Cognotekt maximises business-process automation
for task chains consisting of repetitive,
simple human cognitive behaviour.
We analyse a given business process and determine
We then re-design the process taking into account
To achieve this, we blend rules, deterministic equation systems
As a result, the amount of human labour is reduced,
As a foundation, we use a generic software platform
which can support business processes
in various industries.
As an example, consider the drug safety process in the pharmaceutical industry. Here, we automate the data acquisition and the evaluation of the cases. At the beginning of the process, we digitalise all incoming drug-safety-case texts. We then auto-structure the texts and auto-populate a data template which a human worker reviews and completes.
The case is then automatically evaluated and the need for reporting it to the authorities is computed. Appropriate thresholds ensure that there are no false negative cases, i.e. all cases that need to be seen by a human being are routed accordingly.
Once the cases are evaluated, their distribution to regulators is highly automated using classical business rules.
This process support comes with rigorous business process management to optimise the process, allow it to be changed and to obtain true oversight. Rich data models enable faster and higher quality risk detection that allows pharmaceuticals to thoroughly optimise the indication spectrum of their drugs.
"The drug safety platform you envisage finally gives us
the long expected software for the post-Vioxx age. This is finally the new approach for pharmacovigilance we have been waiting for."
- Top ten pharmaceutical, drug safety executives.
As an other example, consider claims management in the insurance industry. This process is attractive for automation as its main inputs are numbers, codes and short standardised texts amenable to computable formats. We drastically automate data digitisation and structuring and create a chain of fully automated claims processing.
We offer automated claims checking, fraud detection, reimbursement and payment as well as machine decision for the routing to the optimal case expert should the machine not be able to process the case. Only 20-25% of the claims, notably the iterating ones with long reports or those with high value potential, need human intervention.
The remaining human labour can be focused to increase quality and to enable machine learning improvement. The savings potential is huge and customers benefit from a massive reduction of waiting times and better service.
"This is the future of claims management."
"Finally an exciting vision for claims management after decades of stagnation."
- Globally leading primary insurance, claims management executives.