case study in language learning

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Case study research on language learning and use - 2014

Duff, P. (2014). Case study research on language learning and use. Annual Review of Applied Linguistics, 34, 233-255. doi:10.1017/S0267190514000051. (*Note page numbers on the downloadable version are not correct). ABSTRACT Case study research has played a very important role in applied linguistics since the field was established, particularly in studies of language teaching, learning,and use. The case in such studies generally has been a person (e.g., a teacher,learner, speaker, writer, or interlocutor) or a small number of individuals on their own or in a group (e.g., a family, a class, a work team, or a community of practice). The cases are normally studied in depth in order to provide an under-standing of individuals’ experiences, issues, insights, developmental pathways,or performance within a particular linguistic, social, or educational context. Rather than discuss constructs, hypotheses, and findings in terms of statistical patterns or trends derived from a larger sample or survey of a population of language learners, as in some quantitative research, a qualitative case study of a person presents a contextualized human profile. Case study has contributed substantially to theory development, generating new perspectives or offering a refutation or refinement of earlier theories in applied linguistics by analyzing linguistic, cultural, and social phenomena associated with children, adolescents, young adults, and older adults. In recent years, the purview of case studies in applied linguistics has expanded to include many previously underrepresented topics, linguistic situations, theoretical perspectives, and populations. This article provides an overview of some traditional areas of coverage and then newer foci in terms of methodology, thematic areas, and findings pertaining to language learners in transnational, multilingual, and diaspora contexts especially.

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Case Study Methods in Researching Language and Education

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• Christian Faltis 3  

Part of the book series: Encyclopedia of Language and Education ((LANG,volume 8))

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Case study research stems from a special interest in individual cases (Stake, 1994). In the field of language and education, case study methods of research have produced some important discoveries about (1) how children and adolescents learn oral and written language; (2) how language teachers draw on perspectives and assumptions to inform their practices, and (3) how what happens outside the classrooms in school settings interacts with the ways that children and adolescents learn and use oral and written language inside of classrooms.

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Faltis, C. (1997). Case Study Methods in Researching Language and Education. In: Hornberger, N.H., Corson, D. (eds) Encyclopedia of Language and Education. Encyclopedia of Language and Education, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-4535-0_14

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Inverse translation and the language student: A case study

Dr. Lucía Pintado Gutiérrez is Assistant Professor in Spanish and Applied Linguistics at Dublin City University, Ireland. Her research explores the developing field of language education and pedagogical translation, in particular the principles on how translation may be utilized as a tool in foreign language, and how it may be developed as a skill for the benefit of FL learners. Her interdisciplinary research includes aspects related to foreign language teaching and learning, translation pedagogy, and learners’ motivation and agency. A second strand of her research lies at the intersection of translation and memory studies. She has published and co-edited work in both areas.

This article explores the agency of the student in translation in language teaching and learning (or TILT). The purpose of the case study discussed here is to gain an overview of students’ perceptions of translation into the foreign language (FL) (also known as “inverse translation”) following a module on language and translation, and to analyse whether there is any correlation between students’ attitude to translation, its impact on their language learning through effort invested, and the improvement of language skills. The results of the case study reveal translation to be a potentially exciting skill that can be central to FL learning and the analysis gives indications of how and why language teachers may optimise the implementation of translation in the classroom. The outcome of the study suggests that further research is needed on the impact of translation in the language classroom focussing on both teachers’ expectations and students’ achievements.

Funding source: Dublin City University

About the author

Funding : This research was funded by a grant from the Faculty of Humanities and Social Sciences, Dublin City University.

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• Published: 15 May 2024

Learning together for better health using an evidence-based Learning Health System framework: a case study in stroke

• Helena Teede 1 , 2   na1 ,
• Dominique A. Cadilhac 3 , 4   na1 ,
• Tara Purvis 3 ,
• Monique F. Kilkenny 3 , 4 ,
• Bruce C.V. Campbell 4 , 5 , 6 ,
• Coralie English 7 ,
• Alison Johnson 2 ,
• Emily Callander 1 ,
• Rohan S. Grimley 8 , 9 ,
• Christopher Levi 10 ,
• Sandy Middleton 11 , 12 ,
• Kelvin Hill 13 &
• Joanne Enticott   ORCID: orcid.org/0000-0002-4480-5690 1  

BMC Medicine volume  22 , Article number:  198 ( 2024 ) Cite this article

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In the context of expanding digital health tools, the health system is ready for Learning Health System (LHS) models. These models, with proper governance and stakeholder engagement, enable the integration of digital infrastructure to provide feedback to all relevant parties including clinicians and consumers on performance against best practice standards, as well as fostering innovation and aligning healthcare with patient needs. The LHS literature primarily includes opinion or consensus-based frameworks and lacks validation or evidence of benefit. Our aim was to outline a rigorously codesigned, evidence-based LHS framework and present a national case study of an LHS-aligned national stroke program that has delivered clinical benefit.

Current core components of a LHS involve capturing evidence from communities and stakeholders (quadrant 1), integrating evidence from research findings (quadrant 2), leveraging evidence from data and practice (quadrant 3), and generating evidence from implementation (quadrant 4) for iterative system-level improvement. The Australian Stroke program was selected as the case study as it provides an exemplar of how an iterative LHS works in practice at a national level encompassing and integrating evidence from all four LHS quadrants. Using this case study, we demonstrate how to apply evidence-based processes to healthcare improvement and embed real-world research for optimising healthcare improvement. We emphasize the transition from research as an endpoint, to research as an enabler and a solution for impact in healthcare improvement.

