A Darling Ventures expert panel co-hosted with The Hive

与The Hive共同主办的Darling Ventures 专家小组

On August 13, we partnered with The Hive to host the first of our three-part series examining the role of data and AI in transforming the US healthcare industry. Part I: Driving Affordability in Healthcare with Data & AI (watch our video recording of the webinar) featured guest speakers with deep and varied expertise from across the industry:

8月13日，我们与The Hive合作举办了由三个部分组成的系列文章的第一个系列，探讨数据和AI在改变美国医疗保健行业中的作用。 第一部分：通过数据和AI推动医疗保健的可负担性 ( 观看我们的网络研讨会视频录像 ) 演讲嘉宾，来自各行各业，具有深厚的专业知识：

• Kaushik Bhaumik (moderator) — CEO of Glide Health, a software startup and recent DV investment transforming the US healthcare industry by making the cost of care more financially transparent for both patients and providers (doctors / clinics / hospitals). Kaushik previously ran Cognizant’s $3B healthcare business.

  Kaushik Bhaumik(主持人)-Glide Health的首席执行官 ，该公司是软件初创公司，最近通过DV投资使美国和美国(医生/诊所/医院)的医疗费用在财务上更加透明，从而改变了美国医疗保健行业。 Kaushik之前经营过Cognizant的$ 3B医疗保健业务。

• Patrick Spoletini (panelist) — VP and Senior Partner with IBM Watson Health Consulting with over 27 years of healthcare industry (payer and provider) and management consulting experience ranging from strategy, operations, and compliance to digital transformation, change management, and large-scale program management.

  Patrick Spoletini(全景专家)— IBM Watson Health Consultin的副总裁兼高级合伙人，拥有超过27年的医疗保健行业(付款人和提供者)和管理咨询经验，涉及从战略，运营和合规性到数字化转型，变更管理和大型规模计划管理。

• Larry Bridge (panelist) — Partner at Bridgehealth Partners with more than 30 years of experience in healthcare, including leadership of several payer organizations, provider-based plans with integrated physicians, medical centers and hospitals.

  Larry Bridge(panelist)— Bridgehealth Partners的合伙人 ，在医疗保健领域拥有30多年的经验，包括多个付款机构的领导，与集成医师，医疗中心和医院相关的基于提供者的计划。

The panelists spent the hour discussing the key drivers behind the escalating cost of care in the US and the role of data and AI in helping payors, providers, and patients make healthcare more accessible and affordable. Some of our key takeaways include the following:

小组成员花了一个小时讨论了美国医疗费用不断上涨的背后的主要驱动因素，以及数据和AI在帮助付款人，提供者和患者使医疗服务更加容易获得和负担得起方面的作用。 我们的一些关键要点包括：

1. Why the US Spends So Much on Healthcare but Achieves So Little

   为什么美国在医疗保健上花这么多钱却收效甚微

Healthcare spending in the US is among the highest of all developed nations, coming in at an astonishing $3.5 trillion annually, which amounts to approximately $10K per US resident. Despite this astronomical figure, Americans have a lower life expectancy compared to other nations and higher degrees of affliction across a range of maladies. The irony of this truth — that Americans spend much more but aren’t markedly healthier than citizens of peer countries — was the motivation behind our moderator’s first question: Why does the US spend so much but achieve so little in providing sufficient care?

美国的医疗保健支出在所有发达国家中名列前茅，每年达到惊人的3.5万亿美元，相当于每位美国居民约1万美元。 尽管有这样的天文数字，但与其他国家相比，美国人的预期寿命较低，而在一系列疾病中的痛苦程度更高。 具有讽刺意味的是，美国人花了更多的钱，但并不比同龄国家的公民明显健康。这是我们主持人提出的第一个问题的动机：美国为什么花这么多钱却却在提供足够的照顾方面却收效甚微？

Panelist Larry Bridge outlined two root causes:

小组成员拉里·布里奇(Larry Bridge)概述了两个根本原因：

• The healthcare system in the US has evolved piecemeal, rather than being engineered美国的医疗保健系统是逐步发展的，而不是经过精心设计的
• The key components of healthcare are individual, private market companies whose incentives are often misaligned with the goal of keeping costs low医疗保健的关键组成部分是个人，私人市场公司，其激励机制常常与降低成本的目标相抵触。

He went on to describe the US healthcare system as follows:

他接着描述了美国的医疗体系如下：

“Picture a large rock wall, made up of rocks of all different sizes, some big and some small, […] and a lot of mortar in-between trying to hold it together. Now compare that to a finely engineered brick wall.”

