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von KI im Gesundheitswesen: Anwendungsfälle, Datengrundlagen und Governance"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-e1de76132f","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EErfahren Sie, wie Unternehmen im Gesundheitswesen KI in der klinischen Versorgung, im Betrieb, beim Zugang und in der Forschung einsetzen – und was nötig ist, um diese Initiativen verantwortungsvoll zu skalieren.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip","content_chip_copy"]}},":itemsOrder":["title_v2","text","container"]},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-98b482e57b",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-6a91e94849","youtubeVideoId":"CBaLM0qkIr0","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-83bc179660","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container border-top","layout":"SIMPLE","id":"container-d0e9672e36",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-cc4fcc323b","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EKI-Themen nach Branche:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-f6f26cabc2","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-advertising\"\u003EKI in der Werbebranche\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-cybersecurity/\"\u003EKI in der Cybersicherheit\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-finance/\"\u003EKI KI im Finanzsektor\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-manufacturing/\"\u003EKI in der Fertigung\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-retail/\"\u003EKI im Handel\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-e0f34dc1c1","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text__0":{"id":"text-f88f644f67","text":"\u003Cp\u003EUnternehmen im Gesundheitswesen müssen heute knapper werdende Margen, sich wandelnde Erstattungsmodelle, steigenden Personal- und Arbeitsdruck sowie wachsende Erwartungen hinsichtlich Zugang, Ergebnissen und betrieblicher Effizienz bewältigen.\u003C/p\u003E\n\u003Cp\u003EVor diesem Hintergrund arbeitenVor diesem Hintergrund arbeiten Teams mit Hochdruck daran, moderne KI-Technologien in die Praxis zu überführen. Teams zügig daran, neuere Formen der KI einzusetzen. In der \u003Ca href=\"https://www.snowflake.com/en/future-ai-interoperability-healthcare/\"\u003EUmfrage von Snowflake und Hakkoda aus dem Jahr 2026\u003C/a\u003E unter 183 Führungskräften im US-Gesundheitswesen gaben 77 % an, dass ihre Unternehmen in \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/generative-ai/\"\u003Egenerative KI\u003C/a\u003E oder \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/agents/\"\u003EAgentic AI\u003C/a\u003E investiert haben oder dies planen, wobei klinische Dokumentation, die Automatisierung administrativer Workflows und Revenue-Cycle-Prozesse zu den Prioritäten gehören. Diese Investitionen erweitern eine Funktionslandschaft, die bereits prädiktives \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/machine-learning/\"\u003Emaschinelles Lernen\u003C/a\u003E, \u003Ca href=\"https://www.snowflake.com/de/fundamentals/natural-language-processing/\"\u003EVerarbeitung natürlicher Sprache\u003C/a\u003E und \u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/computer-vision/\"\u003EComputer Vision\u003C/a\u003E umfasst.