ORCID Profile
0000-0002-5824-4900
Current Organisation
Deakin University
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Publisher: SAGE Publications
Date: 25-07-2017
Abstract: This systematic review sought to identify whether health care reforms led to improvement in the emergency department (ED) length of stay (LOS) and elective surgery (ES) access in Australia, Canada, New Zealand, and the United Kingdom. The review was registered in the PROSPERO database (CRD42015016343), and nine databases were searched for peer-reviewed, English-language reports published between 1994 and 2014. We also searched relevant “grey” literature and websites. Included studies were checked for cited and citing papers. Primary studies corresponding to national and provincial ED and ES reforms in the four countries were considered. Only studies from Australia and the United Kingdom were eventually included, as no studies from the other two countries met the inclusion criteria. The reviewers involved in the study extracted the data independently using standardized forms. Studies were assessed for quality, and a narrative synthesis approach was taken to analyze the extracted data. The introduction of health care reforms in the form of time-based ED and ES targets led to improvement in ED LOS and ES access. However, the introduction of targets resulted in unintended consequences, such as increased pressure on clinicians and, in certain instances, manipulation of performance data.
Publisher: BMJ
Date: 10-2021
DOI: 10.1136/BMJHCI-2021-100444
Abstract: To date, many artificial intelligence (AI) systems have been developed in healthcare, but adoption has been limited. This may be due to inappropriate or incomplete evaluation and a lack of internationally recognised AI standards on evaluation. To have confidence in the generalisability of AI systems in healthcare and to enable their integration into workflows, there is a need for a practical yet comprehensive instrument to assess the translational aspects of the available AI systems. Currently available evaluation frameworks for AI in healthcare focus on the reporting and regulatory aspects but have little guidance regarding assessment of the translational aspects of the AI systems like the functional, utility and ethical components. To address this gap and create a framework that assesses real-world systems, an international team has developed a translationally focused evaluation framework termed ‘Translational Evaluation of Healthcare AI (TEHAI)’. A critical review of literature assessed existing evaluation and reporting frameworks and gaps. Next, using health technology evaluation and translational principles, reporting components were identified for consideration. These were independently reviewed for consensus inclusion in a final framework by an international panel of eight expert. TEHAI includes three main components: capability, utility and adoption. The emphasis on translational and ethical features of the model development and deployment distinguishes TEHAI from other evaluation instruments. In specific, the evaluation components can be applied at any stage of the development and deployment of the AI system. One major limitation of existing reporting or evaluation frameworks is their narrow focus. TEHAI, because of its strong foundation in translation research models and an emphasis on safety, translational value and generalisability, not only has a theoretical basis but also practical application to assessing real-world systems. The translational research theoretic approach used to develop TEHAI should see it having application not just for evaluation of clinical AI in research settings, but more broadly to guide evaluation of working clinical systems.
Publisher: Springer Science and Business Media LLC
Date: 10-01-2019
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 02-05-2023
Abstract: Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision‐making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension‐related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real‐time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.
Publisher: AME Publishing Company
Date: 03-2020
Publisher: Elsevier BV
Date: 04-2022
Publisher: CSIRO Publishing
Date: 2016
DOI: 10.1071/AH15135
Abstract: Considering the grim scenario of burgeoning health-care costs and cost-cutting measures by the Australian Government, there is a clear case to invest and research into disciplines that will ensure sustainability of the public health system. There is evidence that integrated health care contributes to a cost-efficient and quality health system because of potential benefits like streamlined care for patients, efficient use of resources, a better cover of patients and improved patient safety. However, integrated health care as a notion is submerged in the disciplines of public health and primary care. In reality, it is a distinct concept acting as a bridge between primary and secondary care. This article argues it is time for the discipline of integrated health care to be recognised on its own and investment be driven into the establishment of integrated care centres.
