No abstract

Effective implementation of artificial intelligence in behavioral healthcare delivery depends on overcoming challenges that are pronounced in this domain. Self and social stigma contribute to under-reported symptoms, and under-coding worsens ascertainment. Health disparities contribute to algorithmic bias. Lack of reliable biological and clinical markers hinders model development, and model explainability challenges impede trust among users. In this perspective, we describe these challenges and discuss design and implementation recommendations to overcome them in intelligent systems for behavioral and mental health.

No abstract

IMPORTANCE Significant concern has been raised that crisis standards of care policies aimed at guiding resource allocation may be biased against people based on race/ethnicity. OBJECTIVE To evaluate whether unanticipated disparities by race or ethnicity arise from a single institution's resource allocation policy. DESIGN, SETTING, AND PARTICIPANTS This cohort study included adults (aged Ն18 years) who were cared for on a coronavirus disease 2019 (COVID-19) ward or in a monitored unit requiring invasive or noninvasive ventilation or high-flow nasal cannula between May 26 and July 14, 2020, at 2 academic hospitals in Miami, Florida. EXPOSURES Race (ie, White, Black, Asian, multiracial) and ethnicity (ie, non-Hispanic, Hispanic). MAIN OUTCOMES AND MEASURES The primary outcome was based on a resource allocation priority score (range, 1-8, with 1 indicating highest and 8 indicating lowest priority) that was assigned daily based on both estimated short-term (using Sequential Organ Failure Assessment score) and longer-term (using comorbidities) mortality. There were 2 coprimary outcomes: maximum and minimum score for each patient over all eligible patient-days. Standard summary statistics were used to describe the cohort, and multivariable Poisson regression was used to identify associations of race and ethnicity with each outcome. RESULTS The cohort consisted of 5613 patient-days of data from 1127 patients (median [interquartile range {IQR}] age, 62.7 [51.7-73.7]; 607 [53.9%] men). Of these, 711 (63.1%) were White patients, 323 (28.7%) were Black patients, 8 (0.7%) were Asian patients, and 31 (2.8%) were multiracial patients; 480 (42.6%) were non-Hispanic patients, and 611 (54.2%) were Hispanic patients. The median (IQR) maximum priority score for the cohort was 3 (1-4); the median (IQR) minimum score was 2 (1-3). After adjustment, there was no association of race with maximum priority score using White patients as the reference group (Black patients: incidence rate ratio [IRR], 1.00; 95% CI, 0.89-1.12; Asian patients: IRR, 0.95; 95% CI. 0.62-1.45; multiracial patients: IRR, 0.93; 95% CI, 0.72-1.19) or of ethnicity using non-Hispanic patients as the reference group (Hispanic patients: IRR, 0.98; 95% CI, 0.88-1.10); similarly, no association was found with minimum score for race, again with White patients as the reference group (Black patients: IRR, 1.01; 95% CI, 0.90-1.14; Asian patients: IRR, 0.96; 95% CI, 0.62-1.49; multiracial patients: IRR, 0.81; 95% CI, 0.61-1.07) or ethnicity, again with non-Hispanic patients as the reference group (Hispanic patients: IRR, 1.00; 95% CI, 0.89-1.13). CONCLUSIONS AND RELEVANCE In this cohort study of adult patients admitted to a COVID-19 unit at 2 US hospitals, there was no association of race or ethnicity with the priority score underpinning (continued) Key Points Question Is there an association of race and/or ethnicity with priority scores based on both short-term and longerterm estimated mortality used for resource allocation under crisis standards of care? Findings In this retr...

Background: Coronavirus has serially overtaken our metropolitan hospitals. At peak, patients with acute respiratory distress syndrome may outnumber mechanical ventilators. In our Miami Hospital System, COVID-19 cases have multiplied for 4 weeks and elective surgery has been suspended.Methods: An Otolaryngologic Triage Committee was created to appropriately allocate resources to patients. Hospital ethicists provided support. Our tumor conference screened patients for nonsurgical options. Patients were tested twice for coronavirus before performing urgent contaminated operations. N95 masks and protective equipment were conserved when possible.Patients with low-grade cancers were advised to delay surgery, and other difficult decisions were made.Results: Hundreds of surgeries were canceled. Sixty-five cases screened over 3 weeks are tabulated. Physicians and patients expressed discomfort regarding perceived deviations from standards, but risk of COVID-19 exposure tempered these discussions. Conclusions: We describe the use of actively managed surgical triage to fairly balance our patient's health with public health concerns.