Top | 7 | |
Newsletter 12/08/2024 | If you find this article of value, please help keep the blog going by making a contribution at GoFundMe or Paypal |
Back to Contents |
The Many Controversies Surrounding UnitedHealthcare, Part 2
2. Denial of Claims On November 14, 2023, a class action lawsuit [pdf will open] was filed in United States District Court, District of Minnesota, on behalf of the surviving family members and the Estate of Gene B. Lokken; the Estate of Dale Henry Tetzloff; and all others yet unnamed who may have befallen similar circumstances. The central claim of the lawsuit, as reported by CBS News, November 20, 2023, is that "UnitedHealthcare... knowingly used a faulty artificial intelligence algorithm to deny elderly patients coverage for extended care deemed necessary by their doctors." Specifically, on page 3, paragraph 6, of the Complaint, Plaintiffs allege that: 6. Defendants’ AI Model, known as “nH Predict,” determines Medicare Advantage patients’ coverage criteria in post-acute care settings with rigid and unrealistic predictions for recovery. Relying on the nH Predict AI Model, Defendants purport to predict how much care an elderly patient ‘should’ require, but overrides real doctors’ determinations as to the amount of care a patient in fact requires to recover. As such, Defendants make coverage determinations not based on individual patient’s needs, but based on the outputs of the nH Predict AI Model, resulting in the inappropriate denial of necessary care prescribed by the patients’ doctors. Defendants’ implementation of the nH Predict AI Model resulted in a significant increase in the number of post-acute care coverage denials. According to Wikipedia, "nH Predict is a computer program developed by NaviHealth." Furthermore, naviHealth is an entity that comes under the umbrella of UnitedHealth. As reported by ArsTechnica, November 16, 2023 UnitedHealth had "acquired NaviHealth in 2020." At the heart of Plaintiffs' allegations is the fact that nH Predict determines patient care. As stated in the Complaint at Page 13, paragraph 35, the health determinations made by nH Predict are the final decisions about any patient's care and cannot be overridden by human employees. 35. Upon information and belief, the nH Predict AI Model applies rigid criteria from which Defendants’ employees are instructed not to deviate. The employees who deviate from the nH Predict AI Model prediction are disciplined and terminated, regardless of whether the additional care for a patient is justified. Yet, as per the complaint at Page 14, paragraph 38, the decisions about patient care made by nH Predict are reversed 90% of the time upon review by appropriate review boards. As CBS noted in its report cited above, this fact represents the deployment of "an AI model known by the company to have a 90% error rate." Upon information and belief, over 90 percent of patient claim denials are reversed through either an internal appeal process or through federal Administrative Law Judge (ALJ) proceedings. This demonstrates the blatant inaccuracy of the nH Predict AI Model and the lack of human review involved in the coverage denial process. Plaintiffs further allege that the reliance on a faulty machine to determine health coverage, as opposed to trained and competent human individuals, is tantamount to a fraud being perpetrated on those patients whose care was determined by an algorithm. Plaintiffs make this clear at Page 14, paragraphs 39 and 40.
39. Defendants fraudulently misled their insureds into believing
that their health plans would individually assess their claims and pay
for medically necessary care. Plaintiffs contend at Page 19, paragraph 72, that the practices alleged in the Complaint are so widespread that it would be impossible to name them all. Plaintiffs, through their attorneys, do intend to locate additional members of the injured class. 72. The Class is so numerous that their individual joinder herein is impracticable. On information and belief, members of the Class number in the thousands throughout the United States and the named states. The precise number of Class members and their identities are unknown to Plaintiffs at this time but may be determined through discovery. Class members may be notified of the pendency of this action by mail and/or publication through the distribution records of Defendants and third-party retailers and vendors. It has been widely reported that the words "Delay, Deny, Defend," were written on bullet casings found at the scene of Brian Thompson's murder. These words are the main title of a book written by Jay M. Feinman and published in 2010 about how insurance companies conspire to deny claims made by patients. The subtitle of the work is: "Why Insurance Companies Don't Pay Claims and What You Can Do About It." Below is a brief synopsis of the book found and for sale on Amazon. Jay M. Feinman, a legal scholar and insurance expert, explains how these trends developed, how the government ought to fix the system, and what the rest of us can do to protect ourselves. He shows that the denial of valid claims is not occasional or accidental or the fault of a few bad employees. It's the result of an increasing and systematic focus on maximizing profits by major companies such as Allstate and State Farm. I used the word "Indolence" to introduce this essay. As defined by the Merriam-Webster Dictionary, the meaning of indolence is "inclination to laziness." And isn't this, among their other sins, exactly what the management of UnitedHealthcare and UnitedHealthGroup are guilty of: Being too damn lazy and too damn cheap to let actual humans to do the jobs humans are best suited to doing? And did Brian Thompson pay for these transgressions with his very life? The shooter may have well have been an aggrieved family member (or agent thereof) whose loved one or ones suffered and died due to the indolence of their health insurer. The clues do point us in that direction. Other clues, however, point us in another direction. Click through to Part 3 to find those other clues.
| ||
¯\_(ツ)_/¯¯ Gerald Reiff |
Back to Top | ← previous post | next post → |
If you find this article of value, please help keep the blog going by making a contribution at GoFundMe or Paypal |