Medical Malpractice Lawsuits: How Not to Change the Law but Level the Playing Field

If nothing else, the medical malpractice landscape illustrates how the law is exploited by parties on both sides. First and foremost are plaintiffs.(1) Whether victims of medical errors or errors of nature, undoubtedly they are entitled to feel aggrieved and to seek legal counsel. In the meantime, some sue for malpractice because, even if a claim has no attributable merit, there is a deep pocket. These plaintiffs fear no reprisals for greed.

Plaintiff attorneys know all about the deep pocket. Their mantra is “if we don’t win, you don’t pay.” They represent a medical malpractice claim for a contingency fee so that an unfortunate injured party has the resources to be made whole. Plaintiff attorneys represent 85,000 medical malpractice cases per year. The 55,000 claims that are eventually dropped for a variety of reasons, some with prejudice, and the 2000 cases that are lost in court, are excused by their altruism. In the meantime, plaintiff attorneys make $2.1 billion per year just from the contingency fees in claims that are settled, about 27,000 claims. There is another $400 million in contingency fees from 1000 plaintiff verdicts.(2) Although the law prohibits frivolous claims, these attorneys are not dissuaded. Virtually no countersuit has ever been filed.(3)

Defense attorneys are no better. For them, the 85,000 medical malpractice lawsuits are their opportunity to access the deep pocket. They know that each lawsuit filed by a plaintiff attorney requires a defense, so they contract with medical malpractice insurance companies.(4) Although they prevail in many claims that have no merit, filing a countersuit for malicious prosecution of a frivolous claim is not in the game plan.(5) A defense attorney would sooner disparage a doctor who suggests a countersuit for being vengeful than disparage a plaintiff attorney who files s frivolous claim for being less than altruistic. In the meantime, defense attorneys make about $1.1 billion per year from their contracts with malpractice carriers.

Medical experts are physicians.(6) They know medicine or, at least, they claim to. They contract with both types of attorneys. Eighty-five thousand medical experts prepare affidavits of merit. An equal number refute them. Medical experts regard themselves as exceptional authorities who are the epitome of self-regulation in the medical profession; however, the medical profession regards them as “hired guns.” There is ample evidence for biased opinions. Hardly any, however, are held accountable for bias by peer review, although it does occasionally happen.(7) In the meantime, medical experts make at least $100,000 per year.(8)

Malpractice insurance companies are the deep pocket, but they are hardly victims.(9) All legal strategies are influenced by them. When a settlement is convenient for them, even when the claim is meritless, they would sooner throw defendants under the bus than defend a completely defensible frivolous allegation. Not only do attorneys comply, but, likely, they make the recommendation to settle. Furthermore, because of cooperation clauses in policies, doctors are forced to agree. In the meantime, malpractice carriers increase premiums, make $25 billion per year, and pay no taxes on reserves.(10)

Formerly, the healthcare system consisted of private hospitals and private practices managed by market forces. Now, it is made up of networks of hospitals, management systems, and physician groups regulated by the Affordable Care Act. Malpractice insurance is a substantial overhead cost. Many of the nation’s largest networks are self-insured — Mayo Clinic, Kaiser, Henry Ford, Johns Hopkins, Cleveland Clinic, and Mass General, to name a few.(11) They save money, but they also enjoy all the advantages of malpractice carriers, as previously discussed. The network is the employer for the physician, the contractee in health insurance agreements, the provider for the patient, and, in the event of a lawsuit, the one defendant who hires the attorney for all others. In the meantime, networks save $600 billion per year by self-insuring, malpractice claims increase, and so do medical costs.(12)

Doctors, also, game the system. Understandably, all need medical malpractice insurance to remain in practice, and, depending on specialty and location, coverage can cost more than $100,000 per year.(13) To be relieved of this financial burden, doctors join networks.(14) Because networks are paid by capitation, their unwitting patients never know that they are bargaining chips in a network’s contract negotiations with health insurance companies, Medicare, and Medicaid. Nor do patients know about “resource-based practice guidelines,” aka, best practices,(15) which are shortcuts of the standard of care designed to lower costs for the network, not to increase quality of care for patients. Still, physicians willingly comply. In the meantime, these doctors are handsomely paid by networks,(16) and, if not, become medical experts.

