Health economics is now an established aspect of healthcare decision making in many countries around the world. In addition to formal evaluation guidelines to assess whether prospective medicines, devices and medical technologies are cost effective in terms of health outcomes, health economic methods are increasingly being applied to healthcare and broader human service program evaluation and policy analysis.

We provide a comprehensive range of health economic services across conventional research and evaluation settings and into the government and health related human service sectors where applied health economic methods continue to evolve and add social value.

Our projects are shaped around our core foundations in health economic methods and integration with our services across program evaluation and policy analysis, and through our state-of-the-art machine learning platform.

Health Economic Evaluation

Health Economic Evaluation continues to thrive both in Australia and internationally. The well-established trends of people living longer and government support programs increasing in scope and cost continues to intensify the focus of achieving optimal outcomes within budget and service delivery constraints.

In many program areas, there are also fundamental changes in progress due to new flexible funding arrangements and increasing interagency coordination.
We partner with our clients to provide a scalable range of health economic services supporting complex program evaluation and management with outcome focused transparency and evidence.

Our services include established methods of Health Economic Evaluation, advanced modelling and data science including:

  • Cost Effectiveness Analysis (CEA)
  • Cost Utility Analysis (CUA)
  • Cost Benefit Analysis (CBA)
  • Data linkage
  • Machine Learning based predictive analytics
  • Health Technology Assessment
  • Burden of disease studies
  • Economic decision analysis and modelling
    • Decision trees
    • Markov modelling
    • Monte Carlo simulation/patient level modelling
    • Discrete choice analysis
  • Established methods of evaluating uncertainty through sensitivity analyses and scenarios including multi-parameter and probabilistic sensitivity analysis
    • Biostatistics
      • Quantitative and qualitative data analysis
      • Statistical inference and hypothesis testing
    • Epidemiology

Policy Analysis

Health Economic methods are also an established and growing component of evidence based policy bringing an extension of outcomes based cost effectiveness to government program and policy initiatives. Policy analysis is increasingly utilising interagency data linkage and advanced analytics to bring new light to client outcomes and pathways across programs and the interrelationship with other government and non-government sectors.

We bring applied methods of health economics to our policy analysis projects, integrated with leading edge modelling and data science including:

  • Evaluation Frameworks in context of policy process and strategic roles of stakeholders
  • Overlap, integration and optimization of changing roles of the NGO sector in government health and human service delivery
  • Policy design, pilot programs, implementation and delivery
  • Research and health economic evaluation underpinning policy
  • Introduction of innovative technologies, systems capabilities, and funding models and corresponding impact on service model design, planning and delivery
  • Early intervention, evaluation and program refinement
  • Service planning, budget analysis and projections
  • Development of evaluation plans
  • International perspective through global initiatives, trends and research
  • Workforce, policy and incentive analysis
  • Experience with large health and government datasets as well as cutting edge analytic and modelling software
  • Powerful predictive analytics through our data science machine learning platform
  • Our consultants are also experienced in large scale healthcare and government project management and provide a tailored level of end to end management to ensure project delivery.

Data Science

Machine learning and artificial intelligence is commonplace in our everyday lives, going largely unnoticed. These methods are at the core of search engines, social media, our smart phone virtual assistants and across medical datasets and technologies.

In healthcare, specialist doctors are increasingly assisted by a wide range of machine learning based systems, such as imaging diagnostics where research is indicating the exceptional capacity for ‘learning’ to recognise subtle clinical characteristics and the outstanding accuracy of identification in new images, at levels of value to the most experienced clinicians.

These methods support analysis and pattern recognition with related powerful approaches for validating resulting predictive models that can be applied immediately to new data as they become available, providing rapid re-validation and ongoing refinement and ‘learning’.

In program and health economic evaluations, these methods are enabling similar ground-breaking identification and accuracy of predictive analytics, including across growing sources of large and linked government administrative datasets, and increasing supplementary sources of less structured data.

At Époque, we have been building on our core strengths in health economic methods and data management technologies and have established and continue to develop our machine learning platform, providing ground-breaking powerful techniques across our projects. These methods are delivering state-of-the-art predictive analytics supporting more accurate program targeting and earlier identification and intervention points, with potential to significantly contribute to better individual and program outcomes, increased program effectiveness, and related cost effectiveness.

We are continuing to develop our data science platform and integration with our health economic modelling and policy analysis projects. These techniques are as promising and significant to program evaluation, management and analysis as for wider applications of machine learning and artificial intelligence.

And we are excited about the new dimensions and potential this brings to our projects and our core mission.

To bring new insights, provide evidence and deliver increasing social value.