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Objective: Post-licensure vaccine safety surveillance of adverse events following immunisation is critical to ensure public safety and confidence in vaccines. This paper aims to describe the governance structure and data linkage methodology behind the establishment of the largest linked vaccine safety surveillance data resource in Australia - The Vaccine Safety Health Link (VSHL)., Methods: The Vaccine Safety Health Link contains linked records from the Australian Immunisation Register with records from hospital, perinatal, mortality, and notifiable disease datasets in near real-time. Linkage is done by the Centre for Victorian Data Linkage who receive the datasets in an identifiable format which then undergo standardisation, enrichment, linkage, quality assurance and de-identification, prior to being supplied for analysis., Results: The VSHL data resource allows sensitive and rapid analysis of a broad spectrum of suspected adverse events to ensure the safety of all vaccines administered. It is also used to refute spurious concerns where no associations are found, upholding trust, and maintaining vaccine confidence., Conclusions: The Vaccine Safety Health Link's surveillance design complements existing vaccine safety surveillance methods. Challenges encountered and lessons learnt using Vaccine Safety Health Link would benefit linkage projects globally., Implications for Public Health: In its first two years, The Vaccine Safety Health Link has been used for 14 vaccine safety investigations. Studies into these conditions would not have otherwise been possible. The Vaccine Safety Health Link also partners with the Global Vaccine Data Network™ for approved collaborative studies with a combined population of over 300 million people., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Social media offers a rich source of real-time health data, including potential vaccine reactions. However, extracting meaningful insights is challenging due to the noisy nature of social media content. This paper explores using large language models (LLMs) and prompt engineering to detect personal mentions of vaccine reactions. Different prompting strategies were evaluated on two LLM models (GPT-3.5 and GPT-4) using Reddit data focused on shingles (zoster) vaccines. Zero-shot and few-shot learning approaches with both standard and chain-of-thought prompts were compared. The findings demonstrate that GPT-based models with carefully crafted chain-of-thought prompts could identify the relevant social media posts. Few-shot learning helped GPT4 models to identify more of the marginal cases, although less precisely. The use of LLMs for classification with lightweight supervised pretrained language models (PLMs) found that PLMs outperform LLMs. However, a potential benefit in using LLMs to help identify records for training PLMs was revealed, especially to eliminate false negatives, and LLMs could be used as classifiers when insufficient data exists to train a PLM.

Background: Collecting information on adverse events following immunization from as many sources as possible is critical for promptly identifying potential safety concerns and taking appropriate actions. Febrile convulsions are recognized as an important potential reaction to vaccination in children aged <6 years., Objective: The primary aim of this study was to evaluate the performance of natural language processing techniques and machine learning (ML) models for the rapid detection of febrile convulsion presentations in emergency departments (EDs), especially with respect to the minimum training data requirements to obtain optimum model performance. In addition, we examined the deployment requirements for a ML model to perform real-time monitoring of ED triage notes., Methods: We developed a pattern matching approach as a baseline and evaluated ML models for the classification of febrile convulsions in ED triage notes to determine both their training requirements and their effectiveness in detecting febrile convulsions. We measured their performance during training and then compared the deployed models' result on new incoming ED data., Results: Although the best standard neural networks had acceptable performance and were low-resource models, transformer-based models outperformed them substantially, justifying their ongoing deployment., Conclusions: Using natural language processing, particularly with the use of large language models, offers significant advantages in syndromic surveillance. Large language models make highly effective classifiers, and their text generation capacity can be used to enhance the quality and diversity of training data., (©Sedigh Khademi, Christopher Palmer, Muhammad Javed, Gerardo Luis Dimaguila, Hazel Clothier, Jim Buttery, Jim Black. Originally published in JMIR AI (https://ai.jmir.org), 30.08.2024.)