\u003C/p\u003E\n\u003Cp\u003EAnwendungsfälle im Gesundheitswesen stellen jedoch an jede Form von KI besonders hohe Anforderungen. Geschützte Patienteninformationen sind über verschiedene Systeme und Unternehmen verteilt, die klinische Bedeutung kann sich je nach Zeitpunkt und Behandlungsumfeld ändern, und ein unvollständiges oder fehlerhaftes Ergebnis kann schwerwiegende Folgen haben.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/authors/jesse-cugliotta/\"\u003EJesse Cugliotta\u003C/a\u003E, Global Head of Healthcare and Life Sciences bei Snowflake, \u003Ca href=\"https://www.snowflake.com/en/news/press-releases/snowflake-research-reveals-85-percent-of-healthcare-leaders-view-interoperability-as-foundational-to-scaling-ai/\"\u003Edrückt es so aus\u003C/a\u003E: „Im gesamten Gesundheitswesen hält KI Einzug in betriebliche Umgebungen, und Führungskräfte legen dabei strengere Maßstäbe an.“ KI im Gesundheitswesen hängt davon ab, wie Daten, Modelle, Menschen und Kontrollmechanismen zusammenwirken.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-ai-supports-clinical-care":{"id":"title-v2-5541d789c0","additionalClasses":"anchor-title anchor-title--how-ai-supports-clinical-care","type":"heading2","lines":["Wie KI die klinische Versorgung unterstützt"],":type":"snowflake-site/components/title-v2"},"text_how-ai-supports-clinical-care_0":{"id":"text-1aa27fe395","text":"\u003Cp\u003EKlinische KI kann Behandlungsteams dabei unterstützen, große Mengen an Patientendaten zu analysieren, auffällige Muster zu erkennen und den Aufwand bei der Aufbereitung des relevanten Behandlungs-Kontexts drastisch zu reduzieren. Der Grad menschlicher Einbindung und Überwachung bleibt hoch — insbesondere wenn Ergebnisse Diagnosen oder Therapieentscheidungen beeinflussen können.\u003C/p\u003E\n\u003Ch3\u003EMedizinische Bildgebung und diagnostische Unterstützung\u003C/h3\u003E\n\u003Cp\u003EComputer-Vision-Modelle können bei der Analyse von Röntgenaufnahmen, CT-Scans, MRT-Befunden, Ultraschallbildern und digitalen Pathologie-Schnitten unterstützend mitwirken. Je nach Anwendung kann die KI einen Verdachtsbefund markieren, einen relevanten Bereich umreißen, Bilder vergleichen oder dabei helfen, Untersuchungen zu priorisieren, die möglicherweise dringend überprüft werden müssen.\u003C/p\u003E\n\u003Cp\u003EDiese Tools arbeiten im Kontext der umfassenderen klinischen Patientenakte. Ein Befund auf einem Bild muss im Zusammenhang mit Symptomen, Laborwerten, früheren Untersuchungen, Medikamenten und der Krankengeschichte interpretiert werden. Multimodale Modelle beginnen, diese Art der umfassenderen Analyse zu unterstützen, indem sie medizinische Bilder mit Text und strukturierten klinischen Daten kombinieren.\u003C/p\u003E\n\u003Cp\u003EKI kann auch beim Management von Bildgebungs-Workloads helfen. Indem ein System Untersuchungen mit potenziell akuten Befunden identifiziert, kann es diese in der Auswertungs-Warteschlange nach vorne verschieben, während Routinefälle in ihrer normalen Reihenfolge zur Überprüfung verfügbar bleiben. Der:die Radiolog:in oder ein:e andere:r zugelassene:r Ärzt:in interpretiert weiterhin die Untersuchung und bestimmt die klinische Relevanz des Ergebnisses.\u003C/p\u003E\n\u003Ch3\u003EKlinische Entscheidungsunterstützung und Risikoprädiktion\u003C/h3\u003E\n\u003Cp\u003EPrädiktive Modelle für maschinelles Lernen schätzen die Wahrscheinlichkeit eines Ereignisses anhand von Informationen, die bereits in der klinischen Patientenakte vorhanden sind. Zu den gängigen Anwendungen gehört die Identifizierung von Patient:innen, bei denen das Risiko einer Verschlechterung, einer Wiedereinweisung, einer Sepsis, medikamentenbedingter Schäden oder eines Fortschreitens der Krankheit besteht.