Richard Turner

Background: Length of stay (LoS) prediction is vital for optimizing surgical patient flow and resource allocation. Artificial intelligence (AI) and machine learning (ML) models may enhance LoS prediction, yet their validity and clinical utility in General Surgery remain unclear. Methods: Embase, Medline, Scopus, and Web of Science were systematically searched to June 2025. Studies employing AI or ML to predict LoS or discharge timing in adult General Surgery cohorts were included. Data on model development, predictor and target variables, performance, and validation were extracted. Risk of bias was appraised using PROBAST + AI. A checklist was developed to guide future LoS‐prediction research. Results: Nine of 4115 screened records met inclusion criteria. Classifier models with low overall risk of bias achieved moderate discrimination (AUROC 0.71–0.83), whereas regression models performed poorly (RMSE 4.6–12.7 days). Methodological weaknesses were frequent, including data leakage, temporally invalid predictors, biased patient selection, limited external validation, and incomplete performance reporting. Important predictors commonly included ASA score, age and operative duration; psychosocial and home‐support features were rarely considered. Outcome definitions and intended applications varied substantially, limiting direct comparison of performance. Models predicting discharge within short, dynamically reassessed time horizons may achieve greater accuracy, but offer limited capacity for longer‐term bed forecasting. No included study benchmarked ML or AI predictions against usual clinician LoS estimates. Low‐bias models were retrospective and generally internally validated, so performance in prospective or external cohorts remains unknown. More complex neural network models did not consistently outperform simpler methods. This review identifies recurring barriers to prospective clinical application for routine real‐world use. Conclusion: AI and ML models can moderately predict LoS for General Surgery patients, but generalizability is limited by methodological bias, incomplete reporting, and non‐standard outcome definitions. Current models appear more suited to hospital‐wide forecasting than bedside decision support. Future development should use prospectively available predictors, clinically meaningful outcomes, transparent performance reporting, and external validation. The proposed framework highlights strategies to avoid common methodological flaws in models published to date.

Background: There is compelling evidence that treating high‐grade squamous intraepithelial lesions (HSIL), the anal squamous cell cancer (ASCC) precursor, reduces the risk of developing ASCC. Using high‐resolution anoscopy (HRA), surgical excision, ablative and topical therapies are widely used to treat HSIL. Objectives: With ASCC incidence increasing and new guidelines on screening high‐risk patients, we sought to evaluate the efficacy and safety of HSIL treatment modalities. Data Sources: EMBASE, MEDLINE, and Cochrane databases were searched for English‐language, original studies specifically on HSIL or anal intraepithelial neoplasia (AIN) treatment from inception until December 31, 2025. Review Methods: Two reviewers independently reviewed abstracts and full‐text articles with a third reviewer for discrepancies. Studies were evaluated for efficacy, recurrence and safety. Results: Sixty‐four original papers were evaluated, of which five were randomised controlled trials (two directly comparing different modalities). Numbers in each study and overall quality were low, and extensive heterogeneity existed in methodology. Side effects were common, but significant complications only occurred with surgical excision and photodynamic therapy. Conclusions: There is insufficient evidence to clearly identify any gold standard HSIL treatment modality. Regardless of the modality, treatment overall is safe, but recurrence is high. Studies directly comparing modalities with standardisation of methodologies and outcome measures are required, especially in light of increasing incidence of ASCC and up‐take of HRA.