Publisher: AME Publishing Company
Date: 09-2021
DOI: 10.21037/JHMHP-21-31
Publisher: Oxford University Press (OUP)
Date: 04-11-2020
DOI: 10.1093/JAMIA/OCZ192
Abstract: As the efficacy of artificial intelligence (AI) in improving aspects of healthcare delivery is increasingly becoming evident, it becomes likely that AI will be incorporated in routine clinical care in the near future. This promise has led to growing focus and investment in AI medical applications both from governmental organizations and technological companies. However, concern has been expressed about the ethical and regulatory aspects of the application of AI in health care. These concerns include the possibility of biases, lack of transparency with certain AI algorithms, privacy concerns with the data used for training AI models, and safety and liability issues with AI application in clinical environments. While there has been extensive discussion about the ethics of AI in health care, there has been little dialogue or recommendations as to how to practically address these concerns in health care. In this article, we propose a governance model that aims to not only address the ethical and regulatory issues that arise out of the application of AI in health care, but also stimulate further discussion about governance of AI in health care.
Publisher: Elsevier BV
Date: 2020
DOI: 10.1016/J.ACAP.2019.08.008
Abstract: Clinically focused faculty (full-time clinical faculty and clinician educators) comprise an increasing proportion of academic faculty, yet they underutilize mentorship nationally. The aims of this study were to test and refine a program theory for an institutional mentorship program for junior clinically focused faculty and to understand the facilitators and barriers of sustained participation. We conducted a qualitative study using a realist evaluation approach. Between July and December 2017, we performed in-depth semistructured interviews of 2 participant groups from a junior faculty mentorship program at our institution: 1) those who attended more than two thirds of the program sessions and 2) those who only attended 1 session. We used inductive thematic analysis to identify key context and program mechanisms that led to meaningful outcomes for faculty mentorship. We interviewed 23 junior faculty representing 15 pediatric specialties. We identified 4 contextual themes (past personal experience, current competing priorities, institutional culture, and gaps in support and resources), 3 mechanisms (connecting with faculty, sharing ideas and strategies, and self-reflecting), and 3 outcomes (sense of community, acquired tools and skills, and broadened perspectives), which we organized into a programmatic theory representing the program's impact on participants. Themes that emerged were consistent between both groups. A mentorship program that provided junior faculty with opportunities to connect, share ideas and strategies, and self-reflect led to improvement in meaningful outcomes for clinically focused faculty. Our program theory provides a basis for institutions seeking to build a mentorship program targeted towards this increasing proportion of junior faculty.
Publisher: JMIR Publications Inc.
Date: 31-08-2022
Abstract: espite immense progress in artificial intelligence (AI) models, there has been limited deployment in health care environments. The gap between potential and actual AI applications is likely due to the lack of translatability between controlled research environments (where these models are developed) and clinical environments for which the AI tools are ultimately intended. e previously developed the Translational Evaluation of Healthcare AI (TEHAI) framework to assess the translational value of AI models and to support successful transition to health care environments. In this study, we applied the TEHAI framework to the COVID-19 literature in order to assess how well translational topics are covered. systematic literature search for COVID-19 AI studies published between December 2019 and December 2020 resulted in 3830 records. A subset of 102 (2.7%) papers that passed the inclusion criteria was s led for full review. The papers were assessed for translational value and descriptive data collected by 9 reviewers (each study was assessed by 2 reviewers). Evaluation scores and extracted data were compared by a third reviewer for resolution of discrepancies. The review process was conducted on the Covidence software platform. e observed a significant trend for studies to attain high scores for technical capability but low scores for the areas essential for clinical translatability. Specific questions regarding external model validation, safety, nonmaleficence, and service adoption received failed scores in most studies. sing TEHAI, we identified notable gaps in how well translational topics of AI models are covered in the COVID-19 clinical sphere. These gaps in areas crucial for clinical translatability could, and should, be considered already at the model development stage to increase translatability into real COVID-19 health care environments.
Publisher: JMIR Publications Inc.