Background: Intussusception is the primary cause of acute bowel obstruction in infants. The majority of cases <2 years of age are classed as idiopathic, with viral infection implicated as one of the causes. Coronavirus disease 2019 (COVID-19) public health measures led to significant decreases in communicable disease prevalence. During these times, reductions in intussusception frequency were greater than would be expected with our previous understanding of its infectious etiology., Methods: We conducted a retrospective, multistate, ecological study over a 12-year period. Monthly case numbers of "intussusception"-coded admissions (code K56.1; International Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification) were acquired from state-wide admissions data sets from New South Wales, Victoria, and Queensland, representing 77.62% of the eligible Australian population. These counts within differing jurisdictional lockdowns were compared with non-lockdown periods in order to investigate a correlation between intussusception frequency and lockdown periods., Results: We found a negative association between intussusception frequency and lockdown periods in both eligible states. The largest reductions were seen in the <2-year age groups, with Victoria experiencing a 62.7% reduction (rate ratio, 0.37; P < .001) and New South Wales a 40.1% reduction (0.599; P = .006) during lockdown times. Controls for variations in lockdown restrictions between both regional and metropolitan areas also showed expected decreases., Conclusions: Our ecological study demonstrates significant decreases in the frequency of pediatric intussusception admissions during the COVID-19 lockdown periods. The unexpected magnitude of the reductions suggests that the true proportion of infectious disease-caused idiopathic intussusception is greatly underestimated., Competing Interests: Potential conflicts of interest. B. T. and J. Buttery report grants from the Bill & Melinda Gates Foundation, PATH US, the Australian government Department of Health, GSK, the World Health Organization (WHO), and the University of Melbourne (research grant from Partnership in Research Indonesia and Melbourne [PRIME]); support for travel and/or meetings from the WHO Advisory Committee for Vaccines Against AMR and the Australian Government Department for Foreign Affairs and Trade; and participation on a data and safety monitoring board for Vakzine Project Management and on the WHO Advisory Committee for Vaccines Against AMR. N. W. reports funding from Australia's Medical Research Future Fund for a coronavirus disease 2019 (COVID-19) vaccine trial and participated on data and safety monitoring board for a clinical vaccine trial of a COVID-19 vaccine manufactured by Bionet Asia. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

In Victoria, Australia, jurisdictional vaccine safety service is conducted by SAEFVIC (Surveillance of Adverse Events Following Vaccination in the Community). SAEFVIC developed a public Vaccine Safety Report (saefvic.online/vaccinesafety) to present key surveillance information. This study applies an interdisciplinary learning health system approach to evaluate the report, taking into consideration public expressions of concern on social media.

We used text analysis to examine trends and self-reporting in Twitter regarding COVID-19 disease and COVID-19 vaccines' effects on fertility. Trends and self-reporting related to COVID19 vaccines indicates that further research is needed to enable women to feel they have sufficient information to make decisions regarding vaccination, together with social listening and engagement with women's concerns regarding vaccination effects.

Surveillance of Adverse Events Following Vaccination in the Community (SAEFVIC), Victoria's vaccine safety service for reporting adverse events following immunisation (AEFI), has provided integrated spontaneous surveillance and clinical services for individuals affected by AEFI since 2007. We describe SAEFVIC's response to the COVID-19 vaccine program, and reflect on lessons learned for vaccine safety. The massive scale of the Australian COVID-19 vaccine program required rapid adaptations across all aspects of SAEFVIC's vaccine safety services. Collection of AEFI reports was streamlined and expanded, incorporating both spontaneous and active surveillance data. Dramatically increased report volumes were managed with additional staffing, and innovations to automate, filter, and triage reports for priority follow up. There were two major adverse events of special interest (AESI): thrombosis with thrombocytopaenia syndrome and myocarditis, with multiple other AESI also investigated. Rapid escalation mechanisms to respond to AESI were established, along with AESI-specific databases for enhanced monitoring. Vaccine education and training resources were developed and public-facing vaccine safety reports updated weekly. Frequent communication with local and national government and regulatory bodies, and consultation with specialist groups was essential. The COVID-19 vaccine program has highlighted the importance of vaccine safety in supporting public confidence in vaccines and informing evidence-based immunisation policy. Supporting the COVID-19 vaccine program has required flexibility in adapting to policy changes and evolving vaccine safety signals, careful triage and prioritisation, informatics innovation, and enhanced engagement with the public regarding vaccine safety. Long-term investment to continue strengthening vaccine safety systems, building on lessons learned, will be essential for the ongoing success of Australian vaccination programs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Laemmle-Ruff, Lewis, Clothier, Dimaguila, Wolthuizen, Buttery and Crawford.)

