Introduction Advance Care Planning (ACP) is a patient-centered process that enables individuals to consider their goals and priorities and make plans with their healthcare team for future care. Oncology clinicians care for critically ill patients and are central to facilitating and documenting ACP. While Australian and international guidelines strongly support ACP in oncology, clinician engagement and documentation of ACP remain suboptimal. This study aimed to explore oncology clinicians’ ACP knowledge, decision-making, and practice, including factors influencing engagement in ACP. Methods A qualitative study was conducted using semi-structured interviews with oncology clinicians from three metropolitan hospitals in New South Wales, Australia. Participants were purposefully sampled and interviewed between November 2021 and March 2022. Interviews were audio-recorded, transcribed, and analyzed using deductive thematic analysis guided by the Theoretical Domains Framework (TDF). An inductive approach was subsequently applied to develop explanatory subthemes within identified domains. Results Eleven clinicians participated, including Medical/Radiation Oncology Consultants (54.5%) and Advanced Trainees (45.5%). Interview length ranged from 17.5 to 35.5 minutes. A total of 380 quotes were coded across all 14 TDF domains, generating 33 explanatory subthemes. Four domains were most influential: Knowledge and Skills, Social/Professional Role and Identity, Environmental Context and Resources, and Social Influences. Although all participants recognized ACP as best-practice care, engagement was influenced by limited formal training and legal literacy, uncertainty regarding responsibility for initiating and documenting ACP, variable inconsistent interdisciplinary communication, workload and time constraints, electronic medical record functionality, and patient characteristics (i.e.,psychological state, health literacy, cultural complexity). Conclusion ACP in oncology is shaped by complex, interdependent socio-cultural and organizational factors. Applying the TDF identified key behavioral and contextual determinants. Improving ACP requires multilevel strategies targeting clinician capability, role clarity, and system-level supports, including structured education, clearer documentation processes, improved electronic medical record functionality, and integration of ACP into routine multidisciplinary care.

Objectives Generative Artificial Intelligence (Gen AI) has become an increasingly prevalent conversation in healthcare over the past few years. Though there have been research projects and articles exploring the administrative and clinical uses of such technologies, there has been little exploration of health professional perspectives, hopes and concerns. This study sought to explore perspectives and examine the barriers and enablers of Gen AI in healthcare. Methodology Australian health professionals participated in a mixed-methods study. A survey (n=31) explored the Six Dimensions of Healthcare Quality Framework, capturing quantitative (Likert-scale responses) and qualitative (free-text) data. Semi-structured interviews (n=10) explored participant perceptions of Gen AI. Quantitative data was analysed using descriptive statistics. Qualitative data was thematically analysed. Results Most survey respondents (74.14 %) reported having used Gen AI to support their work, but only a few (25.81 %) reported organisational supports for use of these technologies. Analysis of the qualitative data aligned with the survey responses. Five themes were generated through thematic analysis, aligning with health professional’s perceived use of Gen AI chatbots, benefits, risks, as well as drivers of safe use and opportunities for the future. Conclusion Health professionals see potential for using Gen AI to support their work, with enthusiasm about the potential of Gen AI to reduce workloads, particularly in offloading administrative tasks. There is also awareness that Gen AI chatbots pose risks both at the individual level such as limited capability in using these technologies and at the organisational level such as lack of training to support in upskilling, and systemic concerns around policy gaps. Public Interest Summary Generative Artificial Intelligence (Gen AI) is increasingly topical in all aspects of life, and the health sector is no exception. Though there have been research projects focusing on Gen AI in healthcare, there has been little exploration of health professional views and concerns. This study spoke to health professionals and found that though there is a lot of interest in potential applications of Gen AI in healthcare, particularly in administrative offloading and clinical support, however, the benefits don’t yet outweigh the risks. Software developers must work alongside health professionals in developing a substantially beneficial program to support the safe use of Gen AI in healthcare as well as be well supported on an organisational level. There are also opportunities to develop education to build health professionals capacity to use GenAI safely and effectively, and for health service organisations to develop guidance and policies to clearly articulate what safe use looks like.

