Does the Doctor Need a Boss?

The traditional model of medical delivery, in which the doctor is trained, respected, and compensated as an independent craftsman, is anachronistic. When a patient has multiple ailments, there is no longer a simple doctor-patient or doctor-patient-specialist relationship. Instead, there are multiple specialists who have an impact on the patient, each with a set of interdependencies and difficult coordination issues that increase exponentially with the number of ailments involved. Patients with multiple diagnoses require someone who can organize the efforts of multiple medical professionals. It is not unreasonable to imagine that delivering health care effectively, particularly for complex patients, could require a corporate model of organization. At least two forces stand in the way of robust competition from corporate health care providers. First is the regime of third-party fee-for-service payment, which is heavily entrenched by Medicare, Medicaid, and the regulatory and tax distortions that tilt private health insurance in the same direction. Consumers should control the money that purchases their health insurance, and should be free to choose their insurer and health care providers. Second, state licensing regulations make it difficult for corporations to design optimal work flows for health care delivery. Under institutional licensing, regulators would instead evaluate how well a corporation treats its patients, not the credentials of the corporation's employees. Alternatively, states could recognize clinician licenses issued by other states. That would let corporations operate in multiple states under a single set of rules and put pressure on states to eliminate unnecessarily restrictive regulations

Learning-Assisted Automated Reasoning with Flyspeck

The considerable mathematical knowledge encoded by the Flyspeck project is combined with external automated theorem provers (ATPs) and machine-learning premise selection methods trained on the proofs, producing an AI system capable of answering a wide range of mathematical queries automatically. The performance of this architecture is evaluated in a bootstrapping scenario emulating the development of Flyspeck from axioms to the last theorem, each time using only the previous theorems and proofs. It is shown that 39% of the 14185 theorems could be proved in a push-button mode (without any high-level advice and user interaction) in 30 seconds of real time on a fourteen-CPU workstation. The necessary work involves: (i) an implementation of sound translations of the HOL Light logic to ATP formalisms: untyped first-order, polymorphic typed first-order, and typed higher-order, (ii) export of the dependency information from HOL Light and ATP proofs for the machine learners, and (iii) choice of suitable representations and methods for learning from previous proofs, and their integration as advisors with HOL Light. This work is described and discussed here, and an initial analysis of the body of proofs that were found fully automatically is provided