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

Author

Stretton, Brandon, Booth, Andrew E. C., Satheakeerthy, Shrirajh, Howson, Sarah, Evans, Shaun, Kovoor, Joshua, Akram, Waqas, McNeil, Keith, Hopkins, Ashley, Zeitz, Kathryn, Leslie, Alasdair, Psaltis, Peter, Gupta, Aashray, Tan, Sheryn, Teo, Melissa, Vanlint, Andrew, Chan, Weng Onn, Zannettino, Andrew, O’Callaghan, Patrick G., Maddison, John, Gluck, Samuel, Gilbert, Toby, and Bacchi, Stephen

Published
2024
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4. Near-death experiences after cardiac arrest: a scoping review

Author

Kovoor, Joshua G., Santhosh, Sanjana, Stretton, Brandon, Tan, Sheryn, Gouldooz, Hasti, Moorthy, Sylviya, Pietris, James, Hannemann, Christopher, Yu, Long Kiu, Johnson, Rhys, Reddi, Benjamin A., Gupta, Aashray K., Wagner, Morganne, Page, Gregory J., Kovoor, Pramesh, Bastiampillai, Tarun, Maddocks, Ian, Perry, Seth W., Wong, Ma-Li, Licinio, Julio, and Bacchi, Stephen

Published
2024
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7. Sociocultural and Demographic Factors Predict Readmissions for General Surgery Patients

Author

Kovoor, Joshua G., Bacchi, Stephen, Gupta, Aashray K., Nann, Silas D., Stretton, Brandon, Chong, Esther H. L., Hewitt, Joseph N., Bhanushali, Ameya, Nathin, Kayla, Aujayeb, Nidhi, Lu, Amy, Ovenden, Christopher D., John, Athul, Reid, Jessica L., Gluck, Samuel, Liew, Danny, Reddi, Benjamin A., Hugh, Thomas J., Dobbins, Christopher, Padbury, Robert T., Hewett, Peter J., Trochsler, Markus I., and Maddern, Guy J.

Published
2023
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16. Depression after stoma surgery: a systematic review and meta-analysis

Author

Kovoor, Joshua G., Jacobsen, Jonathan Henry W., Stretton, Brandon, Bacchi, Stephen, Gupta, Aashray K., Claridge, Brayden, Steen, Matthew V., Bhanushali, Ameya, Bartholomeusz, Lorenz, Edwards, Suzanne, Asokan, Gayatri P., Asokan, Gopika, McGee, Amanda, Ovenden, Christopher D., Hewitt, Joseph N., Trochsler, Markus I., Padbury, Robert T., Perry, Seth W., Wong, Ma-Li, Licinio, Julio, Maddern, Guy J., and Hewett, Peter J.

Published
2023
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17. Delayed return of bowel function after general surgery in South Australia

Author

Kovoor, Joshua G., Bacchi, Stephen, Stretton, Brandon, Gupta, Aashray K., Jacobsen, Jonathan Henry W., To, Minh-Son, Goh, Rudy, Hewitt, Joseph N., Ovenden, Christopher D., Warren, Leigh, Marshall-Webb, Matthew, Jones, Karen L., Reddi, Benjamin A., Liew, Danny, Dobbins, Christopher, Padbury, Robert T., Hewett, Peter J., Hugh, Thomas J., Trochsler, Markus I., and Maddern, Guy J.

Published
2024
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21. Surgery’s Rosetta Stone: Natural language processing to predict discharge and readmission after general surgery

Author

Kovoor, Joshua G., Bacchi, Stephen, Gupta, Aashray K., Stretton, Brandon, Nann, Silas D., Aujayeb, Nidhi, Lu, Amy, Nathin, Kayla, Lam, Lydia, Jiang, Melinda, Lee, Shane, To, Minh-Son, Ovenden, Christopher D., Hewitt, Joseph N., Goh, Rudy, Gluck, Samuel, Reid, Jessica L., Khurana, Sanjeev, Dobbins, Christopher, Hewett, Peter J., Padbury, Robert T., Malycha, James, Trochsler, Markus I., Hugh, Thomas J., and Maddern, Guy J.

