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.

翻译：医疗资源供需失衡是全球性挑战，在发展中国家尤为严峻。政府与学术界虽已采取多种措施增加医疗供给、优化资源配置，但现有方案往往存在被动僵化之困。平行诊疗系统的出现为破解上述难题提供了新路径，通过释放数据价值实现资源优化配置。该系统由医学导向操作系统（MOOS）、医学导向场景工程（MOSE）与医学导向大模型（MOLMs）三部分组成，可实现数据采集、流转与赋能。本文提出，通过平行诊疗系统的数据赋能机制，有望实现医疗资源配置均衡。具体路径包括：（1）依托MOOS中数字医生与机器人医生提升供给能力；（2）基于MOSE的主动诊疗模式识别个体潜在需求；（3）利用MOLMs大模型优化供需匹配。为验证该方法的有效性，我们以设施可达性为视角开展资源优化配置案例研究。结果表明，平行诊疗系统可使医疗资源可达性提升高达300%。