Confucius says

"Study the past if you would define the future"

We call this evidence-based decision making. Unfortunately, there are a number of barriers to this.

- Data Fragmentation
- Time
- Data quality and expertise
- Privacy concerns

"Study the past if you would define the future"
- Confucius

We call this "evidence based decision making".
Unfortunately, there are a number of barriers to this
Data fragmentation
While there's plenty of data, its in silos and never comes together to aid in making decisions
Time
The time and effort required involves an army of people or lengthy consultancy engagements
Data quality & expertise
The skills needed to validate your data and derive real insight are in short supply and difficult to find
Privacy concerns
Personally identifiable data can be very powerful so protections need to be in place to protect individuals
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Government

cohortiQ was born from DataStart, a program run right out of the Department of Prime Minister and Cabinet. By using open government data in conjunction with specific social services data, cohortiQ is linking datasets together to create rich timelines.

With the recent release of 10% of the MBS/PBS dataset, cohortiQ is able to leverage an unprecedented level of detail about our healthcare system and is currently preparing a case study demonstrating the power of linked data for measuring and predicting human outcomes.

HealthCare &
AGED-Care

Aged care is changing rapidly. People are living, and staying healthy, for longer. We all want our Autumn Years to be golden. Government funding models are changing quickly to cope with our changing demographics, empowering the consumers of aged care.

cohortiQ is currently working with some of Australia's leading aged care providers to breath life into existing research papers in the aged care space. We are linking open government data and existing research to shine a light on the reality of living longer lives as it relates to employment, and help promote the efficacy of programs delivered by aged care providers.




Social Services

What happens when you look at ALL the data for opportunities for early interventions? cohortiQ is helping some of Australia's leading social service providers transform their big data into big opportunities using Machine Learning. We are examining areas such as domestic violence and community welfare programs to enable rapid and interactive assessment of the efficacy of existing programs.

We are also working hard to highlight the gaps in Australia's social services to help improve Australia's social safety net.




Consultants

We understand consultants. We understand that finding, delivering, implementing and tracking change requires a multidisciplinary approach. Often the greatest hurdle is getting everyone in the same room, on the same page.

Not only does the inMotion platform allow you to deliver your results instantly to all the right people, inMotion gives you and your clients a tool for continued engagement and collaboration. Easily plug in advanced visualisations, BI and machine learning to let your clients explore, discover and implement your recommendations.

We understand that continued and ongoing engagement with your clients depends on clear insights and open channels of communication.

"cohortiQ did a brilliant job of succinctly bringing data to life using visualisations that were simple to understand.

The output we saw on the Portal challenged preconceived ideas on the factors that influenced performance while also bringing to light new variables otherwise unseen"
inMotion constructs timelines that allow you to visualise individual and cohort journeys along with their outcomes
Target Longer Term Outcomes

The cohortiQ solution starts with your existing data and organises it as patient or client timelines. This is the basis for measuring longer term outcomes.

Find Predictors

Once data is organised into timelines, the cohortiQ machine learning engine goes to work finding important sets of characteristics for cohorts of people at a higher risk of a measured outcome.
The inMotion platform can identify multiple cohorts, all with thier own characteristics and risk factors
Long Term Outcomes

Shared timeline characteristics are central for analysing which groups of people benefit the most (and least) from the programs which you run.

These groups share multiple characteristics that lead to specific outcomes and are ideal for targeted early interventions.

Multiple Cohorts

Our proprietary machine learning engine doesn't just find one cohort with one set of characteristics, it finds many of them.

From these you can select the groups with the most relevant characteristics for your organisation to target.
Combining machine learning with the human element brings context and delivers more useful insights
Machine Assisted Learning

As the cohortiQ engine surfaces previously hidden insight in your data, you can select which characteristics are more or less relevant within the context of your own requirements.

This is a powerful feature that allows you to interact with the machine learning to increase the value of results. This "man in the loop" concept is now regarded as the missing link that takes machine learning to the next level.

Sharing and Collaboration

With the inMotion platform, refining and sharing insights is as easy as liking or sharing on Facebook.
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