\u003C/p\u003E\n\u003Cp\u003EDer Score selbst ist dabei nur eine Eingabe. Ein:e als hohes Risiko eingestufte:r Patient:in benötigt je nach Zustand und Umfeld möglicherweise ein engmaschigeres Monitoring, eine ärztliche Überprüfung oder zusätzliche Tests. Zur Unterstützung dieser Entscheidung benötigt das System möglicherweise aktuelle Beobachtungen, frühere Behandlungen, aktive Medikamente und den Zeitpunkt der letzten Eingriffe – und nicht einfach nur einen Diagnosecode oder einen historischen Versicherungsanspruch.\u003C/p\u003E\n\u003Cp\u003EDie Kalibrierung ist besonders wichtig. Zwei Patient:innen mit demselben Risikowert sollten eine ähnliche Wahrscheinlichkeit für das prognostizierte Ergebnis aufweisen, während die Performance über demografische Gruppen, Pflegeumgebungen und klinische Subpopulationen hinweg zuverlässig bleiben sollte. Ohne diese Kalibrierung kann ein Modell insgesamt präzise erscheinen, während es bei einzelnen Patient:innen schlecht abschneidet.\u003C/p\u003E\n\u003Ch3\u003EPersonalisierte und präzise Versorgung\u003C/h3\u003E\n\u003Cp\u003EKI kann Ärzt:innen dabei helfen, die vielen Faktoren zu analysieren, die das Ansprechen auf eine Behandlung beeinflussen, darunter Diagnosen, Laborergebnisse, Medikamente, Krankengeschichte, Biomarker und genomische Informationen. In der Onkologie können Modelle beispielsweise bei der Behandlungsstratifizierung helfen, indem sie Muster in Tumoreigenschaften und früheren Ergebnissen identifizieren. Bei chronischen Erkrankungen kann KI dabei helfen, Veränderungen beim Risiko oder der Therapietreue aufzudecken, die darauf hindeuten, dass ein Behandlungsplan überprüft werden sollte.\u003C/p\u003E\n\u003Cp\u003EPersonalisierung hängt auch von einer longitudinalen Patientenakte ab. Informationen aus einer isolierten Behandlung erfassen selten den gesamten Verlauf einer Erkrankung, insbesondere wenn ein:e Patient:in von mehreren Unternehmen betreut wird. Die Verknüpfung von klinischen, pharmazeutischen, Abrechnungs- und patientengenerierten Daten bietet Modellen ein vollständigeres Bild früherer Behandlungen und Ergebnisse.\u003C/p\u003E\n\u003Cp\u003EDie daraus resultierende Empfehlung erfordert weiterhin eine klinische Beurteilung. Präferenzen, Kontraindikationen und Lebensumstände von Patient:innen sind möglicherweise nicht vollständig in den Daten abgebildet, selbst wenn das Modell Zugriff auf eine detaillierte Krankengeschichte hat.\u003C/p\u003E\n\u003Ch3\u003EKlinische Dokumentation und Ambient-KI\u003C/h3\u003E\n\u003Cp\u003EDie klinische Dokumentation nimmt Zeit in Anspruch, die Ärzt:innen sonst mit Patient:innen oder für andere Pflegeaufgaben aufwenden könnten. Ambient-Documentation-Systeme nutzen Spracherkennung, Verarbeitung natürlicher Sprache (NLP) und generative KI, um Arzt-Patienten-Gespräche automatisch zu erfassen und direkt einen Dokumentationsentwurf zu erstellen.\u003C/p\u003E\n\u003Cp\u003EEin gut durchdachtes System kann die Behandlung in relevante Abschnitte gliedern, Medikamente oder Folgemaßnahmen extrahieren und den Entwurf mit Informationen verknüpfen, die bereits in der elektronischen Patientenakte (ePA) vorhanden sind. Das medizinische Fachpersonal prüft, bearbeitet und gibt die Notiz anschließend frei, bevor sie final in die Patientenakte übernommen wird.