Bridging the Divide: Unintended Consequences of the Shift to Home-Based Telemedicine.

OBJECTIVE: To evaluate the impact on health care access of the change in telemedicine delivery from a clinic-based model, in which patients connect with their healthcare provider from local telemedicine clinics, to a home-based model, in which patients independently connect from their homes. STUDY DESIGN: In this retrospective analysis, we compared relative uptake in telemedicine services in Period 1 (01/01/2019 to 03/15/2020, prepandemic, clinic-based model) vs Period 2 (03/16/2020 to 06/30/2022, home-based model) within a tertiary pediatric hospital system. Using multivariable logistic regression, we investigated the influence of telemedicine delivery model on patient sociodemographic characteristics of completed telemedicine visits. RESULTS: We analyzed 400 539 patients with 1 406 961 completed outpatient encounters (52% White, 35% Black), of which 62 920 (4.5%) were telemedicine. In the clinic-based model (Period 1), underserved populations had greater likelihoods of accessing telemedicine: Hispanic ethnicity (OR = 1.41, P = .028) vs reference group non-Hispanic, Medicaid (OR = 2.62, P < .001) vs private insurance, and low-income neighborhood (OR = 3.40, P < .001) vs medium-income. In aggregate, telemedicine utilization rapidly increased from Period 1 (1.5 encounters/day) to Period 2 (107.9 encounters/day). However, underserved populations saw less relative increase (Medicaid [OR = 0.28, P < .001], Hispanic [OR = 0.53, P < .001], low-income [OR = 0.23, P < .001]). CONCLUSIONS: We observe that the clinic-based model offers more equitable access, while the home-based model offers more absolute access, suggesting that a hybrid model that offers both home-based and clinic-based services may result in more absolute and equitable access to telemedicine.

The Association of Workload and Outcomes in the Pediatric Cardiac ICU.

OBJECTIVES: Healthcare workload has emerged as an important metric associated with poor outcomes. To measure workload, studies have used bed occupancy as a surrogate. However, few studies have examined frontline provider (fellows, nurse practitioners, physician assistants) workload and outcomes. We hypothesize frontline provider workload, measured by bed occupancy and staffing, is associated with poor outcomes and unnecessary testing. DESIGN: A retrospective single-center, time-stamped orders, ordering provider identifiers, and patient data were collected. Regression was performed to study the influence of occupancy on orders, length of stay, and mortality, controlling for age, weight, admission type, Society of Thoracic Surgery-European Association for Cardio-Thoracic Surgery Congenital Heart Surgery Mortality score, diagnosis, number of surgeries, orders, provider staffing, attending experience, and time fixed effects. SETTING: Twenty-seven bed tertiary cardiac ICU in a free-standing children's hospital. PATIENTS: Patients (0-18 yr) admitted to the pediatric cardiac ICU, January 2018 to December 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 16,500 imaging and 73,113 laboratory orders among 1,468 patient admissions. Median age 6 months (12 d to 5 yr), weight 6.2 kg (3.7-16.2 kg); 840 (57.2%) surgical and 628 (42.8%) medical patients. ICU teams consisted of 16 attendings and 31 frontline providers. Mortality 4.4%, median stay 5 days (2-11 d), and median bed occupancy 89% (78-93%). Every 10% increase in bed occupancy had 7.2% increase in imaging orders per patient (p < 0.01), 3% longer laboratory turn-around time (p = 0.015), and 3 additional days (p < 0.01). Higher staffing (> 3 providers) was associated with 6% less imaging (p = 0.03) and 3% less laboratory orders (p = 0.04). The number of "busy days" (bed occupancy > 89%) was associated with longer stays (p < 0.01), and increased mortality (p < 0.01). CONCLUSIONS: Increased bed occupancy and lower staffing were associated with increased mortality, length of stay, imaging orders, and laboratory turn-around time. The data demonstrate performance of the cardiac ICU system is exacerbated during high occupancy and low staffing.

The Effects of Discrete Work Shifts on a Nonterminating Service System.

Hospital emergency departments (EDs) provide around-the-clock medical care, and as such are generally modeled as nonterminating queues. However, from the care provider's point of view, ED care is not a never-ending process, but rather occurs in discrete work shifts and may require passing unfinished work to the next care provider at the end of the shift. We use data from a large, academic medical center ED to show that the patients' rate of service completion varies over the course of the physician shift. Further, patients that have experienced a physician handoff have a higher rate of service completion than non-handed off patients. As a result, a patient's expected treatment time is impacted by when in the physician's shift treatment begins. We also show that patients that have been handed off are more likely to revisit the ED within three days, suggesting that patient handoffs lower clinical quality. Lastly, we use simulation to show that shift length and new-patient cutoff rules can be used to reduce handoffs, but at the expense of system throughput.

Emergency department provider and facility variation in opioid prescriptions for discharged patients.

STUDY OBJECTIVE: To study the variation in opioid prescribing among emergency physicians and facilities for discharged adult ED patients. METHODS: We conducted a retrospective analysis of ED visits from five U.S. hospitals between January and May 2014 using records from Data to Intelligence (D2i). We examined physician- and facility-level variation in opioid prescription rates for discharged ED patients. We calculated unadjusted opioid prescription rates at the physician and facility levels and used a multivariable mixed-effect logistic regression model to examine within-facility physician variation in opioid prescription adjusting for patient and situational factors including time of presentation, ED census, and physician workload. RESULTS: In 47,304 visits across five EDs, median patient age was 40 years old (IQR 28,55), and 89% had some form of insurance. There were 17,098 (36%) ED discharges with at least one opioid prescription. The unadjusted facility-level opioid prescription rate ranged from 24%-46%. Among 253 ED physicians, the adjusted opioid prescription rate varied from 22%-76%. Increased physician workload is related to decreased odds of opioid prescription at ED discharge for the lowest (<3 patients) and moderate (6-9 patients) physician workload levels, while the association weakened with increasing levels of workload. CONCLUSION: There was substantial physician and facility variation in opioid prescription for discharged adult ED patients. Emergency physicians were less likely to prescribe opioids when their workload was lower, and this effect diminished at high workload levels. Understanding situational and other factors that explain this variation is important given the rising U.S. opioid epidemic and the need for urgent intervention.