Conclusions

The Australian Stroke program has nationally improved stroke care since 2007, showcasing the value of integrated LHS-aligned approaches for tangible impact on outcomes. This LHS case study is a practical example for other health conditions and settings to follow suit.

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Internationally, health systems are facing a crisis, driven by an ageing population, increasing complexity, multi-morbidity, rapidly advancing health technology and rising costs that threaten sustainability and mandate transformation and improvement [ 1 , 2 ]. Although research has generated solutions to healthcare challenges, and the advent of big data and digital health holds great promise, entrenched siloes and poor integration of knowledge generation, knowledge implementation and healthcare delivery between stakeholders, curtails momentum towards, and consistent attainment of, evidence-and value-based care [ 3 ]. This is compounded by the short supply of research and innovation leadership within the healthcare sector, and poorly integrated and often inaccessible health data systems, which have crippled the potential to deliver on digital-driven innovation [ 4 ]. Current approaches to healthcare improvement are also often isolated with limited sustainability, scale-up and impact [ 5 ].

Evidence suggests that integration and partnership across academic and healthcare delivery stakeholders are key to progress, including those with lived experience and their families (referred to here as consumers and community), diverse disciplines (both research and clinical), policy makers and funders. Utilization of evidence from research and evidence from practice including data from routine care, supported by implementation research, are key to sustainably embedding improvement and optimising health care and outcomes. A strategy to achieve this integration is through the Learning Health System (LHS) (Fig.  1 ) [ 2 , 6 , 7 , 8 ]. Although there are numerous publications on LHS approaches [ 9 , 10 , 11 , 12 ], many focus on research perspectives and data, most do not demonstrate tangible healthcare improvement or better health outcomes. [ 6 ]

Monash Learning Health System: The Learn Together for Better Health Framework developed by Monash Partners and Monash University (from Enticott et al. 2021 [ 7 ]). Four evidence quadrants: Q1 (orange) is evidence from stakeholders; Q2 (green) is evidence from research; Q3 (light blue) is evidence from data; and, Q4 (dark blue) is evidence from implementation and healthcare improvement

In developed nations, it has been estimated that 60% of care provided aligns with the evidence base, 30% is low value and 10% is potentially harmful [ 13 ]. In some areas, clinical advances have been rapid and research and evidence have paved the way for dramatic improvement in outcomes, mandating rapid implementation of evidence into healthcare (e.g. polio and COVID-19 vaccines). However, healthcare improvement is challenging and slow [ 5 ]. Health systems are highly complex in their design, networks and interacting components, and change is difficult to enact, sustain and scale up. [ 3 ] New effective strategies are needed to meet community needs and deliver evidence-based and value-based care, which reorients care from serving the provider, services and system, towards serving community needs, based on evidence and quality. It goes beyond cost to encompass patient and provider experience, quality care and outcomes, efficiency and sustainability [ 2 , 6 ].

The costs of stroke care are expected to rise rapidly in the next decades, unless improvements in stroke care to reduce the disabling effects of strokes can be successfully developed and implemented [ 14 ]. Here, we briefly describe the Monash LHS framework (Fig.  1 ) [ 2 , 6 , 7 ] and outline an exemplar case in order to demonstrate how to apply evidence-based processes to healthcare improvement and embed real-world research for optimising healthcare. The Australian LHS exemplar in stroke care has driven nationwide improvement in stroke care since 2007.

An evidence-based Learning Health System framework

In Australia, members of this author group (HT, AJ, JE) have rigorously co-developed an evidence-based LHS framework, known simply as the Monash LHS [ 7 ]. The Monash LHS was designed to support sustainable, iterative and continuous robust benefit of improved clinical outcomes. It was created with national engagement in order to be applicable to Australian settings. Through this rigorous approach, core LHS principles and components have been established (Fig.  1 ). Evidence shows that people/workforce, culture, standards, governance and resources were all key to an effective LHS [ 2 , 6 ]. Culture is vital including trust, transparency, partnership and co-design. Key processes include legally compliant data sharing, linkage and governance, resources, and infrastructure [ 4 ]. The Monash LHS integrates disparate and often siloed stakeholders, infrastructure and expertise to ‘Learn Together for Better Health’ [ 7 ] (Fig.  1 ). This integrates (i) evidence from community and stakeholders including priority areas and outcomes; (ii) evidence from research and guidelines; (iii) evidence from practice (from data) with advanced analytics and benchmarking; and (iv) evidence from implementation science and health economics. Importantly, it starts with the problem and priorities of key stakeholders including the community, health professionals and services and creates an iterative learning system to address these. The following case study was chosen as it is an exemplar of how a Monash LHS-aligned national stroke program has delivered clinical benefit.

