The current coronavirus pandemic, COVID-19, has changed our daily lives in the blink of an eye. It has also brought widespread attention back to how the health care system and market dynamics impact how care is accessed and delivered, mercilessly revealing points of weakness in our systems and preparedness. Among the many hard lessons being learned right now, two facts become apparent: our system is weak without adequate staffing and reliable technology. The feature articles in this issue focus on staffing in oncology care and a recent predictive modeling project to reduce readmissions.
With resources and funding for health care facilities increasingly constrained, oncology practices are progressively employing advanced practice providers (APPs) to improve practice workflow, increase efficiency, and enable physicians to focus on complex patient care. Oncology/hematology stands out among medical specialties in its acceptance of APPs to assist in delivering care. To better understand the roles of APPs in oncology practices, several studies have focused on identifying the total number of oncology APPs and their scope of work. In the present study, Ajeet Gajra, MD, FACP, and colleagues conducted a survey of community oncologists to assess oncologists’ views of the scope of practice of APPs, the role of APPs in patient treatment and support, and their impact on practice workflow. Understanding potential variations in the scope of practice for APPs, authors note, may help establish a benchmark against which future changes are measured.
With electronic health records, computer models can collect patient information and forecast patients with an increased risk of readmission. This novel approach offers the ability to decrease costs and improve the quality of care. Proven interventions include 48-hour nurse callbacks and 5-day follow-up appointments, which reduced unplanned oncology hospital readmissions in nonsurgical patients. At present, most predictive models are implemented upon discharge, however, recent literature shows that predicting risk scores during hospitalization is more beneficial in preventing readmissions. Kelsey Jarrold, MSISE, and colleagues designed a comprehensive pathway utilizing an internally developed predictive model to proactively reduce oncology readmissions. The developed pathway supported enhanced discharge planning for patients identified as high-risk by targeting opportunities in prioritizing patients, communication between the care team, medication reconciliation, and patient expectations. These interventions included day of discharge visit, follow-up calls, and follow-up visits.
Much of our society is paralyzed by the pandemic, but we must continue to advance the discourse on how to improve care delivery and collaboration among stakeholders so that we come out stronger on the other side.
