Due to the valuable and sensitive nature of its data, the Australian healthcare sector is increasingly targeted by cyberattacks. Existing cybersecurity evaluation methods often lack the specificity required to address the unique vulnerabilities within this sector, especially in terms of engaging stakeholders and fostering a proactive security culture. These evaluations often overlook psychological empowerment, which enhances individuals’ confidence in managing cybersecurity.This study aims to develop a tailored cybersecurity self-assessment index for the Australian healthcare system. It will focus on enhancing psychological empowerment alongside technical assessments to improve overall sector resilience against cyber threats.Using a design science research approach, the index was developed using expert reviews, online surveys, and in-depth interviews with key stakeholders, including healthcare providers, consumers, and government entities. This iterative process involved identifying gaps in existing cybersecurity measures and designing an index to address technical and human factors.The index’s evaluation through a pilot study revealed that it effectively raised awareness and empowered individuals within the healthcare sector to take ownership of cybersecurity practices. Participants reported increased confidence in managing cybersecurity risks and found the index’s actionable recommendations helpful in improving their security posture. However, challenges related to its applicability across diverse healthcare environments and regulatory constraints were identified.The Australian Healthcare Cybersecurity Self-Assessment Index shows promise as a tool for strengthening cybersecurity in the healthcare sector by integrating psychological empowerment with technical assessments. Further research is needed to refine the tool, incorporate quantitative data, and explore its scalability across different healthcare settings and global applications.

Communication theorists discern six types of dialogue that occur between participants in discourse with each other: persuasion, inquiry, deliberation, information-seeking, eristic, and discovery. Although social media use in health care settings has been investigated from diverse perspectives, few studies have analysed social media interactions as dialogues and the implications for individualised care. The objective of this study is to identify the extent to which the six types of dialogue can be discerned from the self-reported social media activities of health care providers and the implications for individualised care. To realise the objectives of this study, transcripts of the interview of health care providers that use social media were thematically analysed using NVivo software. Findings reveal that each of the six types of dialogue is discernible from the social media posts of health care providers, and these dialogues help narrow the communication gap between stakeholders in health care. This suggests the use of social media in health care can be analysed using dialogue theory and paves the way for future research to discover precursors that trigger a dialogue shift from one type to another and a new way to assess the effectiveness of social media.

The Australian healthcare sector is a complex mix of government departments, associations, providers, professionals, and consumers. Cybersecurity attacks, which have recently increased, challenge the sector in many ways; however, the best approaches for the sector to manage the threat are unclear. This study will report on a semi-structured focus group conducted with five representatives from the Australian healthcare and computer security sectors. An analysis of this focus group transcript yielded four themes: 1) the challenge of securing the Australian healthcare landscape; 2) the financial challenges of cybersecurity in healthcare; 3) balancing privacy and transparency; 4) education and regulation. The results indicate the need for sector-specific tools to empower the healthcare sector to mitigate cybersecurity threats, most notably using a self-evaluation tool so stakeholders can proactively prepare for incidents. Despite the vast amount of research into cybersecurity, little has been conducted on proactive cybersecurity approaches where security weaknesses are identified weaknesses before they occur.

Remote Patient Monitoring (RPM), which leverages the Internet of Medical Things (IoMT) and autonomous systems, has grown in popularity recently. In RPM, the IoMT sense a patient’s biophysical data and transmits it in real time while the autonomous system processes the data for clinical notifications and storage. However, RPM deployments face two diverse challenges: how to present continuous data so that healthcare professionals can quickly interpret data streams and how to manage a great deal of missing data that occurs in RPM. Several studies suggested techniques for imputing missing data in static databases, which are unsuitable for RPM. A method for constantly streaming healthcare data to medical experts involves summarizing vital signs information into a numerical score, such as the Modified Early Warning Score (MEWS), which may be visually displayed to highlight MEWS patterns over a certain period. However, a MEWS chart is simplistic and more sophisticated ways to present data visually for straightforward interpretation are needed. This research proposes a solution for the visualization and missing data challenges by identifying patterns in the RPM data. First, a pattern-matching technique is proposed to address the missing data by considering the correlation and variability of the vital signs, resulting in a comparable correct match rate. Second, we transform the observed raw physiological vital signs data into concepts we call trust, frequency, trend, and slope parameters for visualization and automated alerts. The proposed approach can better support clinical decision-making than the MEWS. Comprehensive visualization approaches and missing data solutions can improve the quality and dependability of patient risk assessments.

