Clinical Informatics needs to be a competency for Intensive care training.

Clinical informatics is a cornerstone in the delivery of safe and quality critical care in Australia and New Zealand. Recent advances in the field of clinical informatics, including new technologies that digitise healthcare data, improved methods of capturing and storing these data, as well as innovative analytic methods using machine learning and artificial intelligence, present exciting new opportunities to leverage data for improving the delivery of critical care and patient outcomes. However, ICU training in Australian and New Zealand does not adequately address capability gaps in this area, potentially leaving future intensivists without the necessary skills to provide leadership in the application of informatics within ICUs. This highlights the need to examine how competency in clinical informatics can be incorporated into ICU training, potentially through a range of activities such as curriculum redesign, the formal project, and workshops or datathons. Further work to identify relevant informatics competencies and methods to develop and assess these competencies within ICU training is needed.

Mortality among people admitted to Australian intensive care units for reasons other than COVID-19 during the COVID-19 pandemic: a retrospective cohort study.

OBJECTIVE: To investigate in-hospital mortality among people admitted to Australian intensive care units (ICUs) with conditions other than coronavirus disease 2019 (COVID-19) during the COVID-19 pandemic. DESIGN: National, multicentre, retrospective cohort study; analysis of data in the Australian and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation (ANZICS CORE) Adult Patient Database. SETTING, PARTICIPANTS: Adults (16 years or older) without COVID-19 admitted to Australian ICUs, 1 January 2016 - 30 June 2022. MAIN OUTCOME MEASURES: All-cause in-hospital mortality, unadjusted and relative to the January 2016 value, adjusted for illness severity (Australian and New Zealand Risk of Death [ANZROD] and hospital type), with ICU as a random effect. Points of change in mortality trends (breakpoints) were identified by segmental regression analysis. RESULTS: Data for 950 489 eligible admissions to 186 ICUs were available. In-hospital mortality declined steadily from January 2016 to March 2021 by 0.3% per month (P < 0.001; March 2021 v January 2016: adjusted odds ratio [aOR], 0.70; 95% confidence interval [CI], 0.62-0.80), but rose by 1.4% per month during March 2021 - June 2022 (P < 0.001; June 2022 v January 2016: aOR, 1.03; 95% CI, 0.90-1.17). The rise in mortality continued after the number of COVID-19-related ICU admissions had declined; mortality increased in jurisdictions with lower as well as in those with higher numbers of COVID-19-related ICU admissions. CONCLUSION: The rise in in-hospital mortality among people admitted to Australian ICUs with conditions other than COVID-19 from March 2021 reversed the improvement of the preceding five years. Changes to health service delivery during the pandemic and their consequences should be investigated further.

Pre-medical emergency team activations - Patient characteristics, outcomes and predictors of deterioration.

BACKGROUND: Pre-medical emergency team (MET) calls are an increasingly common tier of Rapid Response Systems, but the epidemiology of patients who trigger a Pre-MET is not well understoof. OBJECTIVES: This study aims to examine the epidemiology and outcomes of patients who trigger a pre-MET activation and identify risk factors for further deterioration. METHODS: This is a retrospective cohort study of pre-MET activations in a university-affiliated metropolitan hospital in Australia, between 13 April 2021 and 4 October 2021. A multivariable regression model was used to identify variables associated with further deterioration, defined as a MET call or Code Blue within 24 h of pre-MET activation. RESULTS: From a total of 39 664 admissions, there were 7823 pre-MET activations (197.2 per 1000 admissions). Compared to inpatients that did not trigger a pre-MET, the patients were older (68.8 vs 53.8 years, p < 0.001), were more likely to be male (51.0 vs 47.6%, p < 0.001), had an emergency admission (70.1% vs 53.3%, p < 0.001), and were under a medical specialty (63.7 vs 54.9%, p < 0.001). They had a longer hospital length of stay (5.6 vs 0.4 d, p < 0.001) and higher in-hospital mortality (3.4% vs 1.0%, p < 0.001). A pre-MET was more likely to progress to a MET call or Code Blue if it was activated for fever, cardiovascular, neurological, renal, or respiratory criteria (p < 0.001), if the patient was under a paediatric team (p = 0.018), or if there had been a MET call or Code Blue prior to the pre-MET activation (p < 0.001). CONCLUSION: Pre-MET activations affect almost 20% of hospital admissions and are associated with a higher risk of mortality. Certain characteristics may predict further deterioration to a MET call or Code Blue, suggesting the potential for early intervention via clinical decision support systems.

Rapid response teams: A review of data collection practice in Victoria, Australia.