Politicians are no strangers to medical malpractice. Medical malpractice is part of health policy.(17) Health policy involves tort reforms. Politicians legislate tort reforms. Tort reforms have little impact on the number of malpractice claims filed. Many are attorneys. Some, like Dick Durbin, begin their careers litigating medical malpractice.(18) In the meantime, politicians are paid to fix problems, not to profit from them.(19)

Next is organized medicine — aka, the American Medical Association (AMA).(20) Its strategy focuses on tort reforms. Also, according to it, best practices ensure competence and have little to do with costs.(21) In its 2024 annual report, entitled “Why We Fight,” there is not a single reference to peer review or medical malpractice.(22) Its board of trustees includes executives of networks, medical experts, and attorneys. It is no wonder that 85% of physicians choose not to belong to the AMA.(23) In the meantime, its total revenues exceed $500 million per year.

Last but not least is the tort system. The most notorious medical malpractice verdict in history is for $229.6 million.(24) Because of motions and rulings during this trial, the jury never learned the truth that this complication was caused by circumstances beyond the control of any defendant.(25) Defendants remain silent under penalty of law. It takes three years to litigate and another two years for an appeal to overturn it.(26) The purpose for the tort system is to determine truth and to make injured parties whole. In the meantime, the tort system makes $443 billion per year.(27)

The cost of medical malpractice litigation is $56 billion per year.(28) For many, this is just the cost of doing business. However, the cost of doing business includes avoidable costs and unavoidable costs. Frivolous lawsuits are avoidable costs. Those who make this assertion are devoid of business acumen. Curiously, the $56 billion figure has not changed since 2010.(29) Why can it not be recalculated?

In the meantime, I developed CCC+C.(30) The CCC+C method, whose name stands for “Collate, Compare, Calculate, and Certify,” is a proposed process for objectively evaluating medical malpractice lawsuits. This is not the often cited “4 Cs.”(31) CCC+C manages frivolous lawsuits by distinguishing, with 95% confidence, a medical error from an error of nature.

Methodology

CCC+C is a decision-making tool. It incorporates ACE+V (analysis, comparison, evaluation and verification), statistical analysis, the law, and the Hippocratic Oath, which also are decision-making tools. ACE+V analyzes fingerprints.(32) So does CCC+C, except the fingerprints are figurative and are found by medical complications. CCC+C uses statistics and the Hippocratic Oath, which are principles in medicine, to determine duty, breach of duty, harm, and proximate cause, which are principles in law.(33) Just as ACE+V has four steps: analyze, compare, evaluate and verify, CCC+C, also, has four steps: collate, compare, calculate, and certify.

Step 1: Collate

This step determines the standard of care, the medical intervention and duty. Then it organizes the standard of care and the medical intervention into 10 phases, each with a specific duty. The “collate” step includes the following phases:

1. The presentation phase. The duty is to perform a history and physical examination and to identify all risk factors, including preexisting conditions, relevant at the initial encounter for this medical condition.

2. The investigation phase. The duty is to perform a complete medical work-up (e.g., laboratory studies, imaging studies, consultations) applicable to the presentation phase.

3. The interpretation phase. The duty is to understand the relevance of results from the investigation phase as they relate to the presentation phase.

4. The diagnostic phase. The duty is to arrive at an appropriate diagnosis and prognosis based on what is discovered in the presentation, investigation, and interpretations phases.

5. The deliberation phase. The duty is to determine alternative medical interventions applicable to the diagnostic phase.

6. The informed consent phase. The duty is to disclose risks, benefits, and complications of these alternatives to the patient and/or to the patient’s representative.

7. The selection phase. The duty is to select the safest, most effective management from among these choices with the approval of the patient and/or the patient’s representative.

8. The technical phase. The duty is to exercise due caution in each detail of management in the presentation selected.

9. The recovery phase. The duty is to respond to progress and to any complications that follow the technical phase.

10. The discharge phase. The duty is to determine that the patient has recovered sufficiently from this medical condition and to arrange follow-up appointments and appropriate referrals.

Step 2: Compare

Step 2 determines harm and breach of duty. Using the Hippocratic Oath, background risk, relative risk, and incident risk as the framework, CCC+C compares each phase of the standard of care to its counterpart in the medical intervention.