Emotional harm and discomfort in therapeutic extended realities (XR) remains underexamined, even as immersive tools are increasingly deployed in healthcare contexts. We frame therapeutic XR as EmotionTech and reflect on 12 cases from 9 researchers and designers through interviews and workshops. We locate four concerns for emotional harm and identify ways to address them: how to talk about emotion, when to talk about emotion, whose emotions are centred, and which emotions are valued. Building on these themes and therapeutic XR as one form of EmotionTech, we propose strategies to legitimise concerns for emotional safety in design and research practice, legitimise knowers by recognising diverse perspectives and situated experiences, and leveraging ambiguity in design and training tools that foster reflexivity rather than closure. These strategies together reposition design responsibility in EmotionTech innovation and make visible its potential to cause emotional discomforts and harms.

Professional learning environments offer a unique opportunity for exploring professional competencies due to the use of workplace data, which captures important aspects of professional performance. This data is different from that used in formal learning analytics contexts, as it is not collected by systems designed to understand learning. Instead, workplace data represents the digital fingerprint professionals leave behind when interacting with technologies to do their jobs. Harnessing this data to support workplace learning is challenging for a range of reasons, including being able to identify meaningful metrics to identify individual performance and scaffold the use of this data to support professional learning interventions on knowledge and performance. Generative AI (GenAI) has great potential to enhance professional learning analytics by addressing some of the challenges inherent in using workplace data. These challenges include needing to process large amounts of unstructured data to understand individual performance and transform this data into interfaces and interventions that can support learning. In our paper, we modify Clow’s learning analytics cycle to inform a modified framework describing the intersection of learning analytics with professional learning. Subsequently, we illustrate the potential power of GenAI for supporting professional learning across the framework through three case studies in health professions education.

Background The growing digitization of health data has expanded opportunities for professional learning and performance improvement. While they provide new means for improving the quality and safety of health care, these new capabilities for data analysis and performance monitoring come with risks and may exacerbate existing ethico-legal concerns about fairness, accountability, privacy, and more. Objective This study aims to develop an ethico-legal framework for the evaluation of professional performance that is cognizant of these concerns and addresses the needs of relevant stakeholders. The study will assess the acceptability, comprehensiveness, and potential utility of the framework from the perspective of end users and subject matter experts. Methods This study will use existing evidence on ethico-legal considerations surrounding secondary uses of health data for performance improvement and management to draft the framework. We will conduct 2 focus groups with end users (eg, health professionals and administrators) and subject matter experts (eg, clinical ethicists and legal practitioners). These focus groups will ask participants to reflect on the framework’s structure and comprehension, intended audience, comprehensiveness and relevance regarding ethical and legal principles, limitations, and utility and acceptability as a step-by-step guide. Study participants may also opt for one-on-one interviews for any reason. This feedback will be thematically analyzed using open coding and verified by an independent reviewer at the focus groups, followed by constant comparisons of feedback from this study to concepts and interrelationships in data previously collected. Results Recruitment for this study is scheduled from August to December 2025. The analysis, compilation, and dissemination of higher-order themes, concepts, and outcomes is planned for after publication of this protocol, after each interview or focus group has been transcribed and coded line by line. Conclusions This study seeks to create an actionable tool that is readily translatable to clinical practice in collaboration with end users and subject matter experts. The proposed methodology is a low-resource coapproach that could be iteratively refined to ensure that the proposed framework continues to support robust and efficient use of performance data while respecting the different contexts in which practice analytics may be delivered. This systematic approach to principle-led evaluation of performance and conduct could inform technology-neutral governance capable of addressing perennial concerns about fairness, privacy, and transparency when using health data for professional learning and performance management. International Registered Report Identifier (IRRID) DERR1-10.2196/82167