Australian Stroke Learning Health System

Internationally, the application of LHS approaches in stroke has resulted in improved stroke care and outcomes [ 12 ]. For example, in Canada a sustained decrease in 30-day in-hospital mortality has been found commensurate with an increase in resources to establish the multifactorial stroke system intervention for stroke treatment and prevention [ 15 ]. Arguably, with rapid advances in evidence and in the context of an ageing population with high cost and care burden and substantive impacts on quality of life, stroke is an area with a need for rapid research translation into evidence-based and value-based healthcare improvement. However, a recent systematic review found that the existing literature had few comprehensive examples of LHS adoption [ 12 ]. Although healthcare improvement systems and approaches were described, less is known about patient-clinician and stakeholder engagement, governance and culture, or embedding of data informatics into everyday practice to inform and drive improvement [ 12 ]. For example, in a recent review of quality improvement collaborations, it was found that although clinical processes in stroke care are improved, their short-term nature means there is uncertainty about sustainability and impacts on patient outcomes [ 16 ]. Table  1 provides the main features of the Australian Stroke LHS based on the four core domains and eight elements of the Learning Together for Better Health Framework described in Fig.  1 . The features are further expanded on in the following sections.

Evidence from stakeholders (LHS quadrant 1, Fig.  1 )

Engagement, partners and priorities.

Within the stroke field, there have been various support mechanisms to facilitate an LHS approach including partnership and broad stakeholder engagement that includes clinical networks and policy makers from different jurisdictions. Since 2008, the Australian Stroke Coalition has been co-led by the Stroke Foundation, a charitable consumer advocacy organisation, and Stroke Society of Australasia a professional society with membership covering academics and multidisciplinary clinician networks, that are collectively working to improve stroke care ( https://australianstrokecoalition.org.au/ ). Surveys, focus groups and workshops have been used for identifying priorities from stakeholders. Recent agreed priorities have been to improve stroke care and strengthen the voice for stroke care at a national ( https://strokefoundation.org.au/ ) and international level ( https://www.world-stroke.org/news-and-blog/news/world-stroke-organization-tackle-gaps-in-access-to-quality-stroke-care ), as well as reduce duplication amongst stakeholders. This activity is built on a foundation and culture of research and innovation embedded within the stroke ‘community of practice’. Consumers, as people with lived experience of stroke are important members of the Australian Stroke Coalition, as well as representatives from different clinical colleges. Consumers also provide critical input to a range of LHS activities via the Stroke Foundation Consumer Council, Stroke Living Guidelines committees, and the Australian Stroke Clinical Registry (AuSCR) Steering Committee (described below).

Evidence from research (LHS quadrant 2, Fig.  1 )

Advancement of the evidence for stroke interventions and synthesis into clinical guidelines.

To implement best practice, it is crucial to distil the large volume of scientific and trial literature into actionable recommendations for clinicians to use in practice [ 24 ]. The first Australian clinical guidelines for acute stroke were produced in 2003 following the increasing evidence emerging for prevention interventions (e.g. carotid endarterectomy, blood pressure lowering), acute medical treatments (intravenous thrombolysis, aspirin within 48 h of ischemic stroke), and optimised hospital management (care in dedicated stroke units by a specialised and coordinated multidisciplinary team) [ 25 ]. Importantly, a number of the innovations were developed, researched and proven effective by key opinion leaders embedded in the Australian stroke care community. In 2005, the clinical guidelines for Stroke Rehabilitation and Recovery [ 26 ] were produced, with subsequent merged guidelines periodically updated. However, the traditional process of periodic guideline updates is challenging for end users when new research can render recommendations redundant and this lack of currency erodes stakeholder trust [ 27 ]. In response to this challenge the Stroke Foundation and Cochrane Australia entered a pioneering project to produce the first electronic ‘living’ guidelines globally [ 20 ]. Major shifts in the evidence for reperfusion therapies (e.g. extended time-window intravenous thrombolysis and endovascular clot retrieval), among other advances, were able to be converted into new recommendations, approved by the Australian National Health and Medical Research Council within a few months of publication. Feedback on this process confirmed the increased use and trust in the guidelines by clinicians. The process informed other living guidelines programs, including the successful COVID-19 clinical guidelines [ 28 ].

However, best practice clinical guideline recommendations are necessary but insufficient for healthcare improvement and nesting these within an LHS with stakeholder partnership, enables implementation via a range of proven methods, including audit and feedback strategies [ 29 ].

Evidence from data and practice (LHS quadrant 3, Fig.  1 )

Data systems and benchmarking : revealing the disparities in care between health services. A national system for standardized stroke data collection was established as the National Stroke Audit program in 2007 by the Stroke Foundation [ 30 ] following various state-level programs (e.g. New South Wales Audit) [ 31 ] to identify evidence-practice gaps and prioritise improvement efforts to increase access to stroke units and other acute treatments [ 32 ]. The Audit program alternates each year between acute (commencing in 2007) and rehabilitation in-patient services (commencing in 2008). The Audit program provides a ‘deep dive’ on the majority of recommendations in the clinical guidelines whereby participating hospitals provide audits of up to 40 consecutive patient medical records and respond to a survey about organizational resources to manage stroke. In 2009, the AuSCR was established to provide information on patients managed in acute hospitals based on a small subset of quality processes of care linked to benchmarked reports of performance (Fig.  2 ) [ 33 ]. In this way, the continuous collection of high-priority processes of stroke care could be regularly collected and reviewed to guide improvement to care [ 34 ]. Plus clinical quality registry programs within Australia have shown a meaningful return on investment attributed to enhanced survival, improvements in quality of life and avoided costs of treatment or hospital stay [ 35 ].

