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Professor Ian Scott
Professor

Ian Scott

Email: 
Phone: 
+61 7 3176 7355

Overview

Background

Ian Scott is the Director of Internal Medicine and Clinical Epidemiology at the Princess Alexandra Hospital and a Professor with the Faculty of Medicine. He is a consultant general physician with clinical interests in in health services evaluation and improvement, clinical guidelines, clinical decision-making, evidence-based medicine, low value care, quality use of medicines, non-invasive cardiology, advance care planning, and older patient care. He currently chairs the Queensland Clinical Networks Executive, is the inaugural chair of the Australian Deprescribing Network, Metro South Clinical AI Working Group, and Queensland Health Sepsis AI Working Group and is a founding member of the Australian and New Zealand Affiliate of the US Society to Improve Diagnosis in Medicine (ANZA-SIDM). He is also a member of Queensland Health System Quality, Safety and Performance Management Committee and the Quality and Safety Committee and the Digital Health Advisory Group of the Royal Australasian College of Physicians (RACP). He is a past President of the Internal Medicine Society of Australia and New Zealand and past member of the MBS Review Taskforce for Cardiac Services. He has led multi-site quality improvement collaboratives in acute cardiac care including both hospitals and Divisions of General Practice. He has been involved at senior level on various high-level committees in establishing policies for Queensland Health and/or RACP on electronic discharge summaries, clinical handover, clinical indicators, evaluation of physician performance, chronic disease management, perioperative medicine, medical assessment and planning units, and patient flow through emergency departments. He has published over 270 peer-reviewed articles, presented to over 150 national and international meetings, and is a recipient of several NHMRC and government research grants.

Availability

Professor Ian Scott is:
Available for supervision

Fields of research

Qualifications

  • Postgraduate Diploma in Education, The University of Queensland
  • Masters (Coursework) of Education, The University of Queensland
  • Royal Australasian College of Physicians, Royal Australasian College of Physicians

Research interests

  • Clinical decision making

    Investigation into how clinicians reason, the cognitive biases that may afflict that reasoning and ways for mitigating such bias, and the sociocognitive aspects of decision-making

  • Low value care

    Investigation into the drivers and manifestations of low value care (ie care that is ineffective, harmful or disproportionately costly for marginal benefit) and methods for reducing it

  • Advance care planning

    Investigation into how clinicians and patients can promote and participate in shared decision-making around end of life care which accounts for patient values and preferences and avoids unnecessary or unwanted invasive interventions in the last years of life.

  • Evidence-informed clinical practice

    Investigation into how clinicians can be assisted in ensuring their clinical practice aligns with best available research evidence of the effectiveness and safety of clinical interventions

  • Diagnostic error

    Investigation into the cognitive and system-related factors that predispose clinicians to making diagnostic error which currently affect around 1 in 10 diagnostic decisions, with potential to cause patient harm.

  • Using artificial intelligence to improve clinical decision-making

    Investigation into how predictive analytics using artificial intelligence, in particular machine learning, can be used to improve clinical decision-making.

Research impacts

I have investigated several quality anfd safety improvement (QSI) topics with publications influencing clinical and policy decisions, cited in 93 countries by 160 institutions (including Harvard, Stanford, Johns Hopkins Universities), 23 publications receiving 41 mentions in policy documents, 11 in top 5% of all outputs (Altmetric 2019). I was lead author of the first systematic review of effectiveness of acute medical units (AMU) and co-authored the first operational standards for AMUs in 2006 (with regular updates), both initiatives prompting many Australian hospitals to establish such units. I co-authored the first Cochrane review of early invasive versus conservative strategies for non-ST-elevation acute coronary syndromes in the stent era in 2016, wrote the first evidence-based Australian guide in perioperative medicine, and reported a case-control study suggesting increased cardiac risk with perioperative use of angiotensin antagonists (now being investigated in the first randomised trial). I have led and researched major QSI reforms within a large tertiary hospital which, within 12 months, increased percentage of patients with ED length of stay of <4 hours from 32% (worst in the country) to 62% (near top), decreased in-hospital mortality from 2.3% to 1.7%, and identified novel predictors of better outcomes. We undertook a study, with Health Roundtable and CSIRO, of 11 million acute presentations which validated a national emregency access target of 82%, which was then adopted by QH and subsequently by other states.