Commonly used digital health technologies, such as electronic health record systems (EHRs) and patient portals, as well as custom built digital decision aids, have the potential to enhance person-centered shared decision-making (SDM) in cancer care. However, there is little evidence in the literature on how these technologies are used for SDM or how best they can be designed and integrated into workflows and practice. This may be due to the nature of SDM, which is fundamentally human interactions and conversations that produce desired human outcomes. Technology must, therefore, be non-intrusive while supporting the human decision-making process. This study examined how digital technologies can help cancer care professionals improve shared decision-making (SDM) in oncology consultations. Healthcare professionals who treat cancer patients in Sydney, Australia, were invited to participate in online co-design focus group meetings. During these sessions, they shared their experiences using digital technologies for shared decision-making (SDM) and provided suggestions to improve their use of digital technologies. The session recordings were transcribed and then analyzed using qualitative thematic analysis. The findings indicated that various digital technologies, such as electronic health record systems (EHRs), mobile devices, and patient portals, are used by cancer care professionals to help improve patients’ understanding of their disease and available care options. Digital technologies can both improve and undermine SDM. Current systems are generally not designed to support SDM. Key issues such as data integration and interoperability between systems negatively impact the ability of digital technologies to support SDM. Emerging technologies such as Generative Artificial Intelligence (AI) were discussed as potential facilitators of SDM by automating the gathering and sharing of information with patients and between health professionals. This research indicates that digital technologies have the potential to impact SDM in oncology consultations. However, this potential has not yet been fully realized, and significant modifications are required to optimize their usefulness in person-centered SDM.

BackgroundCommonly used digital health technologies, such as electronic health record systems and patient portals as well as custom-built digital decision aids, have the potential to enhance person-centered shared decision-making (SDM) in cancer care. SDM is a 2-way exchange of information between at least a clinician and the patient and a shared commitment to make informed decisions. However, there is little evidence in the literature on how technologies are used for SDM or how best they can be designed and integrated into workflows and practice. This may be due to the nature of SDM, which is fundamentally human interactions and conversations that produce desired human outcomes. Therefore, technology must be nonintrusive while supporting the human decision-making process. ObjectiveThis study examined how digital technologies can help cancer care professionals improve SDM in oncology consultations. MethodsHealth care professionals who treat patients with cancer were invited to participate in online co-design focus group meetings. During these sessions, they shared their experiences using digital technologies for SDM and provided suggestions to improve their use of digital technologies. The session recordings were transcribed and then analyzed using qualitative thematic analysis. The 3-talk SDM model, which consists of 3 steps—team talk, option talk, and decision talk—was used as the guiding framework. This approach was chosen because the 3-talk SDM model has been adopted in Australia. The researchers walked the participants through the SDM model and discussed their routine clinical workflows. ResultsIn total, 9 health care professionals with experience treating patients with cancer and using technologies participated in the study. Two focus groups and 2 interviews were conducted in 2024. Three themes and 7 subthemes were generated from the thematic analysis. The findings indicated that various digital technologies, such as electronic health record systems, mobile devices, and patient portals, are used by cancer care professionals to help improve patients’ understanding of their disease and available care options. Digital technologies can both improve and undermine SDM. Current systems are generally not designed to support SDM. Key issues such as data integration and interoperability between systems negatively impact the ability of digital technologies to support SDM. Emerging technologies such as generative artificial intelligence were discussed as potential facilitators of SDM by automating information gathering and sharing with patients and between health professionals. ConclusionsThis research indicates that digital technologies have the potential to impact SDM in oncology consultations. However, this potential has not yet been fully realized, and significant modifications are required to optimize their usefulness in person-centered SDM. Although technology can facilitate information sharing and improve the efficiency of consultation workflows, it is only part of a complex human communication process that needs support from multiple sources, including the broader multidisciplinary cancer team.

BACKGROUND:The rapid digitisation of healthcare has resulted in the capture of a vast amount of health data, which are increasingly being used for secondary purposes, such as quality improvement and performance management. OBJECTIVES:This study examined the legal and ethical considerations that affect if and how health professionals and administrators implement and use their performance data from the perspective of these stakeholder groups. ELIGIBILITY CRITERIA:The search strategy focused on the use of health data (1) for quality improvement and performance management, (2) by health professionals and (3) discussion of ethicolegal concerns. SOURCES OF EVIDENCE:A scoping review was conducted of three medical databases (Medline, Scopus and Embase) in April 2023, updated in June 2024. CHARTING METHODS:Included articles were first charted against 12 descriptive variables and then thematically analysed against the 16 substantive and procedural values of the Ethics Framework for Big Data in Health and Research (the Framework). RESULTS:We identified 16 articles that explored 5/7 procedural and 8/9 substantive values of the Framework. Health professionals were mostly concerned with the fairness of data comparisons defined as the use of accurate and risk-adjusted datasets and the contextualisation of performance data against clinical experiences. Health administrators additionally emphasised the importance of good governance and data stewardship to improving professional engagement with performance data, but privacy remains a key barrier. CONCLUSIONS:The growing interest in using health data for quality improvement and performance management requires health services to address barriers to utilisation of performance data. Legal and ethical concerns must be balanced and prioritised in collaboration with end users for performance data to be accepted as a valid form of quality and performance assessment. Although privacy remains a key issue, these fears can be effectively managed by restricting public reporting on performance to only what is essential for public assurance.

