Achieving equity: patient demographics and outcomes after surgical and non-surgical procedures in South Australia, 2022.

BACKGROUND: Although modern Australian healthcare systems provide patient-centred care, the ability to predict and prevent suboptimal post-procedural outcomes based on patient demographics at admission may improve health equity. This study aimed to identify patient demographic characteristics that might predict disparities in mortality, readmission, and discharge outcomes after either an operative or non-operative procedural hospital admission. METHODS: This retrospective cohort study included all surgical and non-surgical procedural admissions at three of the four major metropolitan public hospitals in South Australia in 2022. Multivariable logistic regression, with backwards selection, evaluated association between patient demographic characteristics and outcomes up to 90 days post-procedurally. RESULTS: 40 882 admissions were included. Increased likelihood of all-cause, post-procedure mortality in-hospital, at 30 days, and 90 days, were significantly associated with increased age (P < 0.001), increased comorbidity burden (P < 0.001), an emergency admission (P < 0.001), and male sex (P = 0.046, P = 0.03, P < 0.001, respectively). Identification as ATSI (P < 0.001) and being born in Australia (P = 0.03, P = 0.001, respectively) were associated with an increased likelihood of 30-day hospital readmission and decreased likelihood of discharge directly home, as was increased comorbidity burden (P < 0.001) and emergency admission (P < 0.001). Being married (P < 0.001) and male sex (P = 0.003) were predictive of an increased likelihood of discharging directly home; in contrast to increased age (P < 0.001) which was predictive of decreased likelihood of this occurring. CONCLUSIONS: This study characterized several associations between patient demographic factors present on admission and outcomes after surgical and non-surgical procedures, that can be integrated within patient flow pathways through the Australian healthcare system to improve healthcare equity.

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 2.5-year period examined the frequency with which three common endocrinological investigations [thyroid stimulating hormone (TSH), HbA1c, 25-hydroxy Vitamin D3] were performed in this population, including the frequency of duplicate tests within a given admission, and the frequency of abnormal test results. The Medicare Benefits Schedule was used to calculate the cost associated with these tests. RESULTS: There were 28,564 individual admissions included in the study. Individuals ≥65 years old were the majority of inpatients in whom the selected tests were performed (80% of tests). TSH was performed in 6730 admissions, HbA1c was performed in 2259 admissions, and vitamin D levels were performed in 5632 admissions. There were 6114 vitamin D tests performed during the study period, of which 2911 (48%) returned outside the normal range. The cost associated with vitamin D level testing was $183,726. Over the study period, 8% of tests for TSH, HbA1c, and Vitamin D were duplicates (where a second test was performed within a single admission), which was associated with a cost of $32,134. CONCLUSIONS: Tests for common endocrinological abnormalities are associated with significant healthcare costs. Avenues by which future savings may be pursued include the investigation of strategies to reduce duplicate ordering and examining the rationale and guidelines associated with ordering tests such as vitamin D levels.

The Efficacy, Adverse Effects and Economic Implications of Oral Versus Intravenous Methylprednisolone for the Treatment of Optic Neuritis: A Systematic Review.

INTRODUCTION: Optic neuritis may occur in a variety of conditions, including as a manifestation of multiple sclerosis. Despite significant research into the efficacy of corticosteroids as a first-line treatment, the optimal route of administration has not been well defined. This review aims to explore the efficacy, adverse effects and economic implications of using oral versus intravenous methylprednisolone to treat acute optic neuritis. METHODS: A systematic search of the databases PubMed/MEDLINE, Embase and CENTRAL was performed to July 2022, prior to data collection and risk of bias analysis in accordance with the PRISMA guidelines. RESULTS: Six articles fulfilled the inclusion criteria. The results showed that in the treatment of acute optic neuritis, oral methylprednisolone has a non-inferior efficacy and adverse effect profile in comparison to intravenous methylprednisolone. In a cost analysis, oral methylprednisolone to be more cost-effective than intravenous methylprednisolone. CONCLUSIONS: Oral methylprednisolone has comparable efficacy and adverse effect profiles to intravenous methylprednisolone for the treatment of optic neuritis. The analysis suggests oral administration is more cost-effective than intravenous administration; however, further analyses of the formal cost-benefit ratio are required.

