Comparing frequency of booster vaccination to prevent severe COVID-19 by risk group in the United States.

There is a public health need to understand how different frequencies of COVID-19 booster vaccines may mitigate the risk of severe COVID-19, while accounting for waning of protection and differential risk by age and immune status. By analyzing United States COVID-19 surveillance and seroprevalence data in a microsimulation model, here we show that more frequent COVID-19 booster vaccination (every 6-12 months) in older age groups and the immunocompromised population would effectively reduce the burden of severe COVID-19, while frequent boosters in the younger population may only provide modest benefit against severe disease. In persons 75+ years, the model estimated that annual boosters would reduce absolute annual risk of severe COVID-19 by 199 (uncertainty interval: 183-232) cases per 100,000 persons, compared to a one-time booster vaccination. In contrast, for persons 18-49 years, the model estimated that annual boosters would reduce this risk by 14 (10-19) cases per 100,000 persons. Those with prior infection had lower benefit of more frequent boosting, and immunocompromised persons had larger benefit. Scenarios with emerging variants with immune evasion increased the benefit of more frequent variant-targeted boosters. This study underscores the benefit of considering key risk factors to inform frequency of COVID-19 booster vaccines in public health guidance and ensuring at least annual boosters in high-risk populations.

The Limits of Clinician Vigilance as an AI Safety Bulwark.

This Viewpoint examines the potential problems of clinician reliance on the use of artificial intelligence (AI) in health care and offers suggestions on how AI could be designed to promote clinician vigilance.

COVID-19 and Patient Safety-Lessons From 2 Efforts to Keep People Safe.

This Viewpoint examines the similarities between medical errors and COVID-19 and discusses lessons applicable to ongoing and emerging health challenges.

Will Generative Artificial Intelligence Deliver on Its Promise in Health Care?

Importance: Since the introduction of ChatGPT in late 2022, generative artificial intelligence (genAI) has elicited enormous enthusiasm and serious concerns. Observations: History has shown that general purpose technologies often fail to deliver their promised benefits for many years ("the productivity paradox of information technology"). Health care has several attributes that make the successful deployment of new technologies even more difficult than in other industries; these have challenged prior efforts to implement AI and electronic health records. However, genAI has unique properties that may shorten the usual lag between implementation and productivity and/or quality gains in health care. Moreover, the health care ecosystem has evolved to make it more receptive to genAI, and many health care organizations are poised to implement the complementary innovations in culture, leadership, workforce, and workflow often needed for digital innovations to flourish. Conclusions and Relevance: The ability of genAI to rapidly improve and the capacity of organizations to implement complementary innovations that allow IT tools to reach their potential are more advanced than in the past; thus, genAI is capable of delivering meaningful improvements in health care more rapidly than was the case with previous technologies.

The Hospitalist Movement 25 Years Later.

Hospitalists are generalists who specialize in the care of hospitalized patients. In the 25 years since the term hospitalist was coined, the field of hospital medicine has grown exponentially and established a substantial footprint in the medical community. There are now more hospitalists than practicing physicians in any other internal medicine subspecialty. Several key forces catalyzed the growth in the field of hospital medicine, including the quality, safety, and value movements; residency duty hour restrictions; the emergence of electronic health records; and the COVID-19 pandemic. Looking ahead, we see new opportunities in the realms of technology and telemedicine, and challenges persist in regard to balancing financial considerations with increasing workload and burnout. Hospitalists must remain nimble and seize emerging opportunities to continue supporting the field's prominence and growth.

Oral Nirmatrelvir and Ritonavir in Nonhospitalized Vaccinated Patients With Coronavirus Disease 2019.

BACKGROUND: Treatment of coronavirus disease 2019 (COVID-19) with nirmatrelvir plus ritonavir (NMV-r) in high-risk nonhospitalized unvaccinated patients reduced the risk of progression to severe disease. However, the potential benefits of NMV-r among vaccinated patients are unclear. METHODS: We conducted a comparative retrospective cohort study using the TriNetX research network. Patients ≥18 years of age who were vaccinated and subsequently developed COVID-19 between 1 December 2021 and 18 April 2022 were included. Cohorts were developed based on the use of NMV-r within 5 days of diagnosis. The primary composite outcome was all-cause emergency room (ER) visit, hospitalization, or death at a 30-day follow-up. Secondary outcomes included individual components of primary outcomes, multisystem symptoms, COVID-19-associated complications, and diagnostic test utilization. RESULTS: After propensity score matching, 1130 patients remained in each cohort. A primary composite outcome of all-cause ER visits, hospitalization, or death in 30 days occurred in 89 (7.87%) patients in the NMV-r cohort compared with 163 (14.4%) patients in the non-NMV-r cohort (odds ratio: .5; 95% confidence interval: .39-.67; P < .005) consistent with 45% relative risk reduction. A significant reduction in multisystem symptom burden and subsequent complications, such as lower respiratory tract infection, cardiac arrhythmia, and diagnostic radiology testing, were noted in NMV-r-treated patients. There was no apparent increase in serious complications between days 10 and 30. CONCLUSIONS: Treatment with NMV-r in nonhospitalized vaccinated patients with COVID-19 was associated with a reduced likelihood of ER visits, hospitalization, or death. Complications and overall resource utilization were also decreased.

