{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:12:37Z","timestamp":1742998357411,"version":"3.40.3"},"publisher-location":"Wiesbaden","reference-count":36,"publisher":"Springer Fachmedien Wiesbaden","isbn-type":[{"type":"print","value":"9783658335960"},{"type":"electronic","value":"9783658335977"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-658-33597-7_18","type":"book-chapter","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T05:03:06Z","timestamp":1647406986000},"page":"413-430","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["K\u00fcnstliche Intelligenz in der haus\u00e4rztlichen Versorgung"],"prefix":"10.1007","author":[{"given":"Jasmin","family":"Hennrich","sequence":"first","affiliation":[]},{"given":"Anna L.","family":"Kauffmann","sequence":"additional","affiliation":[]},{"given":"Christoph","family":"Buck","sequence":"additional","affiliation":[]},{"given":"Torsten","family":"Eymann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,17]]},"reference":[{"key":"18_CR1","first-page":"553","volume-title":"Duale Reihe \u2013 Allgemeinmedizin und Familienmedizin","author":"H-H Abholz","year":"2017","unstructured":"Abholz, H.-H., Altiner, A., Bachmann, C., Bartels, S., Baum, E., Becker, A., Beyer, M., Ewert, W., Huhn, W., Lorenz, G., & Pillau, H. (2017). Definition der Allgemeinmedizin. In M. Kochen (Hrsg.), Duale Reihe \u2013 Allgemeinmedizin und Familienmedizin (S. 553\u2013557). Georg Thieme."},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Ahmed, M. N., Toor, A. S., O\u2019Neil, K., & Friedland, D. (2017). Cognitive computing and the future of health care: The cognitive power of IBM Watson has the potential to transform global personalized medicine. IEEE pulse, 8(3), 4\u20139.","DOI":"10.1109\/MPUL.2017.2678098"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Aljarboa, S., & Miah, S. J. (2018). Acceptance of a clinical decision support system for improving healthcare services in Saudi Arabia (S.\u00a0144\u2013148). 4th Asia-Pacific World Congress on Computer Science and Engineering, June 20\u201322, Hanoi Vietnam, Higher Education Forum.","DOI":"10.1109\/APWConCSE.2017.00032"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"ALQahtani, D. A., Rotgans, J. I., Mamede, S., ALAlwan, I., Magzoub, M. E. M., Altayeb, F. M., Mohamedani, M. A., & Schmidt H. G. (2016). Does time pressure have a negative effect on diagnostic accuracy. Journal of the Association of American Medical Colleges, 91(5), 710\u2013716.","DOI":"10.1097\/ACM.0000000000001098"},{"issue":"6","key":"18_CR5","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1057\/palgrave.ejis.3000717","volume":"16","author":"A Bhattacherjee","year":"2007","unstructured":"Bhattacherjee, A., & Hikmet, N. (2007). Physicians\u2019 resistance toward healthcare information technology: A theoretical model and empirical test. European Journal of Information Systems, 16(6), 725\u2013737.","journal-title":"European Journal of Information Systems"},{"issue":"8","key":"18_CR6","doi-asserted-by":"publisher","first-page":"2358","DOI":"10.1016\/j.arth.2018.02.067","volume":"33","author":"SA Bini","year":"2018","unstructured":"Bini, S. A. (2018). Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? The Journal of Arthroplasty, 33(8), 2358\u20132361.","journal-title":"The Journal of Arthroplasty"},{"issue":"3","key":"18_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2196\/12802","volume":"21","author":"C Blease","year":"2019","unstructured":"Blease, C., Kaptchuk, T. J., Bernstein, M. H., Mandl, K. D., Halamka, J. D., & DesRoches, C. M. (2019). Artificial intelligence and the future of primary care: Exploratory qualitative study of UK general practitioners\u2019 views. Journal of Medical Internet Research, 21(3), 1\u201310.","journal-title":"Journal of Medical Internet Research"},{"issue":"2","key":"18_CR8","first-page":"79","volume":"16","author":"C Bryan","year":"2008","unstructured":"Bryan, C., & Boren, S. A. (2008). The use and effectiveness of electronic clinical decision support tools in the ambulatory\/primary care setting: A systematic review of the literature. Informatics in Primary Care, 16(2), 79\u201391.","journal-title":"Informatics in Primary Care"},{"key":"18_CR9","unstructured":"Bundes\u00e4rztekammer. (2018). Behandlungsfehler-Statistik. Bundes\u00e4rztekammer (B\u00c4K, Hrsg.). https:\/\/www.bundesaerztekammer.de\/patienten\/gutachterkommissionen-schlichtungsstellen\/behandlungsfehler-statistik\/2018\/. Zugegriffen: 27. Nov. 2020."