Background:Electronic medical records (EMRs) are a potentially rich source of information on an individual's health care providers' clinical activities. These data provide an opportunity to tailor web-based learning for health care providers to align closely with their practice. There is increasing interest in the use of EMR data to understand performance and support continuous and targeted education for health care providers. Objective:This study aims to understand the feasibility and acceptability of harnessing EMR data to adaptively deliver a web-based learning program to early-career physicians. Methods:The intervention consisted of a microlearning program where content was adaptively delivered using an algorithm input with EMR data. The microlearning program content consisted of a library of questions covering topics related to best practice management of common emergency department presentations. Study participants were early-career physicians undergoing training in emergency care. The study design involved 3 design cycles, which iteratively changed aspects of the adaptive algorithm based on an end-of-cycle evaluation to optimize the intervention. At the end of each cycle, an online survey and analysis of learning platform metrics were used to evaluate the feasibility and acceptability of the program. Within each cycle, participants were recruited and enrolled in the adaptive program for 6 weeks, with new cohorts of participants in each cycle. Results:Across each cycle, all 75 participants triggered at least 1 question from their EMR data, with the majority triggering 1 question per week. The majority of participants in the study indicated that the online program was engaging and the content felt aligned with clinical practice. Conclusions:The use of EMR data to deliver an adaptive online learning program for emergency trainees is both feasible and acceptable. However, further research is required on the optimal design of such adaptive solutions to ensure training is closely aligned with clinical practice.

Aim To determine the feasibility of using population-based linked data to measure an Australian multidisciplinary set of 26 colorectal cancer (CRC) quality indicators.Methods Data were obtained on adult patients diagnosed with CRC (ICD-10-AM codes C18-C20) between July 1, 2005 and December 31, 2019 from the New South Wales (NSW) Cancer Registry. The NSW Cancer Registry data were linked to the Clinical Cancer Registry, Admitted Patient Data Collection, and death records. The feasibility assessment included (1) mapping required variables to available data, (2) review of publicly available reports to identify routine reporting of the indicators, (3) assessment of data completeness and coverage using proportional analyses, and (4) pilot test calculation of feasible indicators where data exist.Results Data mapping found that 14 indicators were potentially feasible. Linked data were available for 38,430 patients to test eight surgical indicators and 8489 patients to test six neoadjuvant therapy indicators. The data required to measure these indicators had significant limitations in data coverage, completeness, and quality, rendering the calculations unreliable and some implausible. The data completeness for staging ranged from 74% to 85%, and almost one half of diagnosis dates were illogical. Overall, six of the 26 indicators were feasible and reliable to measure. These addressed unplanned reoperation/readmission, colonoscopy, surgical mortality, and survival.Conclusion This study identified six clinically relevant quality indicators feasible to measure using NSW population-based data. However, these indicators were surgical processes and outcomes. There are insufficient data to produce adequate and clinically meaningful quality measurements for a multidisciplinary CRC team, particularly in diagnostic workup, neoadjuvant therapy, and supportive care.

