EAGER: Data-Driven Learning and Decision Making in Healthcare

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The goal of this EArly-Grant for Exploratory Research (EAGER) award is to create a new generation of learning and decision making tools in healthcare systems enabled by growing availability of data. Making data-driven learning and decision making an integral part of healthcare has profound potential to both improve the quality of medical care and to reduce healthcare costs. Despite this, state-of-the art methods are still insufficient for achieving broad acceptance, and significant impact of evidence-based decision making in medical settings is lacking. The fundamental methodological and scientific challenges that this project aims to investigate lie at the intersection of multiple disciplines: decision processes, machine learning, and statistics. This project helps advance this multidisciplinary research area and also develops teaching units for training a new cohort of researchers in this high impact space. On the other hand, this project has the potential to impact other domains beyond healthcare. In particular, the emphasis on dynamic learning and optimal decisions from large amount of data make its findings relevant to a number of domains such as finance, marketing, and electronic commerce. Due to advances in technology and government incentives a large amount of data on patients' conditions is becoming available electronically. On the other hand advances in machine learning and statistics allow the design of "predictive systems": algorithms that can learn generalizable patterns by sifting through a large number of patient profiles and provide accurate future forecasts about clinical adverse events, treatment outcomes, or demand for healthcare services. These predictions can be produced in real-time as new data is captured and can help guide decisions in healthcare systems. However, when statistical learning models are used to guide decisions such as medical treatments, clinicians make decisions based on their predictions that can change subsequent patient data that are collected and used to "re-train" the predictive system, thereby updating the forecast probabilities at the presence of new evidence. Current predictive systems in practice fall in one of the following two categories: (1) they are never re-trained post first installation; or (2) they are periodically re-trained with the arrival of new data. However, the first approach leads to poor forecasts when the new data arrives and circumstances around the decisions change. On the other hand, recent advances in the theory of multi-armed bandits, reinforcement learning, and their applications to digital advertising and recommendation systems indicate that the second approach can also lead to low quality predictions due to the endogeneity of the decision making process. The new dynamic-learning and decision making tools developed in this project will be robust against these challenges by proper modeling of the interactions between the data elements, the decisions, and the consequences of the decisions on new data.