Zusammenfassung
Das Problem der Beweisführung in der Medizin wird diskutiert. Kriterien werden eingeführt, um Evidenzstufen für CAM-Modalitäten zuzuweisen. In der westlichen Medizin stellen Befunde aus Laborstudien die höchste Evidenzstufe für einen mutmaßlichen Wirkmechanismus und die Beziehung zwischen „Behandlung“ und „Ergebnissen“ dar. Im Gegensatz dazu spiegelt in nichtwestlichen Medizinsystemen „Evidenz“ die Werte und Überzeugungen der Ursprungskultur wider. Wichtige Unterschiede zwischen quantitativer und qualitativer Evidenz werden beschrieben. Spezielle Probleme im Zusammenhang mit der Literaturrecherche zu CAM werden diskutiert, einschließlich der Formulierung einer Frage, der Identifizierung von Ressourcen, die am ehesten relevante Informationen zu einem bestimmten Thema liefern, und der Verwendung von Methoden zur Optimierung und Straffung der Literaturrecherche. Eine klar formulierte Frage ist die Grundlage für jede Literaturrecherche. Wenn die Frage mehrdeutig oder unfokussiert ist, werden wichtige Ressourcen übersehen und relevante Informationen werden verpasst. Wertvolle webbasierte Ressourcen werden identifiziert und praktische Tipps zur Beschaffung aktueller zuverlässiger Informationen werden gegeben. Techniken zur Verwendung von vorgefilterten Datenbanken und Evidenzkartierung werden überprüft. Die Konzepte der Evidenztabelle und des Algorithmus werden eingeführt. Eine Methodik wird vorgeschlagen, um diese Werkzeuge bei der Planung der integrativen psychischen Gesundheitsversorgung zu verwenden. Die Genauigkeit und Qualität der Informationen, die in einen Algorithmus eingegeben werden, bestimmen die Wirksamkeit und Relevanz der von ihm für jeden einzigartigen Patienten erzeugten klinischen Lösungen. Der optimale integrative Versorgungsplan für einen Patienten hängt von der Anamnese, den Symptomen, den Umständen, den Vorlieben und den finanziellen Einschränkungen im Kontext der lokal verfügbaren Gesundheitsressourcen und dem professionellen Urteil und der klinischen Erfahrung des Praktikers ab. Das Kapitel schließt mit einer Diskussion über Fortschritte in der KI-Software und die Auswirkungen der KI auf die Zukunft der psychischen Gesundheitsversorgung.
„Drei Dinge können nicht lange verborgen bleiben: die Sonne, der Mond und die Wahrheit“
(Der Buddha).
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Literatur
ISPOR Connections. (2012). International Society for Pharmacoeconomics and Outcomes Research (ISPOR) (S. 3–4). Retrieved from http://www.ispor.org.
Acion, L., Kelmansky, D., van der Laan, M., Sahker, E., Jones, D., & Arndt, S. (2017). Use of a machine learning framework to predict substance use disorder treatment success. PLoS One, 12(4), e0175383.
Appelboom, G., LoPresti, M., Reginster, J. Y., Sander Connolly, E., & Dumont, E. P. (2014). The quantified patient: A patient participatory culture. Current Medical Research and Opinion, 30(12), 2585–2587.
Banks, J. (1998). Handbook of simulation. Wiley.
Barry, C. A. (2006). The role of evidence in alternative medicine: Contrasting biomedical and anthropological approaches. Social Science & Medicine, 62(11), 2646–2657.
Baumgartner Jr., W. A., Cohen, K. B., Fox, L., Acquaah-Mensah, G. K., & Hunter, L. (2007). Manual curation is not sufficient for annotation of genomic databases. Bioinformatics, 23, i41–i48.
Beckner, W., & Berman, B. (2003). Complementary therapies on the Internet. Churchill Livingstone.
Bernardo, T. M., Rajic, A., Young, I., Robiadek, K., Pham, M. T., & Funk, J. A. (2013). Sco** review on search queries and social media for disease surveillance: A chronology of innovation. Journal of Medical Internet Research, 15(7), e147.
Brailsford, S. C., Harper, P. R., Patel, B., & Pitt, M. (2009). An analysis of the academic literature on simulation and modelling in health care. Journal of Simulation, 3(3), 130–140.
Browman, G. (2001). Development and aftercare of clinical guidelines: The balance between rigor and pragmatism. Journal of the American Medical Association, 286, 1509–1511.
Dilsizian, S. E., & Siegel, E. L. (2014). Artificial intelligence in medicine and cardiac imaging: Harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Current Cardiology Reports, 16, 441.
Doan, S., Conway, M., Phuong, T. M., & Ohno-Machado, L. (2014). Natural language processing in biomedicine: A unified system architecture overview. In R. J. A. Trent (Ed.), Clinical bioinformatics. Springer.