Background: Interest is growing in the use of Artificial Intelligence (AI) technologies in health care. Health AI innovations have been explored in a range of clinical contexts, yet their implementation into routine practice remains challenging. The aim of this study was to understand the factors that influenced the implementation of AI innovations into routine practice in Australian Healthcare organisations, from the perspective of implementers. Methods: The study used a qualitative methodology. AI implementers were identified via an environmental scan of publicly available information, combined with passive snowballing. In-depth research interviews were undertaken between November 2021 and June 2022. Interviews were audio recorded and transcribed into text for data analysis. Transcripts were inductively coded by the researchers, followed by deductive categorisation of the data using the Consolidated Framework for Implementation Research (CFIR). Results: The study identified 11 different AI innovations being introduced in Australian healthcare organisations, and a total of 12 implementers working on the implementation of these innovations were recruited to participate in the study. Factors influencing the implementation of AI innovations into routine practice were identified across all five domains of the CFIR framework, but the innovation and implementation process domains were emphasised the most in the data. Implementers faced many barriers integrating their innovations into practice including challenges with stakeholder engagement, data access and other technical hurdles, resourcing constrains and lengthy timeframes for implementation. Discussion: The number of Health AI solutions being implemented in routine practice in Australian healthcare organisations is small relative to the uptake of innovation seen in research and industry. This gap is likely a reflection of the length and complexity of the implementation process for Health AI solutions, and barriers that need to be overcome as part of this process.

BackgroundThe health sector collects a plethora of electronic health data via digital technologies, such as electronic health records (EHRs) and electronic medical records (EMRs). The primary use of EHRs includes supporting service delivery, providing data on patient information, and health care operations. Secondary uses of these systems can include quality improvement activities and research, and possibly inform policy. One underexplored secondary use of data from these systems is to enable health care professionals to understand their performance, reflect on their practice, and potentially support enhanced professional and workplace learning. There is growing interest and an increase in policies to focus on motivating the use of this type of data as part of mandatory Continuing Professional Development. Despite this, the design of EHRs is not conducive to the use of these systems for reflective practice, and there are few best practice guides for how to scaffold the use of these data for secondary use. ObjectiveThe aim of this project is to determine how EHRs and EMRs can be leveraged to enable formative performance feedback for health care professionals. The primary objective is to explore the use of these systems by health care professionals to further understand the current and possible future use of these records for reflective practice, performance feedback, and workplace learning. MethodsThe project will use a mixed methods design to enable a holistic picture of participant behaviors. Study data are being collected over 3 phases. Phase 1 consists of interviewing health care professionals and clinicians about their experiences with EHRs and EMRs. Phase 2 will involve surveying health care professionals about specific EHR features, and phase 3 will encompass workshopping discussions around EMR functionality and design with key informants. Participants for phases 1 and 2 will be a convenience sample of health care professionals who self-select and volunteer to participate in the study. Participants for phase 3 will consist of policy makers, representatives from peak bodies, technology vendors, health care professionals, and others. Data from phase 1 will be thematically analyzed to identify key features of EMR and EHR design for prioritization in phase 2. Phase 2 survey responses will be descriptively analyzed to understand the most important features in record design to support reflective practice. Phase 3 workshop data will be thematically analyzed to identify design insights for EHRs and EMRs that support professional learning. ResultsThe project is currently in its interview phase and is expected to publish results in mid-2025. ConclusionsThe project will generate new knowledge on the extent to which data collected by workplace technologies provide health care professionals with formative performance feedback. It will also develop a conceptual design for EHRs that supports health care professional learning, which could be leveraged by developers of these technologies in future implementations. International Registered Report Identifier (IRRID)DERR1-10.2196/66824

Australia has world-class education for healthcare professionals and is recognised for its strength in digital health research but is yet to fill some important gaps in training healthcare professionals in the safe implementation and use of digital technologies. In this case study, we bring together the perspectives of clinicians, health system leaders, and academics to guide efforts in establishing a digitally enabled workforce in Australia. Building on published evidence, our recommendations include leveraging on recent momentum, building strong partnerships with healthcare organisations, academia, and the digital health industry, and ultimately an expansion of a digitally enabled clinical informatics and digital health workforce.

Artificial Intelligence (AI) has great potential to improve healthcare, but implementation into routine practice remains a challenge. This study scoped the extent to which AI and Natural Language Processing (NLP) is being implemented into routine practice in Australian healthcare organisations. An environmental scan of publicly available data was undertaken to identify AI applications. Publicly available data consisted of news posts from Australian public healthcare organisations and conference proceedings from key research organisations. Two researchers reviewed and analysed posts related to AI applications to create a list of potential implementation case studies. The final list of AI applications was reviewed by a governance committee in order to identify any missing applications. One application was identified by the governance committee and subsequently added. The environmental scan identified eighteen AI applications, of which eleven met all eligibility criteria. Only one application included NLP. Twelve applications were included when the application identified by the governance committee was added to the list. Implementation of AI applications is spread across four broad categories of use: 1) Decision Support, 2) Monitoring Treatment Effectiveness, 3) Personalised Care and 4) Risk Prediction.