Academic difficulty in medical education has potentially negative impacts on students, faculties, and healthcare institutions. A traditional deficit view of the phenomenon offers a limited perspective that narrows student support opportunities and neglects intrapersonal strengths and successes arising from student adversity and struggle. This study explores the lived experiences of medical students and junior doctors who surmounted academic difficulties in medical school, to provide a positive lens on the transformative personal and professional potential of these struggles. A constructivist, phenomenological approach and integrated, strengths-based perspective shaped this qualitative study. Semi-structured participant interviews were conducted with participants at two Australian medical schools. In-depth interview transcripts were analysed using Reflexive Thematic Analysis and Interpretive Phenomenological Analysis to capture participants' in-depth, nuanced experiences. Eighteen medical students and eight junior doctors participated. An iterative analysis process identified three themes: 'In a dark place,' 'Rising out of the ashes,' and 'Becoming a better person and a better doctor.' The first theme captured the emotional toll of academic failure. The second theme described a turning point, where participants intentionally shifted their mindset towards a purpose and achievement possibilities that helped them re-engage with learning. This transformation activated intrapersonal and interpersonal skills, including increased confidence, self-efficacy, and peer support, contributing to academic recovery that did not appear to be related to gendered differences. In the third theme, participants reflected on the longer-term personal and professional benefits that emerged from adversity. Enhanced character traits such as perseverance, motivation, and empathy not only supported their academic success but also enriched their clinical practice. The study findings challenge the prevailing deficit narrative and demonstrate the transformative potential of academic struggles. With appropriate support and a strengths-based educational framework, students can overcome feelings of despair and low confidence, emerging as capable, compassionate, and resilient healthcare professionals.

Emergency Department overcrowding is a critical issue that compromises patient safety and operational efficiency, necessitating accurate demand forecasting for effective resource allocation. This study evaluates and compares three distinct predictive models: Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX), EXtreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) networks for forecasting daily ED arrivals over a seven-day horizon. Utilizing data from an Australian tertiary referral hospital spanning January 2017 to December 2021, this research distinguishes itself by decomposing demand into eight specific ward categories and stratifying patients by clinical complexity. To address data distortions caused by the COVID-19 pandemic, the study employs the Prophet model to generate synthetic counterfactual values for the anomalous period. Experimental results demonstrate that all three proposed models consistently outperform a seasonal naive baseline. XGBoost demonstrated the highest accuracy for predicting total daily admissions with a Mean Absolute Error of 6.63, while the statistical SARIMAX model proved marginally superior for forecasting major complexity cases with an MAE of 3.77. The study concludes that while these techniques successfully reproduce regular day-to-day patterns, they share a common limitation in underestimating sudden, infrequent surges in patient volume.

Objectives: To develop Australian anal cancer screening guidelines for people living with HIV. Methods: In 2023, ASHM Health assembled a committee to create guidelines, based on existing international guidelines and utilizing data from the Study of the Prevention of Anal Cancer (SPANC). SPANC provided Australian‐specific data on different screening methodologies for the detection of anal high‐grade squamous intraepithelial lesions. Results: The guidelines were released in March 2025. They recommend primary high‐risk human papillomavirus (HRHPV) testing with cytology triage for high‐resolution anoscopy. Gay, bisexual and other men who have sex with men (GBM) and trans‐women living with HIV should be offered screening from 35 years of age. Cis‐women, trans‐men and other cis‐men (not GBM) living with HIV should be offered screening from 45 years of age. All anal cancer screening should include annual digital ano‐rectal examination, examination of the peri‐anal region and a thorough medical history. Screening should be repeated every 3 years for those who screen negative. Screening should be discontinued, with shared decision‐making, at age 75 years and/or in individuals with two consecutive negative screening visits who are not currently sexually active. Conclusions: These are the first guidelines to recommend primary HRHPV testing with cytology triage as the screening modality. They will assist clinicians in identifying and screening people living with HIV at higher risk of anal cancer and will enable screening and referral of people living with HIV at highest risk for high‐resolution anoscopy, while screening and treatment services capacity are expanded in Australia.

Background: Emergency laparotomy is associated with high rates of morbidity and mortality. Accurate, individualised risk prediction models can be used to improve shared decision‐making, discharge planning and enhance patient flow. This study used the ANZELA‐QI database to apply novel machine learning models to stratify the risk of adverse outcomes in patients undergoing emergency laparotomy. Methods: Data were extracted from the ANZELA‐QI database. Three machine learning techniques were employed: logistic regression, XGBoost and random forest. Selected clinical and demographic predictor variables were used to train and test the machine learning models in the prediction of mortality, post‐operative ICU admission, non‐return home and prolonged hospitalisation. Results: A total of 8615 cases from 35 hospitals was available from the ANZELA‐QI database. Complete data were available in 5195 cases for mortality, ICU admission and non‐return home outcomes, and 4469 cases for length of stay. In this cohort 2175 (42%) were admitted to ICU, 601 (12%) died, 1483 (29%) did not return to usual place of residence and 2983 (67%) were admitted for over 1‐week post‐operatively. Machine learning models demonstrated the greatest accuracy in the prediction of ICU admission and length of stay. The sensitivity and specificity for ICU admission were 0.7 and 0.74, respectively. For admission longer than one week, the overall accuracy was 75%. Conclusion: This study applied novel machine learning programs to the ANZELA‐QI database to develop risk stratification models for adverse outcomes in patients undergoing emergency laparotomy. The results showed high accuracy for the prediction of prolonged length of stay and post‐operative ICU admission.

