Robin Blythe

Assistant Professor

Duke-NUS Medical School

Singapore

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About

Robin Blythe serves as an Assistant Professor at Duke-NUS Medical School in Singapore. His research primarily focuses on anaemia reduction strategies, costing studies related to anaemia interventions, and the development of clinical prediction models for patient deterioration. Recent publications highlight his work on improving healthcare interventions through innovative prognostic models and addressing implementation challenges in clinical decision support systems.

Articles (10)

Year:

2025

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2025

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2025

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2025

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2024

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2024

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2024

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2023

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2023

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2023

Collaborators (3)

Melissa Baysari

Macquarie University

AUSTRALIA

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Nicole White

Queensland University of Technology

AUSTRALIA

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Leanne M Aitken

Professor of Critical Care

City, University of London

UNITED KINGDOM

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Robin Blythe

Research Interests

Contact this professor

About

Articles (10)

Cost-effective targets for anaemia reduction in 191 countries: a modelling study

Estimated unit costs of anaemia interventions for women of reproductive age in 193 UN member states: a costing study

Getting back on track to meet global anaemia reduction targets: a Lancet Haematology Commission

Resuscitation in Paediatric Septic Shock Using Vitamin C and Hydrocortisone (RESPOND): The RESPOND Randomized Controlled Trial Protocol

Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care

An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation

Prioritising deteriorating patients using time-to-event analysis: prediction model development and internal–external validation

Predictors of acute muscle loss in the intensive care unit: A secondary analysis of an in-bed cycling trial for critically ill patients

Identifying barriers and facilitators to successful implementation of computerized clinical decision support systems in hospitals

Vital signs-based deterioration prediction model assumptions can lead to losses in prediction performance

Collaborators (3)

Melissa Baysari

Nicole White

Leanne M Aitken

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