesigned the Monash LHS-MM through a scoping review, and a four phase Double Diamond codesign process involving stakeholder codesign, integration of evidence-based frameworks, and a test case. The resultant LHS-MM provides a structured approach to assess LHS implementation fidelity and maturity over time, supporting stakeholders to assess and optimise approaches to healthcare improvement. The LHS-MM has now been integrated into an online platform aligned with the CFIR and is being deployed across initiatives to benchmark and drive large-scale healthcare systems change. As both an implementation guide and monitoring tool, the LHS-MM will be a pivotal ingredient inside implementation toolkits currently being developed worldwide, supporting LHSs to fulfil their promise and enable the next frontier of healthcare innovation. Data Availability All data supporting the findings of this study is available within the paper and the accompanying supplementary information. List of Abbreviations LHS Learning Health System LHS-MM Learning Health System Maturity Matrix CFIR Consolidated Framework for Implementation Research RE-AIM Reach, Efficacy, Adoption, Implementation and Maintenance BARS Behaviourally Anchored Scales SRQR Standards for Reporting Qualitative Research ASHC Academic Health Science Centres PROMs / PREMs Patient Reported Outcome/ Experience Measures PBS Pharmaceutical Benefits Scheme MBS Medicare Benefits Schedule ASC Australian Stroke Coalition AuSDaT Australian Stroke Data Tool AuSCR Australian Stroke Clinical Registry (AuSCR) DR Darren Rajit AJ Alison Johnson DC Dominique Caldilhac SR Sandy Reeder JE Joanne Enticott HT Helena Teede Declarations Ethics approval and consent to participate All interviews were recorded, and verbal consent provided at the beginning of the interview. This study was approved by the Monash University Human Research Ethics Committee (Project ID: 19969) who approved the method for verbal consent which is explained in the methods section. Potential participants were invited to take part in the study by an introductory email, and then followed-up by a project researcher to provide study information (including prototypes of the matrix), answer questions and organise mutually agreeable interview times. Verbal consent to recording was obtained before commencing the interview. Consent for publication The authors declare their consent for publication. Availability of data and materials All data supporting the findings of this study is available within the paper and the accompanying supplementary information. Competing interest The authors declare that there is no conflict of interest regarding the publication of this article. Funding D.R. is supported by an Australian Government Research Training Program (RTP) Scholarship. H.T. is funded by an NHMRC Fellowship. This work is also supported by the Australian Government Medical Research Future Fund. The funders of this work did not have any direct role in the design of the study, its execution, analyses, interpretation of the data or decision to submit results for publication. Author Contributions D.R, A.J, J.E and H.T contributed to conceptualisation. H.T obtained funding. A.J conducted the interviews. J.E and H.T contributed to supervision. D.C and S.R provided feedback on the maturity matrix during its development stage and D.C corroborated the stroke test case details. D.R. drafted the manuscript with H.T, and all authors contributed intellectually, revised and approved the manuscript. Acknowledgements The authors would like to acknowledge the input and valuable feedback contributed by the subject matter experts and potential users of the tool that were engaged as part of maturity matrix development. Further, this work has leveraged the Deliver initiative (MRFF RARUR000072) aiming to leave a sustainable learning health system and resultant legacy of better health outcomes and a greater research capacity in regional settings in Australia. Footnotes ↵ * Joint senior author References 1. ↵ Melder A , Robinson T , McLoughlin I , Iedema R , Teede H . An overview of healthcare improvement: unpacking the complexity for clinicians and managers in a learning health system . Internal Medicine Journal . 