\u003C/p\u003E\n\u003Cp\u003EGenerative KI kann verwandte Dokumentationsaufgaben unterstützen, wie etwa die Zusammenfassung einer langen Krankengeschichte, die Erstellung eines Entlassungsberichts oder den Entwurf einer Übergabe. In jedem Fall sollte das System klar zwischen originalen Quellinformationen und generiertem Text unterscheiden, damit das Fachpersonal die Ergebnisse verifizieren kann.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_how-ai-supports-clinical-care_0":{"id":"card-v2-aee9cb60ff","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","text":{"id":"text","text":"\u003Cp\u003EBupa nutzt die AI Data Cloud von Snowflake, um Daten von Teams aus den Bereichen Gesundheitswesen, Versicherungen, Zahnmedizin, Pflegedienste und Gesundheitsdienste zu vernetzen. Mit Snowflake als Herzstück seines föderierten Daten-Ökosystems konnte Bupa die Zeit für die Datenerfassung im Contact Center um 90 % reduzieren, eine Verfügbarkeit der Contact-Center-Daten von 99,9 % erreichen und KI-Anwendungsfälle wie Betrugserkennung, virtuelle Assistenten, klinische Insights und personalisierte Pflegeberatung evaluieren.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Customer Story: Bupa"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Customer Story lesen (Englisch)"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c35f80d5-4aef-4888-8d73-84dc1e903a75/bupa.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Bupa logo","isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card"},"title_how-ai-supports-patient-access-and-care-management":{"id":"title-v2-12f4628c39","additionalClasses":"anchor-title anchor-title--how-ai-supports-patient-access-and-care-management","type":"heading2","lines":["Wie KI den Patientenzugang und das Care Management unterstützt"],":type":"snowflake-site/components/title-v2"},"text_how-ai-supports-patient-access-and-care-management_0":{"id":"text-19a8d6d029","text":"\u003Cp\u003EViele Hürden in der medizinischen Versorgung entstehen bereits vor oder zwischen den eigentlichen Behandlungsterminen. Patient:innen stehen häufig vor der Herausforderung, passende Leistungserbringer zu finden, Termine zu vereinbaren, ihren Versicherungsschutz zu verstehen oder Behandlungsplänen zu folgen — während das Personal Überweisungen und Kontaktaufnahmen über mehrere getrennte Systeme hinweg koordiniert.\u003C/p\u003E\n\u003Ch3\u003EPatientenzugang und Navigation\u003C/h3\u003E\n\u003Cp\u003EKI kann bei der Terminvergabe, der Auswahl geeigneter Leistungserbringer, der Weiterleitung von Überweisungen, der Klärung von Leistungsansprüchen und dem Wartelisten-Management wirksam unterstützen. Ein:e Patient:in, der:die einen Termin sucht, benötigt beispielsweise eine:n Anbieter:in mit der richtigen Fachrichtung, dem passenden Netzwerkstatus, Standort und der entsprechenden Verfügbarkeit. Ein KI-System kann diese Rahmenbedingungen konsolidieren und die verfügbaren Optionen gezielt eingrenzen.\u003C/p\u003E\n\u003Cp\u003EAgentic AI kann darüber hinaus größere Abschnitte des gesamten Workflows autonom koordinieren. Mit Zugriff auf genehmigte Tools könnte ein Agent eine Überweisung prüfen, fehlende Informationen identifizieren, Leistungen überprüfen und einen Termin finden, der den Bedürfnissen der Patient:innen entspricht. Schritte, die klinische Dringlichkeit, ungewöhnliche Deckungsregeln oder Ausnahmen beinhalten, können an eine:n Mitarbeiter:in weitergeleitet werden.