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. The databases Ovid Medline, Ovid Embase, Ovid Emcare, PubMed, CINAHL, IEEE Xplore, and Ei Compendex were searched from 2012 until April 2023. Retrospective derivation, validation, and implementation studies wherein patients experienced falls within a healthcare setting were identified for inclusion. The initial search yielded 2611 publications for title and abstract screening. Full-text screening was conducted on 105 publications, resulting in 26 unique studies that underwent qualitative analyses. Studies applied NLP towards falls risk factor identification, known falls detection, future falls prediction, and falls severity stratification with reasonable success. The NLP pipeline was reviewed in detail between studies and models utilizing rule-based, machine learning (ML), deep learning (DL), and hybrid approaches were examined. With a growing literature surrounding falls prediction in both inpatient and outpatient environments, the absence of studies examining the impact of these models on patient and system outcomes highlights the need for further implementation studies. Through an exploration of the application of NLP techniques, it may be possible to develop models with higher performance in automated falls prediction and detection.

Ophthalmology Operation Note Encoding with Open-Source Machine Learning and Natural Language Processing.

INTRODUCTION: Accurate assignment of procedural codes has important medico-legal, academic, and economic purposes for healthcare providers. Procedural coding requires accurate documentation and exhaustive manual labour to interpret complex operation notes. Ophthalmology operation notes are highly specialised making the process time-consuming and challenging to implement. This study aimed to develop natural language processing (NLP) models trained by medical professionals to assign procedural codes based on the surgical report. The automation and accuracy of these models can reduce burden on healthcare providers and generate reimbursements that reflect the operation performed. METHODS: A retrospective analysis of ophthalmological operation notes from two metropolitan hospitals over a 12-month period was conducted. Procedural codes according to the Medicare Benefits Schedule (MBS) were applied. XGBoost, decision tree, Bidirectional Encoder Representations from Transformers (BERT) and logistic regression models were developed for classification experiments. Experiments involved both multi-label and binary classification, and the best performing model was used on the holdout test dataset. RESULTS: There were 1,000 operation notes included in the study. Following manual review, the five most common procedures were cataract surgery (374 cases), vitrectomy (298 cases), laser therapy (149 cases), trabeculectomy (56 cases), and intravitreal injections (49 cases). Across the entire dataset, current coding was correct in 53.9% of cases. The BERT model had the highest classification accuracy (88.0%) in the multi-label classification on these five procedures. The total reimbursement achieved by the machine learning algorithm was $184,689.45 ($923.45 per case) compared with the gold standard of $214,527.50 ($1,072.64 per case). CONCLUSION: Our study demonstrates accurate classification of ophthalmic operation notes into MBS coding categories with NLP technology. Combining human and machine-led approaches involves using NLP to screen operation notes to code procedures, with human review for further scrutiny. This technology can allow the assignment of correct MBS codes with greater accuracy. Further research and application in this area can facilitate accurate logging of unit activity, leading to reimbursements for healthcare providers. Increased accuracy of procedural coding can play an important role in training and education, study of disease epidemiology and improve research ways to optimise patient outcomes.

The Icepack Test in the Diagnosis of Myasthenia Gravis with Ocular Features: A Systematic Review of Diagnostic Accuracy, Technique, and Economic Utility.

