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 United States is in the midst of a drug overdose epidemic. Although the online availability of drugs has been a growing concern with considerable speculation that digital platforms are contributing to this epidemic, empirical assessments have been lacking. To quantify this impact, we rely on the phased rollout of Craigslist, a major online platform, as an experimental setup. Applying a difference-in-differences approach on a national panel data set for all counties in the United States from 1997 to 2008, we find a 14.9% increase in drug abuse treatment admissions, a 5.7% increase in drug abuse violations, and a 6.0% increase in drug overdose deaths after Craigslist’s entry. The impacts of Craigslist’s entry are larger among women, whites, Asians, and the more educated. Further, the unintended consequences of Craigslist are more likely to accrue in larger, wealthier areas with initially low levels of drug abuse. These findings raise the possibility that the marked growth in U.S. drug abuse may have partially stemmed from the wider availability of illicit drugs online at the very beginning of its evolution. This paper was accepted by David Simchi-Levi, information systems.

We study the effects of rescheduling on no-show behavior in an outpatient appointment system for both new and follow-up patients. Previous literature has primarily focused on new patients and investigated the role of waiting time on no-show probability. We offer a more nuanced understanding of this costly phenomenon. Using comprehensive clinical data, we demonstrate that for follow-up patients, their no-show probability decreases by 10.9 percentage points if their appointments were rescheduled at their own request, but increases by 6.2 percentage points if they were rescheduled by the clinic. New patients, in contrast, are less sensitive to who initiates rescheduling. Their no-show probability decreases by 2.3 percentage points if their appointments were rescheduled at their own request, and increases by 3.2 percentage points—but is statistically insignificant at the 10% level—if they were rescheduled by the clinic. New patients are more concerned about waiting time compared with follow-up patients. For patients whose appointments were not rescheduled, new patients’ no-show probability decreases by 1.3 percentage points if their waiting time is reduced by one week, but the waiting time has a small and statistically insignificant effect on follow-up patients’ no-show probability. Using data-driven simulation, we conduct counterfactual investigation of the impact of allowing active rescheduling on the performance of appointment systems. In particular, allowing the flexibility of patient rescheduling can reduce the overall no-show rate and increase system utilization, but at a cost of increased wait time for new patients. If patients are able to reschedule at least one week in advance, new patients’ wait time is largely reduced, whereas the no-show rate remains the same; this is equivalent to the effect of a 5% increase in the clinic’s capacity.

No abstract

Industrial robots (IRs) are widely used in modern manufacturing systems, and energy problem of IRs is paid more attention to meet requirements of environment protection. Therefore, it is necessary to investigate the approaches to optimize the energy consumption of IRs, and the energy consumption model is the basis for enabling such approaches. Usually, energy consumption modeling for IRs is based on dynamic parameters identification. Meanwhile, the physical parameters, e.g. angle, velocity, acceleration, torque, etc. are all the necessary data of parameter identification. However, since the parts of IRs are not easy to be disassembled and the sensor modules can not be installed easily inside IRs, it is difficult to obtain all such physical parameters through sensing method, in particular the torque data. In this context, a practical energy modeling method by measuring total power for IRs is proposed. This method avoids the problem of directly measuring relevant parameters inside IRs, and the parameter identification process is gradually carried out by several excitation experiments. The experimental results show that the proposed energy modeling method can be used to predict the energy consumption of the process used in robot movement in manufacturing processes, and it can also efficiently support the analysis of the energy consumption characteristics of IRs.