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This August, Stanford University is hosting the Machine Learning for Healthcare (MLHC) conference. This annual conference brings healthcare and data science professionals together to discuss the overlapping challenges of their respective professions. They also discuss the ways that machine learning can help streamline the delivery of healthcare services throughout the United States. One of the biggest issues that needs to be addressed is the future role of machine learning in healthcare litigation.
Healthcare Litigation is an Overlooked but Essential Application of Machine Learning
Clinicians across the United States have lauded machine learning for its contributions to the field of medicine. Dr. Ed Corbett, a board certified medical internist and the Chief Medical Officer of Health Catalyst wrote a post highlighting the role that machine learning plays in the field. Corbett said that electronic records simplified many administrative duties, but did not significantly improve the quality of information available to medical professionals. He said that machine learning is gradually changing this.
Corbett said that the real benefit of machine learning is that it can process massive data sets to help healthcare professionals make better decisions to improve patient treatments, yield more accurate diagnoses and minimize costs without compromising the quality of care. A number of recent developments confirm his claims.
There are a number of ways that machine learning is shaping the medical industry. Google recently developed a new tool that can diagnose cancer with 89% accuracy, which is nearly 20% better than most doctors.
As important as these developments are for healthcare delivery, they seem to be overshadowing the impact that machine learning has on healthcare litigation. Nonetheless, this arena is going to be just as important for a number of reasons:
- Healthcare litigation has a complex impact on healthcare costs. Excessive lawsuits can often drive up healthcare costs. On the other hand, they can also act as a deterrent against poor healthcare practices. This could cut costs in the future.
- Machine learning can ensure only justified lawsuits proceed against healthcare providers. This shields qualified and scrupulous healthcare providers from lawsuits that could drive them out of the profession. This is important, because society depends on the best practitioners to keep their licenses and practices.
- Machine learning can help patients with strong cases make bulletproof arguments more quickly. This will likely expedite settlements, so they will have the finances to receive corrective care more quickly.
- Machine learning is likely to help people with class-action lawsuits make stronger cases against major HMOs, pharmaceutical companies and other healthcare companies with deep pockets. This will provide an incentive for these companies to improve the quality of services they deliver and ensure patients receive adequate restitution when they fall short of their obligations.
Machine learning is having a strong impact on the future of the healthcare profession.
What are some ways that machine learning is being used in healthcare litigation?
Creative attorneys can find a number of applications for machine learning. Here are a few.
- Identifying a pattern of negligence on the part of the defendants
Understanding the pattern of conduct of the defendants is key to winning many cases. For example, in the Essure case, the pharmaceutical companies never followed up with 98% of the women in one of the studies highlighting its effectiveness. Machine learning can help identify a pattern of negligence based on prior mistakes made by other defendants.
- Identifying witnesses to depose based on observable behaviors that are relevant to their case
Consider a hypothetical case where a patient is suing a pharmaceutical company and wants to establish a pattern of deceit based on kickbacks to physicians. They can use machine learning to identify physicians that prescribed medications the same way that doctors that were known to receive kickbacks did. Even if those doctors themselves do not receive kickbacks, they could provide relevant testimony.
- Evaluating the likelihood of success of a particular legal strategy
In the future, healthcare litigators may be able to assign a probability of winning a case based on a particular legal strategy. They will make this determination by looking at other attorneys that followed a similar strategy. Of course, they will need to factor for differences in lies between jurisdictions and other relevant factors.
- Identifying potential plaintiffs that meet profiles of people likely to have winnable cases
There are so many factors that influence the outcome of the case. Most of them boil down to the situation the plaintiff is in and the level of sympathy a jury will have for them. Machine learning can help litigators identify patients that are likely to win at trial.
Machine Learning and AI are Changing Healthcare Litigation
Healthcare litigators have realized that big data is changing their profession in countless ways. They are exploring new ways to use machine learning algorithms to find the most lucrative cases and develop winning strategies.