Health Intelligence

Sankalp Khanna
Team Leader
Health Intelligence

The science behind helping the health system increase productivity and safety through optimising patient, clinician and resource flows, and providing intelligent decision support.

Our Science

As pressure on hospital systems builds due to ageing populations and increased prevalence of chronic diseases, health services are increasingly turning to data analytics driven approaches to inform much needed system evaluation and redesign, address crowding, improve flow through hospitals, and deliver improved patient outcomes. The Health Intelligence team develops and delivers scientifically robust analytics to improve safety, quality and efficiency of our healthcare system. These analytics improve performance and sustainability of the Australian health system by transforming clinical and operational data into knowledge via analytics; optimisation of patient, clinician and resource flows; real-time monitoring for decision support; and prediction and risk stratification tools.

The work demonstrates an intimate knowledge of the Australian health system and associated datasets as well as knowledge of the regulatory frameworks of working with sensitive health data and potential quality issues associated with health data.

Impact on the Health System

Working closely with clinicians and health system administrators, the team has delivered significant impact in the areas of patient flow analytics and hospital avoidance, and is well recognised as leaders in this research space.

There has been significant adoption of developed research. Work on analysing the Winter Bed Crisis was incorporated into the QLD Government’s Winter Bed plan as part of a 5-point plan to improve access to ED during this time of surging demand. A study establishing the relationship between ED timeliness and patient mortality was directly translated into government policy around ED performance targets in QLD, and endorsed by ACEM, AMA, and AHMAC. Several health services have credited this body of research as having provided the evidence based approach that helped them deliver improvements in patient flow. Developed Risk Stratification algorithms have been deployed in primary care to identify eligible patients for the Australian Government Health Care Homes national trial, and in acute care for a trial aimed at informing real-time care to reduce rehospitalisation at a large metropolitan QLD hospital.

Our Solutions

Health System Productivity & Efficiency

  • Predicting patients presenting the health system
  • Demand modelling and simulation to identify bottlenecks/overcrowding
  • Bed configurations for current and future demand
  • Winter bed planning
  • Optimisation of surgery scheduling & utilisation
  • Syndromic surveillance
  • Patient deterioration and vital sign monitoring
  • Hospital Avoidance

Risk stratification to reducing readmissions and preventable hospitalisations

  • Statistical machine learning to combat the burden of chronic disease
  • Automation of care planning in a leaning health system

Evidence Based Healthcare Delivery

  • Evidence driven workflow and health system KPIs
  • Informing policy around proposed changes such as after hours healthcare delivery

Case Studies

Team Members:

Sankalp Khanna

Justin Boyle

David Rolls

Hamed Hassanzadeh

Vahid Riahi

Kristin Edwards

Sandra Louise

Venkata Tadi

 

Sankalp Khanna
Senior Research Scientist focussing on findings used to change hospital Key Performance Indicators.
CSIRO Profile

Justin Boyle
Senior Principal Research Scientist working on demand prediction models used in public hospitals.

David Rolls
Senior Health Statistician investigating predictive models for risk of patient re-hospitalisation and evaluating hospital programmes of care.
CSIRO Profile

Hamed Hassanzadeh
Research Scientist with research interests in patient flow modelling and simulation, and machine learning for clinical decision support.
CSIRO Profile

Vahid Riahi
Postdoctoral research fellow focussing on improving system design through the use of mathematical modelling and optimisation techniques in hospitals.

Kristin Edwards
PhD student jointly supervised with James Cook University studying aeromedical retrieval with the primary aim to develop analytics to support decisions which leads to better health outcomes for patients.

Sandra Louise
Research Scientist using statistical modelling to improve the accessibility, quality and delivery of health care. Sandra is passionate about women’s health.

Venkata Tadi
PhD student, jointly supervised with the University of New South Wales and the Australian Government Department of Health, investigating the physical health and social outcomes of Australians with serious mental illnesses.