Example performance report from the Australian Stroke Clinical Registry: average door-to-needle time in providing intravenous thrombolysis by different hospitals in 2021 [ 36 ]. Each bar in the figure represents a single hospital

The Australian Stroke Coalition endorsed the creation of an integrated technological solution for collecting data through a single portal for multiple programs in 2013. In 2015, the Stroke Foundation, AuSCR consortium, and other relevant groups cooperated to design an integrated data management platform (the Australian Stroke Data Tool) to reduce duplication of effort for hospital staff in the collection of overlapping variables in the same patients [ 19 ]. Importantly, a national data dictionary then provided the common data definitions to facilitate standardized data capture. Another important feature of AuSCR is the collection of patient-reported outcome surveys between 90 and 180 days after stroke, and annual linkage with national death records to ascertain survival status [ 33 ]. To support a LHS approach, hospitals that participate in AuSCR have access to a range of real-time performance reports. In efforts to minimize the burden of data collection in the AuSCR, interoperability approaches to import data directly from hospital or state-level managed stroke databases have been established (Fig.  3 ); however, the application has been variable and 41% of hospitals still manually enter all their data.

Current status of automated data importing solutions in the Australian Stroke Clinical Registry, 2022, with ‘ n ’ representing the number of hospitals. AuSCR, Australian Stroke Clinical Registry; AuSDaT, Australian Stroke Data Tool; API, Application Programming Interface; ICD, International Classification of Diseases; RedCAP, Research Electronic Data Capture; eMR, electronic medical records

For acute stroke care, the Australian Commission on Quality and Safety in Health Care facilitated the co-design (clinicians, academics, consumers) and publication of the national Acute Stroke Clinical Care Standard in 2015 [ 17 ], and subsequent review [ 18 ]. The indicator set for the Acute Stroke Standard then informed the expansion of the minimum dataset for AuSCR so that hospitals could routinely track their performance. The national Audit program enabled hospitals not involved in the AuSCR to assess their performance every two years against the Acute Stroke Standard. Complementing these efforts, the Stroke Foundation, working with the sector, developed the Acute and Rehabilitation Stroke Services Frameworks to outline the principles, essential elements, models of care and staffing recommendations for stroke services ( https://informme.org.au/guidelines/national-stroke-services-frameworks ). The Frameworks are intended to guide where stroke services should be developed, and monitor their uptake with the organizational survey component of the Audit program.

Evidence from implementation and healthcare improvement (LHS quadrant 4, Fig.  1 )

Research to better utilize and augment data from registries through linkage [ 37 , 38 , 39 , 40 ] and to ensure presentation of hospital or service level data are understood by clinicians has ensured advancement in the field for the Australian Stroke LHS [ 41 ]. Importantly, greater insights into whole patient journeys, before and after a stroke, can now enable exploration of value-based care. The LHS and stroke data platform have enabled focused and time-limited projects to create a better understanding of the quality of care in acute or rehabilitation settings [ 22 , 42 , 43 ]. Within stroke, all the elements of an LHS culminate into the ready availability of benchmarked performance data and support for implementation of strategies to address gaps in care.

Implementation research to grow the evidence base for effective improvement interventions has also been a key pillar in the Australian context. These include multi-component implementation interventions to achieve behaviour change for particular aspects of stroke care, [ 22 , 23 , 44 , 45 ] and real-world approaches to augmenting access to hyperacute interventions in stroke through the use of technology and telehealth [ 46 , 47 , 48 , 49 ]. The evidence from these studies feeds into the living guidelines program and the data collection systems, such as the Audit program or AuSCR, which are then amended to ensure data aligns to recommended care. For example, the use of ‘hyperacute aspirin within the first 48 h of ischemic stroke’ was modified to be ‘hyperacute antiplatelet…’ to incorporate new evidence that other medications or combinations are appropriate to use. Additionally, new datasets have been developed to align with evidence such as the Fever, Sugar, and Swallow variables [ 42 ]. Evidence on improvements in access to best practice care from the acute Audit program [ 50 ] and AuSCR is emerging [ 36 ]. For example, between 2007 and 2017, the odds of receiving intravenous thrombolysis after ischemic stroke increased by 16% 9OR 1.06 95% CI 1.13–1.18) and being managed in a stroke unit by 18% (OR 1.18 95% CI 1.17–1.20). Over this period, the median length of hospital stay for all patients decreased from 6.3 days in 2007 to 5.0 days in 2017 [ 51 ]. When considering the number of additional patients who would receive treatment in 2017 in comparison to 2007 it was estimated that without this additional treatment, over 17,000 healthy years of life would be lost in 2017 (17,786 disability-adjusted life years) [ 51 ]. There is evidence on the cost-effectiveness of different system-focussed strategies to augment treatment access for acute ischemic stroke (e.g. Victorian Stroke Telemedicine program [ 52 ] and Melbourne Mobile Stroke Unit ambulance [ 53 ]). Reciprocally, evidence from the national Rehabilitation Audit, where the LHS approach has been less complete or embedded, has shown fewer areas of healthcare improvement over time [ 51 , 54 ].