“想象一堵大的岩墙，它由各种大小的岩石组成，大小不一，[…]中间夹着很多灰浆，试图将其固定在一起。 现在，将其与精心设计的砖墙进行比较。”

The US healthcare system is more like the former than the latter; comprised of private components with individual incentives that are often in conflict. Letting these components operate individually leads to fraud, waste, and abuse, estimated to amount to a dismaying 25% of America’s $3.5T annual spend.

美国的医疗体系比前者更像前者。 由私人成分组成，并且具有经常相互冲突的个人动机。 让这些组件单独运行会导致欺诈，浪费和滥用，估计这相当于美国3.5T年度支出中令人沮丧的25％。

2. The Impact of AI on Care and Cost

2.人工智能对护理和成本的影响

What role, then, can AI play in mitigating this waste? Patrick Spoletini broached this question by first describing two key developments that have empowered AI to have an impact on the quality and cost of care. Unlike in the past, the industry now has access to a wealth of data that simply wasn’t available previously. The gradual accumulation of more and more data points, along with the consolidation and convergence of large payors and providers, have produced substantial data sets that make AI enabled outcomes more reliable. In addition, data scientists now have the computational power to process and analyze this data.

那么，人工智能在减轻这种浪费方面可以发挥什么作用？ 帕特里克·斯波莱蒂尼(Patrick Spoletini)首先描述了两个关键发展，这些发展使AI能够对护理的质量和成本产生影响，从而提出了这个问题。 与过去不同，该行业现在可以访问以前根本无法获得的大量数据。 越来越多的数据点的逐步积累，以及大型付款人和提供者的整合和融合，已经产生了大量的数据集，这些数据集使AI支持的结果更加可靠。 此外，数据科学家现在具有处理和分析此数据的计算能力。

The panelists next addressed how AI can reduce costs from both the administrative and care perspective. Patrick and Larry outlined a number of AI applications on the payor side, including the use of Machine Learning and AI to optimize costly and time-consuming processes such as authorization of care, denial analytics, and revenue integrity. Companies such as Glide Health, a recent DV and Hive investment, offer innovative solutions powered by AI to automate these processes, thereby reducing the cost of previously cumbersome, manual tasks. On the care delivery side, clinicians are using AI to come up with better diagnostics and more targeted therapies. While both panelists have historically seen payors employ AI to a greater degree than providers, Larry and Patrick also agreed that AI use on the clinical side has the potential to drive a lot of future value. According to Patrick,

小组成员接下来从管理和护理的角度探讨了AI如何降低成本。 帕特里克(Patrick)和拉里(Larry)在付款方概述了许多人工智能应用程序，包括使用机器学习和人工智能来优化成本高昂且耗时的流程，例如护理授权，拒绝分析和收入完整性。 诸如DV和Hive近期投资的Glide Health等公司提供了由AI驱动的创新解决方案，以实现这些流程的自动化，从而降低了以前繁琐的手动任务的成本。 在护理提供方面，临床医生正在使用AI提出更好的诊断方法和更有针对性的疗法。 两位专家从历史上看，付款人使用AI的程度要高于提供者，而Larry和Patrick也同意，在临床方面使用AI有可能带来很多未来价值。 根据Patrick所说，

I’ve seen AI been deployed in payors much longer than providers… It’s not as ubiquitous in the provider space, and there’s a lot more upside in the provider space than in the payor space.

我已经看到，在付款人中部署AI的时间比提供者要长得多……在提供者空间中并不普遍，而且在提供者空间中的潜力要比在付款者空间中大得多。

Whether it be in AI-guided diagnosis or AI-guided treatment protocols, the panelists expressed enthusiasm for the future clinical use of AI to improve quality of care and reduce cost.

不论是在AI指导的诊断还是在AI指导的治疗方案中，小组成员都对未来AI在临床上的使用表现出热情，以提高护理质量并降低成本。

3. Challenges to Adopting AI from within the Healthcare Industry

3.在医疗保健行业采用人工智能的挑战

Despite the promise of AI to optimize existing processes, its use has been met with some resistance from within the healthcare industry. One reason for this wariness may stem from an existing internal organizational culture that doesn’t rely on data-driven decision making. Patrick pointed out that even some of his most sophisticated clients who say they want to use data to improve performance don’t measure the success of internal initiatives via data, metrics, or KPIs.

