Feasibility of a Reimbursement Pathway for Mobile Medical Applications (MMA) in Australia

Introduction: Mobile health (mHealth) applications (apps) are currently changing Australian healthcare. mHealth apps which have a therapeutic and diagnostic intended purpose are called mobile medical applications (MMA), and are being integrated into healthcare by patients and practitioners in Australia. MMAs have the potential to decrease the health burden of some chronic conditions as well as improve the delivery of healthcare. Any harms produced by the technology are mainly through the information provided and how it is used in clinical decision-making. The nature of apps presents unique challenges (such as their rapid lifecycle) to regulatory and reimbursement processes. There are currently no policies or frameworks available that can be used to conduct a health technology assessment (HTA) on MMAs. Therefore, the aim of this research was to determine what policy changes and assessment criteria are needed to facilitate the development of a system that evaluates MMAs for regulatory and reimbursement purposes in Australia. Methodology: In order to achieve this overall aim, the research was divided into four parts. Firstly, I reviewed the Australian Therapeutic Goods Administration (TGA) regulation of MMAs by evaluating it against international counterparts and the International Medical Devices Regulator’s Forum’s (IMDRF) guidance document for clinically evaluating medical software. This was achieved through the use of a policy analysis and case studies. The policy analysis evaluated MMA regulations internationally to determine whether the regulatory bodies of the IMDRF members addressed the IMDRF guidance on clinically evaluating software as a medical device (SaMD). The case studies reviewed how different MMAs in Australia and the United States of America (USA) were regulated to determine to what extent the SaMD: Clinical Evaluation (2017) guidance was applied. The second section evaluated existing frameworks for assessing MMAs and determined whether any were suitable for use in HTA and reimbursement decision-making. This was achieved through a methodological systematic review. The systematic searches were conducted in seven bibliographic databases in order to identify literature on MMA evaluation frameworks published between 2008 and 2016. Frameworks were only eligible for inclusion in the review if they evaluated one of the HTA domains of safety, cost-effectiveness and/or effectiveness of an MMA. Once a framework had been included into the review it was evaluated to determine what other elements of an HTA the framework addressed. The third section detailed the creation and testing of an MMA HTA evaluation module which was used to modify the current HTA guidelines in Australia. The use of the module ensures that the technology specific characteristics of apps would be properly appraised during an assessment. The module’s transferability to comparable HTA jurisdictions was also assessed. This was achieved in two stages. The first stage were in-depth interviews with stakeholders (healthcare practitioners, application developers, and policymakers) to determine possible impediments and pathways to MMA reimbursement in Australia. The findings of the interviews were integrated with those from the first and second sections of this research on MMA reimbursement and regulation to create an MMA evaluation module. The fourth and final section determined the feasibility of MMA reimbursement in Australia through the integration and synthesis of all the evidence generated from the preceding three sections. Results: The research found that there were policy gaps in the regulatory and reimbursement criteria used to evaluate MMAs. Regarding current regulatory policy, the TGA does not adequately evaluate MMAs according to the IMDRF criteria. Policy changes to current regulation processes should include an assessment of the harm from misinformation as well as potential risks associated with information and connectivity compatibilities, such as cybersecurity threats. Similarly, there were a number of policy changes that could be made to support the reimbursement of MMAs in Australia. The systematic literature review of MMA evaluation frameworks found that there was a greater need to evaluate the harms posed by MMAs (i.e. misinformation) as well as a fuller consideration of the likely comparator for the technology. Other considerations included, but were not limited to, equity of access to MMAs (i.e. by way of age, literacy, user disability, etc.), as well as the importance of secure and proper management of confidential data. Other technology specific concerns included: the possible effect of software updates on the effectiveness and safety of MMAs and possible variation in app performance on different operating systems (OS), mobile platforms, and generations of the same platform. Interviews conducted with stakeholders sought to explore possible pathways and impediments to MMA reimbursement in Australia and, highlighted a few policy challenges. These included: clarification around where the responsibly lies regarding data ownership, cybersecurity, and professional liability in the use of app data; the digital health literacy of healthcare practitioners, patients, and any other MMA users (i.e. carers); and finally, developing evaluative measures which address the technological evolution of MMAs, such as the technology’s rapid lifecycle and software updates. Contrastingly, the interviews indicated that stakeholders trust the evidence-based approach used by the Australian Medical Services Advisory Committee (MSAC) to conduct HTAs and make public funding decisions and felt it would be an appropriate evaluation mechanism for MMAs. Given these policy concerns, proper evaluation of MMA’s is needed before they can be reimbursed in Australia. To ensure that MMAs are properly evaluated, a module was developed which could modify the current HTA framework employed by MSAC. The module addressed both regulatory and reimbursement policy concerns. This is to ensure that the regulatory issues are addressed, as the current TGA process does not properly evaluate them. The utility of the MMA HTA evaluation module was assessed for adaptation to other comparable HTA jurisdictional bodies, such as the European Economic Area (EEA), Canada, and the United States of America (USA). Minimal modifications would need to be made to the module for it to be used by other HTA agencies in these jurisdictions. These adaptations would include the removal of any of the unique MMA items (e.g. software, updates, cybersecurity) that were already addressed by the jurisdiction’s regulatory authority. Adaptations to the cost-effectiveness domain would be dependent on the individual economic evaluations conducted by the respective jurisdictional HTA agencies, and their individual healthcare contexts. The development of the MMA HTA evaluation module, and the research that informed it, shows that MMA reimbursement in Australia is feasible. Thus, it is feasible to tailor the regulatory and reimbursement processes in Australia to evaluate MMAs properly. Conclusion: In conclusion, it is possible to tailor regulation and reimbursement processes in Australia to address the evaluation of MMAs. These modifications to current processes can be made through a variety of key policy and process changes. One process change would be the adoption of the MMA evaluation module as it is capable of adapting the existing MSAC evaluation framework to assess this technology. Other policy changes would include: facilitating the digital health literacy of MMA users (i.e. healthcare practitioners, patients, carers, etc.); providing clarification around who and where the responsibility lies regarding use of MMAs (i.e. data ownership, professional liability, and cybersecurity), and, finally, stipulating evaluative procedures which address the challenges posed by the ongoing technological evolution of apps (i.e. rapid lifecycle, software updates, etc.).Thesis (Ph.D.) -- University of Adelaide, School of Public Health, 202

