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What is Health Data Science?

At the Centre for Big Data Research in Health (CBDRH), we integrate health data science into everything we do. Our science is an evolving discipline that responds to the growing number of large and complex datasets in health and medicine. Health data science combines methods and tools from health research, statistics and computing sciences to address health and medical problems using data.

Why ‘big data’?

‘Big data’ has no agreed definition but the term is generally applied to data that, by virtue of their size and/or complexity, pose challenges to traditional methods for management and analysis. 

In health, these data include the millions of records that are generated routinely. These include health services, real-time clinical data captured at the point-of-care, genomic data produced in research and clinical settings, as well as health-related data generated by the population at large through technologies such as wearable devices and social media. 

We sit at a key point in history, where big data are poised to become a dominant driver in what happens in health and health care. 

Dr Mark Joseph Hanly

Mark is a lecturer and applied social statistician based at the Centre for Big Data Research in Health, UNSW Sydney. His research focuses on the analysis of large, linked administrative datasets to answer research questions related to the health and social welbeing of populations. Topics of interest include early childhood health and development, health service utilisation, Indigenous health and outcomes for vulnerable populations. As an... view more

Mark is a lecturer and applied social statistician based at the Centre for Big Data Research in Health, UNSW Sydney. His research focuses on the analysis of large, linked administrative datasets to answer research questions related to the health and social welbeing of populations. Topics of interest include early childhood health and development, health service utilisation, Indigenous health and outcomes for vulnerable populations. As an applied social statistician, he is interested in statistical methods for answering causal questions using observational data;  summarising and modelling complex longitudinal data; and innovative approaches for  visualising data and communicating research. Mark convenes and teaches the course HDAT9700 Statistical Modelling II as part of the MSc in Health Data Science.

Mark joined the Centre for Big Data Research in Health from the University of Bristol, where he completed his PhD in Advanced Quantitative Methods for the Health and Social Sciences. His doctoral research focused on novel approaches to correct for nonresponse bias in large household surveys. He also holds an MSc in Applied Social Research from Trinity College Dublin and a BSc (Hons) in Mathematics and Statistics from the National University of Ireland, Maynooth.  

$50,000 CRE REACH The type and timing of first child protection service contacts in Aboriginal children in New South Wales, 2002-2019 (CIA)

$655,934 National Health and Medical Research Council Ideas Grant (2021) Hospital trajectories for 15 million Australians (CIE)

$50,000 Triple I Clinical Academic Group Seed Grant (2020) Open source modelling tools to support policy decision-making throughout the COVID-19 post-pandemic phase (CIA)

$1,248,388 National Health and Medical Research Council Transforming the health system response to child maltreatment: the need for cross-jurisdictional e-cohorts (CID)

$26,000 Australian Research Data Council Disseminating Research in the 21st Century (CIA)

$30K NSW Department of Family and Community Services (FACS) Commissioned Project Analysis report for the ‘Family is Culture’ review of Aboriginal children in Out-of-Home care in NSW. (CIB)

$40,000 UNSW Neuroscience, Mental Health and Addictions and SPHERE Clinical Academic Group Seed Funding Scheme Neurodevelopmental, physical and mental health consequences of early life adversity: cross-sectoral population data linkage to inform health and social policy. (AI)

$23,685 NSW FACS Commissioned Project Their Futures Matter – Trial Cohort (Children in residential care) (CIB)

$10,849 NSW FACS Commissioned Project Their Futures Matter – Early life pathways and outcomes for children in contact with child protection (CIB)

My Qualifications

• PhD Advanced Quantitative Mehtods, University of Bristol 2015
• MSc Applied Social Research, Trinity College Dublin 2011
• BSc Mathematics and Statistics, National University of Ireland Maynooth 2006
• mso-list:l0 level1 lfo1;tab-stops:list 36.0pt"> "Times New Roman";mso-ansi-language:EN-IE">CovidR Contest Winner, European R-Users Meeting (2020) mso-fareast-font-family:"Times New Roman"">
• mso-list:l0 level1 lfo1;tab-stops:list 36.0pt"> "Times New Roman";mso-ansi-language:EN-IE">Datathon winner, National University of Singapore - National University Health System - Massachusetts Institute of Technology (MIT) Healthcare Artificial Intelligence (AI) Datathon (2018) "Times New Roman",serif;mso-fareast-font-family:"Times New Roman"">
• mso-list:l0 level1 lfo1;tab-stops:list 36.0pt"> "Times New Roman";mso-ansi-language:EN-IE">UNSW Medicine and Teaching and Research, Excellence in the Design and Development of Programs, Courses or Other Initiatives Award (2018) mso-fareast-font-family:"Times New Roman"">
• mso-list:l0 level1 lfo1;tab-stops:list 36.0pt"> "Times New Roman";mso-ansi-language:EN-IE">European Survey Research Association, Early career researcher award: runner-up (2013)
• mso-list:l0 level1 lfo1;tab-stops:list 36.0pt"> "Times New Roman";mso-ansi-language:EN-IE">ESRC Advanced Quantitative Methods Studentship £16,590 PA for 3 years (+ Tuition fees) (2011)

My Research Supervision

Supervision keywords, areas of supervision.

ILP/Honours/Masters/PhD

Potential supervision areas

• Analysis of linked data
• Causal inference using observational data
• Innovative data communication (interactive dashboards and apps)

My Engagement

Blog posts and articles in the conversation.