尽管AI承诺优化现有流程，但在医疗行业中遇到了一些阻力。 保持警惕的一个原因可能是由于现有的内部组织文化不依赖于数据驱动的决策。 帕特里克(Patrick)指出，即使是一些最老练的客户，他们表示希望使用数据来提高绩效，也无法通过数据，指标或KPI衡量内部计划的成功与否。

“If you don’t adopt the attitude of ‘I want to be, today or in the future, a data driven organization,’ you don’t have the right culture to start with.”

“如果您不采取'我希望成为今天或将来成为数据驱动型组织'的态度，那么您就没有正确的文化开始。”

Another point of resistance stems from barriers preventing the exchange of information. Data points in isolation have exponentially less predictive power vs. a more robust data set, and there is dramatic potential upside for an organization that is open to exchanging data both internally and externally. Patrick put it succinctly:

另一个阻力点是阻碍信息交流的障碍。 与更强大的数据集相比，孤立的数据点具有指数级的预测能力，而对于愿意在内部和外部交换数据的组织而言，则存在巨大的潜在上行空间。 帕特里克简洁地说：

“You’ve got to be able to exchange data within your organization as well as outside your organization. You have to be intra-operable as well as inter-operable.”

“您必须能够在组织内部以及组织外部交换数据。 您必须是内部可互操作的。”

To combat provider resistance to AI, Patrick suggested showing physicians facts and figures around improvements in AI-assisted outcomes, while also making this data easily digestible to mitigate physician fatigue.

为了对抗提供者对AI的抵抗力，Patrick建议向医生展示有关AI辅助结果改善的事实和数据，同时还使这些数据易于消化以减轻医生的疲劳感。

On the topic of data privacy, both panelists agreed there was a need to balance the value of having more complete patient data with developing protocols and security that protects individuals. Going back to Larry’s earlier point about the evolution of the US healthcare system, Patrick highlighted the difficulty of developing secure systems within a network of separate, often misaligned entities. Positioning privacy as a business imperative, he suggested, rather than a compliance issue may be one way to incentive better data privacy protocols within organizations.

关于数据隐私的话题，两位小组成员都同意，需要在拥有更完整的患者数据的价值与不断发展的协议和保护个人的安全性之间取得平衡。 回到拉里关于美国医疗保健系统发展的早期观点，帕特里克(Patrick)强调了在独立且经常错位的实体网络中开发安全系统的困难。 他建议，将隐私定位为企业的当务之急，而不是合规性问题可能是在组织内激励更好的数据隐私协议的一种方法。

4. The Role of COVID-19 in Accelerating Technology Adoption

4. COVID-19在加速技术采用中的作用

The panelists also touched upon the impact of the pandemic on the adoption rate of new technologies — a timely topic with important ramifications for the current and future state of the US. In terms of new technologies, one major trend observed by both Patrick and Larry was the promotion of preventative care and initiatives to push health care more into the community, in an effort to address patient ailments before they get to the ER. Both panelists were optimistic about the potential for telemedicine, wearables, and better data collection to enable more intelligent decision making among clinicians and practitioners interacting with patients well in advance of a hospital visit.

小组成员还谈到了大流行对新技术采用率的影响，这是一个及时的话题，对美国当前和未来的状态产生了重要影响。 在新技术方面，帕特里克(Patrick)和拉里(Larry)共同观察到的一个主要趋势是促进预防保健和将医疗保健更多地推向社区的举措，以在患者进入急诊室之前解决疾病。 两位小组成员对远程医疗，可穿戴设备和更好的数据收集的潜力感到乐观，以使临床医生和从业人员在与患者进行互动之前能更好地做出明智的决策，从而可以在医院就诊之前进行互动。

More specific to COVID-19, AI is also being used to expedite clinical trials and hasten the path to a set of treatments and vaccines for the virus. Instead of looking for how AI can create a cure, however, Larry emphasized the importance of stepping back and looking at the larger picture of how automation is improving the healthcare system as a whole. He summarized his point of view by saying:

更特定于COVID-19的AI也被用于加速临床试验，并加快了针对该病毒的一系列治疗和疫苗的开发之路。 拉里没有寻找AI如何创造治愈方法，而是强调了退一步的重要性，并着眼于自动化如何整体改善医疗体系的全局。 他说：

“That’s the real promise of all this... it’s not that AI will solve any one thing, or that there’s an AI-enabled solution that will solve COVID-19, but if you look at the myriad of things that are going on out there to tackle this issue, AI seems to be making a lot of them bigger, faster, quicker to getting to results.”

