Ebook: Healthier Lives, Digitally Enabled

2020 has been an unprecedented year in many ways. On top of an already evolving healthcare landscape, nationally and internationally, we have been met with the public and societal health challenge of the COVID-19 pandemic. Not only has this stressed individuals and healthcare systems to their limits but it has also driven an urgent and rapid need to mobilise digital health technology, as well as pressure test digital health in ways and under timeframes not previously imagined. This has its obvious challenges, but it has also created the opportunity for the digital health and informatics community to stand-up and lead. For many, this has meant dealing with digital health projects, such as major electronic medical record (EMR) system implementations at the same time as managing human health. All under very challenging circumstances, while continuing to ensure the safe, effective, and efficient delivery of care in a patient-centred manner. For some others, we have also seen the rapid deployment and uptake of telehealth services, both out of necessity to maintain continuity of care but also to ensure those who need healthcare are still able to access it no matter what the situation or where they are located. Again, it is here that the clinical informatics community has led.

Nationally, the Australasian Institute of Digital Health (AIDH) was launched this year, while all these massive shifts have been occurring all around it. We have continued to be guided under the directives of Australia’s National Digital Health Strategy, and recently observed the release of the Australian Digital Health Agency’s (ADHA) Workforce and Education Roadmap. This signposts the need to support and deliver on upskilling our health workforce in digital health and informatics. Three key ‘horizons’ underpin the roadmap, which describes: the use of health record and consumer data, new ways of working with technology, and transformation. There has perhaps been no better time to advocate for our workforce in digital health.

With the workforce in mind, the annual Australian Health Informatics Conference (HIC) represents the coming together of the nation’s digital health community to shape the agenda, network, learn, share, and showcase current and future initiatives that support the progression of digital health. Under normal circumstances, HIC provides the place to discuss innovation, digital models of care, data driven decision making, and more. However, and perhaps poetically, this will not look the same in 2020. Considering the COVID-19 pandemic, we will see the emergence of State-led satellite events, coming together with an online feature showcase under the umbrella of the ‘Digital Health Institute Summit’. Disruption often drives innovation and having seen the way that the AIDH and the informatics community have embraced digital remote meetings, learning, and social gatherings this year, we have no doubt that the Digital Health Institute Summit will still bring the usual (if not more) vigour, liveliness, and passion for change amongst attendees.

The number of submissions and expressions of interest to present at this year’s Summit has been reflective of the passion to continue to drive the digital agenda. We saw overwhelming interest in the form of academic and scientific paper publications. This is reflected in the calibre and breadth that this year’s publications demonstrate. This volume of papers reflect highly topical themes across various areas and disciplines. Examples include (but are not limited to): digital health in aged care, mental health, COVID-19, public health, and workforce. From the digital perspective, we note familiar topics, such as: wearables, mobile health and remote monitoring, interoperability, and data privacy. As well as an increasing appetite for telehealth, automation, bots, and other applications of artificial intelligence.

Like previous years, this year’s academic program maintains the same high standard of papers that we know and expect from this conference. All submissions were double blind-peer reviewed by three health informatics experts. We continue to acknowledge the tireless efforts of our volunteer reviewers, without whom, this would not be possible. This thanks extends to the spectacular team at the AIDH, who have been instrumental in supporting this process. 37 papers underwent the initial review. Authors then addressed reviewer feedback and were confirmed by the Scientific Program Committee. What you have here is the culmination of 17 included in this volume. Our sincere congratulations and commendations to the authors of these papers for their contributions to digital health.

Mark Merolli

Chris Bain

Louise K. Schaper

Background:

Healthcare is among the leading industries which drives the use of personal devices for work purposes (BYOD). However, allowing BYOD for healthcare workers comes at a cost, as it puts sensitive information assets such as patient data residing on personal devices at risk of potential data breaches.

Objective:

Previous review of the literature has highlighted the dearth of empirical studies in hospital settings regarding BYOD usage. As such, this paper aims to report BYOD usage trends in Australian hospitals through a national survey, first of its kind in Australia.

Methods:

An anonymous survey was conducted online among health IT personnel, asking them about their experiences about BYOD usage in their hospitals. 28 responses were collected based on public Australian hospitals, which included 21 hospital groups and 7 standalone hospitals, likely to represent more than 100 hospitals in total. Survey responses were quantitatively analysed through descriptive statistical analysis and cross tabulation.