In response to the growing problem of potentially inappropriate polypharmacy (PIP) in older patients, I co-authored two literature reviews and four prevalence studies, and established the multidisciplinary Australian Deprescribing Network (ADeN) in 2014 (currently >400 colleagues). In 2015 we published a sentinel paper (560 citations to date, top 1% cited paper worldwide), detailing a method (CEASE protocol) for ceasing or dose reducing inappropriate medications – a process called deprescribing - which has been accepted as the international standard. I have co-authored a systematic review of enablers and barriers to deprescribing by clinicians and published papers that prove the efficacy of CEASE in hospital and primary care settings, the latter in a successful controlled trial involving 5 general practices (world first). In addition to Australian authorities (Aust Medicines Handbook), CEASE has been adopted by US advocates (Lown Institute among others), New Zealand (NZ Health), UK (NHS), Taiwan and Singapore (respective health ministries), and China (Guangdong Pharmaceutical Association). I have recently published a review of EMR-enabled tools for minimising polypharmacy, and am now researching means for identifying patients at high risk of medication harm and machine learning methods to predict better drug dosing.

I have proposed clinician-led strategies for minimising low value care (LVC) later endorsed by the Productivity Commission and the Australian Medical Association. I have researched the extent of LVC in Australian hospitals and, in a landmark paper, exposed the cognitive biases underpinning it, which has informed QH Value-based Care group and NSW Health. I have authored reviews of advance care planning (ACP) detailing its process and benefits, evaluated ACP implementation in a tertiary hospital, and assessed integration into nursing homes.

I have co-authored a review of the impacts of electronic medical records (EMR) in hospital practice and formulated an evidence-based EMR implementation checklist that is assisting other hospitals in their digital transformation (344 reads). More recently, I have established two clinical working groups targeting machine learning models aimed at early detection of sepsis and optimising drug dosing.

Works

Search Professor Ian Scott’s works on UQ eSpace

344 works between 1986 and 2024

1 - 20 of 344 works

2024

Journal Article

Using conversant artificial intelligence to improve diagnostic reasoning: ready for prime time?

Scott, Ian A, Miller, Tim and Crock, Carmel (2024). Using conversant artificial intelligence to improve diagnostic reasoning: ready for prime time?. Medical Journal of Australia. doi: 10.5694/mja2.52401

Using conversant artificial intelligence to improve diagnostic reasoning: ready for prime time?

2024

Journal Article

Evaluating automated machine learning platforms for use in healthcare

Scott, Ian A., De Guzman, Keshia R., Falconer, Nazanin, Canaris, Stephen, Bonilla, Oscar, McPhail, Steven M., Marxen, Sven, Van Garderen, Aaron, Abdel-Hafez, Ahmad and Barras, Michael (2024). Evaluating automated machine learning platforms for use in healthcare. JAMIA Open, 7 (2) ooae031, ooae031. doi: 10.1093/jamiaopen/ooae031

Evaluating automated machine learning platforms for use in healthcare

2024

Journal Article

The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians

Scott, Ian A. and Zuccon, Guido (2024). The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians. Internal Medicine Journal, 54 (5), 705-715. doi: 10.1111/imj.16393

The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians

2024

Journal Article

Achieving large-scale clinician adoption of AI-enabled decision support

Scott, Ian A., van der Vegt, Anton, Lane, Paul, McPhail, Steven and Magrabi, Farah (2024). Achieving large-scale clinician adoption of AI-enabled decision support. BMJ Health & Care Informatics, 31 (1) ARTN e100971, e100971. doi: 10.1136/bmjhci-2023-100971

Achieving large-scale clinician adoption of AI-enabled decision support

2024

Journal Article

How should artificial intelligence be used in Australian health care? Recommendations from a citizens’ jury

Carter, Stacy M., Aquino, Yves Saint James, Carolan, Lucy, Frost, Emma, Degeling, Chris, Rogers, Wendy A., Scott, Ian A., Bell, Katy J. L., Fabrianesi, Belinda and Magrabi, Farah (2024). How should artificial intelligence be used in Australian health care? Recommendations from a citizens’ jury. Medical Journal of Australia, 220 (8), 409-416. doi: 10.5694/mja2.52283

How should artificial intelligence be used in Australian health care? Recommendations from a citizens’ jury

2024

Journal Article

The Adverse Inpatient Medication Event and Frailty (AIME-Frail) risk prediction model

Falconer, Nazanin, Scott, Ian A., Abdel-Hafez, Ahmad, Cottrell, Neil, Long, Duncan, Morris, Christopher, Snoswell, Centaine, Aziz, Ebtihal, Lam, Jonathan Yong Jie and Barras, Michael (2024). The Adverse Inpatient Medication Event and Frailty (AIME-Frail) risk prediction model. Research in Social and Administrative Pharmacy, 20 (8), 796-803. doi: 10.1016/j.sapharm.2024.05.003

