  The imbalance between the supply and demand of healthcare resources is a
global challenge, which is particularly severe in developing countries.
Governments and academic communities have made various efforts to increase
healthcare supply and improve resource allocation. However, these efforts often
remain passive and inflexible. Alongside these issues, the emergence of the
parallel healthcare system has the potential to solve these problems by
unlocking the data value. The parallel healthcare system comprises
Medicine-Oriented Operating Systems (MOOS), Medicine-Oriented Scenario
Engineering (MOSE), and Medicine-Oriented Large Models (MOLMs), which could
collect, circulate, and empower data. In this paper, we propose that achieving
equilibrium in medical resource allocation is possible through parallel
healthcare systems via data empowerment. The supply-demand relationship can be
balanced in parallel healthcare systems by (1) increasing the supply provided
by digital and robotic doctors in MOOS, (2) identifying individual and
potential demands by proactive diagnosis and treatment in MOSE, and (3)
improving supply-demand matching using large models in MOLMs. To illustrate the
effectiveness of this approach, we present a case study optimizing resource
allocation from the perspective of facility accessibility. Results demonstrate
that the parallel healthcare system could result in up to 300% improvement in
accessibility.