Date: 06-07-2022
DOI: 10.2196/42313
Abstract: Despite immense progress in artificial intelligence (AI) models, there has been limited deployment in health care environments. The gap between potential and actual AI applications is likely due to the lack of translatability between controlled research environments (where these models are developed) and clinical environments for which the AI tools are ultimately intended. We previously developed the Translational Evaluation of Healthcare AI (TEHAI) framework to assess the translational value of AI models and to support successful transition to health care environments. In this study, we applied the TEHAI framework to the COVID-19 literature in order to assess how well translational topics are covered. A systematic literature search for COVID-19 AI studies published between December 2019 and December 2020 resulted in 3830 records. A subset of 102 (2.7%) papers that passed the inclusion criteria was s led for full review. The papers were assessed for translational value and descriptive data collected by 9 reviewers (each study was assessed by 2 reviewers). Evaluation scores and extracted data were compared by a third reviewer for resolution of discrepancies. The review process was conducted on the Covidence software platform. We observed a significant trend for studies to attain high scores for technical capability but low scores for the areas essential for clinical translatability. Specific questions regarding external model validation, safety, nonmaleficence, and service adoption received failed scores in most studies. Using TEHAI, we identified notable gaps in how well translational topics of AI models are covered in the COVID-19 clinical sphere. These gaps in areas crucial for clinical translatability could, and should, be considered already at the model development stage to increase translatability into real COVID-19 health care environments.
Publisher: Wiley
Date: 12-2015
DOI: 10.1111/JEP.12482
Abstract: The Remote Primary Health Care Manuals (RPHCM) project team manages the development and publication of clinical protocols and procedures for primary care clinicians practicing in remote Australia. The Central Australian Rural Practitioners Association Standard Treatment Manual, the flagship manual of the RPHCM suite, has been evaluated for accessibility and acceptability in remote clinics three times in its 20-year history. These evaluations did not consider a theory-based framework or a programme theory, resulting in some limitations with the evaluation findings. With the RPHCM having an aim of enabling evidence-based practice in remote clinics and anecdotally reported to do so, testing this empirically for the full suite is vital for both stakeholders and future editions of the RPHCM. The project team utilized a realist evaluation framework to assess how, why and for what the RPHCM were being used by remote practitioners. A theory regarding the circumstances in which the manuals have and have not enabled evidence-based practice in the remote clinical context was tested. The project assessed this theory for all the manuals in the RPHCM suite, across government and aboriginal community-controlled clinics, in three regions of Australia. Implementing a realist evaluation framework to generate robust findings in this context has required innovation in the evaluation design and adaptation by researchers. This article captures the RPHCM team's experience in designing this evaluation.
Publisher: Elsevier BV
Date: 2021
Publisher: F1000 Research Ltd
Date: 29-04-2021
DOI: 10.12688/GATESOPENRES.13134.3
Abstract: Background: Few studies have explicitly examined the implementation of change interventions in low- and middle-income country (LMIC) public health services. We contribute to implementation science by analyzing the implementation of an organizational change intervention in a large, hierarchical and bureaucratic public service in a LMIC health system. Methods: Using qualitative methods, we critically interrogate the implementation of an intervention to improve quality of obstetric and newborn services across 692 facilities in Uttar Pradesh and Bihar states of India to reveal how to go about making change happen in LMIC public health services. Results: We found that focusing the interventions on a discreet part of the health service (labour rooms) ensured minimal disruption of the status quo and created room for initiating change. Establishing and maintaining respectful, trusting relationships is critical, and it takes time and much effort to cultivate such relationships. Investing in doing so allows one to create a safe space for change it helps thaw entrenched practices, behaviours and attitudes, thereby creating opportunities for change. Those at the frontline of change processes need to be enabled and supported to: lead by ex le, model and embody desirable behaviours, be empathetic and humble, and make the change process a positive and meaningful experience for all involved. They need discretionary space to tailor activities to local contexts and need support from higher levels of the organisation to exercise discretion. Conclusions: We conclude that making change happen in LMIC public health services, is possible, and is best approached as a flexible, incremental, localised, learning process. Smaller change interventions targeting discreet parts of the public health services, if appropriately contextualised, can set the stage for incremental system wide changes and improvements to be initiated. To succeed, change initiatives need to cultivate and foster support across all levels of the organisation.
Publisher: Wiley
Date: 03-2022
Abstract: The application of artificial intelligence, and in particular machine learning, to the practice of radiology, is already impacting the quality of imaging care. It will increasingly do so in the future. Radiologists need to be aware of factors that govern the quality of these tools at the development, regulatory and clinical implementation stages in order to make judicious decisions about their use in daily practice.
