Through this research, Lunit is seeking to understand how the needs of breast cancer patient caregivers differ from those of patients. Moreover, we seek to gain insights into the potential impact of fulfilling such needs for caregivers through remote nurse support that can provide evidence-based information.
We asked 330 breast cancer patients and 53 caregivers to participate in an online survey in Korea from 10/2021- 11/2021 to understand various needs throughout their cancer journey. Eight breast cancer patients and caregivers asked 25 questions through a remote nurse support model. Interactions took place either via phone or text, starting 12/20/2021 and is ongoing. The answers to asked questions were drafted by nurses and reviewed by a team of medical doctors to ensure delivery of clinically sound information.
While the top areas needing support during the entire cancer journey (prior, during, post treatment) were generally consistent for both caregivers and patients, caregivers showed a pronounced need for support that would help make treatment decisions specifically during the pre-treatment phase. (See table below) This difference was also reflected in the distribution of questions asked during the remote nurse support service. Caregivers asked questions about treatment options that required significant clinical knowledge to answer. In contrast, questions from patients were concentrated in understanding their cancer and in seeking dietary recommendations to keep their cancer at bay.
We observed early evidence that improving access to evidence-based information for caregivers will not only contribute to improved outcomes for the patient but also have a positive impact on the well-being of caregivers, which again in turn is expected to have a positive impact on the patient.
Terri Kim, Dasom Lee, Hajin Lee, Chan-Young Ock, Hyungkook Yang, Ki Hwan Kim, Jane Shin, Anthony S Paek, Jamie Eunsu Park, Yerin Lee, Hyejin Son, Hongseok Choi, Hyemin Shim
Lunit Inc., Seoul, South Korea
Photometric Transformer Networks and Label Adjustment for Breast Density Prediction
External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms
A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study
Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
Application of artificial intelligence–based computer-assisted diagnosis on synthetic mammograms from breast tomosynthesis: comparison with digital mammograms
Reducing Domain Gap by Reducing Style Bias
유방촬영의 위양성 판정에 관한 전통적 진단보조프로그램과 인공지능 기반 진단보조프로그램의 비교
인공지능 기반 컴퓨터 보조진단을 이용한 선별 유방촬영술에서의 간격암에 대한 후향 분석
Mammographic Surveillance After Breast Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection
Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography
Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?
Retrospective Review of Missed Cancer Detection and Its Mammography Findings with Artificial-Intelligence-Based, Computer-Aided Diagnosis
Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics
Mammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment
Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x