Julie E Weiss1, Martha Goodrich2, Kimberly A Harris3, Rachael E Chicoine4, Marie B Synnestvedt5, Steve J Pyle6, Jane S Chen3, Sally D Herschorn7, Elisabeth F Beaber8, Jennifer S Haas9, Anna N A Tosteson10, Tracy Onega11. 1. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire. Electronic address: julie.weiss@dartmouth.edu. 2. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire. 3. Division of General Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts. 4. University of Vermont and Vermont Cancer Center, Burlington, Vermont. 5. Division of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. 6. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire. 7. University of Vermont and Vermont Cancer Center, Burlington, Vermont; Department of Radiology, University of Vermont, Burlington, Vermont. 8. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington. 9. Division of General Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Harvard T.H. Chan School of Public Health, Boston, Massachusetts. 10. Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire. 11. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire; The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
Abstract
PURPOSE: To assess indication for examination for four breast imaging modalities and describe the complexity and heterogeneity of data sources and ascertainment methods. METHODS: Indication was evaluated among the Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) breast cancer research centers (PRCs). Indication data were reported overall and separately for four breast imaging modalities: digital mammography (DM), digital breast tomosynthesis (DBT), ultrasound (US), and magnetic resonance imaging (MRI). RESULTS: The breast PRCs contributed 236,262 women with 607,735 breast imaging records from 31 radiology facilities. We found a high degree of heterogeneity for indication within and across six data sources. Structured codes within a data source were used most often to identify indication for mammography (59% DM, 85% DBT) and text analytics for US (45%) and MRI (44%). Indication could not be identified for 17% of US and 26% of MRI compared with 2% of mammography examinations (1% DM, 3% DBT). CONCLUSIONS: Multiple and diverse data sources, heterogeneity of ascertainment methods, and nonstandardization of codes within and across data systems for determining indication were found. Consideration of data sources and standardized methodology for determining indication is needed to assure accurate measurement of cancer screening rates and performance in clinical practice and research.
PURPOSE: To assess indication for examination for four breast imaging modalities and describe the complexity and heterogeneity of data sources and ascertainment methods. METHODS: Indication was evaluated among the Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) breast cancer research centers (PRCs). Indication data were reported overall and separately for four breast imaging modalities: digital mammography (DM), digital breast tomosynthesis (DBT), ultrasound (US), and magnetic resonance imaging (MRI). RESULTS: The breast PRCs contributed 236,262 women with 607,735 breast imaging records from 31 radiology facilities. We found a high degree of heterogeneity for indication within and across six data sources. Structured codes within a data source were used most often to identify indication for mammography (59% DM, 85% DBT) and text analytics for US (45%) and MRI (44%). Indication could not be identified for 17% of US and 26% of MRI compared with 2% of mammography examinations (1% DM, 3% DBT). CONCLUSIONS: Multiple and diverse data sources, heterogeneity of ascertainment methods, and nonstandardization of codes within and across data systems for determining indication were found. Consideration of data sources and standardized methodology for determining indication is needed to assure accurate measurement of cancer screening rates and performance in clinical practice and research.
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