Publisher: Elsevier BV
Date: 09-2022
Publisher: SAGE Publications
Date: 2016
Abstract: Major health-care reforms have extended across all Australian public hospitals in recent years. Improving emergency department (ED) access has been a focus of these reforms. This study evaluates how the national reforms have led to improvement in ED access in a regional hospital in remote Australia. Assessing a complex scenario such as national reforms and the challenges faced by the regional hospital to implement these reforms requires in-depth analysis. A realist evaluation theory-based approach was employed, allowing investigation of what, how, why, and for whom change occurred. A case study mixed methods design was adopted within the realist framework to answer these questions about change. The study identified moderate improvement in ED access as a result of the reforms (investment in infrastructure and workforce and the introduction of ED targets). Clinical leadership and support from management were essential for the improvement. Without ongoing investment and clinical redesign activities, however, sustainability of the improvement may prove difficult.
Publisher: F1000 Research Ltd
Date: 10-2020
DOI: 10.12688/GATESOPENRES.13134.2
Abstract: Background: Few studies have explicitly examined the implementation of change interventions in low- and middle-income country (LMIC) public health services. We contribute to implementation science by analyzing the implementation of an organizational change intervention in a large, hierarchical and bureaucratic public service in a LMIC health system. Methods: Using qualitative methods, we critically interrogate the implementation of an intervention to improve quality of obstetric and newborn services across 692 facilities in Uttar Pradesh and Bihar states of India to reveal how to go about making change happen in LMIC public health services. Results: We found that focusing the interventions on a discreet part of the health service (labour rooms) ensured minimal disruption of the status quo and created room for initiating change. Establishing and maintaining respectful, trusting relationships is critical, and it takes time and much effort to cultivate such relationships. Investing in doing so allows one to create a safe space for change it helps thaw entrenched practices, behaviours and attitudes, thereby creating opportunities for change. Those at the frontline of change processes need to be enabled and supported to: lead by ex le, model and embody desirable behaviours, be empathetic and humble, and make the change process a positive and meaningful experience for all involved. They need discretionary space to tailor activities to local contexts and need support from higher levels of the organisation to exercise discretion. Conclusions: We conclude that making change happen in LMIC public health services, is possible, and is best approached as a flexible, incremental, localised, learning process. Smaller change interventions targeting discreet parts of the public health services, if appropriately contextualised, can set the stage for incremental system wide changes and improvements to be initiated. To succeed, change initiatives need to cultivate and foster support across all levels of the organisation.
Publisher: F1000 Research Ltd
Date: 18-06-2020
DOI: 10.12688/GATESOPENRES.13134.1
Abstract: Background: Few studies have explicitly examined the implementation of change interventions in low- and middle-income country (LMIC) public health services. We contribute to implementation science by adding to the knowledge base on strategies for implementing change interventions in large, hierarchical and bureaucratic public services in LMIC health systems. Methods: Using a mix of methods, we critically interrogate the implementation of an intervention to improve quality of obstetric and newborn services across 692 facilities in Uttar Pradesh and Bihar states of India to reveal how to go about making change happen in LMIC public health services. Results: We found that focusing the interventions on a discreet part of the health service (labour rooms) ensured minimal disruption of the status quo and created room for initiating change. Establishing and maintaining respectful, trusting relationships is critical, and it takes time and much effort to cultivate such relationships. Investing in doing so allows one to create a safe space for change it helps thaw entrenched practices, behaviours and attitudes, thereby creating opportunities for change. Those at the frontline of change processes need to be enabled and supported to: lead by ex le, model and embody desirable behaviours, be empathetic and humble, and make the change process a positive and meaningful experience for all involved. They need discretionary space to tailor activities to local contexts and need support from higher levels of the organisation to exercise discretion. Conclusions: We conclude that making change happen in LMIC public health services, is possible, and is best approached as a flexible, incremental, localised, learning process. Smaller change interventions targeting discreet parts of the public health services, if appropriately contextualised, can set the stage for incremental system wide changes and improvements to be initiated. To succeed, change initiatives need to cultivate and foster support across all levels of the organisation.