BACKGROUND: Successful implementation of rapid response teams (RRTs) requires robust data collection and reporting processes. However, there is variation in data collection practice in RRT activity between hospitals, leading to difficulties in quality review, collaboration and research. Although a standardised RRT data collection model would be a key step in addressing this, there is uncertainty regarding existing RRT data collection practice across Victoria. OBJECTIVES: This study was endorsed by Safer Care Victoria (SCV) to evaluate existing RRT data collection practice across Victoria. METHODOLOGY: Between 2016 and 2017, hospitals in Victoria were surveyed on data collection practice for RRT activity. Data collected included the fields populated and the mode of data collection. Qualitative content analysis, utilising a blend of pre-existing frameworks and ground-up data-driven approaches for derivation of a coding frame, was used to identify common categories. Validation of the analysis and results was performed by consultation with stakeholder groups. RESULTS: Twenty five hospitals across 18 health networks contributed data, with a mix of tertiary (9/25), metropolitan (11/25) and rural (5/25) hospitals. Seven hospitals collected data electronically, the remainder using paper with abstraction to electronic spreadsheets. None of the hospitals linked with existing hospital data systems to reduce manual data entry requirements. Dataset size varied from 16 to 97 variables but demonstrated content consistency and could be mapped onto seven key categories (comprising antecedent, afferent, event, post-event, audit, context and patient data). Within each category, there was substantial variation in terminology and variable values, but consistency in the collection of a certain subset of variables. CONCLUSION: Despite broad variation in data collection practice, existing datasets can be readily mapped into seven key categories, with the consistent collection of a subset of variables within each category. These variables could inform the development of a minimum dataset within a standardised RRT reporting framework and accommodate data submission from hospitals of differing resource bases.

Virtual visiting in the intensive care unit: implementation, utilisation and provider acceptance in an Australian metropolitan health service.

We describe the design and implementation of an intensive care unit (ICU) virtual visiting program in a metropolitan ICU in Melbourne, Victoria, Australia, to examine patterns of use, and describe clinician acceptance of this technology. This was a mixed-methods study, comprising a retrospective analysis of virtual visits from 18 August to 30 September 2020. Patterns of utilisation included duration and time of visits, as well as bandwidth used. A post-implementation survey on a Virtual Visiting program based on the technology acceptance model was sent to clinicians; results were reported on separate scales for usefulness and usability. Publicly available telecommunication solutions were unsuitable for virtual visiting, whereas dedicated telehealth solutions needed modification to improve accessibility by patients and families. During the study period, 69 virtual visits were made with a median length of 10 min (range 1-80 min). A total of 72.5% of calls were made during office hours (09:00-17:00 h), with the latest occurring at approximately 21:30 h. Virtual visits required a mean bandwidth of 1224 kbps (download) and 940 kbps (upload), and consumed 0.7 GB (range 0.0-7.0 GB) and 0.5 GB (range 0.0-6.7 GB) of download and upload data. Clinicians reported a mean score of 2 (range 1-4) for perceived usefulness and 3 (range 1-6) for the perceived ease of use. Virtual visiting is a feasible alternative in the ICU, with good acceptance by clinicians. Challenges include safety and usability of videoconferencing platforms, as well as bandwidth requirements. Future health service design should consider support for dedicated virtual visiting solutions, as well as ensuring adequate bandwidth capabilities for this service. Further studies are needed to assess patient and family acceptability of this technology.

Impact of lockdowns on critical care service demand in a metropolitan hospital in Melbourne, Australia.

OBJECTIVE: There is a growing recognition of the impact of lockdowns on non-COVID-19 demand for critical care services. While a reduction in demand has been postulated, there remains a paucity of quantitative data on the extent and nature of this reduction. The present study aims to quantify the impact of lockdown on critical care services, namely ED, intensive care unit (ICU), medical emergency team (MET) and emergency theatre (ET) demand, during the lockdown in Victoria, Australia. METHODS: This is a single-centred, retrospective observational study on critical service demand, comparing activity levels during the lockdown (31 March to 27 October 2020) with the matched time period from 1 year prior. RESULTS: There was a reduction in presentations to ED (27.2%), MET calls (27.4%), ICU patient episodes (14.5%) and ET bookings (5.8%). There was an unexpected increase in ICU admissions for metabolic diagnoses, comprising drug overdoses and diabetic ketoacidosis, and a reduction in respiratory ICU admissions. There was a reduction across all ED triage categories, which included triage 1 and 2 patients, indicating a reduction even in life-threatening and emergency presentations. CONCLUSION: Lockdowns lead to a significant reduction in ICU, MET call and ED demand, and to a lesser extent ET demand. This pattern should be considered in surge capacity and workforce redeployment planning. There are also impacts on public health epidemiology, with potential adverse consequences on mental health and chronic disease management. Further research on the impact of lockdowns on long-term disease outcomes is needed.