Results:

BYOD is allowed in majority of the hospitals, and among all major staff groups, with doctors being the leading group. Participants ranked reasons for allowing BYOD, and most of them were related to improvement in clinical productivity, efficiency and mobility for clinical staff. Challenges were generally related to data security such as patient data breaches and compliance with data security laws, according to them. More than two thirds of hospitals had a cybersecurity officer employed, and CIOs were the most dominant group who held responsibility for managing BYOD within the hospital.

Conclusion:

This paper provides a starting point for better understanding of BYOD usage in a complex healthcare environment based on empirical evidence, one which highlights the security-usability conundrum, confirming previous literature themes.

As people move into advanced old age, they may experience cognitive impairments and frailty, making it difficult for them to live without support from others. Caregivers might decide to use aged care monitoring devices (ACMDs) to support older adults under their care. However, these devices raise privacy concerns as they collect and share sensitive data from the older adult’s private life in order to provide monitoring capabilities. This study involved interviewing formal and informal caregivers who used/may use ACMDs to investigate their views on privacy. The study found that although caregivers consider protecting older adults’ privacy important, they may overlook privacy in order to gain benefits from ACMDs. We argue that ACMD developers should simplify privacy terms and conditions so that caregivers can make well-informed decisions when deciding to use the device. They also should consider providing users with flexible privacy settings so that users can decide what data to collect, whom to share it with and when.

The last decade has seen an explosion in the uptake of digital health interventions (DHI) to address complex chronic diseases. This is particularly true in the case of multiple sclerosis where those living with the disease are increasingly seeking adjuvant treatment options such as lifestyle management. This paper seeks to give perspectives and insights from public health researchers that have engaged in the digital transformation process of a face-to-face lifestyle management educational program. There is a dearth of information regarding the digital transformation of lifestyle educational programs and this is particularly true for programs directed at chronic diseases. A large body of work exists from higher education, an area that has undergone rapid digital transformation of its work, and much can be derived from this field. There is also a well-established field of design methodologies and frameworks available to researchers seeking to design, develop and implement DHIs. This paper provides a practical overview of the synthesis between digital transformation processes in higher education and the application of an existing development framework for DHIs. By describing this process, we hope to fill an existing gap within the literature that will provide a valuable tool for future researchers.

Introduction:

Pain is the most common symptom that patients present with to the emergency department. It is hard to identify patients who have presented in pain to the emergency department when compliance with structured pain assessment is low. An ability to identify patients presenting in pain allows further investigation of the quality of care provided.

Background:

Machine and deep learning techniques are commonly used for text analysis in healthcare. Applications such as the classification of diagnosis and unplanned readmissions from textual medical records have previously been described. In other work, conventional and deep-learning techniques have demonstrated high performance in identifying patients presenting to the emergency department in pain. However, these models have lacked interpretability.

Methods:

This paper proposes the use of machine learning techniques to identify patients who present in pain based upon their initial assessment using interpretable deep learning models.

Results:

The interpretable deep learning model of pain identification was shown to have more accuracy and precision than other machine and deep learning techniques. This technique has significant application to large datasets for the identification of the quality of care and real-time identification of patients presenting in pain to improve their care.

Conversation agents (chat-bots) are becoming ubiquitous in many domains of everyday life, including physical and mental health and wellbeing. With the high rate of suicide in Australia, chat-bot developers are facing the challenge of dealing with statements related to mental ill-health, depression and suicide. Advancements in natural language processing could allow for sensitive, considered responses, provided suicidal discourse can be accurately detected. Here suicide notes are examined for consistent linguistic syntax and semantic patterns used by individuals in mental health distress. Paper contains distressing content.

Hospital overcrowding is a major problem for healthcare systems around the globe. In order to better estimate future demands and adequate resources for coping with such demands, statistical and computerised modelling can be applied. This can then allow healthcare administrators and decision makers to quantify the impacts of various “what-if” scenarios on hospital performance measures. This paper investigates the application of Discrete Event Simulation towards optimising Emergency Department resources while measuring overall length of stay and queuing time of emergency patients as a target performance measure. In particular, we explore strategies for generating historically informed synthetic data that helps the simulation model track patient flow through the target hospital over a future time frame. Using the developed simulation model, several resource configurations are tested using data from one of the busiest emergency departments in the state of Queensland as the baseline while quantifying the impacts of such changes on key patient flow metrics. It was found that adding a single bed (and associated resources) to the emergency department would result in a 23% decrease in average patient treatment delay.

