Medical malpractice lawsuits make doctors very vulnerable in an unsettling future. Since the 1960s, medical malpractice lawsuits caused the unmitigated failure to control health care costs. Even today we have no idea of the number of medical malpractice lawsuits filed each year and, least of all, how many of these lawsuits are non-meritorious. Likely it is intended for us not to know because whatever is measured is managed and those in power do not want these lawsuits managed.
What we do know is that each doctor has a 5 percent chance per year of being sued for medical malpractice. This corresponds to 50,000 lawsuits per year. In 2004, 34 percent of all physicians in the U.S. were sued at some time in their careers. In 2017, this increased to 55 percent. At this rate, in 2066, it will be 100 percent. It is then that those in charge, who permitted this vulnerability, will spring socialized medicine on all of us as the solution. Now is the time to take action. This reminds me of the lyrics in the song “Nights in White Satin,” which is about desperation. I bring it to an end. I do so with CCC+C.
CCC+C is a decision-making algorithm which I developed for personal use in 2005. It compares the standard of care to the medical intervention to determine if they are the same or different with 95 percent confidence. It worked for me precisely as I intended. I have not felt desperate since.
There are four steps in CCC+C:
- Step 1, Collate: This step organizes the standard of care and the medical intervention into 10 discrete phases, each with a respective duty. Fundamental to all phases in the medical intervention is the “first duty”: primum non nocere.
- Step 2, Compare: This step compares phases in the standard of care to their counterparts in the medical intervention. To do so, it uses three variables: (1) background risk, which is the probability that the effect occurs as a random chance represented in each phase in the standard of care, (2) relative risk, which is 1 or greater depending on the harm caused by a phase of the medical intervention, and (3) incident risk, which is the product of background risk and relative risk. As a result of the comparison, the medical intervention is represented by a “test sample” of 10 incident risks.
- Step 3, Conclude: This step proves statistical significance by hypothesis testing using the single-sample t-test. Alpha is 0.05. P-value is the result. When p-value is greater than alpha, the null hypothesis is retained and the medical intervention comports with the standard of care. When p-value is equal to or less than alpha, the null hypothesis is rejected and there is a departure from the standard of care.
- Step 4, Certify: Any investigator prepares and notarizes a report. The report concludes if there is an injury, a duty, a breach of duty, and a proximate cause. Not only does it do this with 95 percent confidence but it determines type I error, type II error, selection bias, avoidance bias, confirmation bias, and heuristic bias. All are threats to validity and all have quantitative, not qualitative, values. From these, merit is calculated.
I developed CCC+C in 2005 as a term paper in statistics as a health care administration graduate student. Not only did I receive an A in the course but it was published in the Journal of Legal Medicine. Each time I used it, the lawsuit was dismissed with prejudice. I never anticipated then the impact it would have today.
Howard Smith is an obstetrics-gynecology physician.