\u003C/p\u003E\n\u003Ch3\u003ECare Management und Population Health\u003C/h3\u003E\n\u003Cp\u003ECare-Management-Teams betreuen umfassende Patientengruppen, identifizieren gezielt Personen mit Kontaktdatenbedarf und koordinieren Versorgungsleistungen über verschiedene Leistungserbringer und Einrichtungen hinweg. Prädiktive Modelle können dabei helfen, Patient:innen mit steigendem Risiko, Lücken in der Vorsorge, verpasste Nachsorgeuntersuchungen oder Muster im Zusammenhang mit vermeidbarer Inanspruchnahme zu identifizieren.\u003C/p\u003E\n\u003Cp\u003EAuf Bevölkerungsebene kann KI Unternehmen dabei helfen, Risiken zu stratifizieren, Programmergebnisse zu bewerten und Ungleichheiten zwischen Patientengruppen zu identifizieren. Diese Analysen können klinische Akten, Abrechnungen, Apothekendaten und soziale Determinanten der Gesundheit einbeziehen.\u003C/p\u003E\n\u003Cp\u003EDie dem Modell zugrunde liegenden Definitionen beeinflussen, wer in der jeweiligen Gruppe erscheint. Eine Maßnahme für Versorgungslücken hängt beispielsweise von der berechtigten Population, den Ausschlusskriterien, dem Messzeitraum und den Nachweisen ab, die als ausreichend erachtet werden, um die Lücke zu schließen. Ein gemeinsamer klinischer und semantischer Kontext hält diese Regeln konsistent, unabhängig davon, ob sie in Analytics-Systemen, Conversational Interfaces oder Agentic Workflows eingesetzt werden.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-ai-supports-healthcare-operations-and-administration":{"id":"title-v2-30be770514","additionalClasses":"anchor-title anchor-title--how-ai-supports-healthcare-operations-and-administration","type":"heading2","lines":["Wie KI den Betrieb und die Verwaltung im Gesundheitswesen unterstützt"],":type":"snowflake-site/components/title-v2"},"text_how-ai-supports-healthcare-operations-and-administration_0":{"id":"text-16d842f81b","text":"\u003Cp\u003EVerwaltungsarbeit prägt die Kosten, die Kapazität und die Zugänglichkeit der Versorgung. Ein Großteil davon umfasst das Sammeln von Informationen, die Anwendung komplexer Regeln und die Koordination von Übergaben über Systeme hinweg – Bedingungen, die den Betrieb im Gesundheitswesen zu einem wichtigen Bereich für generative KI und Agentic AI machen.\u003C/p\u003E\n\u003Ch3\u003EKapazität, Personalplanung und Patientenstrom\u003C/h3\u003E\n\u003Cp\u003EKrankenhäuser und Gesundheitssysteme können prädiktive Modelle nutzen, um die Nachfrage in der Notfallabteilung, die stationäre Belegung, das Entlassungsvolumen und das Nichterscheinen bei Terminen zu prognostizieren. iese Prognosen unterstützen datengestützte Entscheidungen bei der Personalbesetzung, Bettenkapazität, OP-Planung und der Steuerung anderer begrenzter Ressourcen.\u003C/p\u003E\n\u003Cp\u003EDer Patientenfluss hängt von Ereignissen ab, die während des gesamten Behandlungsverlaufs auftreten. Ein verspäteter Test kann die Entlassung verzögern, und der daraus resultierende Bettenengpass kann die Aufnahme in der Notfallabteilung und geplante Aufnahmen beeinträchtigen. KI kann Teams dabei helfen, diese Abhängigkeiten früher zu erkennen, indem sie aktuelle klinische Aktivitäten mit operativen Daten verknüpft.\u003C/p\u003E\n\u003Cp\u003EFür die Personalplanung können Modelle den Workload und den Personalbedarf nach Abteilung oder Zeitraum schätzen, um die entsprechenden Kompetenzen und Kapazitäten an die erwarteten Bedürfnisse der Patient:innen anzupassen.\u003C/p\u003E\n\u003Ch3\u003ERevenue Cycle Management\u003C/h3\u003E\n\u003Cp\u003ERevenue-Cycle-Teams übersetzen die klinische Versorgung in präzise Abrechnungen und managen die für den Zahlungseingang erforderlichen Aufgaben. KI kann bei der klinischen Kodierung, der Leistungserfassung, der Vorbereitung von Abrechnungen, der Vorhersage von Ablehnungen und der Priorisierung von Forderungen unterstützen.