A controversial AI program used to deny elderly people health coverage is now at the center of questions about the shooting of the UnitedHealthcare CEO. Brian Thompson, 50 was gunned down Wednesday outside a Hilton in Midtown Manhattan in what police have described as a'brazen' and'targeted' attack. The killer is still on the loose and the motive is not yet known - but a former-FBI agent told Newsweek that he may have been denied health coverage. UnitedHealthcare became the largest denier of insurance plans in 2023, dismissing one in every three claims. It has now emerged that during the years before that, the company implemented AI software that had a 90 percent denial rate.

Kevin Keenahan, is the Chief Product Officer at Net Health and co-founder of Tissue Analytics, Inc., acquired by Net Health in 2020. Net Health's mission is to harness data for human health. They offer EHR software and predictive, actionable analytics for medical specialties, including rehab therapy, wound care, home health and hospice and employee health. Their solutions are trusted in over 23,000 facilities across the continuum of care, including the nation's leading hospitals, skilled nursing facilities, senior living facilities, home health and hospice agencies and outpatient clinics. Could you share the genesis story of how you got involved in wound care, and how this resulted from your graduate studies at John Hopkins University?

When it comes to skilled nursing facilities (SNFs) in particular and long-term care in general, a human-centered and empathetic approach is not only necessary, it's obligatory. Putting the needs, constraints, and perspectives of patients first enhances the level of care we're able to provide. Perhaps ironically, it is the integration and adaptation of artificial intelligence (AI) that has demonstrated the biggest potential in improving empathy and compassion in the industry. It may be counterintuitive, but we're already seeing a major difference. From Hasbro's AI cat, which is used to remind patients to take their prescription medications, to Paro, a robotic plush seal designed to provide therapeutic services for dementia patients, we're seeing how AI can work effectively alongside healthcare professionals to achieve better patient outcomes.

One important characteristic of coronavirus epidemiology is the occurrence of superspreading events. These are marked by a disproportionate number of cases originating from often-times asymptomatic individuals. Using a rich sequence dataset from the early stages of the Boston outbreak, Lemieux et al. identified superspreading events in specific settings and analyzed them phylogenetically (see the Perspective by Alizon). Using ancestral trait inference, the authors identified several importation events, further investigated the context and contribution of particular superspreading events to the establishment of local and wider SARS-CoV-2 transmission, and used viral phylogenies to describe sustained transmission. Science , this issue p. [eabe3261][1]; see also p. [574][2] ### INTRODUCTION We used genomic epidemiology to investigate the introduction and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the Boston area across the first wave of the pandemic, from March through May 2020, including high-density sampling early in this period. Our analysis provides a window into the amplification of transmission in an urban setting, including the impact of superspreading events on local, national, and international spread. ### RATIONALE Superspreading is recognized as an important driver of SARS-CoV-2 transmission, but the determinants of superspreading—why apparently similar circumstances can lead to very different outcomes—are poorly understood. The broader impact of such events, both on local transmission and on the overall trajectory of the pandemic, can also be difficult to determine. Our dataset includes hundreds of cases that resulted from superspreading events with different epidemiological features, which allowed us to investigate the nature and effect of superspreading events in the first wave of the pandemic in the Boston area and to track their broader impact. ### RESULTS Our data suggest that there were more than 120 introductions of SARS-CoV-2 into the Boston area, but that only a few of these were responsible for most local transmission: 29% of the introductions accounted for 85% of the cases. At least some of this variation results from superspreading events amplifying some lineages and not others. Analysis of two superspreading events in our dataset illustrate how some introductions can be amplified by superspreading. One occurred in a skilled nursing facility, where multiple introductions of SARS-CoV-2 were detected in a short time period. Only one of these led to rapid and extensive spread within the facility, and significant mortality in this vulnerable population, but there was little onward transmission. A second superspreading event, at an international business conference, led to sustained community transmission, including outbreaks in homeless and other higher-risk communities, and was exported domestically and internationally, ultimately resulting in hundreds of thousands of cases. The two events also differed substantially in the genetic variation they generated, possibly suggesting varying transmission dynamics in superspreading events. Our results also show how genomic data can be used to support cluster investigations in real time—in this case, ruling out connections between contemporaneous cases at Massachusetts General Hospital, where nosocomial transmission was suspected. ### CONCLUSION Our results provide powerful evidence of the importance of superspreading events in shaping the course of this pandemic and illustrate how some introductions, when amplified under unfortunate circumstances, can have an outsized effect with devastating consequences that extend far beyond the initial events themselves. Our findings further highlight the close relationships between seemingly disconnected groups and populations during a pandemic: Viruses introduced at an international business conference seeded major outbreaks among individuals experiencing homelessness; spread throughout the Boston area, including to other higher-risk communities; and were exported extensively to other domestic and international sites. They also illustrate an important reality: Although superspreading among vulnerable populations has a larger immediate impact on mortality, the cost to society is greater for superspreading events that involve younger, healthier, and more mobile populations because of the increased risk of subsequent transmission. This is relevant to ongoing efforts to control the spread of SARS-CoV-2, particularly if vaccines prove to be more effective at preventing disease than blocking transmission. ![Figure][3] Schematic outline of this genomic epidemiology study. Illustrated are the numerous introductions of SARS-CoV-2 into the Boston area; the minimal spread of most introductions; and the local, national, and international impact of the amplification of one introduction by a large superspreading event. Analysis of 772 complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from early in the Boston-area epidemic revealed numerous introductions of the virus, a small number of which led to most cases. The data revealed two superspreading events. One, in a skilled nursing facility, led to rapid transmission and significant mortality in this vulnerable population but little broader spread, whereas other introductions into the facility had little effect. The second, at an international business conference, produced sustained community transmission and was exported, resulting in extensive regional, national, and international spread. The two events also differed substantially in the genetic variation they generated, suggesting varying transmission dynamics in superspreading events. Our results show how genomic epidemiology can help to understand the link between individual clusters and wider community spread. [1]: /lookup/doi/10.1126/science.abe3261 [2]: /lookup/doi/10.1126/science.abg0100 [3]: pending:yes

A study of 476 total knee arthroplasty (TKA) patients at three separate institutions found that costs could be reduced by as much as $1,215, length of stay could be cut by as much as 25%, home discharges could increase by 80% and, finally that 91% fewer TKA patients were discharged to a skilled nursing facility. As study which measured the effects of such AI based PreHab, titled "The Role of pre-habilitation with a telerehabilitation system prior to total knee arthroplasty," appears in the November 27, 2018 edition of the Annals of Translational Medicine. According to the study authors, 114 patients were selected to participate in a pre-habilitation program of exercises, nutritional advice, education regarding home safety and reducing medical risks, and pain management skills prior to surgery. A group of 362 patients were selected to participate be the control cohort and did not participate in the pre-hab program. The study was conducted at Northwell Health in San Francisco.

The use of artificial intelligence (AI) technology in health care is poised to soar throughout the globe in the coming years, including to support preventive care in people's homes. The report defines artificial intelligence (AI) as providing "a device or software program the ability to interpret complex data, including images, video, text, and speech or other sounds, and act on that interpretation to achieve a goal." The number of monitoring devices capturing patient data for AI purposes such as predictive analytics will increase exponentially, the report found. Specifically, there were 53,000 such devices in use as of 2017, and there will be 3.1 million as of 2021. "This includes the use of AI for home-based preventive care solutions," an ABI press release stated.