How Reinforcement Learning in Healthcare Needs Human Feedback to Accelerate Adoption
Authors: Amina Khalpey, PhD, Solmaz Rashidi, MBA, Brynne Rozell, BS, Ben Taylor, BSc, PhD, Zain Khalpey, MD, PhD, FACS
Reinforcement learning (RL) is a branch of machine learning, which in turn, is a branch of artificial intelligence that enables machines to learn from their experiences and make decisions based on trial and error. RL models are essentially a learning tool where the computer acts as a decision maker after analyzing data within a restricted environment that then provides a decision on what action to take. This technology has been successfully applied to a wide range of fields, including finance, transportation, and gaming. However, the application of RL in healthcare has been limited due to concerns about the safety and ethics of using machine learning algorithms in patient care.
Implementing Reinforcement Learning into Healthcare Settings
In recent years, there has been a growing interest in using RL in healthcare to improve patient outcomes and reduce costs. RL algorithms can be used to optimize treatment plans, predict disease progression, and improve patient monitoring. However, the effectiveness of RL in healthcare depends on the volume of data, quality of data, and the availability of high-quality feedback from human experts.
In this blog post, we will discuss the importance of human feedback in reinforcement learning for healthcare and how it can help to overcome the challenges of adopting this technology for patient care. We will examine the potential benefits of RL in healthcare, the challenges of implementing RL in healthcare, and the role of human feedback in improving the effectiveness of RL in healthcare.
Benefits of Reinforcement Learning in Healthcare
RL has several potential benefits in healthcare, including improving patient outcomes, reducing healthcare costs, boosting drug discovery, accelerating clinical trials, and enhancing the efficiency of medical decision-making.
Improved Patient Outcomes
RL can be used to optimize treatment plans for individual patients based on their medical history, symptoms, and test results. This can help to improve patient outcomes by ensuring that patients receive the most effective and personalized treatments possible.
For example, RL algorithms can be used to optimize the dosage of medication based on a patient’s response to treatment. This can help to reduce the risk of adverse side effects and ensures that patients receive the optimal amount of medication for their condition. RL can also be used to help tailor the patient’s treatment plan based on their history of diseases, illnesses, demographic data, and environmental factors. This was the early attempt of IBM Watson, which we will discuss in a future blog post.
Reduced Healthcare Costs
RL can also help to reduce healthcare costs by improving the efficiency of medical decision-making. Reinforcement learning can help diagnose patients by providing a system that can learn from patient data to identify patterns in the data that are associated with certain conditions or diseases. This could allow the system to make more accurate diagnoses and reduce the time it takes to diagnose a patient. Additionally, the system could be used to recommend the most effective treatments for a given condition. By automating certain aspects of medical care, such as diagnosis and treatment planning, RL can help to reduce the workload of medical professionals and reduce the overall cost of healthcare. For example, RL algorithms can be used to predict the risk of hospital readmission based on patient data. This can help to reduce the number of unnecessary hospital readmissions and save healthcare providers money on medical expenses.
Accelerating Drug Discovery
As we know, the drug discovery process is both costly and time-intensive. On average it takes 10+ years for a drug to become available in the market. However with the help of RL models, we can accelerate the drug discovery process by optimizing the target properties of generated molecules and assess and test compounds and how they interact with expected drug targets with precision and speed. Thanks to both the availability of computational power and progress in RL models we can dramatically increase the speed of drug discovery.
Clinical Trials
A reinforcement learning model has the capacity to analyze the demographic data, illness data, prescription data, laboratory and environmental data from humans participating within a clinical trial and derive a conclusion on a potential treatment plan, policy, and protocol that optimizes their clinical outcomes. This strategy greatly reduces the time and effort that a clinician would expend within a standard clinical trials protocol.
Enhanced Medical Decision-Making
RL can also enhance medical decision-making by providing physicians with more accurate and reliable information about a patient’s condition. By analyzing large amounts of patient data, RL algorithms can identify patterns and trends that may be missed by human experts.
For example, RL algorithms can be used to analyze medical images and identify early signs of disease that may be missed by human radiologists. This can help to improve the accuracy of medical diagnoses and ensure that patients receive the most effective treatment possible. There’s already been much success with this application in the Oncology space.