We study the patient assignment scheduling (PAS) problem in a random environment that arises in the management of patient flow in hospital systems, due to the stochastic nature of the arrivals as well as the length of stay (LoS) distribution. At the start of each time period, emergency patients in the waiting area of a hospital system need to be admitted to relevant wards. Decisions may involve allocation to less suitable wards, or transfers of the existing inpatients to accommodate higher priority cases when wards are at full capacity. However, the LoS for patients in non-primary wards may increase, potentially leading to long-term congestion. To assist with decision-making in this PAS problem, we construct a discrete-time Markov decision process over an infinite horizon, with multiple patient types and multiple wards. Since the instances of realistic size of this problem are not easy to solve, we develop numerical methods based on approximate dynamic programming. We demonstrate the application potential of our methodology under practical considerations with numerical examples, using parameters obtained from data at a tertiary referral hospital in Australia. We gain valuable insights, such as the number of patients in non-primary wards, the number of transferred patients, and the number of patients redirected to other facilities, under different policies that enhance the system’s performance. This approach allows for more realistic assumptions and can also help determine the appropriate size of wards for different patient types within the hospital system.

Background: The experiences of gay, bisexual, and other men who have sex with men (GBMSM) receiving anorectal surgery are not well understood, including their interaction with surgeons and the impact of surgery on receptive anal intercourse. This pilot exploratory study aimed to understand the experiences of GBMSM who recently received anorectal surgery. Methods: This mixed methods study utilized surveys and semi‐structured interviews with gay and bisexual men who received operative treatment by a general or colorectal surgeon for a benign anorectal condition within Australia. Descriptive and qualitative thematic analysis was employed to assess the impact of anorectal surgery and the experience of the surgical encounter. Results: Participants reported impaired sexual function and reduced overall well‐being after anorectal surgery. They expected surgeons to inquire about their sexual practices and provide additional support and post‐operative education, which most often did not occur. Participants perceived their surgeon to lack understanding regarding their sexual orientation and well‐being. Conclusions: Anorectal surgery has a significant impact on the sexual and overall well‐being of GBMSM. Surgeons should inquire about sexual practices and offer adequate counselling about sexual practices accordingly. Surgeons may require education and training to help improve outcomes for GBMSM patients.

Background: Poor patient flow or patient movement through a healthcare organisation can lead to adverse outcomes for patients and organisational inefficiency. Many hospitals have addressed suboptimal patient flow by increasing resources, such as bed stock and staffing; however, this is an unsustainable approach. In determining the nature of poor patient flow issues, it is important to collect data from healthcare professionals who manage patient flow daily. Doing so provides insights into the current state of patient flow management in its entirety, whilst also helping with the development of sustainable solutions. Methods: Thirteen semi-structured interviews were conducted with healthcare professionals who were directly involved with patient flow at a referring hospital in Tasmania, Australia. Results: Using a thematic analysis method, four major themes were developed. The first major theme was: ‘managing patient flow’ which centered around known and unknown demands on bed availability. The second theme, ‘communication for decisions’, highlighted the essential role of communication in maintaining patient flow. The third theme, ‘tools as enablers and barriers, relates to the number of software programs which can both help and hinder patient flow. The final theme of ‘increasing complexity’ is related to an apparent trend towards greater numbers of patients requiring care of an increasingly specialised nature. Conclusions: The findings of this study provide great insights into patient flow issues, with potential solutions identified to address them.