The Adelaide Score: prospective implementation of an artificial intelligence system to improve hospital and cost efficiency.

Background: The Adelaide Score is an artificial intelligence system that integrates objective vital signs and laboratory tests to predict likelihood of hospital discharge.

Methods: A prospective implementation trial was conducted at the Lyell McEwin Hospital in South Australia. The Adelaide Score was added to existing human, artificial intelligence, and other technological infrastructure for the first 28 days of April 2024 (intervention), and outcomes were compared using parametric, non-parametric and health economic analyses, to those in the first 28 days of April 2023 (control).

Copying in medical documentation: developing an evidence-based approach.

Background: Electronic medical records (EMRs) provide multiple efficiencies in communication to clinicians. The ability to copy and paste text in an EMR can be useful; however, it also conveys a risk of inaccurate documentation. Studies in international settings have described such overuse of copying to result in 'note bloat', with the dilution of relevant clinical information and potential clinical detriment.

Medication shortage behaviour change with multidisciplinary clinician-designed digital notification intervention.

Objectives: To evaluate the effect of a clinician-designed digital notification system on the use of intravenous paracetamol during a medication shortage.

Methods: An in-house digital notification platform was designed through multidisciplinary collaboration. A 4-week pre- and post-implementation methodology was employed to evaluate the effect of the intervention.

Rule-based clinician-developed programmes can facilitate haemodialysis clinical workflows.

There are routine hospital workflows that are not addressed by certain institutional electronic medical records, including the detection of patients requiring haemodialysis who are admitted under non-nephrology services. In this study, the feasibility and performance of a clinician-developed automated haemodialysis patient finder was evaluated. The programme ran with zero downtime for 6 months and had zero false negatives or false positives.

Large language models can effectively extract stroke and reperfusion audit data from medical free-text discharge summaries.

Introduction: Audits are an integral part of effective modern healthcare. The collection of data for audits can be resource intensive. Large language models (LLM) may be able to assist.

The future is bright: artificial intelligence for trainee medical officers in Australia and New Zealand.

Given their frontline role in Australia and Aotearoa New Zealand (ANZ) healthcare, trainee medical officers (TMOs) will play a crucial role in the development and use of artificial intelligence (AI) for clinical care, ongoing medical education and research. As 'digital natives', particularly those with technical expertise in AI, TMOs should also be leaders in informing the safe uptake and governance of AI within ANZ healthcare as they have a practical understanding of its associated risks and benefits. However, this is only possible if a culture of broad collaboration is instilled while the use of AI in ANZ is still in its initial phase.

Comfort feeding in hospitalised people with dementia: a retrospective study of survival following comfort feeding recommendations.

Objectives: Persistent and significant swallowing impairment can occur in individuals with dementia. Determining prognosis and establishing realistic goals of care in this population is complex and comfort feeding may be recommended. This study aimed to establish evidence relating to patient outcomes following recommendation of comfort feeding to aid informed decision making.

Push or pull? Digital notification platform implementation reduces dysglycaemia.

Pushing selected information to clinicians, as opposed to the traditional method of clinicians pulling information from an electronic medical record, has the potential to improve care. A digital notification platform was designed by clinicians and implemented in a tertiary hospital to flag dysglycaemia. There were 112 patients included in the study, and the post-implementation group demonstrated lower rates of dysglycaemia (2.

Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive weekend interprofessional flow team (TAILORED-SWIFT).

Weekend discharges occur less frequently than discharges on weekdays, contributing to hospital congestion. Artificial intelligence algorithms have previously been derived to predict which patients are nearing discharge based upon ward round notes. In this implementation study, such an artificial intelligence algorithm was coupled with a multidisciplinary discharge facilitation team on weekend shifts.

Vital sign measurements demonstrate terminal digit bias and boundary effects.

Objective: The measurement and recording of vital signs may be impacted by biases, including preferences for even and round numbers. However, other biases, such as variation due to defined numerical boundaries (also known as boundary effects), may be present in vital signs data and have not yet been investigated in a medical setting. We aimed to assess vital signs data for such biases.

Systolic blood pressure levels and mortality in Australian medical inpatients.