},{"issue":"12","key":"18_CR10","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1001\/archinternmed.2009.130","volume":"169","author":"LP Casalino","year":"2009","unstructured":"Casalino, L. P., Dunham, D., Chin, M. H., Bielang, R., Kistner, E. O., Karrison, T. G., Ong, M. K., Sarkar, U., McLaughlin, M. A., & Meltzer, D. O. (2009). Frequency of failure to inform patients of clinically significant outpatient test results. Archives of Internal Medicine, 169(12), 1123\u20131129.","journal-title":"Archives of Internal Medicine"},{"key":"18_CR11","first-page":"48","volume":"122","author":"P Densen","year":"2011","unstructured":"Densen, P. (2011). Challenges and opportunities facing medical education. Transactions of the American Clinical and Climatological Association, 122, 48\u201358.","journal-title":"Transactions of the American Clinical and Climatological Association"},{"issue":"7362","key":"18_CR12","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1136\/bmj.325.7362.472","volume":"325","author":"M Deveugele","year":"2002","unstructured":"Deveugele, M., Derese, A., van den Brink-Muinen, A., Bensing, J., & de Maeseneer, J. (2002). Consultation length in general practice: Cross sectional study in six European countries. British Medical Journal, 325(7362), 472\u2013474.","journal-title":"British Medical Journal"},{"issue":"2","key":"18_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0211223","volume":"14","author":"A Dreher","year":"2019","unstructured":"Dreher, A., Theune, M., Kersting, C., Geiser, F., & Weltermann, B. (2019). Prevalence of burnout among German general practitioners: Comparison of physicians working in solo and group practices. PLoS ONE, 14(2), 1\u201313. https:\/\/doi.org\/10.1371\/journal.pone.0211223","journal-title":"PLoS ONE"},{"issue":"2","key":"18_CR14","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1148\/rg.2017160130","volume":"37","author":"BJ Erickson","year":"2017","unstructured":"Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017). Machine learning for medical imaging. Radiographics, 37(2), 505\u2013515. https:\/\/doi.org\/10.1148\/rg.2017160130","journal-title":"Radiographics"},{"key":"18_CR15","unstructured":"Eurostat. (2017). Health care expenditure by financing scheme, Eurostat (Hrsg.). https:\/\/appsso.eurostat.ec.europa.eu\/nui\/submitViewTableAction.do. Zugegriffen: 27. M\u00e4rz 2020."},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Graber, M. L. (2013). The incidence of diagnostic error in medicine. British Medical Journal Quality and Safety, 22(2), ii21\u2012ii27.","DOI":"10.1136\/bmjqs-2012-001615"},{"issue":"13","key":"18_CR17","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1001\/archinte.165.13.1493","volume":"165","author":"ML Graber","year":"2005","unstructured":"Graber, M. L., Franklin, N., & Gordon, R. (2005). Diagnostic error in internal medicine. Archives of Internal Medicine, 165(13), 1493\u20131499.","journal-title":"Archives of Internal Medicine"},{"key":"18_CR18","volume-title":"Clinical reasoning in the health professions","author":"J Higgs","year":"1995","unstructured":"Higgs, J., & Jones, M. (1995). Clinical reasoning in the health professions. Butterworth Heinemann Ltd."},{"issue":"10","key":"18_CR19","first-page":"1","volume":"7","author":"G Irving","year":"2017","unstructured":"Irving, G., Neves, A. L., Dambha-Miller, H., Oishi, A., Tagashira, H., Verho, A., & Holden, J. (2017). International variations in primary care physician consultation time: A systematic review of 67 countries. British Medical Journal Open, 7(10), 1\u201315.","journal-title":"British Medical Journal Open"},{"key":"18_CR20","unstructured":"Kassen\u00e4rztliche Bundesvereinigung. (2016). Deutschlandweite Projektion 2030; Arztzahlenentwicklung in Deutschland, Kassen\u00e4rztliche Bundesvereinigung (KBV, Hrsg.). https:\/\/www.google.com\/search?client=firefox-b-d&q=summer+aller+haus%C3%A4rzte+deutschland. Zugegriffen: 18. Jan. 2020."},{"key":"18_CR21","unstructured":"Kassen\u00e4rztliche Bundesvereinigung. (2019). Anteil der Arztgruppen an der Anzahl ambulanter Vertrags\u00e4rzte in Deutschland im Jahr 2018, Kassen\u00e4rztliche Bundesvereinigung (KBV, Hrsg.). https:\/\/de.statista.com\/statistik\/daten\/studie\/206924\/umfrage\/anteil-der-arztgruppen-an-der-anzahl-der-vertragsaerzte\/. Zugegriffen: 20. M\u00e4rz 2020."},{"issue":"7553","key":"18_CR22","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436\u2013444.","journal-title":"Nature"},{"key":"18_CR23","unstructured":"Matzer, M., & Litzel, N. (2020). K\u00fcnstliche Intelligenz gegen Covid-19; So helfen KI-Modelle und Algorithmen im Kampf gegen das Corona-Virus, bigdata insider (Hrsg.). https:\/\/www.bigdata-insider.de\/so-helfen-ki-modelle-und-algorithmen-im-kampf-gegen-das-corona-virus-a-929623\/. Zugegriffen: 5. Nov. 2020."