Background risk is the key.(34) It is the known occurrence rate of a complication in the general population and it is found in medical literature. No matter the complication, there is always a background risk. Because the standard of care is the benchmark for excellence, if performance in a phase of the medical intervention is the same as in the standard of care, any complication can only be the background risk. If performance increases risk over the standard of care, the risk of a complication is greater than the background risk.

Relative risk is the difference.(35) When corresponding phases are the same, the relative risk is 1.0. When they are different, risks are increased, sometimes ever so slightly, but the relative risk is greater than 1.0. The standard for the relative risk of a complication is the relative risk of a medical error, which is 100%/ background risk. One-hundred percent is the fingerprint for a medical error.

Incident risk quantifies the difference.(36) Incident risk = relative risk × background risk. When repeated for each phase of the medical intervention, there is a sample of 10 incident risks, which collectively represent the medical intervention. Some are equal to the background risk, which is the fingerprint for an error of nature. Others are greater than the background risk.

The following sequence is used in Step 2: Compare:

1. The presentation phase. Standard of care = background risk (µ). Medical intervention = background risk × relative risk₁ (≥1) = incident risk₁ (≥µ).

2. The investigation phase. Standard of care = background risk (µ). Medical intervention = background risk × relative risk₂ (≥1) = incident risk₂ (≥µ).

3. The interpretation phase. Standard of care = background risk (µ). Medical intervention = background risk × relative risk₃ (≥1) = incident risk₃ (≥µ).

4. The diagnostic phase. Standard of care = background risk (µ). Medical intervention = background risk × relative risk₄ (≥1) = incident risk₄ (≥µ).

5. The deliberation phase. Standard of care = background risk (µ). Medical intervention = background risk × relative risk₅ (≥1) = incident risk₅ (≥µ).

6. The informed consent phase. Standard of care = background risk (µ). Medical intervention = background risk × relative risk₆ (≥1) = incident risk₆ (≥µ).

7. The selection phase. Standard of care = background risk (µ). Medical intervention = background risk × relative risk₇ (≥1) = incident risk₇ (≥µ).

8. The technical phase. Standard of care = background risk (µ). Medical intervention = background risk × relative risk₈ (≥1) = incident risk₈ (≥µ).

9. The recovery phase. Standard of care = background risk (µ). Medical intervention = background risk × relative risk₉ (≥1) = incident risk₉ (≥µ).

10. The discharge phase. Standard of care = background risk (µ). Medical intervention = background risk × relative risk₁₀ (≥1) = incident risk₁₀ (≥µ).

Step 3: Calculate

This step determines causation. The single-sample t-test(37) and hypothesis testing(38) establish a statistically significant difference between the background risk, representing the standard of care, and the collective incident risks in the test sample, representing the medical intervention. The result is the p value.(39)

Although some incident risks equal the background risk and others are greater than the background risk, this represents a sampling variability.(40) A sampling variability causes a difference, but not necessarily a statically significant one. A statistically significant difference results when differences between two groups are not random. Once statistical difference is determined, there is no longer a sampling variability, Statistical difference separates an error of nature from a medical error. It is the fingerprint for causation.

There are also pre-existing conditions. They may lead to very serious and even fatal errors apparently unrelated to the medical intervention. Some are random errors of nature, but some are acts of contributory negligence(41) and some are acts of collateral negligence.(42) If not caused by the medical intervention, per se, they can be exacerbated by it. CCC+C proves any relationship.

1. The null hypothesis (Ho). The null hypothesis always assumes there is no change between the sample tested and the general population. It states, there is no statistically significant difference between the background risk, representing the standard of care, and this sample of 10 incident risks, representing the medical intervention; therefore, the adverse outcome is an error unrelated to the medical intervention and the medical intervention comports with the standard of care. There is no malpractice.

2. The Alternate Hypothesis (Ha). The alternate hypothesis is the default hypothesis. It states, there is a statistically significant difference; hence, the adverse outcome is a medical error and the medical intervention departs from the standard of care. There is malpractice.