Within the field of stroke in Australia, there is indirect evidence that the collective efforts that align to establishing the components of a LHS have had an impact. Overall, the age-standardised rate of stroke events has reduced by 27% between 2001 and 2020, from 169 to 124 events per 100,000 population. Substantial declines in mortality rates have been reported since 1980. Commensurate with national clinical guidelines being updated in 2007 and the first National Stroke Audit being undertaken in 2007, the mortality rates for men (37.4 deaths per 100,000) and women (36.1 deaths per 100,0000 has declined to 23.8 and 23.9 per 100,000, respectively in 2021 [ 55 ].

Underpinning the LHS with the integration of the four quadrants of evidence from stakeholders, research and guidelines, practice and implementation, and core LHS principles have been addressed. Leadership and governance have been important, and programs have been established to augment workforce training and capacity building in best practice professional development. Medical practitioners are able to undertake courses and mentoring through the Australasian Stroke Academy ( http://www.strokeacademy.com.au/ ) while nurses (and other health professionals) can access teaching modules in stroke care from the Acute Stroke Nurses Education Network ( https://asnen.org/ ). The Association of Neurovascular Clinicians offers distance-accessible education and certification to develop stroke expertise for interdisciplinary professionals, including advanced stroke co-ordinator certification ( www.anvc.org ). Consumer initiative interventions are also used in the design of the AuSCR Public Summary Annual reports (available at https://auscr.com.au/about/annual-reports/ ) and consumer-related resources related to the Living Guidelines ( https://enableme.org.au/resources ).

The important success factors and lessons from stroke as a national exemplar LHS in Australia include leadership, culture, workforce and resources integrated with (1) established and broad partnerships across the academic-clinical sector divide and stakeholder engagement; (2) the living guidelines program; (3) national data infrastructure, including a national data dictionary that provides the common data framework to support standardized data capture; (4) various implementation strategies including benchmarking and feedback as well as engagement strategies targeting different levels of the health system; and (5) implementation and improvement research to advance stroke systems of care and reduce unwarranted variation in practice (Fig.  1 ). Priority opportunities now include the advancement of interoperability with electronic medical records as an area all clinical quality registry’s programs needs to be addressed, as well as providing more dynamic and interactive data dashboards tailored to the need of clinicians and health service executives.

There is a clear mandate to optimise healthcare improvement with big data offering major opportunities for change. However, we have lacked the approaches to capture evidence from the community and stakeholders, to integrate evidence from research, to capture and leverage data or evidence from practice and to generate and build on evidence from implementation using iterative system-level improvement. The LHS provides this opportunity and is shown to deliver impact. Here, we have outlined the process applied to generate an evidence-based LHS and provide a leading exemplar in stroke care. This highlights the value of moving from single-focus isolated approaches/initiatives to healthcare improvement and the benefit of integration to deliver demonstrable outcomes for our funders and key stakeholders — our community. This work provides insight into strategies that can both apply evidence-based processes to healthcare improvement as well as implementing evidence-based practices into care, moving beyond research as an endpoint, to research as an enabler, underpinning delivery of better healthcare.

Availability of data and materials

Not applicable

Abbreviations

Australian Stroke Clinical Registry

Confidence interval

• Learning Health System

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Acknowledgements

The following authors hold National Health and Medical Research Council Research Fellowships: HT (#2009326), DAC (#1154273), SM (#1196352), MFK Future Leader Research Fellowship (National Heart Foundation #105737). 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 information

Helena Teede and Dominique A. Cadilhac contributed equally.

Authors and Affiliations

Monash Centre for Health Research and Implementation, 43-51 Kanooka Grove, Clayton, VIC, Australia

Helena Teede, Emily Callander & Joanne Enticott

Monash Partners Academic Health Science Centre, 43-51 Kanooka Grove, Clayton, VIC, Australia

Helena Teede & Alison Johnson

Stroke and Ageing Research, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Level 2 Monash University Research, Victorian Heart Hospital, 631 Blackburn Rd, Clayton, VIC, Australia

Dominique A. Cadilhac, Tara Purvis & Monique F. Kilkenny

Stroke Theme, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, VIC, Australia

Dominique A. Cadilhac, Monique F. Kilkenny & Bruce C.V. Campbell

Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, Parkville, VIC, Australia

Bruce C.V. Campbell

Department of Medicine, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Victoria, Australia

School of Health Sciences, Heart and Stroke Program, University of Newcastle, Hunter Medical Research Institute, University Drive, Callaghan, NSW, Australia

Coralie English

School of Medicine and Dentistry, Griffith University, Birtinya, QLD, Australia

Rohan S. Grimley

Clinical Excellence Division, Queensland Health, Brisbane, Australia

John Hunter Hospital, Hunter New England Local Health District and University of Newcastle, Sydney, NSW, Australia

Christopher Levi

School of Nursing, Midwifery and Paramedicine, Australian Catholic University, Sydney, NSW, Australia

Sandy Middleton

Nursing Research Institute, St Vincent’s Health Network Sydney and and Australian Catholic University, Sydney, NSW, Australia

Stroke Foundation, Level 7, 461 Bourke St, Melbourne, VIC, Australia

Kelvin Hill

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HT: conception, design and initial draft, developed the theoretical formalism for learning health system framework, approved the submitted version. DAC: conception, design and initial draft, provided essential literature and case study examples, approved the submitted version. TP: revised the manuscript critically for important intellectual content, approved the submitted version. MFK: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. BC: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. CE: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. AJ: conception, design and initial draft, developed the theoretical formalism for learning health system framework, approved the submitted version. EC: revised the manuscript critically for important intellectual content, approved the submitted version. RSG: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. CL: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. SM: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. KH: revised the manuscript critically for important intellectual content, provided essential literature and case study examples, approved the submitted version. JE: conception, design and initial draft, developed the theoretical formalism for learning health system framework, approved the submitted version. All authors read and approved the final manuscript.