Doshi, P., Kickersin, K., Healy, D., Vedula, S., & Jefferson, T. (2013, June). Restoring invisible and abandoned trials: A call for people to publish the findings, BMJ, 13, 346.
Doshi, P., Shamseer, L., Jones, M., & Jefferson, T. (2018, April 26). Restoring biomedical literature with RIAT. BMJ, 361, k1742.
Gantz, J., & Reinsel, D. (2011). Extracting value from chaos. IDC Iview, 1142, 9–10. Good Practices Task Force. Value Health. 2015b;18(1):5–16.
Graham, J. (2016). Artificial intelligence, machine learning, and the FDA. Retrieved from https://www.forbes.com/sites/theapothecary/2016/08/19/artificial-intelligence-machine-learning-and-the-fda/#4aca26121aa1.
Gray, G. (2004). Concise guide to evidence-based psychiatry. American Psychiatric Publishing.
Guyatt, G., & Rennie, D. (Eds.). (2002). Users’ guide to the medical literature: Essentials of evidence-based clinical practice. AMA Press.
Harrison, J. R., Lin, Z., Carroll, G. R., & Carley, K. M. (2007). Simulation modeling in organizational and management research. The Academy of Management Review, 32(4), 1229–1245.
Hayes, M. J., & Prasad, V. (2018). Financial Conflicts of Interest at FDA Drug Advisory Committee Meetings. Cent Rep. 48(2):10–13.
Jadad, A., & Gagliardi, A. (1998). Rating health information on the Internet: Navigating to knowledge or to Babel? Journal of the American Medical Association, 279, 611–614.
Jašović-Gašić, M., Dunjic-Kostić, B., Pantović, M., Cvetić, T., Marić, N. P., & Jovanović, A. A. (2013). Algorithms in psychiatry: State of the art. Psychiatria Danubina, 25(3), 280–283.
Katz, D. L., Williams, A. L., Girard, C., Goodman, J., Comerford, B., Behrman, A., et al. (2003). The evidence base for complementary and alternative medicine: Methods of evidence map** with applications to CAM. Alternative Therapies in Health and Medicine, 9(4), 22–30.
Kayyali, B., Knott, D., & Kuiken, S. V. (2013). The big-data revolution in US health care: Accelerating value and innovation. Retrieved from http://www.mckinsey.com/industries/healthcare-systems-and-services/ourinsights/the-big-data-revolution-in-us-health-care.
Laney, D. (2012). The importance of ‘big data’: A definition. Gartner. Retrieved from https://www.scirp.org/(S(oyulxb452alnt1aej1nfow45))/reference/ReferencesPapers.aspx?ReferenceID=1287330.
Leeper, N. J., Bauer-Mehren, A., Iyer, S. V., LePendu, P., Olson, C., & Shah, N. H. (2013). Practice-based evidence: Profiling the safety of cilostazol by text-mining of clinical notes. PLoS One, 8(5), e63499.
LePendu, P., Iyer, S. V., Bauer-Mehren, A., Harpaz, R., Mortensen, J. M., Podchiyska, T., et al. (2013). Pharmacovigilance using clinical notes. Clinical Pharmacology & Therapeutics, 93(6), 547–555.
Lohr, S. (2016). IBM is counting on its bet on Watson, and paying big money for it. Retrieved from https://www.nytimes.com/2016/10/17/technology/ibm-is-counting-on-its-bet-on-watson-and-paying-big-money-for-it.html.
Lu, Z. (2011). PubMed and beyond: A survey of web tools for searching biomedical literature. Database, 2011, baq036.
Lurie, P. (2018). Suggestions for improving conflict of interest processes in the US Food and Drug Administration Advisory Committees-Past Imperfect. JAMA Internal Medicine, 178(7), 997–998. https://doi.org/10.1001/jamainternmed.2018.1324
Mancano, M., & Bullano, M. (1998). Meta-analysis: Methodology, utility, and limitations. Journal of Pharmacy Practice, 11(4), 239–250.
Manchikanti, L., Kaye, A. D., Boswell, M. V., & Hirsch, J. A. (2015, January–February). Medical journal peer review: Process and bias. Pain Physician, 18(1), E1–E14.
Marshall, D. A. (2012). Getting connected: Systems solutions for generating maximal value from health care resources. In International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Connections. 2012, International Society for Pharmacoeconomics and Outcomes Research (ISPOR) (S. 3–4).
Marshall, D. A., Burgos-Liz, L., IJzerman, M. J., Crown, W., Padula, W. V., Wong, P. K., et al. (2015). Selecting a dynamic simulation modeling method for health care delivery research – part 2: Report of the ISPOR Dynamic Simulation Modeling Emerging Good Practices Task Force. Value in Health, 18(2), 147–160.