2020 ; 50 ( 10 ): 1174 – 1184 . doi: 10.1111/imj.14876 OpenUrl CrossRef PubMed 2. ↵ Braithwaite J , Glasziou P , Westbrook J . The three numbers you need to know about healthcare: the 60-30-10 Challenge . BMC Medicine . 2020 ; 18 ( 1 ): 102 . doi: 10.1186/s12916-020-01563-4 OpenUrl CrossRef PubMed 3. ↵ Damschroder LJ , Reardon CM , Widerquist MAO , Lowery J . The updated Consolidated Framework for Implementation Research based on user feedback . Implementation Science . 2022 ; 17 ( 1 ): 75 . doi: 10.1186/s13012-022-01245-0 OpenUrl CrossRef PubMed 4. ↵ Davies P , Walker AE , Grimshaw JM . A systematic review of the use of theory in the design of guideline dissemination and implementation strategies and interpretation of the results of rigorous evaluations . Implementation Science . 2010 ; 5 ( 1 ): 14 . doi: 10.1186/1748-5908-5-14 OpenUrl CrossRef PubMed 5. ↵ Kirk MA , Kelley C , Yankey N , Birken SA , Abadie B , Damschroder L . A systematic review of the use of the Consolidated Framework for Implementation Research . Implementation Science . 2016 ; 11 ( 1 ): 72 . doi: 10.1186/s13012-016-0437-z OpenUrl CrossRef PubMed 6. ↵ Olsen L , Aisner D , McGinnis JM Institute of Medicine (US) Roundtable on Evidence-Based Medicine . The Learning Healthcare System: Workshop Summary . ( Olsen L , Aisner D , McGinnis JM , eds.). National Academies Press (US) ; 2007 . Accessed July 1, 2024 . http://www.ncbi.nlm.nih.gov/books/NBK53494/ 7. ↵ Cadilhac DA , Bravata DM , Bettger JP , et al. Stroke Learning Health Systems: A Topical Narrative Review With Case Examples . Stroke . 2023 ; 54 ( 4 ): 1148 – 1159 . doi: 10.1161/STROKEAHA.122.036216 OpenUrl CrossRef PubMed 8. Rajit D , Johnson A , Callander E , Teede H , Enticott J . Learning health systems and evidence ecosystems: a perspective on the future of evidence-based medicine and evidence-based guideline development . Health Research Policy and Systems . 2024 ; 22 ( 1 ): 4 . doi: 10.1186/s12961-023-01095-2 OpenUrl CrossRef 9. ↵ Enticott JC , Melder A , Johnson A , et al. A Learning Health System Framework to Operationalize Health Data to Improve Quality Care: An Australian Perspective . Front Med (Lausanne ) . 2021 ; 8 : 730021 – 730021 . doi: 10.3389/fmed.2021.730021 OpenUrl CrossRef PubMed 10. ↵ Enticott J , Johnson A , Teede H . Learning health systems using data to drive healthcare improvement and impact: a systematic review . BMC Health Serv Res . 2021 ; 21 ( 1 ): 200 . doi: 10.1186/s12913-021-06215-8 OpenUrl CrossRef PubMed 11. ↵ McEvoy MD , Dear ML , Buie R , et al. Embedding Learning in a Learning Health Care System to Improve Clinical Practice . Acad Med . 2021 ; 96 ( 9 ): 1311 – 1314 . doi: 10.1097/ACM.0000000000003969 OpenUrl CrossRef PubMed 12. ↵ Ellis LA , Sarkies M , Churruca K , et al. The Science of Learning Health Systems: Scoping Review of Empirical Research . JMIR Med Inform . 2022 ; 10 ( 2 ): e34907 . doi: 10.2196/34907 OpenUrl CrossRef 13. ↵ Vargas C , Whelan J , Brimblecombe J , Allender S . Co-creation, co-design, co-production for public health - a perspective on definition and distinctions . Public Health Res Pract . 2022 ; 32 ( 2 ): 3222211 . doi: 10.17061/phrp3222211 OpenUrl CrossRef PubMed 14. ↵ McEvoy MD , Dear ML , Buie R , et al. Effect of Smartphone App–Based Education on Clinician Prescribing Habits in a Learning Health Care System: A Randomized Cluster Crossover Trial . JAMA Network Open . 