Publisher: Oxford University Press (OUP)
Date: 2023
Abstract: What is the present performance of artificial intelligence (AI) decision support during embryo selection compared to the standard embryo selection by embryologists? AI consistently outperformed the clinical teams in all the studies focused on embryo morphology and clinical outcome prediction during embryo selection assessment. The ART success rate is ∼30%, with a worrying trend of increasing female age correlating with considerably worse results. As such, there have been ongoing efforts to address this low success rate through the development of new technologies. With the advent of AI, there is potential for machine learning to be applied in such a manner that areas limited by human subjectivity, such as embryo selection, can be enhanced through increased objectivity. Given the potential of AI to improve IVF success rates, it remains crucial to review the performance between AI and embryologists during embryo selection. The search was done across PubMed, EMBASE, Ovid Medline, and IEEE Xplore from 1 June 2005 up to and including 7 January 2022. Included articles were also restricted to those written in English. Search terms utilized across all databases for the study were: (‘Artificial intelligence’ OR ‘Machine Learning’ OR ‘Deep learning’ OR ‘Neural network’) AND (‘IVF’ OR ‘in vitro fertili*’ OR ‘assisted reproductive techn*’ OR ‘embryo’), where the character ‘*’ refers the search engine to include any auto completion of the search term. A literature search was conducted for literature relating to AI applications to IVF. Primary outcomes of interest were accuracy, sensitivity, and specificity of the embryo morphology grade assessments and the likelihood of clinical outcomes, such as clinical pregnancy after IVF treatments. Risk of bias was assessed using the Modified Down and Black Checklist. Twenty articles were included in this review. There was no specific embryo assessment day across the studies—Day 1 until Day 5/6 of embryo development was investigated. The types of input for training AI algorithms were images and time-lapse (10/20), clinical information (6/20), and both images and clinical information (4/20). Each AI model demonstrated promise when compared to an embryologist’s visual assessment. On average, the models predicted the likelihood of successful clinical pregnancy with greater accuracy than clinical embryologists, signifying greater reliability when compared to human prediction. The AI models performed at a median accuracy of 75.5% (range 59–94%) on predicting embryo morphology grade. The correct prediction (Ground Truth) was defined through the use of embryo images according to post embryologists’ assessment following local respective guidelines. Using blind test datasets, the embryologists’ accuracy prediction was 65.4% (range 47–75%) with the same ground truth provided by the original local respective assessment. Similarly, AI models had a median accuracy of 77.8% (range 68–90%) in predicting clinical pregnancy through the use of patient clinical treatment information compared to 64% (range 58–76%) when performed by embryologists. When both images/time-lapse and clinical information inputs were combined, the median accuracy by the AI models was higher at 81.5% (range 67–98%), while clinical embryologists had a median accuracy of 51% (range 43–59%). The findings of this review are based on studies that have not been prospectively evaluated in a clinical setting. Additionally, a fair comparison of all the studies were deemed unfeasible owing to the heterogeneity of the studies, development of the AI models, database employed and the study design and quality. AI provides considerable promise to the IVF field and embryo selection. However, there needs to be a shift in developers’ perception of the clinical outcome from successful implantation towards ongoing pregnancy or live birth. Additionally, existing models focus on locally generated databases and many lack external validation. This study was funded by Monash Data Future Institute. All authors have no conflicts of interest to declare. CRD42021256333
Publisher: CSIRO Publishing
Date: 2017
DOI: 10.1071/HC17014
Abstract: ABSTRACT Primary Health Care (PHC) funding in Australia is complex and fragmented. The focus of PHC funding in Australia has been on volume rather than comprehensive primary care and continuous quality improvement. As PHC in Australia is increasingly delivered by hybrid style organisations, an appropriate funding model that matches this set-up while addressing current issues with PHC funding is required. This article discusses and proposes an appropriate funding model for hybrid PHC organisations.