Marshall, D. A., Burgos-Liz, L., IJzerman, M. J., Osgood, N. D., Padula, W. V., Higashi, M. K., et al. (2015). Applying dynamic simulation modeling methods in health care delivery research – The SIMULATE checklist: Report of the ISPOR Simulation Modeling Emerging Good Practices Task Force. Value in Health, 18(1), 5–16.
Matthews, P. M., Edison, P., Geraghty, O. C., & Johnson, M. R. (2014). The emerging agenda of stratified medicine in neurology. Nature Reviews Neurology, 10(1), 15–26.
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
Mewes, H. W. (2013). Perspectives of a systems biology of the brain: The big data conundrum understanding psychiatric diseases. Pharmacopsychiatry, 46(Suppl 1), S2–S9.
Miller, A. L., Crismon, M. L., Rush, A. J., Chiles, J., Kashner, M., Toprac, M., et al. (2004). The Texas medication algorithm project: Clinical results for schizophrenia. Schizophrenia Bulletin, 30, 627–647.
Moore, M., & McQuay, H. (2006). Bandolier’s little book of making sense of the medical evidence. Oxford University Press.
Morley, J., Rosner, A. L., & Redwood, D. (2001). A case study of misrepresentation of the scientific literature: Recent reviews of chiropractic. Journal of Alternative and Complementary Medicine, 7(1), 65–78.
Neill, D. B. (2013). Using artificial intelligence to improve hospital inpatient care. IEEE Intelligent Systems, 28, 92–95.
Ong, J. B. S., Chen, M. I., Cook, A. R., Lee, H. C., Lee, V. J., Lin, R. T., et al. (2010). Real-time epidemic monitoring and forecasting of H1N1-2009 using influenza-like illness from general practice and family doctor clinics in Singapore. PLoS One, 5(4), e10036.
Osgood, N., & Liu, J. (2014). Towards closed loop modeling: Evaluating the prospects for creating recurrently regrounded aggregate simulation models using particle filtering. In Proceedings of the 2014 Winter Simulation Conference. IEEE Press.
Patel, V. L., Shortliffe, E. H., Stefanelli, M., Szolovits, P., Berthold, M. R., Bellazzi, R., et al. (2009). The coming of age of artificial intelligence in medicine. Artificial Intelligence in Medicine, 46, 5–17.
Pathak, J., Bailey, K. R., Beebe, C. E., Bethard, S., Carrell, D. S., Chen, P. J., et al. (2013). Normalization and standardization of electronic health records for high-throughput phenoty**: The SHARPn consortium. Journal of the American Medical Informatics Association, 20(e2), e341–e348.
Pathak, J., Kho, A. N., & Denny, J. C. (2013). Electronic health records-driven phenoty**: Challenges, recent advances, and perspectives. Journal of the American Medical Informatics Association, 20(e2), e206–e211.
Pearson, T. (2011). How to replicate Watson hardware and systems design for your own use in your basement. https://www.ibm.com/developerworks/community/blogs/insideSystemStorage/entry/ibm_watson_how_to_build_your_own_watson_jr_in_your_basement7?lang=en.
Pham-Kanter, G. (2014, September). Revisiting financial conflicts of interest in FDA advisory committees. The Milbank Quarterly, 92(3), 446–470.
Rankin-Box, D. (2006, May). Sha** medical knowledge II: Bias and balance. Complementary Therapies in Clinical Practice, 12(2), 77–79.
Resch, K., Ernst, E., & Garrow, J. (2000). A randomized controlled study of reviewer bias against an unconventional therapy. Journal of the Royal Society of Medicine, 93(4), 164–167.
Sokolowski, J. A., & Banks, C. M. (2009). Principles of modeling and simulation: A multidisciplinary approach. Hoboken: Wiley.
Vieiraa, S., Pinayab, W. H. L., & Mechellia, A. (2017). Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience and Biobehavioral Reviews, 74, 58–75.
Whitaker, R., & Cosgrove, L. (2015). Psychiatry Under the Influence: Institutional corruption, social injury and prescriptions for reform. New York, NY: Palgrave Macmillan.
White, R. W., Tatonetti, N. P., Shah, N. H., Altman, R. B., & Horvitz, E. (2013). Web-scale pharmacovigilance: Listening to signals from the crowd. Journal of the American Medical Informatics Association, 20(3), 404–408.
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Lake, J.H. (2024). Bewertung und Nutzung medizinischer Evidenz in der integrativen psychischen Gesundheitsversorgung: Literaturreview, Evidenztabellen, Algorithmen und das Versprechen der künstlichen Intelligenz. In: Integrative psychische Gesundheitsversorgung. Springer, Cham. https://doi.org/10.1007/978-3-031-52013-6_6
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