A Learning Health System Framework to Operationalize Health Data to Improve Quality Care: An Australian Perspective

Enticott, Joanne; Melder, Angela; Johnson, Alison; Jones, Angela; Shaw, Tim; Keech, Wendy; Buttery, Jim; Teede, Helena

doi:10.3389/fmed.2021.730021

Cited by 27 publications

(31 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The culmination of findings from phase two of this study, systematically informed the development of a list of prioritised interventions that meet specific implementation criteria, set through engagement with the participating organisation’s stakeholders, and their overarching strategic plan [ 24 ]. This feeds into the development of an action plan as well as a communication plan to disseminate the findings more broadly for the organisation.…”

Section: Methodsmentioning

confidence: 99%

Using a Modified Delphi Approach and Nominal Group Technique for Organisational Priority Setting of Evidence-Based Interventions That Advance Women in Healthcare Leadership

Mousa

Teede

Garth

et al. 2022

IJERPH

View full text Add to dashboard Cite

Background: Few studies address how to prioritise organisational interventions that advance women in leadership. We report on the relevance, feasibility and importance of evidence-based interventions for a large healthcare organisation. This study supports the first stage of implementation in a large National Health and Medical Research Council funded initiative seeking to advance women in healthcare leadership. Methods: An expert multi-disciplinary panel comprised of health professionals and leaders from a large healthcare network in Australia participated. The initial Delphi survey was administered online and results were presented in a Nominal Group Technique workshop. Here, the group made sense of the survey results, then evaluated findings against a framework on implementation criteria. Two further consensus surveys were conducted during the workshop. Results: Five priority areas were identified. These included: 1. A committed and supportive leadership team; 2. Improved governance structures; 3. Mentoring opportunities; 4. Leadership training and development; and 5. Flexibility in working. We describe the overall priority setting process in the context of our findings. Conclusions: With evidence and expert input, we established priorities for advancing women in healthcare leadership with a partnering healthcare organisation. This approach can be adapted in other settings, seeking to advance women in leadership.

show abstract

Section: Methodsmentioning

confidence: 99%

Using a Modified Delphi Approach and Nominal Group Technique for Organisational Priority Setting of Evidence-Based Interventions That Advance Women in Healthcare Leadership

Mousa

Teede

Garth

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…8 Discussions and research on the need for learning health systems have focused primarily on highincome countries and clinical care contexts. 8 Existing learning system frameworks generally address topics such as architecture, 9 10 value creation, 11 technological structures (eg, real-time clinical analytics), 12 operationalisation of health data 13 and work and enabling conditions required for learning health systems (eg, QI practices are standard practice). 14 Much is yet to be learnt and understood about effectively deploying, learning from and sustaining practices that deliver quality care at the facility, district or national levels in low-income and middle-income countries.…”

Section: Summary Boxmentioning

confidence: 99%

National learning systems to sustain and scale up delivery of quality healthcare: a conceptual framework

Lattof

Maliqi

Livesley³

et al. 2022

BMJ Glob Health

View full text Add to dashboard Cite

All around the world, health systems fail to provide good quality of care (QoC). By developing learning systems, health systems are able to better identify good practices and to explain how to sustain and scale these good practices. To facilitate the operationalisation of national learning systems, the Network for Improving Quality of Care for Maternal Newborn and Child Health (the Network) developed a conceptual framework for national learning systems to support QoC at scale. The Network facilitated an iterative process to reach consensus on a conceptual framework for national learning systems to sustain and scale up delivery of quality healthcare. Following a landscape analysis, the Network Secretariat and WHO convened two consultative meetings with country partners, technical experts and stakeholders. Based on these inputs, we developed a conceptual framework for national learning systems to support QoC at scale. National learning systems use a variety of approaches to identify practices that have improved QoC at the patient and provider levels. They also facilitate scale up and sustain strategies used successfully to support quality improvement. Despite growing consensus on the importance of learning for QoC, no one has yet detailed how this learning should be operationalised nationally. Our conceptual framework is the first to facilitate the operationalisation of national learning systems so that health systems can begin to develop, adapt and implement mechanisms to learn about what works or fails and to scale up and sustain this learning for QoC.

show abstract

“…Historically, in healthcare, we use different protocols as a baseline to provide the appropriate care to the patients. In continuous learning, we adapt this baseline according to multiple goals par example to increase the organisational efficiency [36]. The stakeholders perform this adaptation in an iterative process using the feedback previously collected.…”

Section: Continuous Learning In Healthcarementioning

confidence: 99%

A comprehensive mHealth solution for OSA patient management

Joymangul

Sekhari

Grasset³

et al. 2022

Preprint

View full text Add to dashboard Cite

Obstructive Sleep Apnea (OSA) is a potentially fatal sleep disease that causes a variety of pathologies. Positive Airway Pressure (PAP) therapy is the preferred treatment for OSA. Nonetheless, when compared to the other 17 therapies, the PAP therapy has one of the lowest levels of compliance. We present 2 novel mHealth solutions for improving this adherence level in CPAP therapy. The first application, Sleep.py Experts, provides a variety of decision support tools to enable tailored patient management for PAP treatment. The end-users for Sleep.py Experts are the multiple stakeholders that are involved in the PAP therapy like the pulmonologist, The second application, Sleep.py, delivers motivational, educational, diet, physical activity and stress management interventions directly to the patient suffering from OSA. We include an innovative serious game to increase the patient’s compliance with the application. Sleep.py provides the most comprehensive collection of personalized interventions for PAP therapy. Another core feature of these applications is to collect feedback from either the experts or the patients, In this research, we also present a framework for creating this feedback and propagating them across various machine learning models in a continuous learning process. This paradigm is adaptable enough to be used in different therapies or fields. To summarise, we offer a clear roadmap for implementing a continuous learning method based on expert-validated outcomes.

show abstract

A Learning Health System Framework to Operationalize Health Data to Improve Quality Care: An Australian Perspective

Cited by 27 publications

References 14 publications

Using a Modified Delphi Approach and Nominal Group Technique for Organisational Priority Setting of Evidence-Based Interventions That Advance Women in Healthcare Leadership

Using a Modified Delphi Approach and Nominal Group Technique for Organisational Priority Setting of Evidence-Based Interventions That Advance Women in Healthcare Leadership

National learning systems to sustain and scale up delivery of quality healthcare: a conceptual framework

A comprehensive mHealth solution for OSA patient management

Contact Info

Product

Resources

About