Beyond data intelligence
Data assets are growing exponentially but our human ability to utilise them effectively has not kept pace. The traditional means (such as caching, batch jobs, pre-aggregation, sampling & de-resolution, long information request cycles, etc. ) simply do not cut it anymore – they are WAY too slow and incomplete.
This problem is real, huge, affects everyone and needs fixing ! Data volumes are going through the roof – and are more and more complex to manage. Using Artificial Intelligence (or AI) to handle this problem will be the next big thing in healthcare and CALYPS intends to be an important part of it !
High complexity & information overload
While solving this is indeed a challenge, we at CALYPS have a way to make this much more manageable.
Hospitals have major issues with efficiency : very often, inappropriate use of resources leads to reduced efficiencies in daily hospital activities, thereby preventing potential benefits as well as sustained higher efficiencies in these activities. Forecasts and activities could be significantly improved with better planning and scheduling tools, which would help hospitals increase their revenues by tens of millions of euros per year. Indications are that efficiency gains of up to 15% across all areas are possible, maybe even higher. Economically, this could result in collective gross margin increases in the tens of billions of euros across all hospitals Europe, mainly due to better use of resources, both human and material.
What we aim to solve
Today’s situation is becoming more and more complicated to manage: too many unforeseen activities, rapidly increasing complexities in context and circumstances, increasingly recurrent peaks of activity, heightened pressure in decision making, growing stress on human resources, sub optimal use of means, increasing unit costs, information in silos, a large and growing volume of untapped and unused data…
This is leading to costly effects which could be avoided, such as the exhaustion of medical and paramedical teams (which sometimes leads to fatal human errors), uncoordinated use of resources (which generates congestion and undesired waiting time), analyses made based on incomplete facts and knowledge that limit the scope of actions or create savings without impact. And always this feeling of spending too much time behind a screen (and not enough with the patient) and having to fill out more and more forms…
To solve this challenge, we must do more than “just” process the data. The artificial intelligence developed by CALYPS (codename CALAI) learns from observed reality, anticipates unexpected events, predicts resulting activities (and their length), accordingly prescribes the best possible allocation of resources and produces a schedule to limit uncertainty to a minimum.
This unique approach drastically reduces the problem of limited information processing capacity by (overwhelmed) humans facing an ever-increasing generation of data. Thanks to CALAI, a hospital can handle complexity much better and more calmly. It anticipates and prepares its resources to respond as effectively as possible to pressure while making a patient’s journey more fluid and concurrently limiting stress on its teams.
Thanks to CALAI, actions are better coordinated, resources are optimally used to significantly reduce losses, time with patients is increased, care is delivered with even better quality, patient readmissions are reduced and resulting economic margins enable the sustaining of higher efficiencies.
With our artificial intelligence tools designed to extract the full potential of their data, help our clients achieve high efficiency gains, high quality work and greater profitability, all without impacting the quality of patient care or damaging their resources (both human and material).
Developing CALAI requires special know-how and broadly ranging skills. CALYPS technology team is made up of system engineers, developers, data architects and data scientists.
In view of the rapid evolution of algorithms and the numerous publications in the field of AI, as well as accelerating focus on the healthcare sector, we maintain various academic and specialist relationships which aide the evolution of CALAI. This, in order to benefit from the latest state of the art scientific developments. Examples are Switzerland’s HEIG-VD and people affiliated with its polytechnic universities ETHZ and EPFL. Links to US based MIT are also being developed. By adopting a broad approach we aim to expand our network of researchers to enable a better understanding of the evolution of this science and be abreast of its latest progress.