“这是所有这些的真正承诺……不是AI可以解决任何一件事，也不是说有AI支持的解决方案可以解决COVID-19，但如果您看到无数的事物，为了解决这个问题，人工智能似乎正在使它们中的许多更大，更快，更快地达到结果。”

The question, then, is perhaps not “how will AI cure COVID-19?”, but “how will AI improve existing processes to get us faster to a more efficient cure?” From that perspective, the future looks bright given the wealth of new AI-empowered innovation.

那么，问题可能不是“人工智能将如何治愈COVID-19？”，而是“人工智能将如何改善现有流程，使我们更快地获得更有效的治愈？” 从这个角度来看，鉴于拥有大量新的人工智能技术的创新，前途一片光明。

5. How the Startup Ecosystem Will Shape the Healthcare Industry

5.创业生态系统将如何塑造医疗保健行业

To round out the discussion, both panelists gave their opinions on whether the proliferation of startups applying AI solutions to problems in the healthcare space would lead to further fragmentation within the industry. While the number of new startups in healthcare offers great promise, the flurry of activity also threatens to exacerbate the ailments of an already decentralized system. In a thoughtful response, Larry suggested that AI-focused startups may avoid the pitfall of producing an excess of options that fail to integrate by looking to improve, rather than invent, solutions.

为了使讨论更加圆满，两位小组成员就将AI解决方案应用于医疗保健领域问题的初创公司的激增是否会导致行业内的进一步分化发表了自己的看法。 尽管医疗保健领域的新创业公司数量令人鼓舞，但活动的忙碌也可能加剧已经分散的系统的弊端。 在一次深思熟虑的回应中，拉里建议，以AI为重点的初创公司可能会避免产生过多的选择，而这些选择由于寻求改进而不是发明解决方案而无法集成，因此会产生陷阱。

“AI is not about bringing something new to the market, as much as it is correcting what’s not working well. Let’s use this to address this problem. Let’s get a better clinical outcome. Let’s get a lower cost outcome. Let’s do this more quickly, more efficiently.”

“ AI并不是要向市场推出新的东西，而是要纠正无法正常工作的东西。 让我们用它来解决这个问题。 让我们获得更好的临床结果。 让我们得到一个成本更低的结果。 让我们更快，更高效地执行此操作。”

Because AI tends to focus on optimizing existing solutions rather than creating niche solutions, the growth of startups in the space has the potential to improve healthcare without leading to dissolution. Patrick went on to suggest that M&A activity and future roll-ups would also prevent further fragmentation. Consolidation of targeted solutions will promote rather than hinder healthcare affordability by making larger organizations more effective while arming them with cutting edge technology.

由于AI倾向于专注于优化现有解决方案而不是创建利基解决方案，因此该领域的初创企业的增长具有改善医疗保健而不导致解散的潜力。 帕特里克(Patrick)继续暗示，并购活动和未来的积累也将防止进一步的分散化。 目标解决方案的整合将通过使大型组织更有效地利用尖端技术武装他们，从而提高而不是阻碍医疗保健的承受能力。

These are just a few of the highlights from a very rich discussion. Overall, the session offered a unique and insightful glimpse into how AI is reshaping the healthcare landscape and addressing the fraud, waste, and abuse that currently plagues it.

这些只是非常丰富的讨论中的一些亮点。 总体而言，本次会议提供了关于AI如何重塑医疗行业格局并解决当前困扰其的欺诈，浪费和滥用的独特而深刻的见解。

If you were unable to attend the webinar, or if this coverage piqued your interest in the role of data and AI in improving affordability, you can find a link to a recording here.

如果您无法参加网络研讨会，或者该报道激起了您对数据和AI在提高可负担性中的作用的兴趣，则可以在此处找到指向录音的链接。

Be on the lookout for Meetup invitations to Parts 2 and 3 of the series:

请注意该系列的第2部分和第3部分的Meetup邀请：

• Part 2: Preventative Healthcare & Lifestyle in our Sensorized World — scheduled for 09/10/2020

  第2部分：我们感知世界中的预防保健和生活方式 -计划于09/10/2020

• Part 3: Personalization of Healthcare and Medicines with Data & AI — date TBD

  第3部分：使用数据和AI对医疗保健和医学进行个性化设置-日期待定