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对话十方医疗呼雅芳:坚持做难而正确的事,用执着熬出数据价值

亿邦智库黄斌 2026-06-03 20:38
亿邦智库黄斌 2026/06/03 20:38

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这篇是亿邦智库对国家级奖项获奖医疗数智化企业十方医疗创始人呼雅芳的深度访谈,整理了十方医疗十余年数字化转型的核心经验与实操干货,具体内容如下:

1. 企业经营的底层逻辑:医疗行业关乎人命,必须坚守良心与医者仁心,尊重医疗行业的特殊性,不能盲目套用普通消费行业的逻辑用数据粗暴管理,要挣对得起良心的钱。

2. 成长发展经验:在行业普遍赚快钱、不在意精细化管理的时候,提前十年布局数字化,自筹资金自建标准、全员通宵整理数据,即便遭遇一半销售团队离职、长期负盈利的困境也坚持,等国家相关规范政策出台后,顺利无缝衔接国家标准,快速拿到试点资质与行业认可。

3. AI落地实操方法:将AI使用纳入绩效考核,要求全员早晚分享AI应用成果,强制推动思维转变,最终实现人机协同,用原有人员就能承接十倍甚至二十倍的业务增长。

十方医疗作为医疗供应链数智化领域的新兴标杆品牌,给医疗行业各类品牌商提供了清晰的发展参考,核心干货如下:

1. 品牌底层建设:以良心和医者仁心作为品牌发展的底层逻辑,契合医疗行业的特殊属性,更容易获得政策、客户与社会的认可,目前十方已经拿到国家级数据要素赛事奖项,是河北省唯一医疗行业可信空间试点。

2. 产品研发方向:瞄准行业真实痛点布局研发,十方精准切入骨科供应链五大痛点:数据碎片化、供应链低效、临床保障弱、医保风控难、数据共享难,研发出的产品获得了可量化的显著成效,库存周转率从0.53次/年提升至1.2次/年,医保拒付率降低35个百分点,市场认可度极高。

3. 盈利与合作模式:十方探索出成熟的三轨盈利模式,客户覆盖生产、流通、医疗、金融、监管全链条,目前已经和纳通集团、河大附属医院等头部机构达成成功合作,未来还将开放标准化SOP,寻找理念契合的合伙人,长期目标冲击独立IPO。

本文给医疗供应链领域的卖家梳理了政策趋势、增长机会与转型注意事项,核心干货如下:

1. 政策趋势判断:早年医疗供应链行业的乱象不可持续,国家必然会出台规范政策,提前布局合规化、数字化的玩家能率先抢占机遇,十方在政策出台前十年就完成了标准与数据积累,政策落地后快速对接,拿到了稀缺的试点资质与行业荣誉。

2. 新增量机会:数据要素是医疗供应链领域新的增长赛道,企业可以通过数据治理解决行业普遍痛点,开辟多元盈利路径,十方的三轨盈利模式可直接参考:第一是给机构提供定制化数据风控服务,第二是帮生产、医疗机构降本增效收取服务费,第三是依托数据能力开展供应链金融服务,目前已经支撑起10亿元级的供应链金融业务。

3. 风险提示:数字化转型前期会遭遇内部团队反对、核心人员流失、短期亏损的阵痛,转型成功的核心是企业一把手坚定信念,提前做好长期投入的准备,才能熬出价值。

本文给医疗生产工厂的数字化转型与业务拓展带来了多方面启示,核心干货如下:

1. 生产与库存端需求明确:当前骨科等医疗细分领域,生产端普遍存在损耗高、库存周转慢的问题,医院端存在高值耗材积压严重的问题,工厂有强烈的数字化优化降本需求,十方的供应链优化服务已经能帮假体厂商把损耗率从8%降至3%,帮医院降低高值耗材积压金额50%以上,工厂对接这类服务可以直接降本增效。

2. 新商业机会:工厂可以和十方这类成熟的医疗数智化企业合作,依托对方的数据溯源能力对接供应链金融服务,解决生产经营中的资金问题,还可以借助对方的客户网络拓展业务渠道,覆盖更多医疗机构客户。

