CT Year in Review
December 15, 2021
CT
X-Ray
From the November 2021 issue of HealthCare Business News magazine
Advanced imaging volumes in the ED created significant patient throughput challenges for Emory Healthcare.
Forming a multidisciplinary team of physicians, nurses, technologists, administrators and a patient family advisor, the healthcare system implemented several changes and began tracking process metrics around the length of time for CT exams and monthly media turnaround time. A phased rollout over half a year saw turnaround time improve, but only for six weeks. Eliminating inefficiencies, however, caused median turnaround times to fall from between 90 and 109 minutes to 82 and 106 minutes. In addition, rad techs saved roughly 268 hours of annualized time.
Racch attributes the lack of sustainment to staff that went on family and medical leave, and said staffing constraints made it difficult to adjust to surging demand when the ED received an influx of patients.
“Sustaining improvements has been challenging. However, the lessons learned established a collaborative ED and radiology department partnership that continues to work on this complex challenge of optimizing ED CT [turnaround times],” he and his colleagues wrote.
The findings were published in RadioGraphics.
Detecting cancer and heart disease in a single low-dose CT scan with AI
Clinicians at Massachusetts General Hospital and engineers at Rensselaer Polytechnic Institute have developed a deep learning algorithm capable of screening for cardiovascular disease risk and cancer from the same scan.
Applied to low-dose CT, the AI solution is designed to expedite the diagnosis process, accelerate treatment and improve patient outcomes, while eliminating the need for additional scanning and subsequently, more radiation exposure.
“Recent studies have shown that the patients diagnosed with cancer have a much greater risk of CVD mortality than the general population. Nevertheless, when the cancer risk population receives cancer screening, their potential CVD risk may be overlooked. Our work shows that deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation for this high-risk group,” Pingkun Yan, an assistant professor of biomedical engineering and member of the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer, told HCB News in May.
Developed at Rensselaer and tested at Massachusetts General Hospital, the algorithm proved highly effective in analyzing the risk for cardiovascular disease and related mortality in high-risk patients undergoing low-dose CT, and was equally sufficient as radiologists in analyzing these images. It also nearly mirrored the performance of dedicated cardiac CT scans when applied to an independent data set collected from 335 patients.
Advanced imaging volumes in the ED created significant patient throughput challenges for Emory Healthcare.
Forming a multidisciplinary team of physicians, nurses, technologists, administrators and a patient family advisor, the healthcare system implemented several changes and began tracking process metrics around the length of time for CT exams and monthly media turnaround time. A phased rollout over half a year saw turnaround time improve, but only for six weeks. Eliminating inefficiencies, however, caused median turnaround times to fall from between 90 and 109 minutes to 82 and 106 minutes. In addition, rad techs saved roughly 268 hours of annualized time.
Quality, speed, and peace of mind
GE HealthCare’s Repair Center Solutions are an ideal complement to your in-house service team. We service a broad range of mobile devices, including monitors and cardiology devices, parts, and portable ultrasound systems and probes.
Racch attributes the lack of sustainment to staff that went on family and medical leave, and said staffing constraints made it difficult to adjust to surging demand when the ED received an influx of patients.
“Sustaining improvements has been challenging. However, the lessons learned established a collaborative ED and radiology department partnership that continues to work on this complex challenge of optimizing ED CT [turnaround times],” he and his colleagues wrote.
The findings were published in RadioGraphics.
Detecting cancer and heart disease in a single low-dose CT scan with AI
Clinicians at Massachusetts General Hospital and engineers at Rensselaer Polytechnic Institute have developed a deep learning algorithm capable of screening for cardiovascular disease risk and cancer from the same scan.
Applied to low-dose CT, the AI solution is designed to expedite the diagnosis process, accelerate treatment and improve patient outcomes, while eliminating the need for additional scanning and subsequently, more radiation exposure.
“Recent studies have shown that the patients diagnosed with cancer have a much greater risk of CVD mortality than the general population. Nevertheless, when the cancer risk population receives cancer screening, their potential CVD risk may be overlooked. Our work shows that deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation for this high-risk group,” Pingkun Yan, an assistant professor of biomedical engineering and member of the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer, told HCB News in May.
Developed at Rensselaer and tested at Massachusetts General Hospital, the algorithm proved highly effective in analyzing the risk for cardiovascular disease and related mortality in high-risk patients undergoing low-dose CT, and was equally sufficient as radiologists in analyzing these images. It also nearly mirrored the performance of dedicated cardiac CT scans when applied to an independent data set collected from 335 patients.
You Must Be Logged In To Post A CommentRegistoO registro é gratuito e fácil. Aproveite os benefícios dos principais produtos novos e inovadores do mundo. Mercado de equipamentos médicos usados. Registrar agora! |
|