2022 ; 5 ( 7 ): e2223099 . doi: 10.1001/jamanetworkopen.2022.23099 OpenUrl CrossRef 15. ↵ Vahidy FS . A Learning Health Care System–Based Approach for Improving Quality of Care Among Patients With Transient Ischemic Attack . JAMA Netw Open . 2020 ; 3 ( 9 ): e2016123 . doi: 10.1001/jamanetworkopen.2020.16123 OpenUrl CrossRef 16. ↵ Enticott J , Braaf S , Johnson A , Jones A , Teede HJ . Leaders’ perspectives on learning health systems: a qualitative study . BMC Health Services Research . 2020 ; 20 ( 1 ): 1087 . doi: 10.1186/s12913-020-05924-w OpenUrl CrossRef PubMed 17. ↵ Rutter H , Savona N , Glonti K , et al. The need for a complex systems model of evidence for public health . The Lancet . 2017 ; 390 ( 10112 ): 2602 – 2604 . doi: 10.1016/S0140-6736(17)31267-9 OpenUrl CrossRef PubMed 18. ↵ Best Practice for PIVC . Accessed June 17, 2024 . https://sites.google.com/monash.edu/bestpracticepivc/home 19. ↵ Deliver: Growing Research in Western Victoria . April 4 , 2024 . Accessed June 17, 2024 . https://deliver.westernalliance.org.au/ 20. ↵ The Double Diamond - Design Council . Accessed August 6, 2024 . https://www.designcouncil.org.uk/our-resources/the-double-diamond/ 21. ↵ Teede H , Cadilhac DA , Purvis T , et al. Learning together for better health using an evidence-based Learning Health System framework: a case study in stroke . BMC Medicine . 2024 ; 22 ( 1 ): 198 . doi: 10.1186/s12916-024-03416-w OpenUrl CrossRef PubMed 22. ↵ Australian Stroke Coalition. Position Statement: Stroke Learning Health System approach in Australia . 2024 . Accessed August 26, 2024 . https://australianstrokecoalition.org.au/wp-content/uploads/2024/03/ASC-Pos-Statement_Stroke-Learning-Health-System-approach-in-Australia.pdf 23. ↵ O’Brien BC , Harris IB , Beckman TJ , Reed DA , Cook DA . Standards for reporting qualitative research: a synthesis of recommendations . Acad Med . 2014 ; 89 ( 9 ): 1245 – 1251 . doi: 10.1097/ACM.0000000000000388 OpenUrl CrossRef PubMed 24. ↵ Rajit D , Reeder S , Johnson A , Enticott J , Teede H . Tools and frameworks for evaluating the implementation of learning health systems: a scoping review . Health Research Policy and Systems . 2024 ; 22 ( 1 ): 95 . doi: 10.1186/s12961-024-01179-7 OpenUrl CrossRef 25. ↵ Lannon C , Schuler CL , Seid M , et al. A maturity grid assessment tool for learning networks . Learning Health Systems . 2021 ; 5 ( 2 ): e10232 . doi: 10.1002/lrh2.10232 OpenUrl CrossRef 26. ↵ Van Citters AD , Buus-Frank ME , King JR , et al. The Cystic Fibrosis Learning Network: A mixed methods evaluation of program goals, attributes, and impact . Learning Health Systems . 2023 ; 7 ( 3 ): e10356 . doi: 10.1002/lrh2.10356 OpenUrl CrossRef PubMed 27. ↵ NIHR Guidance on co-producing a research project . Learning for Involvement . Accessed August 6, 2024 . https://www.learningforinvolvement.org.uk/content/resource/nihr-guidance-on-co-producing-a-research-project/ 28. ↵ Holtrop JS , Estabrooks PA , Gaglio B , et al. Understanding and applying the RE-AIM framework: Clarifications and resources . Journal of Clinical and Translational Science . 2021 ; 5 ( 1 ): e126 . doi: 10.1017/cts.2021.789 OpenUrl CrossRef 29. ↵ Carroll C , Patterson M , Wood S , Booth A , Rick J , Balain S . A conceptual framework for implementation fidelity . Implementation Science . 2007 ; 2 ( 1 ): 40 . doi: 10.1186/1748-5908-2-40 OpenUrl CrossRef PubMed 30. ↵ 30. Frontiers | Use of the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) framework to guide iterative adaptations: Applications, lessons learned, and future directions. Accessed November 14, 2024. https://www.frontiersin.org/journals/health-services/articles/10.3389/frhs.2022.959565/full 31. Muntinga ME , Van Leeuwen KM , Schellevis FG , Nijpels G , Jansen AP . From concept to content: assessing the implementation fidelity of a chronic care model for frail, older people who live at home . BMC Health Services Research . 