Wearable Sleep Technology in Clinical and Research Settings

The accurate assessment of sleep is critical to better understand and evaluate its role in health and disease. The boom in wearable technology is part of the digital health revolution and is producing many novel, highly sophisticated and relatively inexpensive consumer devices collecting data from multiple sensors and claiming to extract information about users' behaviors, including sleep. These devices are now able to capture different biosignals for determining, for example, HR and its variability, skin conductance, and temperature, in addition to activity. They perform 24/7, generating overwhelmingly large data sets (big data), with the potential of offering an unprecedented window on users' health. Unfortunately, little guidance exists within and outside the scientific sleep community for their use, leading to confusion and controversy about their validity and application. The current state-of-the-art review aims to highlight use, validation and utility of consumer wearable sleep-trackers in clinical practice and research. Guidelines for a standardized assessment of device performance is deemed necessary, and several critical factors (proprietary algorithms, device malfunction, firmware updates) need to be considered before using these devices in clinical and sleep research protocols. Ultimately, wearable sleep technology holds promise for advancing understanding of sleep health; however, a careful path forward needs to be navigated, understanding the benefits and pitfalls of this technology as applied in sleep research and clinical sleep medicine

Appraisal of free online symptom checkers and applications for self-diagnosis and triage: An Australian evaluation

