Impact of COVID-19 on patients with metastatic breast cancer: REthink Access to Care and Treatment survey results.

Aim: Patients with metastatic breast cancer (MBC) may be vulnerable to changes in healthcare management, safety standards and protocols that occurred during the COVID-19 pandemic. Materials & methods: The REthink Access to Care & Treatment (REACT) survey assessed USA-based patient perspectives on COVID-19-related impacts to their MBC treatment experience between 27 April 2021 and 17 August 2021. Results: Participants (n = 341; 98.5% females, mean age 50.8 years) reported that overall oncology treatment quality was maintained during the pandemic. Delayed/canceled diagnostic imaging was reported by 44.9% of participants while telemedicine uptake was high among participants (80%). Conclusion: Overall, MBC care was minimally affected by the pandemic, possibly due to the expanded use of telemedicine, informing MBC management for future public health emergencies.

The COVID-19 pandemic has forced healthcare providers to change the way that healthcare is delivered. These changes could particularly affect people with metastatic breast cancer (MBC), an advanced stage of cancer that has spread to other parts of the body. The authors of this study used a web-based survey to ask 341 volunteers with MBC how the pandemic has affected their cancer treatment. The authors found that people with MBC thought that the quality of their care stayed the same during the pandemic. Most people (80%) surveyed were able to use telemedicine, the remote delivery of care by phone or computer, to replace in-person visits to their doctor. However, almost half of people surveyed reported delays or cancellation of their diagnostic imaging appointments. Overall, this study shows that the COVID-19 pandemic did not affect peoples' opinions of their MBC care. Increased use of telemedicine may have contributed to the lack of disruption in care. These findings will help guide MBC care during future public health emergencies.

Real-time multidimensional temporal analysis of complex high volume physiological data streams in the neonatal intensive care unit.

The intensive care of immature preterm infants is a challenging, dynamic clinical task that is complicated because these infants frequently develop a range of comorbidities as they grow and develop after their premature birth. Earliest reliable condition onset detection is a goal within this setting and high frequency physiological analysis is showing potential new pathophysiological indicators for earlier onset detection of several conditions. To realise this, a platform for multi-stream, multi-condition, multi-feature risk scoring is required. In this paper we demonstrate our multi-stream online analytics approach for condition onset detection and demonstrate a user interface approach for patient state that can be available in real-time to support condition risk scoring.

Real-time analysis for intensive care: development and deployment of the artemis analytic system.

The lives of many thousands of children born premature or ill at term around the world have been saved by those who work within neonatal intensive care units (NICUs). Modern-day neonatologists, together with nursing staff and other specialists within this domain, enjoy modern technologies for activities such as financial transactions, online purchasing, music, and video on demand. Yet, when they move into their workspace, in many cases, they are supported by nearly the same technology they used 20 years ago. Medical devices provide visual displays of vital signs through physiological streams such as electrocardiogram (ECG), heart rate, blood oxygen saturation (SpO(2)), and respiratory rate. Electronic health record initiatives around the world provide an environment for the electronic management of medical records, but they fail to support the high-frequency interpretation of streaming physiological data. We have taken a collaborative research approach to address this need to provide a flexible platform for the real-time online analysis of patients' data streams to detect medically significant conditions that precede the onset of medical complications. The platform supports automated or clinician-driven knowledge discovery to discover new relationships between physiological data stream events and latent medical conditions as well as to refine existing analytics. Patients benefit from the system because earlier detection of signs of the medical conditions may lead to earlier intervention that may potentially lead to improved patient outcomes and reduced length of stays. The clinician benefits from a decision support tool that provides insight into multiple streams of data that are too voluminous to assess with traditional methods. The remainder of this article summarizes the strengths of our research collaboration and the resulting environment known as Artemis, which is currently being piloted within the NICU of The Hospital for Sick Children (SickKids) in Toronto, Ontario, Canada. Although the discussion in this article focuses on a NICU, the technologies can be applied to any intensive care environment.

Small-scale testing of RFID in a hospital setting: RFID as bed trigger.

RFID technology shows significant potential for transforming healthcare, yet few studies assess this potential. Our study measured the effectiveness of using RFID as a bed trigger: a tool to accelerate identification of empty beds. We made a small alteration in the discharge process to associate RFID tags with patients and created an RFID-based system that automatically determined discharge time. For each patient, we evaluated the difference in the discharge times recorded manually by the current process and the RFID-based system. The study was conducted on 86 patients over 2 months in 2 physically separate multi-specialty units. Compared to the preexisting process, the RFID-based system identified empty beds >20 minutes earlier 67% of the time with an average of 25 minutes and median of 9 minutes earlier. Hospital leadership defined an improvement of approximately 10 minutes as significant. With minimal investment, our small-scale study lead hospital leadership to begin planning RFID deployment.