The Trial Lawyer Summer 2018 - Page 66

the number of physicians increased by 40 percent, while the increase in the U.S. population grew by only 18 percent. The number of physicians in every state has increased, and in most states the increase in physicians has either matched or outpaced population growth. There is no data to support the claim that capping medical malpractice damages helps to attract or keep doctors. In reality, there are many more doctors practicing in states without damages caps than in those with caps. There is no evidence that medical malpractice lawsuits drive up malpractice premiums. The National Bureau of Economic Research found that “increases in malpractice payments made on behalf of physicians do not seem to be the driving force behind increases in premiums.” Further, Americans for Insurance Reform found that “rate increases were rather driven by the economic cycle of the insurance industry, declining interest rates, and investments.” And, damage caps do not lower premium rates for physicians. Insurance companies pay less money for malpractice clams in states with damages caps, but they do not pass those savings on to doctors by reducing their premiums. After the state of Texas passed legislation capping damages in healthcare malpractice cases in 2003, the nation’s largest medical malpractice carrier told the Texas Insurance Commissioner that caps had a minimal impact on premium rates, while the company announced a 19 percent increase in physicians’ malpractice insurance rates. In fact, the American Insurance Association has acknowledged that, “we have not promised price reductions with tort reform.” Tort reform is a fraud against the American people. It benefits neither the public nor healthcare providers. It simply increases profits for insurance companies and insulates domestic and foreign corporations from compensating people whom they have caused harm. The fraud must be exposed, and all tort reform legislation repealed. When a person who is injured by the negligence or defective product of another takes their case to trial, they are engaging in an extraordinarily heroic act. To file a lawsuit and litigate through trial is not a simple undertaking. The plaintiff will be attacked by the defendant in all sorts of ways, and the case will likely drag on for years. In the meantime, their life will be put on hold. The willingness to go to trial to gain justice is heroic. This truth must be made known to our citizenry. The public must be made to understand that when a person wins a civil case, they win it for all of us, as well as gaining justice for themselves. 10. Predictive Analytics “For the rational study of the law the black-letter man may be the man of the present, but the man of the future is the man of statistics and the master of economics.” — Justice Oliver Wendell Holmes Jr. Predictive analytics is technology that learns from experience and from data to predict the outcome and/or behavior of individuals in order to drive better outcomes or better decisions. 64 x The Trial Lawyer It is essentially “machine learning,” which is exponentially getting faster, better and more efficient. Computers can now look at tendencies and trends and can actually learn. By leveraging the quantitative strength of computers, lawyers can more accurately forecast how events will play out in a case and allow lawyers and their clients to avoid costly mistakes, get a better vision of the strengths and weaknesses of a case, and increase the odds of obtaining a favorable outcome. Predictive analytics uses advanced machine learning algorithms and proven rigorous statistical methods to forecast the probabilities of various outcomes. The probability forecasts produced can aid settlement negotiations and decisions about trial. Potentially, this could save billions of dollars in settlement errors and mitigate the risks of trial. Statistics show an estimated 60 percent of legal cases have settlement value errors. As I noted earlier, 99.75 percent of civil cases are settled. Jury trials today are avoided at all cost due to the perceived unpredictability of a jury. The practice of law includes prediction. Lawyers predictively answer client questions daily such as, “What are the odds of winning this case, and how much do you think this will cost me?” Even Justice Holmes envisioned over a century ago that “the number-crunching masters of economics” will trump the vast majority of lawyers who still rely solely on experience, historical case information, and intuition to predict the outcome of a case. Even the most exceptional lawyers are inherently limited in their capacity to retain and process the information necessary to make well-informed judgments. Computers, while lacking the ability to frame interesting questions or draw conclusions as lawyers, are far better at storing, processing, and summarizing large volumes of information. The technological advancement in computing power and data science has ushered in a new era…the era of Big Data. Google, Facebook, IBM, and countless other technology companies use these new capabilities to market products and ideas with a level of effectiveness never before seen. Predictive analytics is now universally accepted and used widely by many industries to predict outcomes and make better decisions, and was a major factor in predicting the last presidential race. It is imperative that trial lawyers catch up to this data- centric approach found in almost every other industry. The common practice of heavy weighting historical trial outcomes fails to adequately capture present conditions, hampering the accuracy of its predictions. Predictive analytics, unlike historical performance data commonly used for this purpose, takes into account current public sentiment. Real-time predictive analytics provides a great advantage, creating a tool that allows trial lawyers to test the core case arguments identified during discovery against a series of juries, representative of the available jury pool in the location where the trial will take place. A resulting predictive model can be used to inform the settlement negotiations and aid in the decision of moving forward to trial. When cases proceed to trial, the resulting model can be used during jury selection, to insure maximum probabilities of a favorable decision and the largest possible verdict.