Peer Perspectives: Mark Owusu, MD, MPH

Mark Owusu, MD, MPHLocum HospitalistHCA Tristar System at Tristar Summit Medical Center, Hermitage/Nashville, TNTristar Parkridge Medical Center, Chattanooga, TNTristar Hendersonville Medical Center, Hendersonville, TNTristar Greenview Medical Center, Bowling Green, KY

Peer Perspectives: Jane M. Zhu, MD, MPP

Associate Professor of Medicine and Faculty Research LeadDivision of General Internal Medicine and GeriatricsOregon Health & Science University (OHSU) Portland, ORCore Faculty, Center for Health Systems EffectivenessOregon Health & Science University (OHSU) Portland, OR

Peer Perspectives: Elysia Engelage, MD

Elysia Engelage, MD Primary Care Internist, Kaiser Permanente San Francisco (KPSF) Associate Program Director, KP Northern California HIV Medicine Fellowship Core Faculty, KPSF Internal Medicine Residency Program San Francisco, CA 1. What is your current professional position?

Peer Perspectives: David D. Chen, MD, MPH

HospitalistChristianaCare Health SystemWilmington and Newark, DEClinical Assistant ProfessorThomas Jefferson University Hospital-Sidney Kimmel Medical CollegePhiladelphia, PA

Peer Perspectives: Clarissa Barnes, MD, MBA, FACP, ACPA-C

Clarissa Barnes, MD, MBA, FACP, ACPA-C Chief Medical Officer, South Dakota Medicaid Hospitalist, Avera McKennan Hospital and University Health Center Clinical Professor, Internal Medicine Department, University of South Dakota President, American College of Physician Advisors

Peer Perspectives: Camile Gooden, MD, FACP

Physician AdvisorNYU Langone Hospital–SuffolkPatchogue, NYClinical Assistant Professor of MedicineNYU Grossman Long Island School of MedicineMineola, NY1. What is your current professional position?

Peer Perspectives: Bruce Smith, Jr., MD

Bruce Smith, Jr., MD Hospitalist and Attending Physician, Department of Medicine, Cooper University Health Care, Camden, NJ Assistant Professor of Medicine, Cooper Medical School of Rowan University, Camden, NJ 1. What is your current professional position?

Peer Perspectives: Andre Hollingsworth, MD, FACP, FAAP

Internal Medicine Associate Program Director Internal Medicine Section ChiefTrinity Health Grand Rapids HospitalGrand Rapids, MIClinical Assistant ProfessorMichigan State College of Human MedicineGrand Rapids, MI

Mitchell Motooka, MD, FACP

Ambulatory Primary Care, Hawaii Permanente Medical GroupProgram Director Emeritus, Kaiser Permanente Hawaii Residency ProgramUniversity of Hawaii Internal Medicine Residency, 1987–1990University of California San Diego School of Medicine, 1987

Early Career Physicians

Explore ACP benefits and resources unique to internal medicine physicians early in their careers as well as opportunities to engage with peers locally and nationally.Personal and Professional Development Connect with mentors and discover ACP programs to help build your CV and achieve your professional goals.

What You Deserve

Efficacy of Acupuncture for Chronic Spontaneous Urticaria: A Randomized Controlled Trial: Annals of Internal Medicine: Vol 176, No 12

Background: The effectiveness of acupuncture for patients with chronic spontaneous urticaria (CSU), reported in a few small-scale studies, is not convincing. Objective: To investigate whether acupuncture leads to better effects on CSU than sham acupuncture or waitlist control. Design: A multicenter, randomized, sham-controlled trial. (Chinese Clinical Trial Registry: ChiCTR1900022994) Setting: Three teaching hospitals in China from 27 May 2019 to 30 July 2022. Participants: 330 participants diagnosed with CSU. Intervention: Participants were randomly assigned in a 1:1:1 ratio to receive acupuncture, sham acupuncture, or waitlist control over an 8-week study period (4 weeks for treatment and another 4 weeks for follow-up). Measurements: The primary outcome was the mean change from baseline in the Weekly Urticaria Activity Score (UAS7) at week 4. Secondary outcomes included itch severity scores, self-rated improvement, and Dermatology Life Quality Index scores. Results: The mean change in UAS7 (range, 0 to 42) for acupuncture from baseline (mean score, 23.5 [95% CI, 21.8 to 25.2]) to week 4 (mean score, 15.3 [CI, 13.6 to 16.9]) was −8.2 (CI, −9.9 to −6.6). The mean changes in UAS7 for sham acupuncture and waitlist control from baseline (mean scores, 21.9 [CI, 20.2 to 23.6] and 22.1 [CI, 20.4 to 23.8], respectively) to week 4 (mean scores, 17.8 [CI, 16.1 to 19.5] and 20.0 [CI, 18.3 to 21.6], respectively) were −4.1 (CI, −5.8 to −2.4) and −2.2 (CI, −3.8 to −0.5), respectively. The mean differences between acupuncture and sham acupuncture and waitlist control were −4.1 (CI, −6.5 to −1.8) and −6.1 (CI, −8.4 to −3.7), respectively, which did not meet the threshold for minimal clinically important difference. Fifteen participants (13.6%) in the acupuncture group and none in the other groups reported adverse events. Adverse events were mild or transient. Limitation: Lack of complete blinding, self-reported outcomes, limited generalizability because antihistamine use was disallowed, and short follow-up period. Conclusion: Compared with sham acupuncture and waitlist control, acupuncture produced a greater improvement in UAS7, although the difference from control was not clinically significant. Increased adverse events were mild or transient. Primary Funding Source: The National Key R&D Program of China and the Science and Technology Department of Sichuan Province.