The research study deals with electronic health records (EHRs) data breaches, their impact., Electronic health records play an important role in digital healthcare services. However, confidentiality and integrity of sensitive EHRs are critical to ensure patient privacy. Although the existing traditional cybersecurity practices provide some protection, they cannot prevent EHRs data breaches. Therefore, this research's primary focus will be critically reviewing the impact of data breaches and current cybersecurity practices. Finally, the paper's key findings highlight the type of cyberattacks and options to reduce them.

Remote patient monitoring (RPM) has been gaining popularity recently. However, health data acquisition is a significant challenge associated with patient monitoring. In continuous RPM, health data acquisition may miss health data during transmission. Missing data compromises the quality and reliability of patient risk assessment. Several studies suggested techniques for analyzing missing data; however, many are unsuitable for RPM. These techniques neglect the variability of missing data and provide biased results with imputation. Therefore, a holistic approach must consider the correlation and variability of the various vitals and avoid biased imputation. This paper proposes a coherent computation pattern-matching technique to identify and predict missing data patterns. The performance of the proposed approach is evaluated using data collected from a field trial. Results show that the technique can effectively identify and predict missing patterns.

Understanding and integrating physiological data collected from wearable sensors in remote patient monitoring (RPM) is challenging. Data streams may be interrupted due to the sensor's sensitivity, movement, and electromagnetic interference leading to inconsistent, missing, and inaccurate data. Existing approaches to summarize data flows into a single score such as the traditional Modified early warning score (MEWS) is limited. Data visualization approaches have the potential to address this challenge, but few studies have focused on visualization of RPM streams. The study presents a transformation of observed raw RPM physiological data into parameters identified as trust, frequency, slope, and trend. This facilitated visualization and enabled automated assessments of prioritized alerts. Experimental results have shown that the transformations led to the prioritization of clinically significant conditions, and improved visualization has the potential to better support clinical decisions compared with traditional MEWS.

Remote Patient Monitoring (RPM) is an emerging technology paradigm that helps reduce clinician workload by automated monitoring and raising intelligent alarm signals. High sensitivity and intelligent data-processing algorithms used in RPM devices result in frequent false-positive alarms, resulting in alarm fatigue. This study aims to critically review the existing literature to identify the causes of these false-positive alarms and categorize the various interventions used in the literature to eliminate these causes. That act as a catalog and helps in false alarm reduction algorithm design. A step-by-step approach to building an effective alarm signal generator for clinical use has been proposed in this work. Second, the possible causes of false-positive alarms amongst RPM applications were analyzed from the literature. Third, a critical review has been done of the various interventions used in the literature depending on causes and classification based on four major approaches: clinical knowledge, physiological data, medical sensor devices, and clinical environments. A practical clinical alarm strategy could be developed by following our pentagon approach. The first phase of this approach emphasizes identifying the various causes for the high number of false-positive alarms. Future research will focus on developing a false alarm reduction method using data mining.

Causal loop diagrams (CLD) that emerged from systems thinking disciplines have been used to simulate complex inter-dependencies between causal factors in diverse phenomena. This paper highlights a process for generating a casual loop diagrams to represent the quality of electronic health record (EHR) ecosystem in a medical context. The quality inherent in the use of electronic health records for specific clinical purposes is taken to depend on factors including data integrity, reliability, relevance, timeliness and completeness. By improving the electronic health record ecosystem quality, health care providers can enhance their data sharing practices, and personalised patient care, while reducing the probabilities of medical errors. Ultimately the CLD can be used to run multiple simulations for several clinical case scenarios to understand the impact of various case phenomena on the quality of the electronic health record ecosystem.