3. 数字化转型启示:工厂不要等行业普及数字化再行动,可以提前布局,早早搭建数据标准、积累结构化数据,同时要强力推动内部AI应用,实现人机协同,用现有人员承接更多业务,避免业务扩张带来人力成本大幅上涨的问题。

对于医疗数智化服务领域的服务商,本文梳理了行业痛点、成熟解决方案与发展趋势,核心干货如下:

1. 客户核心痛点:当前骨科医疗供应链领域普遍存在五大痛点,分别是数据碎片化、供应链低效、临床保障弱、医保风控难、数据共享难,早期行业没有统一标准,跨系统数据有效流通率仅为30%,各类玩家都有强烈的痛点解决需求。

2. 可参考的成熟解决方案:可以先积累标准化多模态数据集,自主研发产业数智中台与AI智能体矩阵,结合数字孪生、AI预测、区块链存证技术针对性解决痛点,十方的方案已经将跨系统数据有效流通率提升至98%,各项核心指标提升明显,可复制性强。

3. 行业发展趋势:未来数据要素价值会持续释放,AI全面重构业务效率、建设合规可信数据空间实现数据增值变现、跨业态融合打造产业生态是三大核心发展方向,服务商可以沿着这些方向布局,还可以通过输出标准化SOP帮行业新玩家少走弯路,拓展自身业务规模。

十方医疗的实践给医疗产业平台的发展提供了多方面参考,核心干货如下:

1. 行业核心需求:当前医疗供应链行业对平台的核心需求,是提供合规安全的数据流通服务、全链路数据风控服务、跨业态资源对接服务,解决行业长期存在的数据碎片化、共享难、风控能力弱的痛点。

2. 平台运营管理经验:可以借鉴十方的AI落地方法,强力推动内部全员使用AI,将AI应用纳入绩效考核,快速推动员工思维转变,短期内实现人机协同,有效降低人力成本,提升平台的业务承接能力,不需要大幅增加人员就能支撑多倍业务增长。

3. 风向规避与发展方向:平台要提前布局合规化数据建设,紧跟国家政策方向,提前做好数据标准积累,避免政策落地后无法对接的风险;未来可以重点布局可信数据空间,对接产业链上下游不同类型的伙伴,构建涵盖生产、流通、医疗、金融、监管的全链条生态,持续提升平台的核心价值。

本文为医疗供应链数字化、数据要素变现领域的研究提供了一手典型案例与产业新信息,核心干货如下:

1. 产业新动向:数据要素在医疗供应链领域已经实现落地变现,诞生了可复制的成熟商业模式,也就是十方提出的三轨盈利模式:分别是数据服务盈利轨道、供应链优化盈利轨道、创新金融服务盈利轨道,该模式覆盖全产业链客户,已经产生了可量化的经济价值与社会价值,是数据要素产业落地的典型样本。

2. 产业新问题:医疗行业数字化转型的核心阻力不是技术问题,而是人的思维转变问题,转型前期会面临内部反对、核心团队流失、长期亏损的困境,数字化转型本质是一把手工程,转型成功的关键是企业一把手的坚持,这是之前研究中少有的一手实践结论。

3. 研究启示:十方提前十年布局、坚持做难而正确的事、政策出台后快速崛起的成长路径,为研究数据要素领域企业成长规律提供了典型案例,也为研究政策落地与企业能力衔接的相关课题提供了真实样本,具备较高的研究参考价值。

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Quick Summary

This is a deep interview by Ebrun Think Tank with Hu Yafang, founder of Shifang Medical, a digital intelligent healthcare enterprise that has won national-level awards. The interview distills Shifang Medical's core experience and actionable insights from over a decade of digital transformation, as outlined below:

1. Underlying logic of operation: Healthcare is a life-and-death industry that demands unwavering integrity and a patient-centric mindset. Companies must respect the unique attributes of the healthcare sector, avoid blindly applying crude data-driven management models common in consumer industries, and only pursue profits that align with ethical standards.