Publisher: Cold Spring Harbor Laboratory
Date: 26-02-2023
DOI: 10.1101/2023.02.23.23286374
Abstract: Despite immense progress in artificial intelligence (AI) models, there has been limited deployment in healthcare environments. The gap between potential and actual AI applications is likely due to the lack of translatability between controlled research environments (where these models are developed) and clinical environments for which the AI tools are ultimately intended. We have previously developed the Translational Evaluation of Healthcare AI (TEHAI) framework to assess the translational value of AI models and to support successful transition to healthcare environments. In this study, we apply the TEHAI to COVID-19 literature in order to assess how well translational topics are covered. A systematic literature search for COVID-AI studies published between December 2019-2020 resulted in 3,830 records. A subset of 102 papers that passed inclusion criteria were s led for full review. Nine reviewers assessed the papers for translational value and collected descriptive data (each study was assessed by two reviewers). Evaluation scores and extracted data were compared by a third reviewer for resolution of discrepancies. The review process was conducted on the Covidence software platform. We observed a significant trend for studies to attain high scores for technical capability but low scores for the areas essential for clinical translatability. Specific questions regarding external model validation, safety, non-maleficence and service adoption received failed scores in most studies. Using TEHAI, we identified notable gaps in how well translational topics of AI models are covered in the COVID-19 clinical sphere. These gaps in areas crucial for clinical translatability could, and should, be considered already at the model development stage to increase translatability into real COVID-19 healthcare environments.
Publisher: SAGE Publications
Date: 03-12-2019
Abstract: Summary In recent years, there has been massive progress in artificial intelligence (AI) with the development of deep neural networks, natural language processing, computer vision and robotics. These techniques are now actively being applied in healthcare with many of the health service activities currently being delivered by clinicians and administrators predicted to be taken over by AI in the coming years. However, there has also been exceptional hype about the abilities of AI with a mistaken notion that AI will replace human clinicians altogether. These perspectives are inaccurate, and if a balanced perspective of the limitations and promise of AI is taken, one can gauge which parts of the health system AI can be integrated to make a meaningful impact. The four main areas where AI would have the most influence would be: patient administration, clinical decision support, patient monitoring and healthcare interventions. This health system where AI plays a central role could be termed an AI-enabled or AI-augmented health system. In this article, we discuss how this system can be developed based on a realistic assessment of current AI technologies and predicted developments.
Publisher: Wiley
Date: 24-05-2018
DOI: 10.1111/JEP.12772
Abstract: The aim of this evaluation was to assess the acceptability, accessibility, and compliance with the 2014 editions of the Remote Primary Health Care Manuals (RPHCM) in health care centres across remote areas of Northern and Central Australia. To undertake a comprehensive evaluation that considered context, the evaluation used a realist evaluation framework. The evaluation used a variety of methods including interviews and survey to develop and test a programme theory. Many remote health practitioners have adopted standardized, evidence-based practice because of the use of the RPHCM. The mechanisms that led to the use of the manuals include acceptance of the worth of the protocols to their clinical practice, reliance on manual content to guide their practice, the perception of credibility, the applicability of RPHCM content to the context, and a fear of the consequences of not using the RPHCMs. Some remote health practitioners are less inclined to use the RPHCM regularly because of a perception that the content is less suited to their needs and daily practice or it is hard to navigate or understand. The evaluation concluded that there is work to be done to widen the RPHCM user base, and organizations need to increase support for their staff to use the RPHCM protocols better. These measures are expected to enable standardized clinical practice in the remote context.
Publisher: AMPCo
Date: 05-2015
DOI: 10.5694/MJA15.00129
Publisher: SAGE Publications
Date: 26-07-2016
Abstract: Clinical Practice Guidelines are widely used to inform and improve the quality and consistency of clinical practice. Developing and publishing Clinical Practice Guidelines is a complex task involving multiple components. Electronic Content Management Systems are increasingly employed to make this task more manageable. The Content Management System market offers a variety of options for publishing content on the Internet. However, there are limited products that comprehensively address the requirements of publishing Clinical Practice Guidelines. The authors are involved in publishing guidelines for remote clinical practitioners in Australia and present their perspective about identifying an appropriate Content Management System. Several elements essential to addressing their unique editing needs are defined in this article. Unfortunately, customisation is very expensive and laborious: few Content Management System providers can comprehensively meet the needs of Clinical Practice Guidelines publishing. Being pragmatic about the level of functionality a product can offer to support publication is essential.
Publisher: Informa UK Limited
Date: 17-06-2015
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Sandeep Reddy.