Algorithmic Health Decisions: Pros & Cons in Medicare

Introduction

The use of algorithms in healthcare decision-making is growing rapidly. Many healthcare systems are using algorithms to predict outcomes, to prioritize patient care, and to identify patients at risk for certain diseases. This technology can help to improve patient care and to reduce healthcare costs. However, there are also concerns about the use of algorithms in healthcare decision-making. In this article, we will explore the pros and cons of algorithmic decision-making in healthcare.

The Pros of Algorithmic Decision-Making in Healthcare

One of the primary benefits of algorithmic decision-making in healthcare is that it can help to improve patient outcomes. Algorithms can be used to predict patient outcomes, such as the likelihood of developing a certain disease or the likelihood of experiencing a certain complication. This information can help healthcare providers to develop personalized treatment plans that are tailored to the specific needs of each patient.

Another benefit of algorithmic decision-making is that it can help to reduce healthcare costs. By predicting patient outcomes and identifying patients at risk for certain diseases, healthcare providers can take proactive measures to prevent or treat these conditions. This can help to reduce the need for expensive treatments and hospitalizations, which can ultimately save money for both patients and healthcare systems.

The Cons of Algorithmic Decision-Making in Healthcare

Despite these benefits, there are also concerns about the use of algorithms in healthcare decision-making. One concern is that algorithms may not always be accurate. Algorithms are only as good as the data that is used to train them, and if the data is incomplete or biased, the algorithm may produce inaccurate results. This can lead to incorrect diagnoses or treatments, which can have serious consequences for patients.

Another concern is that algorithms may be biased. Algorithms are often trained using historical data, which may reflect historical biases and inequalities. This can lead to algorithmic decision-making that perpetuates these biases and inequalities. For example, a study published in the New England Journal of Medicine found that an algorithm used to identify high-risk patients for extra care underestimated the needs of Black patients by 48%.

Case Study: Medicare Advantage and Healthcare Algorithms

One recent example of the use of algorithms in healthcare decision-making is Medicare Advantage. Medicare Advantage is a government program that allows private insurance companies to offer Medicare coverage to seniors. These insurance companies use algorithms to predict patient outcomes and to prioritize patient care.

However, there are concerns about the use of algorithms in Medicare Advantage. A recent investigation by The Verge found that some insurance companies may be using algorithms to discriminate against sicker patients. The investigation found that some insurance companies were using algorithms to identify patients who were at risk for certain medical conditions, and then steering those patients toward cheaper and less comprehensive healthcare plans. This practice, known as “cherry-picking,” allows insurance companies to maximize profits by avoiding expensive patients.

The investigation also found that some insurance companies were using algorithms to identify patients who were likely to drop out of the program. These patients were then targeted with aggressive marketing campaigns to encourage them to leave the program. This practice, known as “skimping,” allows insurance companies to maximize profits by avoiding patients who are likely to require expensive treatments.

Conclusion

In conclusion, the use of algorithms in healthcare decision-making can have both pros and cons. While algorithms can help to improve patient outcomes and reduce healthcare costs, there are also concerns about accuracy and bias. The recent investigation into Medicare Advantage highlights the potential for algorithms to be used for discriminatory practices. It is important for healthcare systems to carefully consider the use of algorithms and to ensure that they are used in an ethical and responsible manner.