Recruitment of patients to the AVERT DOSE international trial - the Irish experience

Introduction: Mobility training is a complex intervention and recovery post-stroke is multidimensional. AVERT DOSE is the first trial to use an adaptive trial design in stroke rehabilitation and aims to define optimal early intervention regimens for people with mild to moderate ischaemic stroke. Seven Irish sites are participating. Method(s): AVERT DOSE (ACTRN:12619000557134) is a randomised trial that will recruit >2,500 patients internationally. Randomisation is to two groups according to stroke severity. Patients are then randomised to one of four mobility training regimens in each strata and the intervention is delivered for up to 14-days. Primary Outcome: Identification of the intervention regimen that results in higher proportion of favourable outcome at 3-months post-stroke. Seven Irish sites are participating. Result(s): In Ireland, 3 sites are recruiting (SJH, OLOLH, and MMUH) with 4 finalising contracts. Thirteen patients have been recruited to date in Ireland and 265 internationally. Trial set-up has proven complex and variable across Irish sites, with time to ethics approval ranging from 10-37-months. Given the COVID-19 pandemic and international nature of the trial, online training and meetings were necessitated for all Irish sites. Close communication, teamwork and shared responsibilities have supported this process. Flexibility was required with some blinded followup assessments using telehealth. Conclusion(s): Undertaking rehabilitation research requires a dynamic, problem-solving approach, particularly during a pandemic. Irish sites have embraced this opportunity to answer an important stroke research question. In Ireland, shared learning in trial governance should improve future rehabilitation trial readiness. Trial recruitment is expected to gain pace as more Irish and international sites are approved.

Investigating Disparities in Telehealth Among CKD Patients in a US Integrated Healthcare System

Background: Telehealth utilization, including the use of telephone appointments (TAV) and video appointment visits (VAV), are a valuable alternative to in-person visits and were a crucial form of health delivery during the COVID-19 national emergency. Among chronic kidney disease (CKD) patients within an integrated healthcare system, we evaluated the rate of successfully completed telehealth visits and assess differences in adoption using an equity lens. Method(s): A retrospective cohort study was conducted among individuals (age>=18yrs) with CKD stage 3 and 4 receiving care at Kaiser Permanente Southern California (KPSC). We limited our sample to patients with at least one in-person visit within 12 months prior to Mar 1, 2020 and followed them for 1 year. A successfully completed telehealth visit was defined as a >=20 min completed appointment via TAV/VAV. Poisson regression with robust variance error was conducted to estimate the rate ratio for a successful telehealth visit. Result(s): Of 161,088 patients, 74% (N=118,456) had >= 1 successfully completed telehealth visit, 34% of which were VAVs. Younger age, female gender, white race, and English as spoken language were associated with the successful completion of a telehealth visit. Senior persons (85+) were less likely to have a successfully TAV/VAV compared to young adults (18-34 yrs) (RR:0.82;95% CI:0.79-0.86) (Figure). Those having a KPSC online account were more likely to have successful TAV/VAV (RR:1.11;95% CI:1.10-1.12). Medicaid patients had more successful telehealth visits while patients living in neighborhoods with less internet access were marginally less likely to have successful TAV/VAV (p=0.05). Conclusion(s): We observed disparities in adoption of telehealth care among CKD patients within an integrated health system. Our findings suggest that further studies and management strategies are needed to facilitate and improve equitable patient-centered care.

Short-Term Adaptive Emergency Call Volume Prediction

Sudden periods of extreme and persistent changes in the distribution of medical emergencies can trigger resource planning inefficiencies for Emergency Medical Services, causing delayed responses and increased waiting times. Predicting such changes and reacting adaptively can alleviate these adversarial impacts. In this paper, we propose a simple framework to enhance historically calibrated call volume models, the latter a focus of study in the arrival estimation literature, to give more accurate short-term prediction by refitting their residuals into time series. We discuss some justification of our framework from the perspective of doubly stochastic Poisson processes. We illustrate our methodology in predicting the hourly call volume to the 911 call center during the Covid-19 pandemic in NYC, showing how it could improve the performance of baseline historical estimators by close to 50% measured by the out-of-sample prediction error for the next hour. © 2021 IEEE.

Simulating New York City Hospital Load Balancing during COVID-19

In most emergency medical services (EMS) systems, patients are transported by ambulance to the closest most appropriate hospital. However, in extreme cases, such as the COVID-19 pandemic, this policy may lead to hospital overloading, which can have detrimental effects on patients. To address this concern, we propose an optimization-based, data-driven hospital load balancing approach. The approach finds a trade-off between short transport times for patients that are not high acuity while avoiding hospital overloading. In order to test the new rule, we build a simulation model, tailored for New York City's EMS system. We use historical EMS incident data from the worst weeks of the pandemic as a model input. Our simulation indicates that 911 patient load balancing is beneficial to hospital occupancy rates and is a reasonable rule for non-critical 911 patient transports. The load balancing rule has been recently implemented in New York City's EMS system. © 2021 IEEE.