},{"issue":"5","key":"18_CR24","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/S1470-2045(19)30149-4","volume":"20","author":"KY Ngiam","year":"2019","unstructured":"Ngiam, K. Y., & Khor, I. W. (2019). Big data and machine learning algorithms for health-care delivery. The Lancet Oncology, 20(5), 262\u2013273.","journal-title":"The Lancet Oncology"},{"issue":"13","key":"18_CR25","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1056\/NEJMp1606181","volume":"375","author":"Z Obermeyer","year":"2016","unstructured":"Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future \u2013 Big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216\u20131219.","journal-title":"The New England Journal of Medicine"},{"key":"18_CR26","unstructured":"Razzaki, S., Baker, A., Perov, Y., Middleton, K., Baxter, J., Mullarkey, D., Sangar, D., Taliercio, M., Butt, M., Majeed, A., DoRosario, A., Mahoney, M., & Johri, S. (2018). A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis. arXiv preprint arXiv, 1\u201315."},{"issue":"2","key":"18_CR27","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1097\/ACM.0000000000002518","volume":"94","author":"CS Royce","year":"2019","unstructured":"Royce, C. S., Hayes, M. M., & Schwartzstein, R. M. (2019). Teaching critical thinking: A case for instruction in cognitive biases to reduce diagnostic errors and improve patient safety. Journal of the Association of American Medical Colleges, 94(2), 187\u2013194.","journal-title":"Journal of the Association of American Medical Colleges"},{"issue":"6","key":"18_CR28","first-page":"418","volume":"173","author":"H Singh","year":"2013","unstructured":"Singh, H., Giardina, T. D., Meyer, A. N. D., Forjuoh, S. N., Reis, M. D., & Thomas, E. J. (2013). Types and origins of diagnostic errors in primary care settings. Journal of American Medical Association Internal Medicine, 173(6), 418\u2013425.","journal-title":"Journal of American Medical Association Internal Medicine"},{"issue":"23","key":"18_CR29","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1136\/bmjqs-2013-002627","volume":"9","author":"H Singh","year":"2014","unstructured":"Singh, H., Meyer, A. N. D., & Thomas, E. J. (2014). The frequency of diagnostic errors in outpatient care: Estimations from three large observational studies involving US adult populations. British Medical Journal Quality and Safety, 9(23), 727\u2013731.","journal-title":"British Medical Journal Quality and Safety"},{"key":"18_CR30","unstructured":"Singh, H., Onakpoya, I., Thompson, M. J., Graber, M. L., & Schiff, G. (2016). Diagnostic errors. World Health Organization (WHO)."},{"issue":"10114","key":"18_CR31","doi-asserted-by":"publisher","first-page":"2739","DOI":"10.1016\/S0140-6736(17)31540-4","volume":"390","author":"T Lancet","year":"2017","unstructured":"Lancet, T. (2017). Artificial intelligence in health care: Within touching distance. The Lancet, 390(10114), 2739.","journal-title":"The Lancet"},{"key":"18_CR32","doi-asserted-by":"crossref","unstructured":"Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44\u201356.","DOI":"10.1038\/s41591-018-0300-7"},{"issue":"4","key":"18_CR33","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1111\/jep.12747","volume":"23","author":"M van Such","year":"2017","unstructured":"van Such, M., Lohr, R., Beckman, T., & Naessens, J. M. (2017). Extent of diagnostic agreement among medical referrals. Journal of Evaluation in Clinical Practice, 23(4), 870\u2013874.","journal-title":"Journal of Evaluation in Clinical Practice"},{"issue":"1","key":"18_CR34","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.dss.2008.06.004","volume":"46","author":"Z Walter","year":"2008","unstructured":"Walter, Z., & Lopez, M. S. (2008). Physician acceptance of information technologies: Role of perceived threat to professional autonomy. Decision Support Systems, 46(1), 206\u2013215.","journal-title":"Decision Support Systems"},{"issue":"4","key":"18_CR35","first-page":"177","volume":"5","author":"S Yazdani","year":"2017","unstructured":"Yazdani, S., Hosseinzadeh, M., & Hosseini, F. (2017). Models of clinical reasoning with a focus on general practice: A critical review. Journal of Advances in Medical Education & Professionalism, 5(4), 177\u2013184.","journal-title":"Journal of Advances in Medical Education & Professionalism"},{"key":"18_CR36","unstructured":"B\u00f6hm K, Tesch-R\u00f6mer C, Ziese T. Gesundheit und Krankheit im Alter - Beitr\u00e4ge zur Gesundheitsberichterstattung des Bundes. Berlin: Robert Koch-Institut 2009"}],"container-title":["K\u00fcnstliche Intelligenz im Gesundheitswesen"],"original-title":[],"language":"de","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-658-33597-7_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T05:12:14Z","timestamp":1647407534000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-658-33597-7_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783658335960","9783658335977"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-658-33597-7_18","relation":{},"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}