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Motivated by the emerging needs of personalized preventative intervention in many healthcare applications, we consider a multi-stage, dynamic decision-making problem in the online setting with unknown model parameters. To deal with the pervasive issue of small sample size in personalized planning, we develop a novel data-pooling reinforcement learning (RL) algorithm based on a general perturbed value iteration framework. Our algorithm adaptively pools historical data, with three main innovations: (i) the weight of pooling ties directly to the performance of decision (measured by regret) as opposed to estimation accuracy in conventional methods; (ii) no parametric assumptions are needed between historical and current data; and (iii) requiring data-sharing only via aggregate statistics, as opposed to patient-level data. Our data-pooling algorithm framework applies to a variety of popular RL algorithms, and we establish a theoretical performance guarantee showing that our pooling version achieves a regret bound strictly smaller than that of the no-pooling counterpart. We substantiate the theoretical development with empirically better performance of our algorithm via a case study in the context of post-discharge intervention to prevent unplanned readmissions, generating practical insights for healthcare management. In particular, our algorithm alleviates privacy concerns about sharing health data, which (i) opens the door for individual organizations to levering public datasets or published studies to better manage their own patients; and (ii) provides the basis for public policy makers to encourage organizations to share aggregate data to improve population health outcomes for the broader community.