2015 ; 15 ( 1 ): 18 . doi: 10.1186/s12913-014-0662-6 OpenUrl CrossRef PubMed 32. ↵ Guerbaai RA , DeGeest S , Popejoy LL , et al. Evaluating the implementation fidelity to a successful nurse-led model (INTERCARE) which reduced nursing home unplanned hospitalisations . BMC Health Services Research . 2023 ; 23 ( 1 ): 138 . doi: 10.1186/s12913-023-09146-8 OpenUrl CrossRef PubMed 33. ↵ Ryan O , Ghuliani J , Grabsch B , et al. Development, implementation, and evaluation of the Australian Stroke Data Tool (AuSDaT): Comprehensive data capturing for multiple uses . HIM J . 2024 ; 53 ( 2 ): 85 – 93 . doi: 10.1177/18333583221117184 OpenUrl CrossRef 34. ↵ Harris D , Cadilhac DA , Hankey GJ , Hillier S , Kilkenny M , Lalor E . National Stroke Audit: The Australian experience . Clinical Audit . 2010 ; 2 : 25 – 31 . doi: 10.2147/CA.S9435 OpenUrl CrossRef 35. ↵ English C , Hill K , Cadilhac DA , et al. Living clinical guidelines for stroke: updates, challenges and opportunities . Med J Aust . 2022 ; 216 ( 10 ). Accessed July 1, 2024 . https://www.mja.com.au/journal/2022/216/10/living-clinical-guidelines-stroke-updates-challenges-and-opportunities#6 36. ↵ The Centre for Research Excellence to Accelerate Stroke Trial Innovation and Translation . December 14 , 2023 . Accessed July 1, 2024 . https://stroke-trials-cre.org.au/ 37. ↵ Schiller C , Winters M , Hanson HM , Ashe MC . A framework for stakeholder identification in concept mapping and health research: a novel process and its application to older adult mobility and the built environment . BMC Public Health . 2013 ; 13 : 428 . doi: 10.1186/1471-2458-13-428 OpenUrl CrossRef PubMed 38. ↵ National Health and Medical Research Council (NHMRC) . Consumer involvement . 39. ↵ International Assocation for Public Participation (IAP2) . IAP2 Spectrum of Public Participation . https://iap2.org.au/wp-content/uploads/2020/01/2018_IAP2_Spectrum.pdf 40. ↵ James Lind Alliance (JLA). JLA Guidebook . https://www.jla.nihr.ac.uk/jla-guidebook/ 41. ↵ Vogel C , Zwolinsky S , Griffiths C , Hobbs M , Henderson E , Wilkins E . A Delphi study to build consensus on the definition and use of big data in obesity research . Int J Obes . 2019 ; 43 ( 12 ): 2573 – 2586 . doi: 10.1038/s41366-018-0313-9 OpenUrl CrossRef 42. ↵ Nurjono M , Shrestha P , Lee A , et al. Realist evaluation of a complex integrated care programme: protocol for a mixed methods study . BMJ Open . 2018 ; 8 ( 3 ): e017111 . doi: 10.1136/bmjopen-2017-017111 OpenUrl Abstract / FREE Full Text 43. ↵ Maier AM , Moultrie J , Clarkson PJ . Assessing Organizational Capabilities: Reviewing and Guiding the Development of Maturity Grids . IEEE Transactions on Engineering Management . 2012 ; 59 ( 1 ): 138 – 159 . doi: 10.1109/TEM.2010.2077289 OpenUrl CrossRef 44. ↵ Fraser P , Moultrie J , Gregory M . The use of maturity models/grids as a tool in assessing product development capability . In: IEEE International Engineering Management Conference . Vol 1 .; 2002 : 244 – 249 vol.1. doi: 10.1109/IEMC.2002.1038431 OpenUrl CrossRef 45. ↵ Moultrie J , Sutcliffe L , Maier A . A maturity grid assessment tool for environmentally conscious design in the medical device industry . Journal of Cleaner Production . 2016 ; 122 : 252 – 265 . doi: 10.1016/j.jclepro.2015.10.108 OpenUrl CrossRef 46. ↵ Sharma KM , Jones PB , Cumming J , Middleton L . A self-assessment maturity matrix to support large-scale change using collaborative networks in the New Zealand health system . BMC Health Services Research . 2024 ; 24 ( 1 ): 838 . doi: 10.1186/s12913-024-11284-6 OpenUrl CrossRef PubMed 47. L G , In F , D D , Hjm V . Assessment of the implementation fidelity of a strategy to scale up integrated care in five European regions: a multimethod study . BMJ open . 