The Adverse Inpatient Medication Event and Frailty (AIME-Frail) risk prediction model

2024

Journal Article

Powered by <scp>AI</scp>: advancing towards artificial intelligence algorithms in Australian hospital pharmacy

Falconer, Nazanin, Scott, Ian and Barras, Michael (2024). Powered by AI: advancing towards artificial intelligence algorithms in Australian hospital pharmacy. Journal of Pharmacy Practice and Research, 54 (2), 107-109. doi: 10.1002/jppr.1922

Powered by <scp>AI</scp>: advancing towards artificial intelligence algorithms in Australian hospital pharmacy

2024

Journal Article

Risk factors predicting hospital-acquired pressure injury in adult patients: An overview of reviews

Wang, Isabel, Walker, Rachel M., Gillespie, Brigid M., Scott, Ian, Sugathapala, Ravilal Devananda Udeshika Priyadarshani and Chaboyer, Wendy (2024). Risk factors predicting hospital-acquired pressure injury in adult patients: An overview of reviews. International Journal of Nursing Studies, 150 104642, 1-11. doi: 10.1016/j.ijnurstu.2023.104642

Risk factors predicting hospital-acquired pressure injury in adult patients: An overview of reviews

2024

Journal Article

Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain

van der Vegt, Anton H., Campbell, Victoria, Mitchell, Imogen, Malycha, James, Simpson, Joanna, Flenady, Tracy, Flabouris, Arthas, Lane, Paul J., Mehta, Naitik, Kalke, Vikrant R., Decoyna, Jovie A., Es’haghi, Nicholas, Liu, Chun-Huei and Scott, Ian A (2024). Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain. Journal of the American Medical Informatics Association, 31 (2), 509-524. doi: 10.1093/jamia/ocad220

Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain

2024

Journal Article

Monoclonal antibodies for treating early Alzheimer disease—a commentary on recent ‘positive’ trials

Scott, Ian A. (2024). Monoclonal antibodies for treating early Alzheimer disease—a commentary on recent ‘positive’ trials. Age and Ageing, 53 (2) afae023. doi: 10.1093/ageing/afae023

Monoclonal antibodies for treating early Alzheimer disease—a commentary on recent ‘positive’ trials

2024

Journal Article

Deprescribing: a 20‐year retrospective

Scott, Ian A. (2024). Deprescribing: a 20‐year retrospective. Journal of Pharmacy Practice and Research, 53 (6), 320-327. doi: 10.1002/jppr.1906

Deprescribing: a 20‐year retrospective

2024

Journal Article

Reducing potentially inappropriate polypharmacy at a national and international level: the impact of deprescribing networks

McDonald, Emily G., Lundby, Carina, Thompson, Wade, Boyd, Cynthia, Farrell, Barbara, Gagnon, Camille, Herbin, Jennie, Khuong, Ninh, Moriarty, Frank, Pierson, Tiphaine, Scott, Sion, Scott, Ian A., Silvius, Jim, Spinewine, Anne, Steinman, Michael A., Tannenbaum, Cara, Trimble, Johanna, Turner, Justin P. and Reeve, Emily (2024). Reducing potentially inappropriate polypharmacy at a national and international level: the impact of deprescribing networks. Expert Review of Clinical Pharmacology, 17 (5-6), 433-440. doi: 10.1080/17512433.2024.2355270

Reducing potentially inappropriate polypharmacy at a national and international level: the impact of deprescribing networks

2023

Journal Article

Too much versus too little: looking for the “sweet spot” in optimal use of diagnostic investigations

Scott, Ian A., Crock, Carmel and Twining, Matthew (2023). Too much versus too little: looking for the “sweet spot” in optimal use of diagnostic investigations. Medical Journal of Australia, 220 (2), 67-70. doi: 10.5694/mja2.52193

Too much versus too little: looking for the “sweet spot” in optimal use of diagnostic investigations

2023

Journal Article

First do no harm in responding to incidental imaging findings

Scott, Ian A., Slavotinek, John and Glasziou, Paul P. (2023). First do no harm in responding to incidental imaging findings. Medical Journal of Australia, 220 (1), 7-9. doi: 10.5694/mja2.52177

First do no harm in responding to incidental imaging findings

2023

Conference Publication

Development and validation of a machine learning algorithm to optimise the dosing of unfractionated heparin