\u003C/p\u003E\n\u003Cp\u003EDie Verarbeitung natürlicher Sprache kann relevante Diagnosen und Verfahren aus der klinischen Dokumentation extrahieren, während generative KI zusammenfassen kann, warum eine Forderung abgelehnt wurde, und die mit der Entscheidung verbundene Richtlinie des Kostenträgers abrufen kann. Ein Agent kann die unterstützenden Unterlagen zusammentragen, fehlende Dokumentationen identifizieren und einen Widerspruch zur Überprüfung vorbereiten.\u003C/p\u003E\n\u003Cp\u003EDer Quelldatensatz sollte dabei maßgeblich bleiben. Generierte Kodierungen oder Widerspruchstexte sollten immer mit der klinischen Dokumentation und den geltenden Abrechnungsregeln abgeglichen werden, insbesondere wenn Fehler die Erstattung, die Compliance oder die finanzielle Verantwortung der Patient:innen beeinträchtigen könnten.\u003C/p\u003E\n\u003Ch3\u003EVorabgenehmigung und Utilization Management\u003C/h3\u003E\n\u003Cp\u003EDie Vorabgenehmigung erfordert einen kontinuierlichen Informationsaustausch zwischen Leistungserbringern und Kostenträgern, um zu prüfen, ob eine angeforderte Leistung die definierten Erstattungskriterien erfüllt. Der Prozess erstreckt sich oft über elektronische Patientenakten (ePA), Kostenträgerportale, Dokumenten-Repositories und Kommunikationstools.\u003C/p\u003E\n\u003Cp\u003EKI kann erforderliche Details aus der Akte extrahieren, sie mit veröffentlichten Kriterien vergleichen und fehlende Informationen identifizieren, bevor ein Antrag eingereicht wird. Ein Agent könnte dann die Anfrage vorbereiten, ihren Status überwachen und eine Antwort an die zuständigen Mitarbeiter:innen weiterleiten.\u003C/p\u003E\n\u003Cp\u003EKlinische Entscheidungen erfordern klar abgegrenzte Befugnis- und Systemgrenzen. KI kann dabei helfen, Nachweise zusammenzustellen oder einfache Regeln anzuwenden, während Fälle, die klinisches Urteilsvermögen, mehrdeutige Kriterien oder negative Bescheide beinhalten, weiterhin einer qualifizierten menschlichen Überprüfung unterliegen.\u003C/p\u003E\n\u003Ch3\u003EAbrechnungsprüfung und Zahlungsintegrität\u003C/h3\u003E\n\u003Cp\u003EFür Kostenträger im Gesundheitswesen kann KI dabei helfen, Abrechnungen zu bewerten, inkonsistente Informationen zu erkennen und Muster zu identifizieren, die mit Betrug, Verschwendung oder Missbrauch in Verbindung stehen. Modelle können doppelte Abrechnungen, ungewöhnliche Inanspruchnahme, Identitätsprobleme oder das Verhalten von Leistungserbringern, das von einem erwarteten Muster abweicht, markieren.\u003C/p\u003E\n\u003Cp\u003EFalsch-positive Ergebnisse können angemessene Zahlungen verzögern und erheblichen Arbeitsaufwand für Leistungserbringer und Versicherte verursachen, weshalb Untersuchungen die Konfidenz und die Konsequenzen des Signals berücksichtigen sollten. Erklärbarkeit und Überprüfung sind besonders wichtig, wenn ein Modell den Zugang zu Leistungen oder Zahlungen beeinflusst.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-ai-supports-healthcare-research":{"id":"title-v2-5e9371e4df","additionalClasses":"anchor-title anchor-title--how-ai-supports-healthcare-research","type":"heading2","lines":["Wie KI die Forschung im Gesundheitswesen unterstützt"],":type":"snowflake-site/components/title-v2"},"text_how-ai-supports-healthcare-research_0":{"id":"text-dd11cb2198","text":"\u003Cp\u003EDie Forschung im Gesundheitswesen stützt sich zunehmend auf reale Daten (Real-World Data), die im Rahmen der Routineversorgung generiert werden, einschließlich elektronischer Patientenakten (ePA), Abrechnungen, Registern und von Patient:innen berichteten Ergebnissen. KI kann Forscher:innen dabei helfen, relevante Kohorten zu identifizieren, Ergebnisse zu analysieren und Muster in Datensätzen zu finden, die manuell nur schwer zu untersuchen sind.