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Teede, H., Cadilhac, D.A., Purvis, T. et al. Learning together for better health using an evidence-based Learning Health System framework: a case study in stroke. BMC Med 22 , 198 (2024). https://doi.org/10.1186/s12916-024-03416-w

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DOI : https://doi.org/10.1186/s12916-024-03416-w

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Case Study: Monitoring with Zabbix and AI

Artificial intelligence (AI) and data monitoring are working together to digitally transform relationships, businesses, and people. In telecommunications, predictive analysis based on data collection plays a crucial role in development. Starting with version 6.0 of Zabbix, users have benefited from updates in predictive functions and machine learning, which make it possible for them to study the data monitored by Zabbix and integrate it with AI modules.

Danilo Barros, co-founder of Lunio (a Zabbix Certified Partner in Brazil), presented the results of using Zabbix combined with telecom data monitoring through AI and machine learning at Zabbix Conference Brazil in 2022. Keep reading to get the whole story!

Table of Contents

The scenario

With over 600 OLTs (Optical Line Terminals – the fiberoptic infrastructure used by internet providers) as well as 400,000 customers across more than 800 cities and 20 states in Brazil, Lunio’s client manages a staggering amount of data. This monitoring is essential for smooth operations and to guarantee that there are no negative impacts on users and no overload for customer service agents in the event of accidents.

A primary challenge for telecom clients is the overload of calls to customer service in the event of massive network incidents. With so many customers, every precaution must be taken to avoid clogging phone lines during outages or service failures.

“You can’t achieve customer satisfaction under such circumstances, and the Net Promoter Score (NPS) drops drastically.”   Danilo Barros, co-founder of Lunio

Mapping needs

Considering the client’s operational structure, a series of customer needs were identified, focusing on six main points:

1. Automation: With notifications via digital channels for each event 2. Speed: Aiming for improved customer service 3. Operational costs: Budget optimization 4. Root cause analysis: Quick identification of the cause of events 5. Predictability: The ability to analyze problems and identify trends 6. Reporting: Identifying incidents and following regulations from ANATEL (National Telecommunications Agency)

With these interests in mind, it was possible to reassess the use of tools previously employed by the telecom client, which at the time served unique functions in the process. Each tool had its usage and information verification time, which could impact hundreds of users in a massive-scale incident. The key challenges identified by the Lunio team included:

• Integrations: Systems needed to be interconnected
• Integrity: Constant data updates
• Topology: With system mapping through specific programs
• Business rules: Respecting the development of local processes
• Performance: The monitoring and automation of 600,000 assets
• High availability: Dozens of data centers catering to local demand

Once the needs and challenges were identified, it was time to promote change within the client. By integrating systems and using Zabbix to monitor over 600,000 items, understand incidents, and predict potential future errors, the technical teams at Lunio created LunioAI, a “super attendant” with analytical and predictive capabilities as well as the ability to continuously learn.

“This guy (LunioIA) learns from each event, understanding each topology that occurs in the client’s network.”   Danilo Barros, co-founder of Lunio

In the initial response tests, LunioAI was able to analyze and evaluate massive events in a minute and a half. Over time, this was reduced to 30 seconds, making the return to the technical team increasingly swift and positively impacting incident resolution.

The results

Throughout the development and improvement of LunioIA, the operations chain was involved in predictive analyses of potential events on the network, providing technical professionals with the information needed to perform preventive maintenance on monitored items.

LunioIA considers data from integrated systems, FTTH (fiber to the home) environments, data centers, and items, all as part of the Zabbix monitoring environment. It can then diagnose events, understand the severity of an event, and find resolution points – without the need for human resources in the process.

As a result, when physical attendants were contacted by customers experiencing difficulties with the service, instead of going through the entire process to understand what happened, the attendant could perform a search using the customer’s CPF (Individual Taxpayer Registry Identification) and then access a summary of the events, causes, and solutions identified by artificial intelligence combined with data monitoring through Zabbix.

In conclusion

This example happens to come from the telecommunications industry, but it’s not difficult to see how the ability of Zabbix to integrate the data monitored by Zabbix with AI modules can benefit companies in almost any industry.

You can find out more about what we can do across a variety of industries by visiting our website or requesting a demo.

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ML/AI Self Study Links

If you are aware of good online resources that you think we should include, please email us .

The following websites are useful for people interested in machine learning with Python.

Minnesota Supercomputing Institute

The Minnesota Supercomputing Institute has a YouTube channel with a number of tutorials on it. In addition, they regularly offer tutorials and other events. Check out their webpage for current offerings.