Background: The preparation of the current and future health workforce for the possibility of using artificial intelligence (AI) in health care is a growing concern as AI applications emerge in various care settings and specializations. At present, there is no obvious consensus among educators about what needs to be learned or how this learning may be supported or assessed., Objective: Our study aims to explore health care education experts' ideas and plans for preparing the health workforce to work with AI and identify critical gaps in curriculum and educational resources across a national health care system., Methods: A survey canvassed expert views on AI education for the health workforce in terms of educational strategies, subject matter priorities, meaningful learning activities, desired attitudes, and skills. A total of 39 senior people from different health workforce subgroups across Australia provided ratings and free-text responses in late 2020., Results: The responses highlighted the importance of education on ethical implications, suitability of large data sets for use in AI clinical applications, principles of machine learning, and specific diagnosis and treatment applications of AI as well as alterations to cognitive load during clinical work and the interaction between humans and machines in clinical settings. Respondents also outlined barriers to implementation, such as lack of governance structures and processes, resource constraints, and cultural adjustment., Conclusions: Further work around the world of the kind reported in this survey can assist educators and education authorities who are responsible for preparing the health workforce to minimize the risks and realize the benefits of implementing AI in health care., (©Kathleen Gray, John Slavotinek, Gerardo Luis Dimaguila, Dawn Choo. Originally published in JMIR Medical Education (https://mededu.jmir.org), 04.04.2022.)

Background: Person-generated health data (PGHD) are produced by people when they use health information technologies. People who use PGHD may experience changes in their health and care process, such as engagement with their own healthcare, and their sense of social support and connectedness. Research into evaluating those reported effects has not kept up; thus, a method for measuring PGHD outcomes was previously designed and applied to the exemplar case of Kinect-based stroke rehabilitation systems. A key step of the method ensures that the patient's voice is included. Allowing stroke survivors to participate in the development and evaluation of health services and treatment can inform healthcare providers on decisions about stroke care, and thereby improve health outcomes., Objective: This paper presents the perspectives of stroke survivors and clinicians on the anticipated effects of stroke survivors' use of PGHD from a poststroke simulated rehabilitation technology., Methods: This study gathered the perspectives of stroke survivors and clinicians through three focus groups and three interviews, recruited for convenience. Participants were also asked questions intended to encourage them to comment on the initial items of the patient-reported outcome measure-PGHD. Deductive thematic analysis was performed., Results: This paper has further demonstrated that outcomes of using PGHD can be measured. For instance, stroke survivors described that using PGHD could result in positive, negative and nil effects on their health behaviours. Survivors and clinicians had varying perspectives in three of the six themes presented, and emphasise the importance of allowing stroke survivors to participate in the evaluation of digital health services., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: An established and well-known method for usability assessment of various human-computer interaction technologies is called heuristic evaluation (HE). HE has been adopted for evaluations in a wide variety of specialized contexts and with objectives that go beyond usability. A set of heuristics to evaluate how health information technologies (HITs) incorporate features that enable effective patient use of person-generated health data (PGHD) is needed in an era where there is a growing demand and variety of PGHD-enabled technologies in health care and where a number of remote patient-monitoring technologies do not yet enable patient use of PGHD. Such a set of heuristics would improve the likelihood of positive effects from patients' use of PGHD and lower the risk of negative effects., Objective: This study aims to describe the development of a set of heuristics for the design and evaluation of how well remote patient therapeutic technologies enable patients to use PGHD (PGHD enablement). We used the case of Kinect-based stroke rehabilitation systems (K-SRS) in this study., Methods: The development of a set of heuristics to enable better use of PGHD was primarily guided by the R3C methodology. Closer inspection of the methodology reveals that neither its development nor its application to a case study were described in detail. Thus, where relevant, each step was grounded through best practice activities in the literature and by using Nielsen's heuristics as a basis for determining the new set of heuristics. As such, this study builds on the R3C methodology, and the implementation of a mixed process is intended to result in a robust and credible set of heuristics., Results: A total of 8 new heuristics for PGHD enablement in K-SRS were created. A systematic and detailed process was applied in each step of heuristic development, which bridged the gaps described earlier. It is hoped that this would aid future developers of specialized heuristics, who could apply the detailed process of heuristic development for other domains of technology, and additionally for the case of PGHD enablement for other health conditions. The R3C methodology was also augmented through the use of qualitative studies with target users and domain experts, and it is intended to result in a robust and credible set of heuristics, before validation and refinement., Conclusions: This study is the first to develop a new set of specialized heuristics to evaluate how HITs incorporate features that enable effective patient use of PGHD, with K-SRS as a key case study. In addition, it is the first to describe how the identification of initial HIT features and concepts to enable PGHD could lead to the development of a specialized set of heuristics., (©Gerardo Luis Dimaguila, Kathleen Gray, Mark Merolli. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 28.07.2020.)