Credentialing Internal Medicine Physicians to Expand Long-Acting Reversible Contraceptive Access

Science v. Ideology in Court: Mifepristone and the U.S. Food and Drug Administration

Comparative Effectiveness of Diabetes Drugs: The Use and Misuse of Observational Research

Effect of Complementary Interventions to Redesign Care on Teamwork and Quality for Hospitalized Medical Patients: A Pragmatic Controlled Trial: Annals of Internal Medicine: Vol 176, No 11

Background: Multiple challenges impede interprofessional teamwork and the provision of high-quality care to hospitalized patients. Objective: To evaluate the effect of interventions to redesign hospital care delivery on teamwork and patient outcomes. Design: Pragmatic controlled trial. Hospitals selected 1 unit for implementation of interventions and a second to serve as a control. (ClinicalTrials.gov: NCT03745677) Setting: Medical units at 4 U.S. hospitals. Participants: Health care professionals and hospitalized medical patients. Intervention: Mentored implementation of unit-based physician teams, unit nurse–physician coleadership, enhanced interprofessional rounds, unit-level performance reports, and patient engagement activities. Measurements: Primary outcomes were teamwork climate among health care professionals and adverse events experienced by patients. Secondary outcomes were length of stay (LOS), 30-day readmissions, and patient experience. Difference-in-differences (DID) analyses of patient outcomes compared intervention versus control units before and after implementation of interventions. Results: Among 155 professionals who completed pre- and postintervention surveys, the median teamwork climate score was higher after than before the intervention only for nurses (n = 77) (median score, 88.0 [IQR, 77.0 to 91.0] vs. 80.0 [IQR, 70.0 to 89.0]; P = 0.022). Among 3773 patients, a greater percentage had at least 1 adverse event after compared with before the intervention on control units (change, 1.61 percentage points [95% CI, 0.01 to 3.22 percentage points]). A similar percentage of patients had at least 1 adverse event after compared with before the intervention on intervention units (change, 0.43 percentage point [CI, −1.25 to 2.12 percentage points]). A DID analysis of adverse events did not show a significant difference in change (adjusted DID, −0.92 percentage point [CI, −2.49 to 0.64 percentage point]; P = 0.25). Similarly, there were no differences in LOS, readmissions, or patient experience. Limitation: Adverse events occurred less frequently than anticipated, limiting statistical power. Conclusion: Despite improved teamwork climate among nurses, interventions to redesign care for hospitalized patients were not associated with improved patient outcomes. Primary Funding Source: Agency for Healthcare Research and Quality.

Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings: A Simulation Study: Annals of Internal Medicine: Vol 176, No 10