The World Health Organisation has recently declared sepsis a global medical emergency. Obtaining quality data to establish the evidence on how clinicians recognise, diagnose, and treat sepsis is still a challenge. This feasibility study aimed to utilise routinely collected data from electronic health records (EHR) to assess the sepsis inpatient care pathway. We conducted a retrospective observational cohort study which included all patients admitted to a private teaching hospital between 2015 and 2018. De-identified patient demographic and clinical data were extracted and analysed. A total of 47 sepsis patients were identified based on diagnoses recorded and a review of clinical notes. A surgical procedure was conducted on more than half of these patients (n=25, 53%). Nearly two-thirds were given antibiotics (n=30, 64%), of which 87% (n=26) were administered within 2-hours of sepsis diagnosis. Eighteen patients were admitted to ICU and 13 of them were diagnosed as septic in ICU. We identified some aspects of EHR data that could be improved. Overall, routinely collected data from clinical information systems provides rich information to assess the sepsis patient care pathway.

Introduction:

Increasing physical activity among posttreatment breast cancer survivors is essential, as greater physical activity reduces the relative risk of cancer-specific mortality. This trial examines how a fitness tracker-based intervention changes the physical activity behaviour of inactive posttreatment breast cancer survivors.

Methods:

Seventeen physically inactive posttreatment breast cancer survivors participated in a randomised cross-over controlled trial. Participants underwent a 12-week intervention of a fitness tracker combined with a behavioural counselling and goal-setting session and 12 weeks of normal activity (control). The primary outcome was the change in physical activity assessed by accelerometry over seven days.

Results:

The intervention achieved a mean increase of 4.5 min/day of moderate-vigorous physical activity, representative of a small-moderate effect (d = 0.34). Changes in time spent as a proportion of the day in light physical activity (-8.3%) and in sedentary behaviour (7.9%), were both significantly different to baseline (t (16) = 3.522, p < 0.01; t (16) = -3.162, p < 0.01).

Conclusion:

Interindividual differences in the change of patterns of physical activity behaviour suggest that only for some, fitness trackers can achieve a change in the level of moderate-vigorous physical activity.

This paper describes the extent to which remote interaction healthcare interventions supported by digital technology are currently being used, or have recently been newly developed for use, in the care of older people in Australia within the context of the existing Australian aged care system and in conjunction with the COVID-19 pandemic. We place emphasis on those interventions associated with primary care provision, and associated healthcare services such as allied health, rather than outreach from jurisdictional health services and acute care. The primary purpose of this study was to gain an indication of the extent and range of such interventions, and provide a pragmatic commentary on their usage. This has enabled the understanding of some characteristics for success, and drivers for rapid adoption of further digital technology interventions, in the aged care sector.

This paper proposes a formal model for expressing policies in digital health. The aim is to support computable expressions of legislative, regulative and organizational policies. The model is grounded in the semantics of deontic logic [1] and in modelling concepts for expressing accountability, specified in the new RM-ODP Enterprise Language standard [2]. An example of privacy consent based on the FHIR consent resource [3] is used to explain the use of these modelling concepts. The example involves multiple stakeholders and illustrates the complexity associated with the use of machine learning and artificial intelligence systems as part of healthcare delivery governed by informed consent policies.

Epilepsy is the most common neurological disorder. The diagnosis commonly requires manual visual electroencephalogram (EEG) analysis which is time-consuming. Deep learning has shown promising performance in detecting interictal epileptiform discharges (IED) and may improve the quality of epilepsy monitoring. However, most of the datasets in the literature are small (n≤100) and collected from single clinical centre, limiting the generalization across different devices and settings. To better automate IED detection, we cross-evaluated a Resnet architecture on 2 sets of routine EEG recordings from patients with idiopathic generalized epilepsy collected at the Alfred Health Hospital and Royal Melbourne Hospital (RMH). We split these EEG recordings into 2s windows with or without IED and evaluated different model variants in terms of how well they classified these windows. The results from our experiment showed that the architecture generalized well across different datasets with an AUC score of 0.894 (95% CI, 0.881–0.907) when trained on Alfred’s dataset and tested on RMH’s dataset, and 0.857 (95% CI, 0.847–0.867) vice versa. In addition, we compared our best model variant with Persyst and observed that the model was comparable.