Tutorials available on their YouTube channel include:

• Python for Scientific Computing
• Advanced Python for Scientific Computing
• Getting Started with PyTorch: Setup
• Getting Started with PyTorch: Example
• Programming with Python
• Programming with Python: Fundamentals and Working with Data
• Programming with Python: Making Choices and Creating Functions
• Programming with Python: Loops, Lists, and Automating Analysis

Below we provide a selection of resources for Python, a general purpose programming language that has become popular for use in data science. One reason for that popularity is the existence of a number of packages for Python that implement various mathematical, statistical, and machine learning-related functions. Another attractive feature of Python is the widespread use of Jupyter notebooks that allow for interactive programming in Python (as well as other languages). Jupyter notebooks are also an easy way to share code and create tutorials for various programming tasks. Of course, Python programs can also be created using standard editors used for programming languages. There will be more on this below.

General Python background

• Python website
• Python Wikipedia Entr y

Getting Started with Python

The first task in getting started with Python is to get access. You can do this by installing it on a machine you have access to, such as your personal computer, or you can get access through a computing facility in your organization. 

Access Python without installing it on your personal machine

Option 1: Google Colab This puts you directly into a Python notebook, which has a link to an introductory video, and to a new Jupyter notebook. Other links on the side of this notebook have links to examples, etc.  If you just want to see the webpage, you can hit the cancel button at the bottom of the notebook. Colab allows you to get started with coding in Python right away. Access to Python's packages and GPUs is provided in this environment, but it is not intended for running compute-intensive programs.

Option 2: MSI Jupyter Notebook Server Those who have an account at the Minnesota Supercomputing Institute can go directly to the Jupyter notebook server. You must have an MSI account to use MSI resources. You must also connect either from a machine on a UMN network or via VPN. See the MSI website for  additional documentation . You can perform moderate-size tasks using this option, but for programs that use more resources, you should create and submit jobs via the batch system at MSI.

Option 3: Code in the Cloud from Anaconda Anaconda also provides an environment that you can use to install and use Python and its packages. If you just want to have access to a Jupyter notebook and play around with learning and light use of Python, this could be another possibility. You will have to create an account with Anaconda.

Install Python on your personal machine

We recommend installing  Anaconda as it will install Python and also install other regularly used packages for scientific computing and data science. It will also help make sure all your packages remain compatible with one another. Much of the functionality of Python, like R, Matlab, and other languages, comes from the packages. 

The Anaconda website also provides a webpage for  Getting started with Anaconda . 

Anaconda takes a fair amount of space on the disk and time to install, so if you have an older computer with limited disk space or a limited internet connection, you should consider lighter-weight options such as  Miniconda .

Alternatively, you can download Python from  python.org .  In that case, you will probably want to use a package manager such as  pip , which comes with Python if you install it from python.org. This gives you more control but requires more understanding of Python, its packages, and how they are managed. 

Learning to program in Python

For those unfamiliar with Python 3 and Jupyter notebooks, this is a relatively easy environment to learn. Learning Python 3 will help you advance your knowledge of data analytics, as most big data platforms and data mining/machine learning projects require a working knowledge of Python. The following resources can help you learn Python and get started in using Python for machine learning.

• Learn Python (book wesbite)
• Google's Python Class (website and videos)
• Python for Data Analysis, 3E (book website)
• W3Schools Python Tutorials (a collection of short Python tutorials on various aspects of the Python language)
• Data Analysis with Python and Pandas (videos) Pandas is a Python package for handling and analyzing data.
• Learn to Program with Python (videos)

Running Jupyter Notebooks

If you have installed Anaconda, you can run Anaconda Navigator and then click on the Jupyter notebook icon to start the Jupyter notebook. (Once you become familiar with a Jupyter notebook, you may want to run JupyterLab, a more advanced Notebook Interface.)  

You can also log onto the MSI Jupyter notebook server. 

• How to Use Jupyter Notebook: A Beginner's Tutorial (webpage)
• Running the Notebook (webpage)  For those not using Anaconda

General Machine Learning

• An introduction to machine learning with the Python library, scikit-learn
• Machine Learning for Absolute Beginners , 3e by Oliver Theobald. 2nd Edition, 2017.
• Stanford machine learning playlist on YouTube  by Andrew Ng
• Introduction to Statistical Learning , by James, Witten, Hastie, Tibshirani, and Taylor. (Available to download with R or Python.) 
• Introduction to Data Mining , 2nd edition by Tan, Steinbach, Karpatne, and Kumar, which has three chapters available online, two of which provide a basic introduction to Classification and Clustering.
• Mathematics for Machine Learning . Deisenroth, Faisal, and Ong.
• Understanding Machine Learning: From Theory to Algorithms , Shalev-Schwartz and Sai Ben-David. Choose the download option on the bottom left. 
• The website,  KDD Nuggets , has many resources, including additional links to Free Books for Machine Learning . 

