Foundational work on causal inference and dynamic treatment regimes presents a promising pathway towards precision medicine. In a precision-medicine system, decision rules might be algorithmically individualized based on an optimal rule previously learned from non-experimental or experimental data. However, there is some resistance to the notion that implementation of an optimal regime, successfully learned from the data, will result in better expected outcomes on average, compared to existing human-decision rules: care providers may be inclined to override the treatment recommendations provided by the identified optimal regimes, based on their privileged patient observations. In this talk, I will review existing methodology for learning optimal regimes and clarify the validity of the care provider's skepticism. Then, I will present methodology for leveraging human intuition by identifying a super-optimal regime using data generated by either nonexperimental or experimental studies, and clarify when a fusion of such data is beneficial. The superoptimal regime will indicate to a care provider -- in an algorithm-assisted decision setting -- precisely when expected outcomes would be maximized if the care provider would override the optimal regime recommendation and, importantly, when the optimal regime recommendation should be followed regardless of the care-provider's assessment.