The Future of Healthcare AI: An Honest, Straightforward Q&A

Q:The first wave of EHR adoption has focused primarily on digitizing the patient record—with a more recent focus on building interactive clinical decision support capabilities. Development and implementation of CDS applications currently requires clinical staff to observe trends in data, develop protocols to act on these trends, and work with technical staff to codify the logic into executable form. As NLP and computer vision capabilities become more advanced, algorithms will identify and propose actions reflecting patterns in the data. [Will] AI technology…ultimately support an unsupervised learning approach in the EHR to identify trends and possible responses at both the patient and population level?

A:有一件事要记住，这与普遍的看法相反:我们并没有因为采用电子病历而在医疗保健领域淹没在有价值的数据之中。人工智能需要数据的广度和深度才能发挥作用——大量的患者行和大量关于这些患者的事实，也就是特征。谷歌的自动驾驶汽车算法在模拟器中行驶了8000万英里，才触碰到真正铺设的道路，它们仍然需要人类来监督汽车。您的医疗服务提供者每年收集您的数据多少次?在美国，你平均每年看3次医生或医院。在一年中的其他362天里，您的医疗服务提供者不会收集您的数据。在你3次访问期间，供应商收集了一些数据。身高，体重，吸烟状况，血压，年龄，性别，姓名和地址。实验室测试大约有1到10个测量值(这些测量值在AI世界中被称为“特征”)。一两个ICD诊断代码。 Maybe a CPT code or two. Maybe a digital image—X-ray, CT, MRI. A physician’s clinical note. Maybe a microbiology or pathology diagnostic test along with a text report about the findings. A pharmacy order for a medication.

在人工智能数据的世界里，数据的广度和深度对人工智能模型的准确性至关重要，这并不是太多的数据。此外，我们认为人工智能成功的关键在于临床记录的分析。但是临床记录中的质量、准确性、数量和客观、可计算的信息最多是值得怀疑的。临床记录很少超过半页——这在人工智能世界中并不是一个非常多的信息，而且它完全是主观的，除非它引用了一个客观的衡量标准，如实验室测试或血压读数。它们几乎完全是由临床医生撰写的。

On very rare occasions, a patient might have a full or partial genome sequence, so that’s an important but largely missing data set in today’s healthcare.

因此，在传统的临床会诊中，我们收集大约50个数据点——特征——关于一个病人，每年只收集3次。也就是说，每年的数据量还不到100mb。特斯拉每小时收集25gb的汽车数据。

We facilitated a study in Alberta that concluded that EHRs represent only 8 percent of the features and facts that we need for precision medicine and population health—and that 8 percent was gracious. We think the number is even lower. By the way, we should all be struck down by lightning if we continue to add mouse clicks to the backs of clinicians in our attempts to collect more data about patients and their care. #nomoreclicks

The bottom line: EHRs are not the holy grail of data for AI that we like to think they are. They are barely scratching the data surface in terms of what we need to fully leverage the potential of AI for healthcare. EHR data is better than nothing, but it’s not nearly enough. We need to bathe the patient and the health ecosystem in passive sensors that stream that data into a technology platform that was designed from the ground up to support analytics, decision support, and AI. EHRs were not designed for that purpose, technically or functionally.

在很大程度上，人工智能算法可以归结为某种形式的模式识别，然后建议人类对模式做出反应，或者在自主人工智能的情况下，由计算机对模式进行干预。飞机上的自动驾驶是模式识别和计算机自动干预的经典例子——不断输入数据来描述飞机的俯仰、横摇、偏航、位置、目的地和速度，然后输入算法来保持飞机的飞行路线。飞机上的自动驾驶软件每秒对这些数据流进行100次以上的采样。已经退役的F-117隐形战斗机的空气动力不稳定程度达到了没有人工智能(AI)算法监控飞行遥测、持续调整飞机上的飞行控制系统、保持飞机稳定的程度。AI算法需要数据——大量的数据——才能充分发挥其潜力。

The term “unsupervised learning” can be misleading. Just as is the case in human learning, unsupervised learning can only be realistically applied in situations in which the consequences of mistakes and the unsupervised adaption to those mistakes are insignificant. Could we place an autonomous car on the road and let its AI algorithms learn in an unsupervised fashion? Yes, technically that’s possible, but imagine the operational consequences of doing so. To some people, it implies that AI algorithms can be unleashed on data and somehow magically learn something, but learning requires a distinct understanding between “correct” and “incorrect,” which comes from observing, acting, monitoring the outcome of the act, determining whether the act was correct or incorrect in the context of the desired outcome, then adapting so that the incorrect action is not repeated in future, similar scenarios. Generally speaking, the history of unsupervised learning in AI would suggest that the best AI can do without supervision is identify patterns in data that might otherwise escape human recognition. It does not mean that those patterns will be useful. Unsupervised learning can generate hypotheses from data, but it will be up to humans and other downstream AI algorithms to test those hypotheses. At this time in healthcare, given our current data environment, unsupervised learning is best used to generate hypotheses that humans would otherwise not identify.

Q;Is AI just a passing trend, with potential to make only incremental changes over the status quo? Or will AI unlock new possibilities for humankind?

