{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T02:23:39Z","timestamp":1776824619637,"version":"3.51.2"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643682648","type":"print"},{"value":"9781643682655","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,6]]},"abstract":"<jats:p>Cancer screening and timely follow-up of abnormal results can reduce mortality. One barrier to follow-up is the failure to identify abnormal results. While EHRs have coded results for certain tests, cancer screening results are often stored in free-text reports, which limit capabilities for automated decision support. As part of the multilevel Follow-up of Cancer Screening (mFOCUS) trial, we developed and implemented a natural language processing (NLP) tool to assist with real-time detection of abnormal cancer screening test results (including mammograms, low-dose chest CT scans, and Pap smears) and identification of gynecological follow-up for higher risk abnormalities (i.e. colposcopy) from free-text reports. We demonstrate the integration and implementation of NLP, within the mFOCUS system, to improve the follow-up of abnormal cancer screening results in a large integrated healthcare system. The NLP pipelines have detected scenarios when guideline-recommended care was not delivered, in part because the provider mis-identified the text-based result reports.<\/jats:p>","DOI":"10.3233\/shti220112","type":"book-chapter","created":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:32:09Z","timestamp":1654594329000},"source":"Crossref","is-referenced-by-count":5,"title":["Natural Language Processing to Identify Abnormal Breast, Lung, and Cervical Cancer Screening Test Results from Unstructured Reports to Support Timely Follow-up"],"prefix":"10.3233","author":[{"given":"Courtney J.","family":"Diamond","sequence":"first","affiliation":[{"name":"Department of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States"}]},{"given":"John","family":"Laurentiev","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Brigham and Women\u2019s Hospital, Boston, MA, United States"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Brigham and Women\u2019s Hospital, Boston, MA, United States"}]},{"given":"Amy","family":"Wint","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States"}]},{"given":"Kimberly A.","family":"Harris","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States"}]},{"given":"Tin H.","family":"Dang","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States"}]},{"given":"Amrita","family":"Mecker","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States"}]},{"given":"Emily B.","family":"Carpenter","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States"}]},{"given":"Anna N.","family":"Tosteson","sequence":"additional","affiliation":[{"name":"The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, Lebanon, NH, United States"}]},{"given":"Adam","family":"Wright","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States"}]},{"given":"Jennifer S.","family":"Haas","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States"}]},{"given":"Steven J.","family":"Atlas","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States"}]},{"given":"Li","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of General Internal Medicine, Brigham and Women\u2019s Hospital, Boston, MA, United States"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2021: One World, One Health \u2013 Global Partnership for Digital Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220112","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T09:32:10Z","timestamp":1654594330000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"ISBN":["9781643682648","9781643682655"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220112","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]}}}