Temporary telehealth initiatives during COVID-19 have been life-changing for many people in Australia; for the first time Frail, Homebound, and Bedridden Persons (FHBP) equitably received primary healthcare services, like Australians without a disability. However, government changes to telehealth funding mean that since July 2020 telehealth is only available for those who have attended a face-to-face appointment in the last 12 months, thus excluding FHBP. This paper illustrates the reported health exclusion and marginalisation of FHBP. We reviewed the literature and surveyed 164 Australian adults (27% homebound people and 73% affiliated persons) to ascertain their opinions and thoughts on potential strategies to tackle issues associated with FHBP’s current circumstances. Results demonstrate that digital technologies and telehealth services are ethical imperatives. Policymakers, clinicians, and health researchers must work with end-users (community-based participation) to create an inclusive healthcare service.

There is a need to improve the digital capabilities of the health workforce through training and education. Until now there has not been a national strategy that addresses the digital capability gaps in the existing and emerging health workforce. This paper describes the development of a national strategy to improve the digital capabilities of Australia’s health workforce. A mixed-method approach was used to incorporate the findings of a literature review, stakeholder interviews, online and offline workshops, consumer interviews, and surveys to develop the national strategy. Various stakeholder groups across all Australian jurisdictions were engaged in its development. The final strategy consists of key principles, a three-horizon framework reflecting the maturity levels, and a digital profile framework articulating the expectations of the many stakeholders in health.

The current legacy ICT framework structures in healthcare are often siloed and do not allow information to flow easily between business analytics and clinical systems, affecting critical decision making.Western Sydney Local Health District (WSLHD) has numerous electronic database systems for business analytics including tracking individual patients waiting for treatment in the emergency department (ED). Administrators of hospital business data report ED performance measures in a weekly static feedback report to clinical and executive staff due to current legacy systems and manual resource allocation processes. The remit of the project was to prototype a system that could integrate data sources from the current QlikSense Dashboard into an interoperable mobile app with the future intention of direct impact on clinical care decision making for the emergency department. A series of meetings between business analytics unit and clinical staff were used to inform a set of requirements for information workflow systems integration to be used on the project. Stimulated patient data that matched typical data feeds from the system was used to develop a prototype interoperable HL7 messaging mobile app that would report waiting patients in their triage categories in near real time. This working protype with synthetic scenarios and data will inform a future deployable production system with information for the patient journey from the ED waiting room into available hospital beds. As most applications are either designed for business analytics or clinical workflows, integrating information data sources into one mobile application that could meet the needs of both clinical and business performance was novel and integral. This proof of concept project successfully integrated the information systems necessary for both purposes and informs future requirements for an interoperable and deployable cross-platform mobile app.

Pulmonary rehabilitation is a behavioral intervention that can improve symptom control and quality of life for patients with chronic obstructive pulmonary disease (COPD), but access, uptake and adherence are problematic. Our team has pursued the development of a mobile phone-based intervention (mobile pulmonary rehabilitation, mPR) with iterative design and a pilot study. The mPR intervention is delivered through two technologies: text messages (SMS) and a smartphone application. Our user-centered design analysis of pilot study data led to several insights. First, patients’ replies to the SMS suggested that messages were anthropomorphised and provided social support. Second, the smartphone application could help patients by clearly visualizing the exercise program, alternative exercises, and progress to date. We demonstrate the design iterations made to meet these requirements and we present feedback obtained from experts and from four COPD patients. We discuss implications for the design of mobile pulmonary rehabilitation interventions.

E-health technologies have potential to provide scalable and accessible interventions for youth mental health. As part of an ecosystem of e-screening and e-therapy tools for New Zealand young people, a dialog agent, Headstrong, has been designed to promote resilience with methods grounded in cognitive behavioral therapy and positive psychology. This paper describes the architecture underlying the chatbot. The architecture supports a range of over 20 activities delivered in a 4-week program by relatable personas. The architecture provides a visual authoring interface to its content management system. In addition to supporting the original adolescent resilience chatbot, the architecture has been reused to create a 3-week ‘stress-detox’ intervention for undergraduates, and subsequently for a chatbot to support young people with the impacts of the COVID-19 pandemic, with all three systems having been used in field trials. The Headstrong architecture illustrates the feasibility of creating a domain-focused authoring environment in the context of e-therapy that supports non-technical expert input and rapid deployment.

To realise the benefits of digital health, the health workforce needs to evolve, adapt and develop their digital proficiency. As the largest workforce in health, nurses and midwives are well positioned to lead as an agile digital healthcare workforce. The objective of this work is to describe how individual nurses and midwives, organisations and education providers could use the newly developed National Nursing and Midwifery Digital Health Capability Framework to build digital health capability. The paper concludes with an international perspective on the framework.