Background: Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models. Objective: To estimate changes in predictive model performance with use through 3 common scenarios: model retraining, sequentially implementing 1 model after another, and intervening in response to a model when 2 are simultaneously implemented. Design: Simulation of model implementation and use in critical care settings at various levels of intervention effectiveness and clinician adherence. Models were either trained or retrained after simulated implementation. Setting: Admissions to the intensive care unit (ICU) at Mount Sinai Health System (New York, New York) and Beth Israel Deaconess Medical Center (Boston, Massachusetts). Patients: 130 000 critical care admissions across both health systems. Intervention: Across 3 scenarios, interventions were simulated at varying levels of clinician adherence and effectiveness. Measurements: Statistical measures of performance, including threshold-independent (area under the curve) and threshold-dependent measures. Results: At fixed 90% sensitivity, in scenario 1 a mortality prediction model lost 9% to 39% specificity after retraining once and in scenario 2 a mortality prediction model lost 8% to 15% specificity when created after the implementation of an acute kidney injury (AKI) prediction model; in scenario 3, models for AKI and mortality prediction implemented simultaneously, each led to reduced effective accuracy of the other by 1% to 28%. Limitations: In real-world practice, the effectiveness of and adherence to model-based recommendations are rarely known in advance. Only binary classifiers for tabular ICU admissions data were simulated. Conclusion: In simulated ICU settings, a universally effective model-updating approach for maintaining model performance does not seem to exist. Model use may have to be recorded to maintain viability of predictive modeling. Primary Funding Source: National Center for Advancing Translational Sciences.

In Memoriam: Dr. Frank Davidoff: Leading Annals as It Entered the 21st Century

The Case for Flourishing in the Time of Wellness

Fatal Drug Overdose Risks of Health Care Workers in the United States: A Population-Based Cohort Study: Annals of Internal Medicine: Vol 176, No 8

Background: Despite an unprecedented increase in drug overdose deaths in the United States, the risks faced by U.S. health care workers, who often have access to controlled prescription drugs, are not known. Objective: To estimate risks for drug overdose death among health care workers relative to non–health care workers. Design: Prospective cohort study. Setting: United States. Participants: Health care workers (n = 176 000) and non–health care workers (n = 1 662 000) aged 26 years or older surveyed in 2008 and followed for cause of death through 2019. Measurements: Age- and sex-standardized drug overdose deaths were determined for 6 health care worker groups (physicians, registered nurses, other treating or diagnosing health care workers, health technicians, health care support workers, and social or behavioral health workers) and non–health care workers. Adjusted drug overdose death hazards (and 95% CIs) were also evaluated, with adjustment for age, sex, race/ethnicity, marital status, education, income, urban or rural residence, and region. Results: Approximately 0.07% of our study sample died of a drug overdose during follow-up. Among health care workers, annual standardized rates of drug overdose death per 100 000 persons ranged from 2.3 (95% CI, 0 to 4.8) for physicians to 15.5 (CI, 9.8 to 21.2) for social or behavioral health workers. Compared with those for non–health care workers, the adjusted hazards of total drug overdose death were significantly increased for social or behavioral health workers (adjusted hazard ratio, 2.55 [CI, 1.74 to 3.73]), registered nurses (adjusted hazard ratio, 2.22 [CI, 1.57 to 3.13]), and health care support workers (adjusted hazard ratio, 1.60 [CI, 1.19 to 2.16]), but not for physicians (adjusted hazard ratio, 0.61 [CI, 0.19 to 1.93]), other treating or diagnosing health care workers (adjusted hazard ratio, 0.93 [CI, 0.44 to 1.95]), or health technicians (adjusted hazard ratio, 1.13 [CI, 0.75 to 1.68]). Results were generally similar for opioid-related overdose deaths and unintentional overdose deaths. Limitation: Unmeasured confounding, uncertain validity of cause of death, and one-time assessment of occupation. Conclusion: Registered nurses, social or behavioral health workers, and health care support workers were at increased risk for drug overdose death, suggesting the need to identify and intervene on those at high risk. Primary Funding Source: National Heart, Lung, and Blood Institute.

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ACIP releases 2024 Adult Immunization Schedule featuring new vaccines

Put words in our mouth

‘Annals: Spotlight on 2023’ collection and video now available

Study finds no link between elective surgery and dementia risk in seniors

MKSAP Quiz: 1-year history of widespread joint, muscle pain

Study of DAPT after TIA or minor stroke finds high-risk subgroup benefits more, has higher bleeding rates

Lung cancer screening led to more downstream procedures, complications in practice than in research

Put words in our mouth

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Many in-hospital deaths from nonventilator HAP may not be preventable | ACP Hospitalist

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Women less likely than men to receive opioids while hospitalized | ACP Hospitalist

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Spurious hyperphosphatemia in a multiple myeloma patient | ACP Hospitalist

Rapid uptitration of GDMT safe in hospitalized HF patients with afib, analysis finds | ACP Hospitalist

EEG, fMRI newly identified responses in patients with disorders of consciousness | ACP Hospitalist

Bloodstream infection, hemodialysis associated with worse outcomes in C. auris | ACP Hospitalist

Adapt to autism | ACP Hospitalist