The Challenge Statement
The challenge in the NDIS space is counter-intuitive. The point of uniqueness of healthcare service providers is to offer more and more personalised participant experience and choice. Whilst this fosters well-being and a high level of service, it is also not sustainable due to human capital expenses, shortage of digital solutions, and a capped ability to charge for services. We believe that it is this trajectory of ever-increasing personalisation will ultimately make NDIS service providers unsustainable and therefore actually reduce the availability, quality, and responsiveness of service delivery.
The Solution Statement
The solution to this challenge is a strategic thought change (in many ways counter-intuitive) to simplify the choices provided to participants, standardise treatment and resource plans (and deal with exceptions to this via human capital), as well as use historical medical data and computing power to commoditise large parts of the lifecycle.
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The majority of treatment plans will follow a similar path to either recovery or wellbeing
NDIS service providers are motivated to increase utilisation and resource optimisation of their team
Historical data can accurately predict (based on sufficiently documented needs) future outcomes
What is maica.ai
maica.ai is a digital platform that uses machine learning and artificial intelligence to accurately predict participant’s treatment plan and resourcing requirements as well as intelligently schedules all available human resources. This will reduce the operational human capital burden (which is cost-incurring) for NDIS service providers, allow them to focus on delivering services (which is revenue-generating) by using structured historical data and the enormous computing power available today to increase efficiency and therefore providing the participant better healthcare services.
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An omni-channel platform for participants to self-manage the following:
Personal Details
Plan Details/Statements/Invoice History
Service Times/Resources (this will be constrained to allow the AI-driven algorithms to continually optimise the workforce (care workers), an example is provided below:
A participant may currently have a service scheduled for
Thursday at 3pm. This time is no longer suitable, so rather than being able to suggest/request a new time, maica.ai will present up to 3 options to select from which fall within the optimised workforce management plan. The participant will then be able to select the time suitable to their needs.A participant may currently have a service scheduled with a particular care worker with whom they do not wish to work with anymore. The maica.ai platform will, similar to the above, propose a number of available care workers who are suitable for the work to be performed, are scheduled to be in the geographic area at the scheduled time, and meet all the above-mentioned constraints also.
A historical medical database that is fed (participant’s to be de-identified) by medical practitioners, researchers, and other relevant parties to provide the underlying data of success/failure of particular treatment strategies and their outcomes.
Any exceptions to the above process, which is underpinned by AI-driven platforms, will be handled via existing human processes. This will continue to exist but will be dramatically reduced by commoditising the majority use case.
Automatic
Invoicegeneration, claiming, and payment engine which allows healthcare providers to register their bank account details for fast payments within hours of submitting anInvoice. We anticipate that this function will ultimately be replaced by the NDIS payment system (currently under RFP) but some healthcare providers may elect to remain on our solution regardless.A published API which will accept a set of structured parameters and return a treatment/resource plan as well as schedule. This uses a machine-learning model similar to https://docs.aws.amazon.com/machine-learning/latest/dg/tutorial.html by using Amazon’s ML technology platforms (very likely but design yet to be confirmed).
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Overall, maica.ai will follow the design philosophy of simplifying the thinking and offering to make NDIS providers more efficient. This is similar to the design principle that http://xero.com has brought to the accounting space online. In this case, it has been simplified to be highly functional/suitable without the end user needing to be an accountant. |
How are we building maica.ai
To be completed
Why are we building maica.ai
The current healthcare provider market is struggling to provide care services at a level that is financially sustainable whilst also giving participants freedom of choice to create their own wellbeing journeys. It is our belief that by making healthcare providers more efficient, we can ultimately contribute to the greater wellbeing of the people who need it whilst also building a commercially profitable platform offering the following key services:
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The current trajectory is not sustainable and a circuit-breaker is required to revitalise the healthcare service provider model to ultimately provide participants with the services they need to the level needed to have real positive impact. The vast majority of software solutions aim to address the challenge of providing participants with more and ever-expanding choice; we believe this needs to be constrained and the lifecycle simplified to allow for a flourishing provider sector. |
The Commercial Model
The commercial model can be found here and is based on three separate revenue streams, including:
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