Which families delay sending their child to school and why? We crunched the numbers . (The Conversation, April 2019)

Preschool benefits Indigenous children more than other types of preschool care . (The Conversation, December 2020)

Evaluating COVID-19 exit strategies using open-source modelling tools . (The UK Public Health Rapid Support Team Blog Post, August 2020)

Australia must vaccinate 200,000 adults a day to meet October target: new modelling . (The Conversation, February 2021)

Australian vaccine rollout needs all hands on deck after laterst Astra Zeneca news, mass vaccination hubs included . (The Conversation, April 2021)

Using Shiny in Healthcare: Examples from the 2021 Shiny Contest  (R Studio blog, August 2021)

In the media

How big data can change intensive care . (UNSW Newsroom, July 2018)

'A gift of time': Children who start school later fare better, study finds . (Sydney Morning Herald, April 2019)

1 in 4 families delay thier child's school entry -- and older children are more schol-ready: big data study . (UNSW Newsroom, April 2019)

Should kids start school later?  (The Educator magazine, June 2019)

UNSW wins international COVID-19 data science competition . (thesphere.com.au, June 2020)

Dr Mark Hanly talks COVOID R package (eRUM 2020) . (UNSW Newsroom, June 2020)

'What's surprising is the scale of the problem': One in seven children at risk of harm by five years old . (The Sydney Morning Herald, June 2020)

Nurses accuse Commonwealth of slowing vaccine rollout and treating them as handmaidens  (ABC AM, February 2021)

Emailed COVID-19 vaccine certificate part of rollout plan, as Australia gets closer to first vaccinations  (ABC News, February 2021)

Is Australia's goal of vaccinating the entire adult population by October achievable? (The Guardian, February 2021)

Mass vaccination could rescue the snail-paced rollout (The Medical Republic, April 2021)

My Teaching

mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-IE;mso-fareast-language: EN-GB;mso-bidi-language:AR-SA"> HDAT9700 Statistical Modelling II . Lead course developer, convenor and lecturer.

Publications

• Journal articles 28
• Conference Papers 4
• Conference Presentations 2
• Conference Abstracts 6
• Preprints 1

ORCID as entered in ROS

Research Activities

Funded by NHMRC Project Grant.

Promoting positive early childhood development is fundamental to improving life opportunities and outcomes for Aboriginal Australians. However, national data show that a significant proportion of Aboriginal children have markers of developmental vulnerability at school entry and this tracks through to poor literacy and numeracy outcomes across all schooling years. We currently lack information about the key drivers of positive early childhood development in Aboriginal children, and the features of local communities and early childhood service…

UNSW Sydney NSW 2052 Australia Telephone +61 2 93851000 Authorised by Deputy Vice-Chancellor (Research) UNSW CRICOS Provider Code: 00098G ABN: 57 195 873 179

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Health Data Science Student Hub

| Master of Science in Health data Science

| Clinical Artificial Intelligence

| Continuous Professional development

The Master of Science in Health Data Science will prepare you to find the right data, unlock hidden insights and use this information to better support clinical care, inform health policy and improve population health. This degree covers the entire pipeline from comprehension of complex health issues through to data wrangling and management, machine learning, data analytics, data modelling and data communication.

At the Centre for Big Data Research in Health (CBDRH) we aim to enhance the health and wellbeing of all, by maximising the productive use of all possible sources of health big data in medical research.

Our master’s degree, graduate diploma and graduate certificate in health data science are pioneering programs that examine data-driven solutions to complex health problems. At UNSW, we’re leading this new approach to healthcare.

Check out the videos below to learn more!

Applications of Health Data Science

Industry perspectives of Health Data Science

Hear our student’s stories

Casual Academic Talent Pool - Medicine - Centre for Big Data Research in Health

Apply now Job no: 511514 Work type: casual Location: Sydney, NSW Categories: Tutor, Lecturer, Other, Demonstrator

The Centre for Big Data Research in Health within the Faculty of Medicine & Health is seeking expressions of interest from qualified individuals for casual teaching opportunities in the Master of Science in Health Data Science program.

We encourage Expressions of Interest from new and experienced tutors, demonstrators, markers and lecturers who are excited to be a part of an integrated teaching team.

About the role

Your role is to provide casual teaching support to the course convenors. Your duties may include demonstrating in tutorials, workshops or practical classes, marking, and other miscellaneous course related activities.

You will need:

• A degree in statistics, biostatistics, epidemiology, data science, or a related discipline, and/or relevant work experience.
• Demonstrated strong interpersonal skills and ability to communicate efficiently with students and course convenors
• Proficiency with software applications commonly used in teaching (e.g. Office 365: Word, Excel, Teams, and learning management systems such as Moodle), as well as software packages (R, Python or SAS)
• Commitment to follow the  UNSW Code of Conduct  which deals with confidentiality, discrimination, health and safety, harassment, etc 
• Willingness to complete Workplace Health & Safety training and other relevant training as required by UNSW before commencing work.

UNSW is committed to evolving a culture that embraces equity and supports a diverse and inclusive community where everyone can participate fairly, in a safe and respectful environment. We welcome candidates from all backgrounds and encourage applications from people of diverse gender, sexual orientation, cultural and linguistic backgrounds, Aboriginal and Torres Strait Islander background, people with disability and those with caring and family responsibilities. UNSW provides workplace adjustments for people with disability, and access to flexible work options for eligible staff.

The University reserves the right not to proceed with any appointment.

Position Description

Advertised: 07 Sep 2022 AUS Eastern Standard Time Applications close:

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