Background: Person-generated health data (PGHD) are health data that people generate, record, and analyze for themselves. Although the health benefits of PGHD use have been reported, there is no systematic way for patients to measure and report the health effects they experience from using their PGHD. Patient-reported outcome measures (PROMs) allow patients to systematically self-report their outcomes of a health care service. They generate first-hand evidence of the impact of health care services and are able to reflect the real-world diversity of actual patients and management approaches. Therefore, this paper argues that a PROM of utilizing PGHD, or PROM-PGHD, is necessary to help build evidence-based practice in clinical work with PGHD., Objective: This paper aims to describe a method for developing PROMs for people who are using PGHD in conjunction with their clinical care-PROM-PGHD, and the method is illustrated through a case study., Methods: The five-step qualitative item review (QIR) method was augmented to guide the development of a PROM-PGHD. However, using QIR as a guide to develop a PROM-PGHD requires additional socio-technical consideration of the PGHD and the health technologies from which they are produced. Therefore, the QIR method is augmented for developing a PROM-PGHD, resulting in the PROM-PGHD development method., Results: A worked example was used to illustrate how the PROM-PGHD development method may be used systematically to develop PROMs applicable across a range of PGHD technology types used in relation to various health conditions., Conclusions: This paper describes and illustrates a method for developing a PROM-PGHD, which may be applied to many different cases of health conditions and technology categories. When applied to other cases of health conditions and technology categories, the method could have broad relevance for evidence-based practice in clinical work with PGHD., (©Gerardo Luis Dimaguila, Kathleen Gray, Mark Merolli. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 07.05.2020.)

Patient-reported outcome measures (PROMs) contribute to improving the evidence base in many areas of clinical care. An area where PROMs can help build the evidence base is in person-generated health data (PGHD) that is available to people who engage with many health technologies. An important use case for PGHD outcomes is in simulated poststroke rehabilitation that use body-tracking technologies. This study gathered stroke survivor perceptions-through 2 focus groups and 5 interviews-on PGHD effects from a leading poststroke simulated rehabilitation technology. Deductive thematic analysis was performed. The findings show that PGHD outcomes of utilising PGHD can be measured. Moreover, the findings provided a deeper understanding of PGHD effects and broadened the scope through the perceptions of stroke survivors themselves. This work will further refine and validate the development of a PROM for utilising PGHD for poststroke simulated rehabilitation systems, and has wider relevance and application.