Challenges of Implementing RL in Healthcare
Despite its potential benefits, implementing RL in healthcare is not without its challenges. There are several factors that need to be considered when implementing RL in healthcare, including data quality, data regulations, geopolitical considerations, algorithmic bias, and ethical considerations.
Role of Human Feedback in RL for Healthcare
Given the challenges of implementing RL in healthcare, human feedback is critical for the successful adoption of this technology for patient care. Human feedback can help to address many of the challenges associated with implementing RL in healthcare, including data quality, algorithmic bias, and ethical considerations. Creating these Ai-driven systems is a constantly evolving process which improves much faster with human feedback.
Data Quality
The effectiveness of RL in healthcare depends on the quality of the data used to train the algorithms. Inaccurate or incomplete data can lead to inaccurate predictions and suboptimal treatment plans. Therefore, it is essential to ensure that the data used to train RL algorithms is of high quality and representative of the patient population.
Human feedback can help reinforcement learning models in two ways: The first is to take the experience of a human and teach the machine right from wrong, avoiding algorithmic bias. The second is to improve the quality of the data used to train RL algorithms by removing anomalies, redundancies, null values, and exception based data that would deviate the algorithm from learning what matters. Human experts can review the data used to train the algorithms and identify any inaccuracies or biases. Additionally, human experts can provide feedback on the output of the algorithms, which can help to refine the algorithms and improve their accuracy.
Data Regulations And Geopolitical Considerations
As discussed above, the quality of data is critical, but equally critical is the volume and variety of data to ensure an equitable, comprehensive, and complete data set which accounts for both 1st party and 3rd party data. Also, depending on the region and/or market, a user’s ability to access and use the vast and diverse data sets may be limited.
Another potential positive impact of implementing RL in the healthcare space is giving time back to the healthcare professionals. As we all know, there is a massive shortage of healthcare workers due to the growing population of aging adults. By leveraging RL, we can reallocate the repetitive, manual, and laborious tasks to the machines, creating capacity for physicians to focus their energy and time on patient care, high-value & high-touch care, and attend to the growing population across the globe. This strategy also has the potential of creating a happier workforce because their focus is more on fulfilling healthcare responsibilities leaving the paperwork and the odious task of analyzing data points to the machines.
Addressing Algorithmic Bias
Another challenge of implementing RL in healthcare is the risk of algorithmic bias. RL algorithms can be biased if they are trained on data that is not representative of the patient population. This can lead to inaccurate predictions and suboptimal treatment plans for certain groups of patients.
Human feedback can also help to address algorithmic bias in RL for healthcare. Human experts can review the output of the algorithms and identify any biases that may be present. They can then provide feedback on how to adjust the algorithms to reduce bias and improve their accuracy and hone in on the features and attributions of the model that matter most, assigning a categorical weight to ensure feature biases are avoided.
Ensuring Ethical Considerations
Human feedback is also critical for ensuring that RL in healthcare is implemented in a responsible and ethical manner. Human experts can provide feedback on how to ensure that patient privacy and autonomy are protected, and that the impact of RL on the job market is minimized.
In addition, human feedback can help to ensure that the use of RL in healthcare is transparent and accountable. Human experts can review the algorithms and provide feedback on how they were developed and how they make decisions. This can help to build trust in the use of RL in healthcare and ensure that it is used in a responsible and ethical manner.
Conclusion
Reinforcement learning has the potential to revolutionize healthcare by improving patient outcomes, reducing healthcare costs, accelerated discovery, speedier clinical trials, and enhancing the efficiency of medical decision-making. However, the successful adoption of RL in healthcare depends on the volume and variety of data sets, and the availability of high-quality feedback from human experts.
Human feedback is critical for addressing the challenges of implementing RL in healthcare, including data quality, algorithmic bias, and ethical considerations. Human experts can provide feedback on the data used to train the algorithms, the output of the algorithms, and the ethical implications of using RL in healthcare.
Therefore, it is essential to involve human experts in the development and implementation of RL in healthcare to ensure that this technology is used in a responsible and ethical manner. By working together, we can harness the power of RL to improve patient outcomes and transform the healthcare industry for the better.
