How AI Will Drive Less 'Satisficing' In Healthcare

Introduction:

In this edition of our healthcare data innovations newsletter we explore the predictions that underpin the hottest topic in healthcare, personalized medicine. As the cost of predictions decreases its utilization and accuracy in complex environments continues to grow.

The Cost of Prediction:

One of the most exciting developments in healthcare is the decreasing cost of prediction. As technology advances and the costs of computation and data acquisition continue to decrease, the affordability of predictive analytics and machine learning algorithms continues to improve. This reduction in cost opens up new possibilities for leveraging the power of data to make accurate predictions in healthcare.

Separating Prediction from Judgment:

Machine learning operates on the basis of IF-THEN logic. The more input factors (IFS) involved, especially in complex environments, the greater the number of possible outcomes (THENs). Navigating through multiple IFs requires skill in designing automation that ensures statistical accuracy—an area where humans often struggle on a daily basis. A critical aspect of integrating machine predictions into healthcare workflows is the clear distinction between prediction and human judgment. While machines excel at processing vast amounts of data and identifying patterns, humans bring their expertise and contextual understanding to make informed decisions. This complementary relationship is a delicate balance; health systems that effectively separate prediction from judgment within their workflows by accurately leveraging machine and human complements will lead the charge on patient care outcomes and enhance the overall patient and staff experience.

The Challenge of "Satisficing":

"Satisficing", a term coined by Herbert Simon in 1956, refers to decisions that are less than ideal but deemed good enough given the circumstances. It's a common occurrence in various aspects of our lives, including healthcare.

Take, for example, the process of scheduling a flight. The ideal scenario would involve arriving at the airport just in time, with all factors perfectly aligned. However, due to the high level of uncertainty—traffic conditions, security wait times, gate changes, and flight timeliness—we often find ourselves arriving early. To cater to frequent and high-net-worth travelers, airlines have introduced airport lounges as a means of enhancing the travel experience. These lounges provide comfort and amenities, but they are a response to the limitations of our current predictive abilities. Once enough data is widely available and predictions are accurate enough in traffic, parking, security wait times, and plane locations the need for them vanishes.

Similarly, healthcare has its own version of airport lounge satisficing. For instance, the need for biopsies, CT scans and MRIs arises because the level of trust in predictions for highly complex cases is not yet fully established. These tests provide valuable insights into a patient's condition, but they often come with a trade-off. While they can help diagnose and monitor certain conditions, they also expose patients to radiation or require invasive procedures. Healthcare professionals must weigh the benefits of obtaining detailed information against the potential risks and patient discomfort, ultimately making decisions that strike a balance between accuracy and practicality. While these procedures are necessary, they represent a compromise between the desire for precise predictions and the limitations of current technology.

However, as the field of predictive analytics continues to evolve, and data proliferates, we can envision a future where complexity is tamed and our understanding becomes near-finite. With advancements in machine learning and the availability of extensive datasets, we have the potential to revolutionize healthcare decision-making. Imagine a healthcare system where we can confidently predict the most effective treatments, reducing unnecessary procedures and minimizing patient discomfort.

Embracing the Future:

By embracing the power of data-driven predictions, healthcare organizations can transform the way care is delivered. Striking the right balance between machine predictions and human judgment will be crucial in driving positive patient care outcomes and optimizing the overall healthcare experience with less system-level "satisficing". Through robust infrastructure, training, and a commitment to leveraging the best of both human and machine capabilities, we can revolutionize healthcare and create an environment that ensures equitable, personalized medicine for all.