{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T15:09:51Z","timestamp":1789657791266,"version":"4.0.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686875","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"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":[[2026,9,17]]},"abstract":"<jats:p>Introduction: Within the German Medical Informatics Initiative, the Broad Consent (BC) enables the secondary use of routine clinical data and biospecimens but raises concerns regarding representativeness and selection bias. This study evaluates BC coverage, consent rates, and representativeness of oncologic patients with a documented BC at a university hospital. Methods: An anonymized dataset from the University Hospital M\u00fcnster covering the period from September 2021 to September 2025 was analyzed. The study population comprised 38,819 adult oncology patients, of whom 4,835 had a documented BC. Representativeness was assessed by comparing the BC sample with the overall population using Chi-square goodness-of-fit tests and Cohen\u2019s w across demographic and clinical characteristics. Results: The overall BC coverage rate was 12.46%, with higher coverage among inpatients (17.74%) compared to outpatients (9.56%). Among the documented BC cases, consent rates were high, with 96.27% of BC patients agreeing and 3.73% declining. BC module-specific consent was consistently high, except for lower acceptance of third-country transfer options. All examined attribute distributions showed statistically significant differences. However, effect sizes varied: gender (w = 0.07) and age groups (w = 0.26) indicated small deviations, suggesting good representativeness. In contrast, case status (w = 0.44) and ICD-10 diagnosis groups (w = 0.86) revealed moderate to large discrepancies. Discussion: These results indicate limited representativeness of the BC sample, particularly for clinical characteristics, and suggest potential selection bias. Uneven BC implementation across clinical departments is a likely contributing factor. Future evaluations should reassess these effects after full implementation of BC to improve representativeness and support valid secondary data use.<\/jats:p>","DOI":"10.3233\/shti261012","type":"book-chapter","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T14:48:19Z","timestamp":1789656499000},"source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of the Broad Consent Distribution at a University Hospital: Is it Representative or Biased?"],"prefix":"10.3233","author":[{"given":"Justin S.","family":"Hoffmann","sequence":"first","affiliation":[{"name":"Department of Information Systems, University of M\u00fcnster, M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Lenz","sequence":"additional","affiliation":[{"name":"Department of Palliative Care, University Hospital M\u00fcnster, M\u00fcnster, Germany"},{"name":"West German Cancer Center, University Hospital M\u00fcnster, M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antje","family":"Duda","sequence":"additional","affiliation":[{"name":"West German Cancer Center, University Hospital M\u00fcnster, M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annalen","family":"Bleckmann","sequence":"additional","affiliation":[{"name":"West German Cancer Center, University Hospital M\u00fcnster, M\u00fcnster, Germany"},{"name":"Department of Medicine A (Hematology, Hemostasiology, Oncology and Pulmonology), University Hospital M\u00fcnster, M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole","family":"Niehoff","sequence":"additional","affiliation":[{"name":"Central Biobank of the Medical Faculty of M\u00fcnster, University of M\u00fcnster, M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lena","family":"Clever","sequence":"additional","affiliation":[{"name":"Department of Information Systems, University of M\u00fcnster, M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3108-8311","authenticated-orcid":false,"given":"Dominik","family":"Heider","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of M\u00fcnster, M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1557-8097","authenticated-orcid":false,"given":"Tobias J.","family":"Brix","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of M\u00fcnster, M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences 2026: BIG DATA, small data, Your Data; Transitions in the age of biomedical AI"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI261012","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T14:48:20Z","timestamp":1789656500000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI261012"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,17]]},"ISBN":["9781643686875"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti261012","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,17]]}}}