2. Growth lessons: When the industry generally prioritized quick profits over refined management, Shifang invested in digital transformation a decade ahead of the curve. It self-funded the development of internal standards, with all employees working around the clock to organize data. The company persisted even through crises including the departure of half its sales team and prolonged negative profitability. When national regulatory standards were released, Shifang seamlessly aligned with the new requirements and quickly secured pilot qualifications and industry recognition.

3. Practical AI implementation: Shifang integrated AI usage into employee performance reviews, requiring all staff to share AI application outcomes morning and night to force a shift in mindsets. This approach ultimately achieved effective human-machine collaboration, enabling the company to support 10x to 20x business growth with its existing workforce.

As an emerging benchmark brand in digital intelligent healthcare supply chains, Shifang Medical provides clear development guidance for brand owners across the healthcare industry. Key takeaways are as follows:

1. foundational brand building: Building a brand on integrity and patient-centric values aligns with the unique attributes of healthcare, making it easier to earn policy support, customer trust and social recognition. Today, Shifang has won a national award for data element innovation and is Hebei Province's only accredited trusted data space pilot in the healthcare sector.

2. Product R&D direction: Shifang focused its R&D on solving real industry pain points, specifically targeting five core challenges in orthopedic supply chains: fragmented data, inefficient supply chains, weak clinical support, difficult medical insurance risk control, and poor data sharing. Its solutions have delivered measurable, significant results: inventory turnover increased from 0.53 turns per year to 1.2 turns per year, and medical insurance claim rejection rates dropped by 35 percentage points, earning strong market recognition.

3. Profitability and partnership models: Shifang has developed a mature three-track profitability model that serves clients across the entire value chain: manufacturing, distribution, healthcare, finance and regulation. It has already established successful partnerships with leading institutions including Naton Medical Group and the Affiliated Hospital of Hebei University. Going forward, Shifang will open its standardized SOPs to partner with aligned industry players, with a long-term goal of pursuing an independent IPO.

This article outlines policy trends, growth opportunities and key transformation considerations for sellers in the healthcare supply chain space, with key insights as follows:

1. Policy trend analysis: The chaotic practices that dominated the healthcare supply chain industry in earlier years are unsustainable, and national regulatory standards were inevitable. Players that invest early in compliance and digital transformation will be the first to capture first-mover advantage. Shifang completed standard building and data accumulation a full decade before policy implementation, enabling it to quickly align with new rules and secure rare pilot qualifications and industry honors when regulations came into effect.

2. New growth opportunities: Data elements represent an emerging growth track for the healthcare supply chain industry. Companies can unlock diversified revenue streams by solving common industry pain points through data governance. Shifang's three-track profitability model offers a replicable framework: 1) providing customized data risk control services to institutions; 2) charging service fees for cost reduction and efficiency improvement for manufacturers and medical institutions; 3) offering supply chain finance services backed by data capabilities. This model now supports a RMB 1 billion-level supply chain finance business.

3. Risk warning: Early-stage digital transformation will bring growing pains including internal resistance, core talent departure and short-term losses. The key to a successful transformation is unwavering commitment from the top leader, and advance preparation for long-term investment, to deliver long-term value.

This article offers multi-faceted insights for medical manufacturing factories pursuing digital transformation and business expansion, with key takeaways as follows:

1. Clear demand for production and inventory optimization: In medical sub-sectors such as orthopedics, manufacturers generally face high product损耗 and slow inventory turnover, while hospitals struggle with severe overstock of high-value consumables, creating strong demand for digital optimization to cut costs. Shifang's supply chain optimization services have already reduced prosthesis manufacturer损耗 rates from 8% to 3%, and cut high-value consumable overstock for hospitals by more than 50%. Partnering with such providers can deliver immediate cost reduction and efficiency gains for factories.

2. New business opportunities: Factories can partner with established digital intelligent healthcare players like Shifang to access supply chain finance services powered by the partner's data tracing capabilities, solving working capital constraints for operations. They can also leverage the partner's existing customer network to expand business channels and reach more medical institution clients.

3. Digital transformation insights: Factories do not need to wait for industry-wide digital adoption to act. They can start early to build data standards and accumulate structured data, while aggressively driving internal AI adoption to achieve human-machine collaboration. This allows existing staff to support increased business volume, avoiding the sharp surge in labor costs that comes with traditional business expansion.

