Defining a financial forecasting model for healthcare insurance companies

This document concerns an exploratory research towards the application of collaborative Markov chains as a forecasting model within the field of healthcare insurance. The model proposed is based on both predicting care demand and associated institutional pathway traversal. A system of collaborative Markov chains allows the user to jointly model several probabilistic elements that share dependencies. It describes a collection of Markov chains in which the state of a certain chain within the collection influences transition probabilities in other chains within the collection. It allows the use of different types of techniques to estimate forecasting parameters as an input within one model. It entails a modular structure which allows the user to perform case-based analyses. Simulation of simplified proof-of-concept cases has shown accurate predictive behaviour. Due to the fact that Markov chains and consequently systems of collaborative Markov chains are probabilistic in nature, simulation of a sufficient number of sampling replication yields results that tend to follow a Normal distribution. The statistical nature of the simulation results lends itself perfectly for consecutive statistical post-processing. Systems of collaborative Markov chains provide in modelling complex probabilistic systems in which several dependencies might exist. Current challenges within the application of systems of collaborative Markov chains involve the complexity in terms of the number of parameters to estimate and associated running times. Additionally the existence of potential “inactive elements” with respect to the field of healthcare insurance introduces additional challenges in parameter estimation of the model.

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