BACKGROUND: The diagnosis of myasthenia gravis (MG) may be challenging and require multiple specialised testing modalities. Accessing these investigations can involve significant waiting time and costs. The bedside icepack test (IPT) has been proposed to assist with the diagnosis of MG with ocular features, and may prove an economically viable; however, there have been there is heterogeneity in the literature evaluating the IPT. OBJECTIVES: A systematic review was performed examining the accuracy, described techniques, and economic implications of the IPT for the diagnosis of MG with ocular features. METHOD: The databases EMBASE, PubMed, and the Cochrane Library were searched from inception to July 2022. The systematic review adhered to PRISMA guidelines. Eligibility determination was undertaken with a standardised form using appropriate inclusion criteria. The Cochrane risk of bias assessment tool for diagnostic test accuracy was employed to evaluate studies that presented the diagnostic performance of the IPT. The Johanna Briggs Institute Critical Appraisal Checklist for Economic Evaluations was used for the assessment of studies presenting economic evaluations of the IPT. RESULTS: 20 articles met the specified criteria and included a total of 1264 participants. The IPT had a sensitivity ranging from 38.5% to 100%. Specificity was found to be > 95% in six studies. Excluding two outlier results of 25% and 31.3%, the lowest specificity recorded was 62.5%. The most commonly described method of evaluating the IPT involved applying ice to both eyelids and using a >2 mm change as a threshold for a positive test (evaluated with a ruler). There were no adverse effects described with the IPT. There were no studies that formally examined the economic implications of the IPT. CONCLUSIONS: The IPT is a well-tolerated and readily available diagnostic tool that has an important role in the evaluation of possible MG with ocular features in specific contexts. Despite limited economic evaluation of this test, it is likely the use of the IPT may result in significant financial and time savings.

Health Economic Implications of Artificial Intelligence Implementation for Ophthalmology in Australia: A Systematic Review.

PURPOSE: The health care industry is an inherently resource-intense sector. Emerging technologies such as artificial intelligence (AI) are at the forefront of advancements in health care. The health economic implications of this technology have not been clearly established and represent a substantial barrier to adoption both in Australia and globally. This review aims to determine the health economic impact of implementing AI to ophthalmology in Australia. METHODS: A systematic search of the databases PubMed/MEDLINE, EMBASE, and CENTRAL was conducted to March 2022, before data collection and risk of bias analysis in accordance with preferred reporting items for systematic ceviews and meta-analyses 2020 guidelines (PROSPERO number CRD42022325511). Included were full-text primary research articles analyzing a population of patients who have or are being evaluated for an ophthalmological diagnosis, using a health economic assessment system to assess the cost-effectiveness of AI. RESULTS: Seven articles were identified for inclusion. Economic viability was defined as direct cost to the patient that is equal to or less than costs incurred with human clinician assessment. Despite the lack of Australia-specific data, foreign analyses overwhelmingly showed that AI is just as economically viable, if not more so, than traditional human screening programs while maintaining comparable clinical effectiveness. This evidence was largely in the setting of diabetic retinopathy screening. CONCLUSIONS: Primary Australian research is needed to accurately analyze the health economic implications of implementing AI on a large scale. Further research is also required to analyze the economic feasibility of adoption of AI technology in other areas of ophthalmology, such as glaucoma and cataract screening.

Effect of medical student debt on mental health, academic performance and specialty choice: a systematic review.

OBJECTIVES: With the high and rising total cost of medical school, medical student debt is an increasing concern for medical students and graduates, with significant potential to impact the well-being of physicians and their patients. We hypothesised that medical student debt levels would be negatively correlated with mental health and academic performance, and would influence career direction (ie, medical specialty choice). DESIGN: We performed a systematic literature review to identify articles that assessed associations between medical student mental health, academic performance, specialty choice and debt. The databases PubMed, Medline, Embase, Scopus and PsycINFO were searched on 12 April 2017, for combinations of the medical subject headings Medical Student and Debt as search terms. Updates were incorporated on 24 April 2019. RESULTS: 678 articles were identified, of which 52 met the inclusion criteria after being reviewed in full text. The majority of studies were conducted in the USA with some from Canada, New Zealand, Scotland and Australia. The most heavily researched aspect was the association between medical student debt and specialty choice, with the majority of studies finding that medical student debt was associated with pursuit of higher paying specialties. In addition, reported levels of financial stress were high among medical students, and correlated with debt. Finally, debt was also shown to be associated with poorer academic performance. CONCLUSIONS: Medical student debt levels are negatively associated with mental well-being and academic outcomes, and high debt is likely to drive students towards choosing higher paying specialties. Additional prospective studies may be warranted, to better understand how educational debt loads are affecting the well-being, career preparation and career choices of physicians-in-training, which may in turn impact the quality of care provided to their current and future patients.