Employee ambidexterity (EA) is becoming increasingly recognised as a significant factor in enhancing individual and organisational performance across diverse industries. Ambidexterity refers to the capacity to exploit and explore organisational resources simultaneously. Scholars from diverse industry sectors have been motivated to delve deeper into the topic of EA due to its growing popularity. The objective of conducting a scoping review was to scrutinise the existing literature and identify the key drivers and constraints that impact EA to thrive in the changing work landscape. The insights gained from this review can assist decision-makers in formulating effective strategies to cultivate the ambidexterity skills of their workforce and achieve desirable outcomes. This review adheres to the PRISMA-ScR protocol. Articles were obtained from databases including Scopus, Web of Science, and EBSCOhost (Academic Search Complete, Business Source Complete). The body of literature concerning EA is in its nascent stage. 23 articles assessing EA's performance outcomes were identified using targeted search terms and thorough screening. After conducting a thorough thematic analysis using the iterative categorisation (IC) technique, tailored for scoping a review, we successfully identified twenty-nine factors contributing to the enhancement of EA, meticulously organised into five distinct categories: organisational factors, social connectedness, employee behaviour, employee personality, and work environment related factors. Similarly, we discovered four factors that impede EA: functional tenure, team identification, bounded discretion, and conscientiousness. Our findings underscore the profound impact of employee ambidexterity on distinct types of performance. Among the sixteen types of performance reported to be enhanced by EA, ten are linked to individual performance, while six are tied to organisational performance. Notably, our analysis revealed that nearly all studies have relied on cross-sectional research methods except for one. However, we advocate for the exploration of longitudinal studies as they hold the promise of offering a more comprehensive understanding of EA. The paper presents valuable insights into how to cultivate ambidextrous capabilities in the workforce for unparalleled success in today's rapidly evolving work environment. Additionally, it identifies several intriguing avenues for future research that could further elucidate and bridge existing knowledge gaps.

Remote patient monitoring involves the collection of data from wearable sensors that typically requires analysis in real time. The real-time analysis of data streaming continuously to a server challenges data mining algorithms that have mostly been developed for static data residing in central repositories. Remote patient monitoring also generates huge data sets that present storage and management problems. Although virtual records of every health event throughout an individual's lifespan known as the electronic health record are rapidly emerging, few electronic records accommodate data from continuous remote patient monitoring. These factors combine to make data analytics with continuous patient data very challenging. In this chapter, benefits for data analytics inherent in the use of standards for clinical concepts for remote patient monitoring is presented. The openEHR standard that describes the way in which concepts are used in clinical practice is well suited to be adopted as the standard required to record meta-data about remote monitoring. The claim is advanced that this is likely to facilitate meaningful real time analyses with big remote patient monitoring data. The point is made by drawing on a case study involving the transmission of patient vital sign data collected from wearable sensors in an Indian hospital.

Understanding the value of social media in health care has been a conundrum. Much of the literature in this area focuses on the use of social media for promotion, with very few studies seeking to elucidate how social media yields value in health care settings. This article draws on concepts from 18th century linguist Mikhail Bahktin to explain that social media acts like a Carnival in suspension of behavioral norms, and the provision of a forum for the proliferation of diverse dialogues. As a Carnival, social media plays an important role in encouraging dialogues that would not be appropriate within other spaces in the health care system. As such, social media is playing a pivotal role in changing norms toward shared care and patient empowerment.

Data sharing between financial services organisations has led to a proliferation of third party data service providers that are not parties to transactions but facilitate interactions between them by analysing, manipulating or storing data related to transactions. This has led to widespread legal, technological and sociocultural changes in that sector broadly described as Open-Banking initiatives. Third party service providers have not emerged in the healthcare sector in the same way. This study reports preliminary results of a Delphi study comprising healthcare and financial experts to explore the extent to which third party providers in healthcare is beneficial and feasible. Ensuring the quality of data service provided by third parties was seen to be a critical success factor. A causal loop model was used to describe the inter-dependent factors underpinning this factor. Further investigations to augment the model with Consumer Data Rights and validate empirically are underway.