The health sector is highly digitized, which is enabling the collection of vast quantities of electronic data about health and well-being. These data are collected by a diverse array of information and communication technologies, including systems used by health care organizations, consumer and community sources such as information collected on the web, and passively collected data from technologies such as wearables and devices. Understanding the breadth of IT that collect these data and how it can be actioned is a challenge for the significant portion of the digital health workforce that interact with health data as part of their duties but are not for informatics experts. This viewpoint aims to present a taxonomy categorizing common information and communication technologies that collect electronic data. An initial classification of key information systems collecting electronic health data was undertaken via a rapid review of the literature. Subsequently, a purposeful search of the scholarly and gray literature was undertaken to extract key information about the systems within each category to generate definitions of the systems and describe the strengths and limitations of these systems.

BACKGROUND:Over two-thirds of people present to their primary care physician (or general practitioner; GP) as a first point of contact for mental health concerns. However, eating disorders (EDs) are often not identified in a primary care setting. A significant barrier to early detection and intervention is lack of primary care physician training in EDs; compounded by the significant time commitments required for training by already time-poor general practitioners. The aim of the current study was to pilot and evaluate a microlearning programme that can be delivered to general practitioners with high workloads to help support patients with, or at risk of, developing an ED. METHODS:Fifty-one Australian general practitioners aged between 25-to-60 years old were recruited. Participants completed a baseline questionnaire to ascertain their experience working in general practice and with EDs. Participants then completed an online programme consisting of a series of 10 case studies (vignettes) delivered over a 6-10 week period related to various facets of ED care. Following conclusion of the programme, participants were asked to complete an evaluative questionnaire related to the content of the programme; perceived knowledge, confidence, willingness-to-treat, skill change; and their overall experience of microlearning. RESULTS:All 51 GPs completed the programme and reached completion criteria for all vignettes, 40 of whom completed the programme evaluation. Participants indicated improved skill, confidence, willingness-to-treat, and knowledge following the completion of the pilot programme. Almost all (97.5%; n = 39) found microlearning to be an effective method to learn about EDs; with 87.5% (n = 35) of participants reporting they felt able to apply what was learnt in practice. Qualitative feedback highlighted the benefit of microlearning's flexibility to train general practitioners to work with complex health presentations, specifically EDs. CONCLUSIONS:Findings from the current study lend support to the use of microlearning in medical health professional training; notably around complex mental health concerns. Microlearning appears to be an acceptable and effective training method for GPs to learn about EDs. Given the significant time demands on GPs and the resulting challenges in designing appropriate training for this part of the workforce, this training method has promise. The pre-existing interest in EDs in the current study sample was high; future studies should sample more broadly to ensure that microlearning can be applied at scale.

BACKGROUND Medical students are often taught clinical reasoning implicitly, rather than through a formal curriculum. Like qualified health professionals, they engage in a wide range of information seeking and other practices as part of the clinical reasoning process. This increasingly includes seeking out information online and being informed by anecdotal information from social media or peer groups. The aim of this research was to investigate how anecdotes and icon arrays influenced the clinical reasoning process of medical students deciding to prescribe a hypothetical new drug. METHODS A cross-sectional survey design was used. The survey required participants to respond to six hypothetical clinical scenarios in which they were asked to prescribe a hypothetical drug “polypill” for a specific patient. The order of delivery of the six scenarios was randomised for each participant. In response to each scenario, participants indicated how effective they perceived each drug to be. The study received ethics approval from the University of Sydney Human Research Ethics Committee: Protocol No: 2019/001. All participants provided written informed consent before agreeing to participate in the study. RESULTS A total of 56 medical students fully completed the survey. Statistical analysis of the responses indicated that the icon array may be effective for highlighting how the polypill reduces CVD risk, reducing the impact of anecdotes on efficacy judgments. Without the icon array, both the positive and negative anecdotes made participants less willing to prescribe the polypill. CONCLUSIONS Medical student clinical reasoning processes appear to be influenced by anecdotal information and data visualisations. The extent of this influence is unclear, but there may be a need to actively educate students about the influence of these factors on their decision-making as they graduate into a world where they will be increasingly interacting with anecdotal information on social media and visualisations of electronic data.

