Hiring Doctors in E-Healthcare With Zero Budget

TL;DR

This paper introduces three novel mechanisms for allocating expert doctors to low-income patients in e-healthcare settings without any budget, aiming to improve critical medical access globally.

Contribution

It presents the first zero-budget doctor allocation mechanisms for underserved populations, with theoretical proofs and experimental validation.

Findings

TOAM is strategy-proof and has a unique core property

Mechanisms are validated through exhaustive experiments

Addresses healthcare access for below-income groups globally

Abstract

The doctors (or expert consultants) are the critical resources on which the success of critical medical cases are heavily dependent. With the emerging technologies (such as video conferencing, smartphone, etc.) this is no longer a dream but a fact, that for critical medical cases in a hospital, expert consultants from around the world could be hired, who may be present physically or virtually. Earlier, this interesting situation by taking the expert consultancies from outside the hospital had been studied, but under monetary perspective. In this paper, for the first time, to the best of our knowledge, we investigate the situation, where the below income group (BIG) people of the society may be served efficiently through the expert consultancy by the renowned doctors from outside of the hospital under zero budget. This will help us saving many lives which will fulfil the present day need of biomedical research. We propose three mechanisms: Random pick-assign mechanism (RanPAM), Truthful optimal allocation mechanism (TOAM), and Truthful optimal allocation mechanism for incomplete preferences (TOAM-IComP) to allocate the doctor to the patient. With theoretical analysis, we demonstrate that the TOAM is strategy-proof, and exhibits a unique core property. The mechanisms are also validated with exhaustive experiments.