3. Statistical analysis: The “one-sample t-test” is found in most statistical software. Although it is called the one-sample t-test, the test presumes a second hypothetical sample, which represents the standard of care. In the standard of care, all 10 incident risks are equal to the background risk.

4. The level of significance (α) = 0.05. It represents 95% confidence.(100% – 5%).

5. The test sample is the incident risks, 1 thru 10, for the medical intervention.

6. The population mean (µ) = background risk. It represents the standard of care.

7. The p value is the result. If p value is equal to or greater than alpha (0.05), the difference between the background risk and the incident risks in the sample is not statically significant, and the null hypothesis is retained. If p value is less than alpha (0.05), the difference is statistically significant and the null hypothesis is rejected.

Step 4: Certify

This step determines type I and type II errors. It does so through a notarized report that certifies both the results of CCC+C and the judicial standards for admissible scientific evidence, as determined by the Daubert decision.(43)

1. There are three possible conclusions:

   1. The complication is a medical error or it is an error of nature;

   2. The medical intervention comports with, or departs from, the standard of care;

   3. There is, or there is not, proximate cause. These are expressed with 95% confidence.

2. The evidence presented is reliable and is found in medical records.

3. The scientific method and hypothesis testing, upon which CCC+C is based, are generally accepted and established norms.

4. CCC+C has been peer reviewed and is published in scientific literature.

5. The theory behind the process is objective and is not speculation.

6. The burden of proof is preponderance of evidence, which is 50% probability plus a discretionary scintilla; however, when there is 95% confidence, scintilla is 45%.

7. The medical expert is qualified.

8. There are known errors; type I error = X% and type II error = Y%.

Type I and type II errors are what level the playing field.

A medical intervention that comports with the standard of care is a null hypothesis that is true. Type 1 error is the probability of rejecting a “true” null hypothesis. Type I error is alpha. There is always a quantitative value for alpha. The quantitative value is “the level of significance.” The level of significance is a transition point on an axis. The axis represents the general population of all treatments for a medical condition. All points within this level of significance are treatments that depart from the standard of care and all beyond are treatments that comport with the standard of care.

In law, alpha is “preponderance of evidence,” which, in ordinary jargon, is “more likely than not.” This is a sine qua non in law.(44) The level of significance is 0.5, corresponding to a tipping point on the axis of 50%. This is the law.

In science, including CCC+C, the sine qua non is “95% confidence.” This is a level of significance, or alpha, of 0.05.(45) This is science.

The p value is a point on that same axis that represents the medical intervention in question. When the medical intervention comports with the standard of care and the p value is less than alpha, a true null hypothesis is rejected. Once rejected, the “false” alternate hypothesis, which is “the medical intervention departs from the standard of care,” is accepted, but it is accepted by default and without proof. A fact is rejected as a negative and a fiction is accepted as a positive, Type I error is a false positive. A type I error can be said to be an error of omission.

Type II error is different. It is the probability of retaining a “false” null hypothesis. A “false” null hypothesis is a medical intervention that departs from the standard of care. Type II error is a mistake that places the p value beyond the tipping point when it should be within the tipping point. Type II error proves a negative, not by default, as does type I error, but by the process of hypothesis testing. Type II error can be said to be an error of commission.

For an investigator so inclined, relative risks are distorted to, ultimately, miscalculate a p value that ordinarily would be less than alpha. The operational term is “relative risks are distorted,” meaning that the internal heuristic of hypothesis testing is not distorted; the data are distorted. Miscalculations and differences of opinion distort data, but so does intention. This begs other questions regarding motives and malice, questions that level the playing field.

Type II error, also, has a quantitative value. It is beta. Beta = 1 -- power.(46) Power is a very complex and esoteric analytic. Until now, it is rarely, if ever, calculated.(47) Although finders-of -fact know that the quantitative value of type I error is 0.5 or 0.05, the quantitative value of type II error is not known. This does not presume that the quantitative value of type II error cannot be known.(48) In CCC+C, instead of 1-power, there is the FDR, or false discovery rate.(49) It corresponds to a type II error and it is neither complex nor esoteric. The FDR = p value — the p value adjustment.(50)

Because the p value contains all the distortions of incident risks when an investigator tests the null hypothesis to incorrectly retain a false null hypothesis, and because both type II error and the FDR measure the probability of these distortions, it makes sense that FDR and type II error are related. They are related but they are not the same.