Introduction: Online learning is an accessible method that enables medical practitioners to undertake training to develop new, and reinforce existing, knowledge and skills. Early career medical practitioners may find engaging in online learning particularly beneficial as they have a stronger motivation to refine knowledge and skills than their more senior peers. One under-explored mechanism to strengthen the delivery of online learning for medical practitioners is the use of clinical data to tailor learning so it is closely aligned with the individual health professional’s clinical practice. Methodology: This study aimed to evaluate the feasibility of personalising an online learning program for early career doctors working in oncology using electronic medical record (EMR) data. An online program was developed by clinical domain experts that could be triggered using pathology orders and/or results closely aligned to when the test was ordered in clinical practice. The program content was designed to cover three categories: (1) test ordering, (2) interpreting test results, and (3) patient management. Early career medical practitioners undergoing oncology training were recruited to participate in the study. The program was evaluated using metrics captured by the online learning platform, and a post-program survey. Results: All early career medical practitioners eligible to participate in the study consented to participate (n=5). It was feasible to personalise the online program using pathology ordering data. Further, analysis of survey responses indicated that personalising an online learning program using EMR data was acceptable to early career doctors and facilitated engagement with the course. Conclusion: Personalising an online learning program for early career medical practitioners in cancer care using electronic health-record data is both feasible and acceptable.

AimTo develop a priority set of quality indicators (QIs) for use by colorectal cancer (CRC) multidisciplinary teams (MDTs). MethodsThe review search strategy was executed in four databases from 2009-August 2019. Two reviewers screened abstracts/manuscripts. Candidate QIs and characteristics were extracted using a tailored abstraction tool and assessed for scientific soundness. To prioritize candidate indicators, a modified Delphi consensus process was conducted. Consensus was sought over two rounds; (1) multidisciplinary expert workshops to identify relevance to Australian CRC MDTs, and (2) an online survey to prioritize QIs by clinical importance. ResultsA total of 93 unique QIs were extracted from 118 studies and categorized into domains of care within the CRC patient pathway. Approximately half the QIs involved more than one discipline (52.7%). One-third of QIs related to surgery of primary CRC (31.2%). QIs on supportive care (6%) and neoadjuvant therapy (6%) were limited. In the Delphi Round 1, workshop participants (n = 12) assessed 93 QIs and produced consensus on retaining 49 QIs including six new QIs. In Round 2, survey participants (n = 44) rated QIs and prioritized a final 26 QIs across all domains of care and disciplines with a concordance level > 80%. Participants represented all MDT disciplines, predominantly surgical (32%), radiation (23%) and medical (20%) oncology, and nursing (18%), across six Australian states, with an even spread of experience level. ConclusionThis study identified a large number of existing CRC QIs and prioritized the most clinically relevant QIs for use by Australian MDTs to measure and monitor their performance.

Background: Patient portals have been shown to be beneficial for caregivers of children with chronic diseases, simplifying appointment-booking processes and improving communication between healthcare teams and families. However, there is low uptake of patient portals in this community. Despite some research into barriers of portal use among adults with chronic conditions, there is limited understanding of barriers to portal use among parents of children with chronic conditions. This study aimed to explore the perceived barriers to engaging with a patient portal among caregivers of children attending a tertiary children's hospital. Materials and methods: A qualitative study was conducted with a purposive sample of participants (n = 7) who were caregivers of patients at a tertiary children's hospital, and not using the patient portal. Semi-structured interviews were audio recorded, transcribed and deidentified before being thematically analysed using the Technology Acceptance Model (TAM). Results: Six themes emerged; four fit into the two TAM categories: perceived ease of use (technical difficulties and patient portal design) and perceived usefulness (existing system and process, and appropriateness for patient context). Two additional factors were found: awareness of the patient portal and attitudes towards technology. Conclusions: Participants identified barriers to patient portal uptake that broadly aligned with the TAM and with barriers affecting self-managing adults. The need for the portal to be easily integrated into caregivers’ busy schedules appeared to be more evident in this study than in those exploring factors affecting self-managing adults.