Readmission Reduction as a Hospital Quality Measure: Time to Move on to More Pressing Concerns?

The authors of this Viewpoint argue that the focus on hospital readmission rates as a measure of quality during the past decade, although undoubtedly leading to some improvements in care, has had minimal demonstrable benefit and has even distracted clinicians and health system leaders from other crucial quality concerns.

Nailing the nuance on COVID-19.

In messaging the public about COVID-19, brevity and simplicity are crucial. But so too is scientifically accurate and nuanced communication that tells people what the latest numbers mean and how they should influence behavior. Nearly 3 years into the pandemic, public health communicators continue to struggle to get this balance right.

Building a Targeted Automatic e-Consult (TACo) Program.

DRIVING FORCES: Traditional specialty consults are resource intensive and may be delayed or omitted if the treating physician does not recognize the need for specialty advice. Targeted automatic e-consults (TACos) address these limitations by prospectively identifying patients using the electronic health record (EHR) and presenting pertinent information on a dashboard, enabling consultants to provide a virtual consult with written recommendations. The TACo model may improve value by facilitating more expert input without a proportional increase in cost. BUILDING A TACO: Through our experience developing a TACo program, we have identified four key steps. First, identify appropriate conditions that have support from the health system and from frontline clinicians. Second, design the digital infrastructure, including lists and dashboards. Third, create a funding plan to support the consultant's time, either through internal grants, external grants, e-consult billing codes, or some combination of the three. Fourth, pilot on a select number of services, iterate, and scale. CHALLENGES: Funding for TACos has been a major barrier to adoption. Fortunately, new e-consult billing codes may make it possible to recoup as least part of the program's cost. Technological hurdles also exist, particularly in building real-time lists within the EHR to prospectively identify patients based on complex criteria. NEXT STEPS: We look for this model to gain popularity as evidence of clinical and operational benefit mounts. We anticipate reimbursement policies may be updated to support this type of consult. Finally, we expect machine learning to play a role in identifying patients and providing recommendations in the future.

Rates and Characteristics of Medical Malpractice Claims Against Hospitalists.

BACKGROUND: Hospitalists practice in high-stakes and litigious settings. However, little data exist about the malpractice claims risk faced by hospitalists. OBJECTIVE: To characterize the rates and characteristics of malpractice claims against hospitalists. DESIGN, SETTING, AND PARTICIPANTS: An analysis was performed of malpractice claims against hospitalists, as well as against select other specialties, using data from a malpractice claims database that includes approximately 31% of US malpractice claims. MAIN OUTCOMES AND MEASURES: For malpractice claims against hospitalists (n = 1,216) and comparator specialties (n = 18,644): claims rates (using a data subset), percentage of claims paid, median indemnity payment amounts, allegation types, and injury severity. RESULTS: Hospitalists had an annual malpractice claims rate of 1.95 claims per 100 physician-years, similar to that of nonhospitalist general internal medicine physicians (1.92 claims per 100 physician-years), and significantly greater than that of internal medicine subspecialists (1.30 claims per 100 physician-years) (P < .001). Claims rates for hospitalists nonsignificantly increased during the study period (2009-2018), whereas claims rates for four of the five other specialties examined significantly decreased over this period. The median indemnity payment for hospitalist claims was $231,454 (interquartile range, $100,000-$503,015), significantly higher than the amounts for all the other specialties except neurosurgery. The greatest predictor of a hospitalist case closing with payment (compared with no payment) was an error in clinical judgment as a contributing factor, with an adjusted odds ratio of 5.01 (95% CI, 3.37-7.45). CONCLUSION: During the study period, hospitalist claims rates did not drop, whereas they fell for other specialties. Hospitalists' claims had relatively high injury severity and median indemnity payment amounts. The malpractice environment for hospitalists is becoming less favorable.

The Molecular Medicine Investigation Unit: Linking Patient Care and Scientific Inquiry in Physician-Scientist Training.

BACKGROUND: Medical innovation depends on translation, the process of applying clinical insights to solve biological problems, and vice versa, yet existing training programs provide few opportunities for physician-scientists to integrate their clinical and research training. OBJECTIVE: We developed and determined the feasibility and acceptability of a rotation on the Molecular Medicine Investigation Unit (MMIU), a novel program that engages trainees in the deliberate linkage of patient care and scientific inquiry to cultivate their interest and skills in translation. METHODS: Between July 2017 and January 2019, fourth-year medical students and internal medicine residents were offered a 4-week elective rotation on the MMIU. Supervised by 2 part-time faculty, trainees evaluated patients with unusual and perplexing presentations with the goal of generating hypotheses and a research plan to elucidate the underlying mechanisms of disease. We tracked the development of research hypotheses and resulting projects and surveyed participants about their satisfaction with the program. RESULTS: Over 18 months, 21 trainees (11 medical students and 10 residents) participated in the program and evaluated a total of 70 patients. Trainees generated a mechanistic hypothesis in 45 (64%) cases, and this resulted in a patient-centered research project in 38 (54%) cases. Trainees unanimously agreed that the program gave them an opportunity to integrate their clinical and research training, and many expressed that it reinforced their interests in translational research. CONCLUSIONS: With modest funding support, it was feasible to deliver authentic experiences of translational inquiry for medical students and internal medical residents, and these experiences were valued by trainees.