Published
2023
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23. Meeting medical emergency response criteria for hypertension is not associated with an increased likelihood of in-hospital mortality in a tertiary referral center

Author

Tsang, Jin Nuo Joan, primary, Bacchi, Stephen, additional, Ovenden, Christopher D., additional, Goh, Rudy, additional, Kovoor, Joshua G., additional, Gupta, Aashray K., additional, Min Le, Yong, additional, Lam, Antoinette, additional, Stretton, Brandon, additional, To, Minh-Son, additional, Woodman, Richard, additional, Mangoni, Arduino A, additional, and Malycha, James, additional

Published
2024
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41. The future is bright: artificial intelligence for trainee medical officers in Australia and New Zealand.

Author

Kovoor, Joshua G., Smallbone, Harry, Jenkins, Alexander, Stretton, Brandon, Santhosh, Sanjana, Jacobsen, Jonathan H. W., Gupta, Aashray K., Zaka, Ammar, Nann, Silas D., Jiang, Melinda, Luo, Yuchen, Withers, Caitlyn, Ataie, Sara, Nematzadeh, Nasim, Warren, Leigh R., Marshall‐Webb, Matthew, Chan, WengOnn, McNeil, Keith, Gluck, Samuel, and Turner, Richard

Subjects
*ARTIFICIAL intelligence, *MEDICAL education, *CLINICAL medicine, *MEDICAL research, *EXPERTISE
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2024
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43. A brief history of ramping.

Author

Cook, Benjamin, Evenden, James, Genborg, Ruby, Stretton, Brandon, Kovoor, Joshua, Gibson, Kieran, Tan, Sheryn, Gupta, Aashray, Chan, Weng O., Bacchi, Carol, Ittimani, Mana, Cusack, Michael, Maddison, John, Gluck, Samuel, Gilbert, Tony, McNeill, Keith, and Bacchi, Stephen

Subjects
TURNAROUND time, HOSPITAL utilization, RESOURCE allocation, HOSPITAL emergency services, PATIENT care, AMBULANCES, TREATMENT delay (Medicine), TIME
Abstract

'Ramping' is a commonly used term in contemporary Australian healthcare. It is also a part of the public and political zeitgeist. However, its precise definition varies among sources. In the published literature, there are distinctions between related terms, such as 'entry overload' and 'Patient Off Stretcher Time Delay'. How ramping is defined and how it came to be defined have significance for policies and procedures relating to the described phenomenon. Through examination of the history of the term, insights are obtained into the underlying issues contributing to ramping and, accordingly, associated possible solutions. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Perceived and actual value of Student‐led Objective Structured Clinical Examinations.

Author

Stretton, Brandon, Montagu, Adam, Kunnel, Aline, Louise, Jenni, Behrendt, Nathan, Kovoor, Joshua, Bacchi, Stephen, Thomas, Josephine, and Davies, Ellen

Subjects
SUBJECTIVE stress, CRONBACH'S alpha, HUMAN reproduction, MEDICAL students, SEMI-structured interviews
Abstract