Deep Learning with Python

• Deep Learning , by Goodfellow, Bengio, and Courville An online book that gives a good, although demanding, introduction to deep learning.
• Dive into Deep Learning , by by Zhang, Lipton, Li, and Smola. This interactive online book includes concepts, exercises, and code.
• Practical Deep Learning for Coders . This interactive online book also includes concepts, exercises, and code. It now has a part 2,  From Deep Learning Foundations to Stable Diffusion . 
• Deep Learning . This website features instructors Yann LeCun and Alfredo Canziani teaching a course on deep learning in Spring 2020 at the NYU Center for Data Science. It includes YouTube videos, slides, and Jupyter notebooks. The course concerns the latest techniques in deep learning and representation learning.
• Welcome to the UVA Deep Learning Tutorials! The University of Amsterdam has a set of Python notebook tutorials for deep learning. These tutorial notebooks are fairly self-contained but are accompanied by  videos  available on YouTube.
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Can music education boost grades, attendance? A new case study suggests it might

A new case study that included hundreds of Tennessee public schools suggests that music education may be tied to better math and reading scores, along with better attendance and positive social, emotional and behavioral effects.

The study, titled "Face the Music: A Case Study for Expanding Music and Arts in Schools," was the result of a joint effort between the CMA Foundation and the Mr. Holland's Opus Foundation. It examined the challenges and benefits stemming from music and arts education across 589 schools and 23 districts in Tennessee, and also included feedback from music and arts teachers, students and lifelong learners.

“Music and arts education are playing a key role in helping students catch up after the pandemic, from helping with math and reading proficiency to coping with stress and trauma. The research is clear: arts and music classes are a must-have, not a nice-to-have," CMA Foundation Executive Director Tiffany Kerns said in a news release.

The foundations behind the study hope it will serve as a model for other states to boost music and arts education at the state and district level, along with gaining support from nonprofit organizations and philanthropy.

Here are key takeaways from the study.

Related: TN high school band gets surprise invite to perform at CMA Fest as director wins award

A connection between music, arts, math and reading

Data from the majority of districts in the study showed that students enrolled in music and arts programs demonstrated significantly higher proficiency rates in math and literacy. The majority of districts also saw better attendance rates from music and arts students, compared to schoolwide averages. These outcomes align with a handful of other studies. However, the case study report did note an important caveat: Most studies on music and arts participation don't prove it's the cause of improved academics.

Here's a look at the proficiency and attendance numbers the study revealed:

• Roughly 1-in-4 school districts reported 47% of students enrolled in music and arts programs showed reading proficiency, compared to an overall school average of 36%.
• Roughly 7-in-10 school districts reported 46% of students enrolled in music and arts programs excelled in math, compared to an overall school average of 35%.
• Roughly 3-in-4 districts reported increased attendance rates for students enrolled in music and arts programs. The study noted that schools where music and arts students outpaced the school average for attendance saw an average attendance boost of 12%.

Systemic barriers to music and arts education

The case study also outlined factors that hamper access to music and arts education for students and schools. They largely align with trends reported in the most recent National Arts Education Status Report and historical data from the Tennessee Arts Education Data Project, the study said.

The barriers for students identified in the study are:

• Participation requirements, including after-school attendance and prerequisites or auditions
• Housing insecurity
• Transportation challenges
• Participation fees
• Equipment costs

The study also showed that curriculum scheduling is a primary issue facing schools. That can disrupt the ability to offer sequential music and arts classes in every grade, allowing students to explore a variety of options and build upon their skills each year.

Recommendations for action

The study mapped out a series of recommendations for how everyone from parents and students to educators and lawmakers can take action to bolster music and arts education in Tennessee.

For school community members like parents, students and educators, that can range from simply attending music and arts events to show support to advocating for more funding for programs. The study also calls on school and state leaders to partner to fund music and arts education, train teachers and develop partnerships that help sustain those programs in schools. It also challenges philanthropic organizations to fund grants, promote advocacy and awareness and take other steps to partner with and support schools.

"Each of us can support a system of change to enhance music and arts education for a more well-rounded education for our students," the study stated.

A look at the demographics and schools

The demographics of the schools that participated were similar to the state of Tennessee as a whole, but they differed slightly when it came to the population of public school students statewide. Students in the study were:

• 16% Black or African American
• 9% Hispanic or Latino
• 2% other racial identities
• 24% socioeconomically disadvantaged
• 13% engaged in special education services
• 9% English language learners
• 2% experiencing homelessness or in foster care
• 1% identified as migrants or refugees

According to 2022-23 academic year data from the Tennessee Department of Education, here's how Tennessee schools demographics broke down:

• 24% Black and African American
• 14% Hispanic
• 3% other racial identities
• 30% economically disadvantaged
• 14% students with disabilities
• 8% English learners
• Less than 1% in foster care
• 2% experiencing homelessness
• Less than 1% identified as migrants

Here's the full list of districts that participated in the case study:

• Arlington Community Schools
• Bartlett City Schools
• Benton County Schools
• Chester County School District
• Clarksville-Montgomery County School System
• Coffee County School District
• Germantown Municipal School District
• Giles County School System
• Greene County Schools
• Hamilton County Schools
• Jackson-Madison County School District
• Knox County Schools
• Lincoln County Schools
• Marion County Schools
• Maury County Public Schools
• Metro Nashville Public Schools
• Paris Special School District
• Robertson County Schools
• Rutherford County Schools
• Tullahoma City Schools
• Weakley County Schools
• Williamson County School 
• Wilson County Schools

The CMA Foundation is the philanthropic arm of the Country Music Association. More information, along with the foundation's extensive research and initiatives, can be found at cmafoundation.org . Learn more about the Mr. Holland's Opus Foundation at mhopus.org .