Associate Professor Joanne Enticott is a nationally and internationally recognised health and medical translational biostatistician, leading research in big data analytics and Learning Health Systems. She heads the Big Data and Learning Health Systems research stream at the Monash Centre for Health Research and Implementation (MCHRI), a partnership with Monash Health—Australia’s largest maternity network—providing a unique implementation laboratory with over 120 active clinician-researchers. Her work is deeply embedded in women’s health and mental wellbeing, with a strong translational focus on equitable, data-driven healthcare.

She is ranked within the top 0.5% of researcers globally (in all fields) based on research productivity, quality, and impact over the past five years (2025 ScholarGPS). She has authored over 200 publications (56% as co-lead or senior author), with an H-index of 34 and >5,500 citations. She has secured over $39M in research funding, including $25M in the past five years as lead statistician across 14 major grants (9 MRFF, 4 NHMRC). She is building future capacity through current supervision of 7 PhD students (3 primary), 9 completions, and leadership of the Digital Data-Driven Healthcare Graduate Research Industry Partnership, mentoring 11 cross-disciplinary PhD candidates.

A/Prof Enticott’s work exemplifies the integration of AI, machine learning, and classical statistics to advance gender-responsive, sustainable health systems that improve outcomes for women and vulnerable populations.

At MCHRI, she is part of the Executive Team with oversight of strategic direction for the Centre, which has >$50million in current NHRMC and MRFF grants.

My program of research to produce policy informing information from existing big data assets such as routine health data sets is informing policy.​ ​I have established a research program focused on big data analytics and the learning health system, working closely with clinical researchers, service providers and policy makers. My program supports heterogeneous clinical disciplines and I also have a strong research interest in maternal health and mental health.

The past decade has seen enormous advances in the amount of data routinely generated however the ability of clinicians, researchers, policy makers and the community to harness this data to improve healthcare and outcomes at the individual, organisation and population level has been hindered by a lack of system-wide capacity, knowledge and approaches. I lead the development of the evidence-based LHS, led by Monash Partners/ University, that engaged stakeholders, was informed by evidence synthesis and qualitative research and is now being implemented by governments and health services (MRFF $19M up to 2030) for: LHS implementation to deliver enhanced healthcare at home for older people in Australia, and; on major data infrastructure, advancing IT innovations and applying the LHS in maternity care across priority areas, settings and populations. 

I have contributed to large scale programs and brings expertise in pragmatic trial design, biostatistics risk prediction and LHS. I have research utilising healthcare data informed by implementation research funded by the MRFF that is instrumental in: e.g. translating risk prediction tools to improve women’s health – HiPPPP Personalised Medicine meets Public Health in the first 2000 days ($2.5M, 2020-2025); optimising the delivery of antenatal digital interventions in public healthcare, improving equity, access and engagement for better maternal and neonatal health outcomes ($1.5M, 2021-2026).

Since 2020, she has led the MCHRI Biostatistical Consultancy Service, which is a not-for-profit service providing research and statistics support to Monash Health clinicans, staff/students.