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Joint replacement is the most common inpatient surgical treatment in the US.We investigate the clinical pathway optimization for knee replacement, which isa sequential decision process from onset to recovery. Based on episodic claimsfrom previous cases, we view the pathway optimization as an intelligencecrowdsourcing problem and learn the optimal decision policy from data byimitating the best expert at every intermediate state. We develop areinforcement learning-based pipeline that uses value iteration, statecompression and aggregation learning, kernel representation and crossvalidation to predict the best treatment policy. It also provides forecast ofthe clinical pathway under the optimized policy. Empirical validation showsthat the optimized policy reduces the overall cost by 7 percent and reduces theexcessive cost premium by 33 percent.

RL4health: Crowdsourcing Reinforcement Learning for Knee Replacement Pathway Optimization