For service providers in the digital intelligent healthcare sector, this article summarizes core industry pain points, mature solution frameworks and development trends, with key insights as follows:

1. Core client pain points: The orthopedic healthcare supply chain sector currently faces five widespread challenges: fragmented data, inefficient supply chains, weak clinical support, difficult medical insurance risk control, and poor data sharing. In the early stage of industry development, the absence of unified standards left cross-system data flow efficiency at only 30%, creating strong demand for solutions across all types of industry players.

2. Replicable mature solution framework: Providers can first accumulate standardized multi-modal datasets, independently develop an industrial digital intelligence middle platform and AI agent matrix, and address pain points with a combination of digital twin, AI prediction and blockchain evidence storage technologies. Shifang's solution has already boosted cross-system data flow efficiency to 98%, with significant improvements across all core indicators, and the framework is highly replicable.

3. Industry development trends: The value of data elements will continue to grow in the coming years. Three core development directions are: full-scale AI-driven business efficiency improvement, building compliant trusted data spaces to enable data value monetization, and cross-format integration to build industrial ecosystems. Service providers can build their strategies along these lines, and expand their business scale by sharing standardized SOPs to help new industry entrants avoid common pitfalls.

Shifang Medical's practical experience offers multiple insights for the development of healthcare industry platforms, with key takeaways as follows:

1. Core industry demand: The core demand that healthcare supply chain players have for platforms is compliant and secure data circulation services, full-chain data risk control, and cross-format resource matching to solve long-standing industry pain points including fragmented data, poor data sharing and weak risk control capabilities.

2. Platform operation and management lessons: Platforms can adapt Shifang's AI implementation approach, which aggressively drives enterprise-wide AI adoption, integrates AI usage into performance reviews, accelerates mindset shifts among employees, and achieves human-machine collaboration in a short period of time. This effectively reduces labor costs and improves the platform's business carrying capacity, enabling multi-fold business growth without large-scale headcount expansion.

3. Risk mitigation and development direction: Platforms should invest early in compliant data infrastructure, align with national policy priorities, and complete data standard accumulation in advance to avoid the risk of being unable to meet new requirements after policy implementation. Going forward, platforms can prioritize building trusted data spaces, partner with different players across the upstream and downstream industrial chain, build a full-chain ecosystem covering manufacturing, distribution, healthcare, finance and regulation, and continuously enhance the platform's core value.

This article provides first-hand typical case data and new industrial insights for research on digital healthcare supply chains and data element monetization, with key takeaways as follows:

1. New industrial trends: Data elements have already achieved real-world monetization in the healthcare supply chain sector, giving rise to a replicable mature business model: Shifang's three-track profitability framework, which includes three revenue streams: data services, supply chain optimization services, and innovative financial services. The model serves clients across the full industrial chain and has delivered quantifiable economic and social value, making it a representative sample of successful data element industry implementation.

2. New industrial insights: The core barrier to healthcare digital transformation is not technological, but shifting employee mindsets. Early-stage transformation faces headwinds including internal resistance, core team departure and prolonged losses, and digital transformation is essentially a top-leader-led project. The success of transformation hinges on the commitment of the company's top leader, a first-hand practical conclusion rarely captured in prior research.

3. Research implications: Shifang's growth trajectory — investing a decade ahead of the curve, persisting with doing the difficult but right thing, and achieving rapid growth after policy implementation — provides a typical case for research on enterprise growth patterns in the data element sector, and a real-world sample for research on the alignment between policy implementation and enterprise capabilities, offering high research reference value.

Disclaimer: The "Quick Summary" content is entirely generated by AI. Please exercise discretion when interpreting the information. For issues or corrections, please email run@ebrun.com .

