AI-Assisted Decision Making: Healthcare’s Next Frontier

本报告基于健康Catalyst首席数据科学家Jason Jones在2019年医疗保健分析峰会上的演讲，题为“医疗保健领导者的AI:改进领导决策的新前沿”。世界杯葡萄牙vs加纳即时走地

Technology is rapidly transforming every industry, including–perhaps especially–healthcare. In 2015, theWorld Economic Forumconducted asurveyto help predict when transformative technologies would become mainstream. Nearlyhalfof the 800 executives surveyed answered that they expected the first Artificial Intelligence (AI) machine to be on a board of directors of a business by 2025 and the first transplant using a 3D-printed liver is likely by 2024.

Attendees at the 2019Healthcare Analytics Summit(HAS)还没有准备好把决策权交给机器。Healt世界杯葡萄牙vs加纳即时走地h Catalyst首席数据科学家杰森·琼斯(Jason Jones)在他的“医疗保健领袖AI”课程上向与会者提出了这样一个问题:“到2025年，你们的董事会有多大可能让一台机器成为投票成员?”33%的人回答“不可思议”，其余67%的人回答“不确定”。琼斯认为，也许他们需要一台机器来帮助他们回答这个问题。

AI-Assisted Decision Making at the Highest Levels of Leadership

While there may not be a machine at the helm of every major health system by 2025, AI should have a place at the table when it comes to healthcare decision making. A new frontier is expanding AI from artificial intelligence to augmented intelligence. Traditional AI focuses on improving analytics efficiency and effectiveness while augmented intelligence is about improving the decision-making ability of healthcare leaders. AI can help support leaders in driving systemwideoutcomesimprovement and answering important questions such as “Is there more opportunity in readmission or depression?” “How many staff are need in the ED on weekends?” and “How long does a nurse manager need to improve safety culture?”

There is an opportunity for AI to assist decision making in new and innovative ways, but if healthcare leaders aren’t prepared for this step forward, they will continue to underutilize AI and machine learning (ML). Leaders that embrace this paradigm can drive outcomes improvement using augmented intelligence.

The Importance of Strategic Direction

Because healthcare is at a technological tipping point, it is important that healthcare decision makers decide if and how to adopt AI technologies for their organizations. While the conversation around AI-assisted decision making is more meaningful because of increased efficiencies and algorithms, leaders need to implement AI thoughtfully in order to make sure AI supports their goals and values. A good analogy is that while drivers may be able to get somewhere faster because of Google Maps, the technology isn’t going to help them if they don’t know where they’re going in the first place. Healthcare leaders need to be able to clearly state, interrogate, and refine their goals and values along their AI journey.

Challenges of Leadership Reporting

If augmented intelligence can play a role in decision making, the first question to ask is what healthcare leaders need to be able to do and how AI can assist. The two biggest needs from AI are separating signal from noise and making decisions that impact the future (while leveraging data to accomplish both):

• Separating signal from noise– Is this hospital better than that one? Have we improved over time? If we set an improvement goal, is it statistically different from current performance?
• Making decisions that impact the future– Where will we be in a year? Are we satisfied with that? If not, what will we change and when can we expect to see a result? If we’re satisfied, is performance sustainable?

下面的示例报告(图1)显示了如何使用常见的ML算法来帮助回答这些问题。在本例中，x轴表示性能，而y轴表示7个不同的地理位置。水平线代表不同大小的置信度限制，代表不同地理区域之间的大小差异。在图的右边，字母表示这些地理区域在统计上是否彼此不同。此外，灰色钻石代表了这些地区在一年后的自动预测。

虽然这份报告显示了大多数标准季报所包含的一些信息，但它增加了新的维度，为领导人提供了更多信息，用于做出关键决策，同时消除了解读数据的一些认知负担。The report uses a standard ML algorithm calledrecursive partitioning, which is often used in readmission prediction and case management selection. While these ML tools are frequently used at the point of care, few organizations are applying these same models to high-level decision making even though they are capable of providing both types of information.

The example report uses augmented intelligence to help separate signal from noise (by distinguishing the different geographies) and includes data that helps inform future decisions (by showing forecasted predictions of the geographies’ performance in one year). Healthcare leaders can take this information and see that while the performance of some geographies (geographies A, D, and E) are predicted to stay relatively even or decline slightly, others (geographies B and C) are predicted to improve significantly. That information changes the discussion from “How is the organization performing?” to “Are there significant differences between geographies B and C to A, D, and E that need to be accounted for?” and “Is this performance sustainable for those geographies?” While many leaders are reluctant to trust the forecasting of organizational performance to computers, the reality is that they’remuch better在这一点上比人类强。

How AI-Assisted Decision Making Relates to Health Equity

An unexpected application of augmented intelligence relates to healthcare equity. TheAgency for Healthcare Research and Quality(AGRQ) definessix domainsof healthcare quality including safe, effective, person-centered, timely, efficient, and equitable. AHRQ defines the equitable aim for healthcare as “Quality does not vary by personal characteristics, such as gender, ethnicity, geography, and socioeconomic status.” The example report shown in Figure 1 above illustrates how geography impacts healthcare quality. While AI and ML can perpetuate biases in healthcare, it’s also possible that these same tools can improve healthcare equity, particularly when applied to decision making.

Healthcare organizations report on several hundredperformance measures. What if they also knew how equitably they were performing each measure across race, gender, ethnicity, geography, and socioeconomic status? This could be done by using theGiniindex of inequality, but instead of applying it to income equality, it was applied across healthcare performance measures (Figure 2).

图2显示了如何使用基尼指数对绩效指标进行排名，以显示不平等程度较高的领域，然后医疗领导者可以使用该指数确定重点领域。在这个例子中，卫生系统可能将重点放在糖化血红蛋白控制上，这表明了年龄的不平等。A common ML tool called theReceiver Operating Characteristic(ROC) curve is a measure of how well data scientists can predict an outcome. Just like data scientists can use this tool to predict what patients will contract severe sepsis or what patients will be readmitted, they can also predict how equitably the health system can control hemoglobin A1C across the patient population. Armed with that information, healthcare leaders can then improve healthcare inequalities across that measurement and others.

AI的下一个目标是什么?

While many healthcare organizations have implemented AI and ML tools at the point of care, few have been able to apply them to decision making at the highest levels. The good news is that opportunities to do so abound. Augmented intelligence can be applied in a leadership context to help separate signal from noise, make future-oriented decisions, and be applied to some of the most complex problems, such as solving healthcare inequality. In order for healthcare to keep moving forward, healthcare leaders need to encourage the use of these tools both at the point of care and at the highest levels of decision making. As advances in AI allow data scientists new ways to make sense of data, leaders can embrace these technologies to improve healthcare through augmenting leadership decisions.

Additional Reading

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

1. Artificial Intelligence in Healthcare: A Change Management Problem
2. Machine Learning Tools Unlock the Most Critical Insights from Unstructured Health Data
3. A New Era of Personalized Medicine: The Power of Analytics and AI
4. 人工智能如何克服医疗数据安全挑战并提高患者信任
5. Healthcare Data Management: Three Principles of Using Data to Its Full Potential