A:It will and is unlocking new possibilities for humankind. The progress of AI is exceeding Moore’s Law. The capabilities of AI are doubling every six months in. It’s unlike anything I’ve seen in my 34-year career in computer and information science. That said, as I mentioned earlier, healthcare will be left behind if we don’t increase the digitization of the patient—dramatically increase the breadth and depth of health-related data about patients. AI needs large volumes and high-quality data, and for the most part, we don’t have that in healthcare, yet.

Q:How significant a challenge is posed by interoperability (or lack thereof) between different EHR platforms and the widespread application of AI to these systems?

A:Surprisingly, lack of interoperability is not really the problem. We can easily peel the data out of EHRs and expose that data to AI algorithms. At Health Catalyst, we’ve commoditized that; we can do it in our sleep. The problem lies in the inherent limitations of the EHR and the way they are designed. They were designed with the clinical encounter—the billable event—as the center of their data model. So, if you want to apply AI to understand billable events, that design is fine. But if you want to apply AI to understand the patient, you need to completely change the fundamental design of the data model behind EHRs so that the patient is at the center of the model, not the billable encounter. EHRs were not software engineered to support dynamic, intelligent, context-based user interfaces, such as what we see in modern web and mobile software applications. The software code and architecture behind the scenes of EHRs is based on 20-year old technology, at least. This puts inherent limitations on the ability to drive better decision support to clinicians, enabled by AI, into the user interface of EHRs. Also, healthcare is the only data environment that consistently believes we have to push copies of data from one EHR system to another in order to achieve interoperability; to view and access data. But, if you think about modern information systems, like Amazon, Google, and Facebook, they don’t ship and store data locally. They index and then reference data—that is, they “point to data” in its native location. That’s what an HTTP and URL address is all about. It’s an address to the location of data. If the Internet followed EHRs’ approach to interoperability, we would all have a full copy of the world wide web on our laptop computers. So, is interoperability a problem? Yes. But the bigger problem is the fundamental design and engineering of EHRs.

Q:Will these tools live up to the hype and also help reduce the significant computer-based workload physicians now face?

A:当然可以，但前提是我们要增加患者数据的广度和深度。就像自动驾驶把驾驶飞机的任务从飞行员身上解放出来一样，应用于医疗保健的人工智能也可以做到这一点。它可以减轻临床医生的简单诊断和治疗的商品任务和管理任务，以便他们可以监测患者，并在情况需要他们更高的、非商品的专业知识时作出反应。但是，如果我们不更好地实现病人和护理过程的数字化，这一切都不可能实现。

Q:那么电子病历数据中的一些固有偏见呢?我们可以做些什么来确保机器学习算法不会延续(甚至加剧)这些偏见?

A: This is the core problem we face. EHRs were designed with the billable clinical encounter at the center of the data model, not the patient. Clinical notes are notoriously random in terms of their content and quality for the same patients and patient types. Clinical notes are just as random in their content as the humans who write them. Human-assigned ICD diagnosis codes are subjective and highly-impacted by the clinicians and coders who assigns them. Same with CPT codes. So, we have a very limited data set in EHRs which were designed for billing, and we have a data set whose quality is very questionable. Think about the objective and quantitative nature of data that is collected in the telemetry from a rocket or satellite. That data originates in a sensor that was designed and manufactured to capture computable information. It’s not a human, subjectively estimating and entering that telemetry data. So, can we derive AI value from the data in an EHR? Yes, and we are doing it now. Do we face the probability of making false conclusions from the output of that AI? Absolutely, positively. We have to apply AI in the field of healthcare with the rigor of formal experimental design until the data we have about patients is less subjective, deeper, and broader than what’s contained in today’s EHRs. That’s an important topic for another time—data scientists in healthcare need formal training in experimental design to ensure that the results of their AI models are valid.

Q:How will these technologies impact patients’ expectations of data privacy and confidentiality?

A:我是一名患者，我迫不及待地希望人工智能的好处能够改善我的治疗，降低我的医疗成本。在这种情况下，我完全不担心隐私和机密性，作为一个行业，我们需要确保我们不会在患者中制造对隐私的恐惧，从而推迟或抑制AI的发展。我期待着未来，当我沐浴在7×24传感器中，它收集和监测我的健康数据。我期待着将来我拥有并控制这些数据及其隐私设置——我可以决定谁能看到我的数据，谁不能。我期待着将我的健康数据展示给商业上可用的AI算法——AI算法公司争夺我的赞助和订阅——它们可以评估我的健康状况，并建议最佳的治疗计划和疗法。我期待着未来，当我在临床中遇到一位临床医生时，我拥有比那位临床医生更多的健康数据，以及我所认可的人工智能算法的输出，然后和那位临床医生一起，决定什么对我来说是最好的，作为一个人，而不是作为他们的附属病人。医疗保健的发展速度极其缓慢，但我认为这种未来正在显现。For example, look at the bio-integrated sensors technology being developed byJohn Rogers and team at Northwestern. Their products are already being used by sports teams and in clinical trials. We are facing a tipping point of cultural dissatisfaction at healthcare’s glacial pace. The winds of change are blowing harder than ever. This future that I look forward to, is no more than five years away.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

1. The Dangers of Commoditized Machine Learning in Healthcare: 5 Key Differentiators that Lead to Success
2. Data Science for Healthcare: What Today’s Leaders Must Know
3. The Impact of FDA Digital Health Guidance on CDS, Medical Software, and Machine Learning