Introduction: Student‐led Objective Structured Clinical Examinations (OSCEs) provide formative learning opportunities prior to Faculty‐led OSCEs. It is important to undertake quality assurance measurements of peer‐led assessments because, if they are found to be unreliable and invalid, they may have detrimental impacts. The objectives of this study were to explore and evaluate Student‐led OSCEs hosted by fifth‐year medical students. Methods: Student‐led OSCE results were analysed to examine reliability (Cronbach's alpha). The relationship between Student‐led and Faculty‐led OSCEs was evaluated using linear regression. Qualitative data were acquired by survey and semi‐structured interviews and were analysed using an inductive content analysis approach. Results: In total, 85 (94%) of 91 eligible students consented to study participation. Student‐led OSCEs had a low‐moderate reliability [Cronbach alpha = 0.47 (primary care) and 0.61 (human reproduction/paediatrics) (HRH)]. A statistically significant, positive relationship between Student‐led and Faculty‐led OSCE results was observed. Faculty‐led OSCE grades increased by 0.49 (95% CI: 0.18, 0.80) to 1.09 (95% CI: 0.67, 1.52), for each percentage increase in Student‐led OSCE result. Student‐led OSCE participants highly valued the authentic peer‐assessed experience. Reported benefits included a reduction of perceived stress and anxiety prior to Faculty‐led OSCEs, recognition of learning gaps, contribution to overall clinical competency and facilitation of collaboration between peers. Discussion: Student‐led OSCEs are moderately reliable and can predict Faculty‐led OSCE performance. This form of near‐peer assessment encourages the metacognitive process of reflective practice and can be effectively implemented to direct further study. Faculties should collaborate with their student bodies to facilitate Student‐led OSCEs and offer assistance to improve the quality, and benefits, of these endeavours. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Vital sign measurements demonstrate terminal digit bias and boundary effects.

Author

Kleinig, Oliver, To, Minh‐Son, Ovenden, Christopher D., Kovoor, Joshua G., Goh, Rudy, Lam, Lydia, Wenzel, Tara, Tan, Yiran, Harish, Hrishikesh, Gupta, Aashray K., Gluck, Sam, Gilbert, Toby, and Bacchi, Stephen

Subjects
VITAL signs, OXYGEN saturation, MEDICAL care, TERTIARY care, RESEARCH bias, HEART beat, MEASUREMENT errors, ATTITUDES of medical personnel, RESPIRATORY measurements, ELECTRONIC health records, SYSTOLIC blood pressure, PATIENT monitoring, REGRESSION analysis, EVALUATION
Abstract

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. These parameters are clinically significant as they influence care escalation. Methods: Vital signs data (heart rate, respiratory rate, oxygen saturation and systolic blood pressure) were collected from a tertiary hospital electronic medical record over a 2‐year period. These data were analysed using polynomial regression with additional terms to assess for underreporting of out‐of‐range observations and overreporting numbers with terminal digits of 0 (round numbers), 2 (even numbers) and 5. Results: It was found that heart rate, oxygen saturation and systolic blood pressure demonstrated 'boundary effects', with values inside the 'normal' range disproportionately more likely to be recorded. Even number bias was observed in systolic heart rate, respiratory rate and blood pressure. Preference for multiples of 5 was observed for heart rate and blood pressure. Independent overrepresentation of multiples of 10 was demonstrated in heart rate data. Conclusion: Although often considered objective, vital signs data are affected by bias. These biases may impact the care patients receive. Additionally, it may have implications for creating and training machine learning models that utilise vital signs data. [ABSTRACT FROM AUTHOR]

Published
2024
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48. All Aboard: Towards Standardisation of Surgical Resident Onboarding

Author

Kovoor, Joshua G., primary, Bacchi, Stephen, additional, Nann, Silas D., additional, Luo, Yuchen, additional, Stretton, Brandon, additional, Gupta, Aashray K., additional, Zaka, Ammar, additional, Warren, Leigh R., additional, Clarke, Jonathan M., additional, Gluck, Samuel, additional, Vanlint, Andrew S., additional, Chan, WengOnn, additional, and Marshall-Webb, Matthew, additional

Published
2024
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49. No shortcuts: False economy prevention during artificial intelligence implementation in rural Australian health care

Author

Kovoor, Joshua G., primary, Ittimani, Cansy, additional, Godber, Harry, additional, Herath, Asith, additional, Ovenden, Morgan, additional, Ovenden, Christopher D., additional, Hewitt, Joseph N., additional, Zaka, Ammar, additional, Ittimani, Mana, additional, Marshall‐Webb, Matthew, additional, Gupta, Aashray K., additional, Stretton, Brandon, additional, and Bacchi, Stephen, additional

Published
2024
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