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【亿邦原创】十方医疗是一家在2025年国家数据局主办的“数据要素×”大赛中荣获国家级奖项的新兴企业,其《骨科供应链数据集产业生态新动能》项目直击行业五大痛点,实现了库存周转率从0.53次/年提升至1.2次/年、手术备台时间从72小时缩短至5小时、医保拒付率降低35个百分点等关键突破。在创始人呼雅芳的讲述中,这十余年的数字化之路,更像是一场充满孤独与阵痛的“征途”。当行业普遍追求高利润、高周转时,十方医疗却选择了“笨办法”——投入大量资金自建标准、全员通宵整理数据,甚至一度因为管理要求严格而流失半壁江山的销售团队。近日,亿邦智库对呼雅芳进行了深度访谈,透过她极具感染力的讲述,还原了一个医药供应链企业如何成为行业数智化标杆的真实故事。

亿邦智库:在传统的访谈提纲中,我们通常先问战略。但我注意到,您却在你们的成长过程中反复提到“良心”和“医者仁心”。这是否是十方医疗数据的底层逻辑?

呼雅芳:绝对是。我们做的是医药行业,是关乎人命的事情。我自己是学医的,也在部队从过医。这个行业有很强的特殊性。不像卖水、卖面包,可以按消费频次去推算。医疗产品,你得先得了这个病,才能用到。很多产品需要在备货状态下,有的东西甚至可能一辈子都用不上。如果不尊重这个特殊性,盲目用数据去粗暴管理,是对生命的不负责任。做这个行业要挣良心的钱,这是出发点。从这点出发,我们才开始逐步构建起这套管理体系。

亿邦智库:您提到十方医疗“会早于这个行业十年去做管理”,但这个过程非常“惨烈”。具体发生了什么?

呼雅芳:十几年前,这个行业利润非常高,根本不在乎精细化管理。行业有句话叫“北有十方”,但那是做传统业务。我决定转型做数据治理时,公司内部简直是“造反”。

第一,是巨大的投入。整个行业当时都没有信息化,我们自筹资金,投入了大量的人力物力。最痛苦的就是录数据。满库房的产品,你得一个一个录入,没有标准,十个人可能录出十个样子。我们要对产品做RFID芯片,国家没标准,我们自己做码、自己建标准,让它可追溯、可流通。关键是还得要求所有销售人员下单必须按标准来,不能像以前那样随便拿。结果是,销售半壁江山直接断崖式下跌,很多员工觉得你制约了他们,纷纷离职。

第二,是外部的困惑。初时行业整体比较好,一般都在大力挣钱,我们却把重点反而放在干一些“吃力不讨好”的事。很多人不理解”。我找来了志同道合的人,给他们的工资其实不高,但我告诉他们:“你要先认可这个理念,坚持做难而正确的事。我正在做的这一件事,它很难。”

亿邦智库:您提到“先做,等风来”,这是非常有意思的策略。当时您怎么就这么笃定国家政策会来?

呼雅芳:其实不是我预见到了政策,而是当时的条件需要改变,让我觉得这一定不是长久之计。正是在纷乱的环境中,我们不断总结和完善。国家政策出来之前,我们是在摸索;国家政策出来后,我瞬间就起来了。2020年、2021年国家正式发文,因为我们在数据治理的经验、标准早就准备好了,所以很快就接上了轨。这就是“机遇是留给有准备的人”。我们投入了大量精力做RFID芯片,等国家政策覆盖到百分之三五十时,我们就把自己的标准停掉,无缝衔接到了国家标准。目前,我们已获批河北省唯一“医疗行业可信空间”试点,以及河北省首批高质量数据集。在“数据要素×”大赛中,我们的22个多模态标准化数据集实现了跨系统有效流通率从30%提升至98%。

亿邦智库:刚才您讲了很多心路历程。那么在技术落地上,十方医疗具体解决了行业哪些核心问题?有哪些可量化的成效?