The internet has impacted society and changed the way companies and individuals operate on a daily basis. Seeking information online via computer or mobile device is common practice. The phrase ‘Google it’ is now part of modern vernacular and is a resource increasingly utilised by young and old alike. Around 80% of Australian’s search health-related information online as it is convenient, cheap, and available 24/7. Symptom checkers are one tool used by consumers to investigate their health issues. Symptom checkers are automated online programs which use computerised algorithms, asking a series of questions to help determine a potential diagnosis and/or provide suitable triage advice. Recent evidence suggests symptom checkers may not work the way they are intended. Inferior or incorrect healthcare information can potentially have serious consequences on the consumer’s wellbeing and may not have the desired effect of directing consumers to the appropriate point of care. This research evaluated the clinical performance of 36 symptom checkers found on websites and smartphone applications that are freely available for use by the Australian general public. Symptom checkers were exposed to 48 clinical vignettes, generating 1858 symptom checker vignette tests (SCVT). Diagnosis was assessed on the inclusion of the correct diagnosis in the first, the top three or top ten differential diagnoses (n = 1,170 SCVT). Triage advice was assessed on whether the triage category recommended was concordant with our assessment (n = 688 SCVT). The correct diagnosis was listed first in 36% (95% CI 31–42) of SCVT, within the top three in 52% (95% CI 47–59) and within the top ten in 58% (95% CI 53–65). Symptom checkers which claimed to utilise artificial intelligence (AI) outperformed non-AI with the first listed diagnosis being accurate in 46% (95% CI 40–57) versus 32% (95% CI 26–38) of SCVT. Individual symptom checker performance varied considerably, with the average rate of correct diagnosis provided first ranging between 12%–-61%. Triage advice provided was concordant with our assessment in 49% (95% CI 44–54) of SCVT. Appropriate triage advice was provided more frequently for emergency care SCVT at 63% (95% CI 52–71) than for non-urgent SCVT at 30% (95% CI 11–39). Symptom checker performance varied considerably in relation to diagnosis. Triage advice was risk-averse, typically recommending more urgent care pathways than necessary. Given this, symptom checkers may not be working to alleviate demand for health services (particularly emergency services) within Australia—counter to marketing materials of some organisations’ symptom checkers. It is important that symptom checkers do not further burden the healthcare system with inappropriate referrals or incorrect care advice. Although, a balance must be struck as avoiding unsuitable triage advice could potentially result in life-threatening consequences for consumers. Nonetheless, the results of this research make clear that the accuracy of diagnosis and triage advice provided from readily available symptom checkers for the Australian public require improvements before everyday consumers can rely entirely on health information provided via these mediums

Multidisciplinary perspectives on Artificial Intelligence and the law

This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

Health technology assessment framework: adaptation for digital health technology assessment

Tecnologia de salut digital; Avaluació de tecnologia sanitària; MetodologiaTecnología de salud digital; Evaluación de tecnología sanitaria; MetodologíaDigital health technology; Health technology assessment; MethodologyEl objetivo principal de este encargo es desarrollar un marco metodológico específico para la ETS de salud digital (ETSd) y se concreta en los siguientes objetivos específicos: determinar qué dominios, dimensiones y subdimensiones debe considerar la ETSd, con especial atención a los modelos de atención no presencial (MANP), la sald móvil (mSalud) y la inteligencia artificial (IA); definir un marco de estándares de evidencia que deben alcanzar estas tecnologías según la clasificación del riesgo que deriva de su uso.L’objectiu principal d’aquest encàrrec és desenvolupar un marc metodològic específic per a l’ATSd i es concreta en els següents objectius específics: determinar quins dominis, dimensions i subdimensions ha de considerar l’ATSd, amb especial atenció als models d’atenció no presencial (MANP), la salut mòbil (mSalut) i la intel·ligència artificial (IA); definir un marc d’estàndards d’evidència que han d’assolir aquestes tecnologies segons la classificació del risc que deriva del seu ús.The main objective of this assignment is to develop a specific methodological framework for the evaluation of digital health technologies (DHTs), and is specified in the following specific objectives: to determine which domains, dimensions, and sub-dimensions should be considered in DHT evaluation, with special attention to non-face-to-face care models (NFTC), mobile health (mHealth), and artificial intelligence (AI); to define a framework of evidence standards that these technologies must achieve according to the risk classification derived from their use

Background Examples of Literature Searches on Topics of Interest

A zip file of various literature searches & some resources related to our work related to exposure after the Chernobyl accident and as we began looking at helping in Semey Kazakhstan----a collection of literature reviews on various topics we were interested in... eg. establishing a registry of those exposed for longterm follow-up, what we knew about certain areas like genetics and some resources like A Guide to Environmental Resources on the Internet by Carol Briggs-Erickson and Toni Murphy which could be found on the Internet and was written to be used by researchers, environmentalists, teachers and any person who is interested in knowing and doing something about the health of our planet. See more at https://archives.library.tmc.edu/dm-ms211-012-0060

Coastal Carolina University 2014-2015 undergraduate catalog

Coastal Carolina University annually publishes a catalog with information about the university, student life, undergraduate academic programs, and faculty and staff listings