2020 ; 10 ( 3 ). doi: 10.1136/bmjopen-2019-035002 OpenUrl Abstract / FREE Full Text 48. ↵ Grooten L , Vrijhoef HJM , Calciolari S , et al. Assessing the maturity of the healthcare system for integrated care: testing measurement properties of the SCIROCCO tool . BMC Medical Research Methodology . 2019 ; 19 ( 1 ): 63 . doi: 10.1186/s12874-019-0704-1 OpenUrl CrossRef PubMed 49. ↵ Holland JR , Arnold DH , Hanson HR , et al. Reliability of the Behaviorally Anchored Rating Scale (BARS) for assessing non-technical skills of medical students in simulated scenarios . Med Educ Online . 27 ( 1 ): 2070940 . doi: 10.1080/10872981.2022.2070940 OpenUrl CrossRef 50. ↵ Ng AH , Reeder S , Jones A , et al. Consumer and community involvement: implementation research for impact (CCIRI) – implementing evidence-based patient and public involvement across health and medical research in Australia – a mixed methods protocol . Health Research Policy and Systems . 2025 ; 23 ( 1 ): 25 . doi: 10.1186/s12961-025-01293-0 OpenUrl CrossRef 51. ↵ Ramadan N , Arafeh M . Healthcare quality maturity assessment model based on quality drivers . International Journal of Health Care Quality Assurance . 2016 ; 29 ( 3 ). doi: 10.1108/IJHCQA-08-2015-0100 OpenUrl CrossRef 52. Bird M , McGillion M , Chambers EM , et al. A generative co-design framework for healthcare innovation: development and application of an end-user engagement framework . Research Involvement and Engagement . 2021 ; 7 ( 1 ): 12 . doi: 10.1186/s40900-021-00252-7 OpenUrl CrossRef PubMed 53. Bello JO , Grant P . A systematic review of the effectiveness of journal clubs in undergraduate medicine . Canadian Medical Education Journal . 2023 ; 14 ( 4 ): 35 . doi: 10.36834/cmej.72758 OpenUrl CrossRef PubMed 54. Nyanchoka L , Tudur-Smith C , Thu VN , Iversen V , Tricco AC , Porcher R . A scoping review describes methods used to identify, prioritize and display gaps in health research . Journal of Clinical Epidemiology . 2019 ; 109 : 99 – 110 . doi: 10.1016/j.jclinepi.2019.01.005 OpenUrl CrossRef PubMed 55. Elliott JH , Turner T , Clavisi O , et al. Living Systematic Reviews: An Emerging Opportunity to Narrow the Evidence-Practice Gap . PLOS Medicine . 2014 ; 11 ( 2 ): e1001603 . doi: 10.1371/journal.pmed.1001603 OpenUrl CrossRef PubMed 56. McDonald S , Hill K , Li HZ , Turner T . Evidence surveillance for a living clinical guideline: Case study of the Australian stroke guidelines . Health Info Libr J. Published online November 9 , 2023 . doi: 10.1111/hir.12515 OpenUrl CrossRef 57. Bull C , Teede H , Watson D , Callander EJ . Selecting and Implementing Patient-Reported Outcome and Experience Measures to Assess Health System Performance . JAMA Health Forum . 2022 ; 3 ( 4 ): e220326 . doi: 10.1001/jamahealthforum.2022.0326 OpenUrl CrossRef 58. Weiner J , Balijepally V , Tanniru M . Integrating Strategic and Operational Decision Making Using Data-Driven Dashboards: The Case of St. Joseph Mercy Oakland Hospital . Journal of Healthcare Management . 2015 ; 60 ( 5 ): 319 . OpenUrl PubMed 59. Michie S , Richardson M , Johnston M , et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions . Ann Behav Med . 2013 ; 46 ( 1 ): 81 – 95 . doi: 10.1007/s12160-013-9486-6 OpenUrl CrossRef PubMed 60. Parry G , Coly A , Goldmann D , et al. Practical recommendations for the evaluation of improvement initiatives . International Journal for Quality in Health Care . 2018 ; 30 ( Suppl 1 ): 29 . doi: 10.1093/intqhc/mzy021 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted April 10, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following The Monash Learning Health System Maturity Matrix: Codesign of a Tool to Measure and Guide Improvement in Complex Health System Behaviour Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. 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