Barras, Michael, Falconer, Nazanin, Abdel-Hafez, Ahmad, Scott, Ian, Marxen, Sven, Van Garderen, Aaron, Bonilla, Oscar and Canaris, Stephen (2023). Development and validation of a machine learning algorithm to optimise the dosing of unfractionated heparin. 81st FIP World Congress of Pharmacy and Pharmaceutical Sciences, Brisbane, QLD Australia, 24 - 28 September 2023. The Hague, Netherlands: International Pharmaceutical Federation. doi: 10.46542/pe.2023.236.329349

Development and validation of a machine learning algorithm to optimise the dosing of unfractionated heparin

2023

Journal Article

RELEASE (REdressing Long-tErm Antidepressant uSE): protocol for a 3-arm pragmatic cluster randomised controlled trial effectiveness-implementation hybrid type-1 in general practice

Wallis, Katharine A., Donald, Maria, Horowitz, Mark, Moncrieff, Joanna, Ware, Robert S., Byrnes, Joshua, Thrift, Karen, Cleetus, MaryAnne, Panahi, Idin, Zwar, Nicholas, Morgan, Mark, Freeman, Chris and Scott, Ian (2023). RELEASE (REdressing Long-tErm Antidepressant uSE): protocol for a 3-arm pragmatic cluster randomised controlled trial effectiveness-implementation hybrid type-1 in general practice. Trials, 24 (1) 615, 1-14. doi: 10.1186/s13063-023-07646-w

RELEASE (REdressing Long-tErm Antidepressant uSE): protocol for a 3-arm pragmatic cluster randomised controlled trial effectiveness-implementation hybrid type-1 in general practice

2023

Journal Article

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

Strating, Tom, Shafiee Hanjani, Leila, Tornvall, Ida, Hubbard, Ruth and Scott, Ian A. (2023). Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models. BMJ Health & Care Informatics, 30 (1) e100767, e100767. doi: 10.1136/bmjhci-2023-100767

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

2023

Journal Article

Digital health competencies for the next generation of physicians

Scott, Ian A., Shaw, Tim, Slade, Christine, Wan, Tai T., Coorey, Craig, Johnson, Sandra L. J. and Sullivan, Clair M. (2023). Digital health competencies for the next generation of physicians. Internal Medicine Journal, 53 (6), 1042-1049. doi: 10.1111/imj.16122

Digital health competencies for the next generation of physicians

2023

Journal Article

Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework

van der Vegt, Anton H., Scott, Ian A., Dermawan, Krishna, Schnetler, Rudolf J., Kalke, Vikrant R. and Lane, Paul J. (2023). Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework. Journal of the American Medical Informatics Association, 30 (9), 1503-1515. doi: 10.1093/jamia/ocad088

Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework

2023

Journal Article

Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework

van der Vegt, Anton H., Scott, Ian A., Dermawan, Krishna, Schnetler, Rudolf J., Kalke, Vikrant R. and Lane, Paul J. (2023). Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework. Journal of the American Medical Informatics Association, 30 (7), 1349-1361. doi: 10.1093/jamia/ocad075

Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework

Funding

Current funding

  • 2023 - 2028
    RELEASE+: REdressing Long-tErm Antidepressant uSE in general practice
    NHMRC Partnership Projects
    Open grant
  • 2023 - 2027
    Optimising medicine information handover after discharge (REMAIN HOME 2.0)
    MRFF Quality, Safety and Effectiveness of Medicine Use and Medicine Intervention by Pharmacists
    Open grant

Past funding

  • 2019 - 2021
    Personalised Medicine in action: Applying machine learning to develop personalised medication dosing (MSHHS Research Support Scheme grant administered by MSHHS)
    Metro South Hospital and Health Service
    Open grant
  • 2018 - 2022
    Safety, effectiveness of care and resource use among Australian hospitals (Safer Hospitals) (The Hospital Research Foundation grant administered by The University of Adelaide)
    University of Adelaide
    Open grant
  • 2016 - 2018
    Measuring low-value health care for targeted policy action (NHMRC Project Grant administered by The University of Sydney)
    University of Sydney
    Open grant
  • 2013 - 2018
    Telehealth in residential aged care facilities: a pragmatic randomised control trial
    NHMRC Project Grant
    Open grant
  • 2011 - 2013
    A new prescribing technology for older patients
    PA Research Foundation Private Practice Trust Fund Research Support Grants
    Open grant

Supervision

Availability

Professor Ian Scott is:
Available for supervision

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Supervision history

Current supervision

Completed supervision

Media

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