In CCC+C, the “false discovery rate” is the difference between the p value (representing the medical intervention) and alpha (the tipping point). In CCC+C, alpha is the p value adjustment.(51) This makes the FDR a discrete measurable distance between two points. The quantification of distance represents the rate of error that separates the medical intervention from the tipping point. Therefore, the FDR is a reasonable substitute for type II error. However, when the null hypothesis is rejected, the FDR has a negative sign because the p value is always less than alpha. Because type II error is neither positive nor negative, only the integer represents type II error.

In the final analysis, just as type I error has a measurement, type II error has a measurement. Anything that can be measured can be managed. This is why CCC+C does not change the law and why type I and type II errors level the playing field.

Conclusion

The way in which CCC+C does not change the law and how type I and type II errors level the playing field are best explained by example.

Consider a hypothetical medical malpractice case in which the medical intervention has a complication that may be caused by a single performance that differs from the standard of care. If there are others, they are not recognized. This is typical of medical malpractice cases. The premise is the medical intervention either comports with, or departs from, the standard of care and there is a complication, which may, or may not, be a medical error The standard for proof is preponderance of evidence or its equivalent. Threats to validity are to be determined but, basically, they measure miscalculations of performance including differences of opinion and intent.

The defense’s medical expert uses CCC+C to prove that the medical intervention comports with the standard of care. Alpha is 0.05. The null hypothesis is “there is no statistically significant difference between the standard of care and the medical intervention.” The background risk for the complication is 15%. The medical expert uses the relative risk for a medical error, which is 6.66, or 100% / 15%, as the standard to determine that the relative risk in one phase of the medical intervention is 5. All other relative risks are 1. Hence, the test sample has 9 incident risks of 15% and one of 75% p value= 0.171718. Because p value is greater than alpha, the null hypothesis is retained. Type I error is 5%, meaning that, if the null hypothesis is true, there is a 5% probability of rejecting it. The FDR = 0.171718 – 0.05 = 0.121718. Type II error is 12%, meaning that, if the null hypothesis is false, there is a 12% probability of accepting it.

The plaintiff’s medical expert uses conventional inductive reasoning to prove that the medical intervention departs from the standard of care. To analytically compare and contrast the two methods, the defense attorney adapts inductive reasoning to CCC+C to prove the plaintiff’s theory of the case The null hypothesis must be rejected. When deposed, the plaintiff’s medical expert asserts that conclusions are made according to “preponderance of evidence.” Therefore, alpha is 0.5. The plaintiff’s expert also acknowledges 10 duties. However, three duties are breached, the obvious one and two others, which the plaintiff’s expert identifies, but for which they give no reasons for these conclusions. The expert asserts that the complication is a medical error and not a random error of nature. Consequently, the test sample has 7 incident risks of 15% and 3 of 100%. The p value is .040563. The null hypothesis is rejected. Type I error is 50%. The FDR = 0.040563 – 0.5 = –45437. Type II error is 45%.

The type I and type II errors in the defense attorney’s proof stand in stark contrast to the type I and type II errors in the adaptation of the plaintiff attorney’s proof. However, this advantage goes away when there are 3 breaches and alpha is 0.05. The p value is less than alpha. The null hypothesis is rejected. Type 1 error is 5%, and type II error is 1%. The defense would be defeating its own case, if not for one thing. A type II error of 1% presumes that there is only a 1% distortion in the data underlying three breaches. The defense attorney knows that this is not the case in two of the three duties in which claiming relative risks of 6.66 are extreme distortions of relative risks, which can only be 1. Such distortions are obvious to any rational finder -of- fact. By law, it is the plaintiff attorney who has the burden of proof. The duty for the defense attorney is to cast doubt on that proof. Hence, in a status hearing, the defense attorney exercises this duty by asking proof for the breaches of duty alleged by the plaintiff. Defense counsel knows that, to prove a breach, data specific to these two other duties are blatantly distorted. This leaves the plaintiff attorney to either continue with the distortions or dismiss the case. CCC+C does not change the law, but, because of type 1 and type II errors, it levels the playing field.

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