The epidemiology of elevations in blood pressure is incompletely characterized, particularly in Australia. Given the lack of evidence regarding the frequency and the optimal management of in-hospital hypertension, the authors performed a multicenter retrospective cohort study of consecutive medical admissions in South Australia over a 2-year period to investigate systolic blood pressure levels and their association with in-hospital mortality. Among 16 896 inpatients, 76% had at least one systolic blood pressure reading of ≥140 mmHg and 11.

The use of natural language processing in detecting and predicting falls within the healthcare setting: a systematic review.

Falls are a common problem associated with significant morbidity, mortality, and economic costs. Current fall prevention policies in local healthcare settings are often guided by information provided by fall risk assessment tools, incident reporting, and coding data. This review was conducted with the aim of identifying studies which utilized natural language processing (NLP) for the automated detection and prediction of falls in the healthcare setting.

Beware of little expenses: Low-value endocrinological blood tests in geriatric medical inpatients.

Objectives: Blood tests for endocrinological derangements are frequently requested in general medical inpatients, in particular those in the older age group. Interrogation of these tests may present opportunities for healthcare savings.

Methods: This multicentre retrospective study over a 2.

Why do we evaluate 30-day readmissions in general medicine? A historical perspective and contemporary data.

Reducing preventable readmissions is important to help manage current strains on healthcare systems. The metric of 30-day readmissions is commonly cited in discussions regarding this topic. While such thresholds have contemporary funding implications, the rationale for individual cut-off points is partially historical in nature.

Falls and fractures in the acute care setting: A retrospective cohort study.

Objectives: Falls with fracture in hospitalised patients remain a common occurrence with significant morbidity and mortality. Our objectives were to determine the characteristics of patients who suffer falls with fractures in hospital, and to examine whether outcomes in this cohort differ from those of patients who fall without sustaining a fracture.

Methods: Coding data pertaining to a 6-year period (2012-2017) were interrogated.

Antibiotic Prescribing Practices Differ between Patients with Penicillin Intolerance and Penicillin Allergy Labels.

Introduction: Penicillin allergy labels are common. However, many penicillin allergy labels have been applied incorrectly and in fact represent penicillin intolerance. Patients with penicillin intolerance can receive penicillin antibiotics.

Designing the ultimate general medicine model.

The ideal model of care in general medicine remains elusive, perhaps because of interhospital heterogeneity in patient population and resource allocation to both general medicine and the medical subspecialties. We explain why successful interventions at one site are not easily applied in another and recommend a nationally coordinated examination of the best general medicine departments' methods of clinical practice improvement.

Daily estimates of individual discharge likelihood with deep learning natural language processing in general medicine: a prospective and external validation study.

Machine learning, in particular deep learning, may be able to assist in the prediction of the length of stay and timing of discharge for individual patients. Artificial neural networks applied to medical text have previously shown promise in this area. In this study, a previously derived artificial neural network was applied to prospective and external validation datasets.

Mixed-data deep learning in repeated predictions of general medicine length of stay: a derivation study.

The accurate prediction of likely discharges and estimates of length of stay (LOS) aid in effective hospital administration and help to prevent access block. Machine learning (ML) may be able to help with these tasks. For consecutive patients admitted under General Medicine at the Royal Adelaide Hospital over an 8-month period, daily ward round notes and relevant discrete data fields were collected from the electronic medical record.

Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study.

Length of stay (LOS) and discharge destination predictions are key parts of the discharge planning process for general medical hospital inpatients. It is possible that machine learning, using natural language processing, may be able to assist with accurate LOS and discharge destination prediction for this patient group. Emergency department triage and doctor notes were retrospectively collected on consecutive general medical and acute medical unit admissions to a single tertiary hospital from a 2-month period in 2019.

Preparing the future workforce for healthcare in Australia.

Medical education fails to prepare young doctors for the nature of the work they will encounter. Doctors face a rapidly changing medical landscape, which relies more and more upon interprofessional collaboration to optimise patient outcomes and upon non-clinical skills to provide care efficiently and cost effectively. The current response to change is a reactive and resource-intensive effort, where established doctors are directed towards new ways of working.