Introduction: Patient-reported outcome measures (PROMs) allow patients to self-report the status of their health condition or experience independently. A key area for PROMs to contribute in building the evidence base is in understanding the effects of using person-generated health data (PGHD), and using PROMs to measure outcomes of using PGHD has been suggested in the literature. Key considerations inherent in the stroke rehabilitation context makes the measurement of PGHD outcomes in home-based poststroke rehabilitation, which uses body-tracking technologies, an important use case., Objective: This paper describes the development of a preliminary item bank of a PROM-PGHD for Kinect-based stroke rehabilitation systems (K-SRS), or PROM-PGHD for K-SRS., Methods: The authors designed a method to develop PROMs of using PGHD, or PROM-PGHD. The PROM-PGHD Development Method was designed by augmenting a key PROM development process, the Qualitative Item Review, and follows PROM development best practice. It has five steps, namely, literature review; binning and winnowing; initial item revision; eliciting patient input and final item Revision., Results: A preliminary item bank of the PROM-PGHD for K-SRS is presented. This is the result of implementing the first three steps of the PROM-PGHD Development Method within the domains of interest, that is, stroke and Kinect-based simulated rehabilitation., Conclusions: This paper has set out a case study of our method, showing what needs to be done to ensure that the PROM-PGHD items are suited to the health condition and technology category. We described it as a case study because we argue that it is possible for the PROM-PGHD method to be used by others to measure effects of PGHD utilisation in other cases of health conditions and technology categories. Hence, it offers generalisability and has broader clinical relevance for evidence-based practice with PGHD. This paper is the first to offer a case study of developing a PROM-PGHD., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Person- or patient-generated health data (PGHD) are health, wellness, and clinical data that people generate, record, and analyze for themselves. There is potential for PGHD to improve the efficiency and effectiveness of simulated rehabilitation technologies for stroke. Simulated rehabilitation is a type of telerehabilitation that uses computer technologies and interfaces to allow the real-time simulation of rehabilitation activities or a rehabilitation environment. A leading technology for simulated rehabilitation is Microsoft's Kinect, a video-based technology that uses infrared to track a user's body movements., Objective: This review attempts to understand to what extent Kinect-based stroke rehabilitation systems (K-SRS) have used PGHD and to what benefit., Methods: The review is conducted in two parts. In part 1, aspects of relevance for PGHD were searched for in existing systematic reviews on K-SRS. The following databases were searched: IEEE Xplore, Association of Computing Machinery Digital Library, PubMed, Biomed Central, Cochrane Library, and Campbell Collaboration. In part 2, original research papers that presented or used K-SRS were reviewed in terms of (1) types of PGHD, (2) patient access to PGHD, (3) PGHD use, and (4) effects of PGHD use. The search was conducted in the same databases as part 1 except Cochrane and Campbell Collaboration. Reference lists on K-SRS of the reviews found in part 1 were also included in the search for part 2. There was no date restriction. The search was closed in June 2017. The quality of the papers was not assessed, as it was not deemed critical to understanding PGHD access and use in studies that used K-SRS., Results: In part 1, 192 papers were identified, and after assessment only 3 papers were included. Part 1 showed that previous reviews focused on technical effectiveness of K-SRS with some attention on clinical effectiveness. None of those reviews reported on home-based implementation or PGHD use. In part 2, 163 papers were identified and after assessment, 41 papers were included. Part 2 showed that there is a gap in understanding how PGHD use may affect patients using K-SRS and a lack of patient participation in the design of such systems., Conclusions: This paper calls specifically for further studies of K-SRS-and for studies of technologies that allow patients to generate their own health data in general-to pay more attention to how patients' own use of their data may influence their care processes and outcomes. Future studies that trial the effectiveness of K-SRS outside the clinic should also explore how patients and carers use PGHD in home rehabilitation programs., (©Gerardo Luis Dimaguila, Kathleen Gray, Mark Merolli. Originally published in JMIR Rehabilitation and Assistive Technology (http://rehab.jmir.org), 08.05.2018.)

It is important for patients to have access to the health information they generate, for them to be actively engaged in their own healthcare. This is relevant to Kinect-based post-stroke rehabilitation systems; as such there is a need to review the literature based on person-generated management and utilisation. Previous systematic reviews on Kinect for stroke rehabilitation have not used this as part of their criteria. This systematic review fills that gap.