呼雅芳:我们的项目应该讲是精准切入了医疗特别是骨科行业五大痛点:数据碎片化、供应链低效、临床保障弱、医保风控难、数据共享难。

在供应链效率方面,通过数字孪生和AI预测模型,我们将骨科行业的库存周转率从0.53次/年提升至1.2次/年,工具周转天数从30天降至3天,物流空载率从30%降至5%以下。在临床保障方面,我们实现的AI智能备台系统将手术备台时间从72小时缩短至5小时,备台准确率从70%提升至99.8%以上。在医保风控方面,通过全链路数据留痕和区块链存证,医保拒付率较原水平降低35个百分点,医保基金审核准确率提升至99.9%。

这些数据背后,是我们自主研发的《医疗链产业数智中台》和“十方无界·智享AI”智能体矩阵在发挥作用。目前,项目已沉淀结构化数据6TB,核心数据实现分钟级更新。

亿邦智库:当前业界都非常关注对AI的应用。但对于十方医疗来说,AI的落地非常特别,甚至到了“强行”的程度,为什么?

呼雅芳:我认为AI的落地技术根本不是什么问题,是人的问题。人的思维问题解决了,智能体、大模型才能用。我们内部最早在ChatGPT刚出来时,就鼓励员工应用。后来DeepSeek出来后,我们正式要求每个人必须有自己的AI助理。具体措施也很“惨烈”:早会、晚会,每个人必须分享你今天用AI帮你做了什么工作。甚至放在绩效考核里,今天没用就扣钱。刚开始大家都不会用,现在人人都用得可好了。我们还有数字员工的上榜,组织数字员工的组织架构。

现在我们已经是人机协同在工作。员工们很开心,因为大量重复性工作被智能体代替了,他们可以腾出时间思考做更有创造性的工作。这解决了我们一个核心痛点:以前业务量增加十倍、二十倍,就需要增加大量人员;现在还是这点人,因为我们有了数据和AI。

亿邦智库:在数据要素流通和商业变现方面,十方医疗有哪些具体模式?

呼雅芳:我们探索了“三轨盈利模式”,即:

第一,数据服务盈利轨道,就是向医疗机构、企业、监管机构提供定制化数据分析和风控服务。我们已为金融机构提供医疗供应链数据风控支撑,API接口平均响应时间≤2秒,可用性≥99.9%。

第二,供应链优化盈利轨道,主要通过精细化管理,帮助器械生产企业降低生产损耗(某假体厂商损耗率从8%降至3%),帮助医院降低高值耗材积压金额50%以上。

第三,创新金融服务盈利轨道,以数据溯源能力为基础,支撑了10亿元级的供应链金融业务。

我们的客户覆盖了生产、流通、医疗、金融、监管全链条。例如,我们与纳通集团合作实现库存周转提升,与河大附属医院合作实现备台时间缩短,与医保局合作构建风控模型,成功拦截异常医保支出,等等。

亿邦智库:在访谈的最后,您能对后来者提一个一句话的建议吗?

呼雅芳:我就一个词:老板。你要坚持认为这个对,就坚持做下去。很多事,无论高层还是其他人,所有人都不支持你的时候,老板你做还是不做?就在于这儿。数字化转型是一把手工程,跟谁都没关系,就是老板。

我们用了十几年的时间熬出来,并不是我们比别人聪明,而是早干了十年,并坚持了下来,所以现在通过SOP标准流程,希望找到理念相同的资本方或合伙人。这个发展路径,按我们的经验、标准、流程去做,就能少走弯路。

关于未来,我们有三个明确方向:第一,以AI全面重构业务效率;第二,建成可信数据空间,让数据在安全合规的前提下增值变现;第三,探索金融、保险等跨业态融合,构建医疗供应链、金融服务、保险保障协同发展的产业生态。我们的长期目标是实现独立IPO。

未来以来,一步一个脚印踏实去做,不要过多焦虑,也不要对直接效益产生过高的期望。

结语:长期持续地坚守

在访谈的最后,呼雅芳用一段话总结了十方医疗的价值观:“未来以来,一步一个脚印踏实去做。不要过多焦虑,也不要过高期望。”当概念退潮、风口转向,真正能够穿越周期的,永远是那些扎入行业深处、为客户创造真实价值的企业。十方医疗正在用十余年的“数据征途”,努力书写中国医疗医药供应链的“数智”新篇章。亿邦智库将持续关注产业互联网发展与企业数据要素竞争力提升,并报道相关发展的新成果与新案例。

      联系邮箱为:huangbin@ebrun.com


文章来源:亿邦智库

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