\u003C/p\u003E\n\u003Ch3\u003EKlinische Forschung und Studienbetrieb\u003C/h3\u003E\n\u003Cp\u003EKI kann in der Arzneimittelforschung eingesetzt werden und die Durchführbarkeit von Protokollen, die Standortauswahl, die Patientenrekrutierung und das Studien-Monitoring unterstützen. Ein Forschungsteam kann abschätzen, ob an den beteiligten Studienzentren ausreichend geeignete Patienten verfügbar sind, und gezielt Datensätze identifizieren, die die Einschlusskriterien für eine detaillierte Prüfung erfüllen.\u003C/p\u003E\n\u003Cp\u003EDie Verarbeitung natürlicher Sprache kann Informationen aus Notizen und Berichten extrahieren, die in strukturierten Feldern nicht verfügbar sind. Generative KI kann bei der Literaturrecherche oder der Dokumentenvorbereitung helfen, während Agenten genehmigte administrative Schritte über Studiensysteme hinweg koordinieren können.\u003C/p\u003E\n\u003Ch3\u003EReal-World Evidence und Pharmakovigilanz\u003C/h3\u003E\n\u003Cp\u003EReal-World-Evidence-Studien untersuchen, wie Behandlungen außerhalb streng kontrollierter Studien abschneiden. Forscher:innen können KI nutzen, um Ergebnisse über klinische Akten, Abrechnungen und Register hinweg zu analysieren, Patientenkohorten zu vergleichen und Variablen zu identifizieren, die mit Wirksamkeit oder Sicherheit zusammenhängen.\u003C/p\u003E\n\u003Cp\u003EIn der Pharmakovigilanz können die Verarbeitung natürlicher Sprache und maschinelles Lernen dabei helfen, potenzielle unerwünschte Ereignisse in klinischen Berichten, Sicherheitsberichten und anderen Quellen zu identifizieren. Das Signal durchläuft dann einen etablierten Überprüfungsprozess – KI kann Nachweise priorisieren, aber Sicherheitsexpert:innen bewerten die Kausalität und die regulatorische Bedeutung.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_benefits-and-risks-of-ai-in-healthcare":{"id":"title-v2-25bb4e129f","additionalClasses":"anchor-title anchor-title--benefits-and-risks-of-ai-in-healthcare","type":"heading2","lines":["Vorteile und Risiken von KI im Gesundheitswesen"],":type":"snowflake-site/components/title-v2"},"text_benefits-and-risks-of-ai-in-healthcare_0":{"id":"text-c0e16f645c","text":"\u003Cp\u003EKI kann Unternehmen im Gesundheitswesen dabei helfen, Risiken früher zu erkennen, klinische und administrative Belastungen zu reduzieren, den Zugang für Patient:innen zu verbessern, Kapazitäten effektiver zu nutzen und die Forschung zu beschleunigen. Durch das Zusammentragen von Informationen aus verschiedenen Quellen kann sie auch eine koordiniertere und personalisiertere Versorgung unterstützen.\u003C/p\u003E\n\u003Cp\u003EUngenaue Ausgaben, Modell-Bias, Automatisierungs-Bias und Performance-Änderungen können jedoch klinische oder finanzielle Entscheidungen beeinflussen. Unternehmen im Gesundheitswesen müssen außerdem geschützte Gesundheitsinformationen (PHI) schützen, Anforderungen wie \u003Ca href=\"https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html\" target=\"_blank\"\u003EHIPAA\u003C/a\u003E und \u003Ca href=\"https://www.snowflake.com/de/data-governance/regulatory-compliance/gdpr/\"\u003EDSGVO\u003C/a\u003E einhalten, Zustimmungen und zulässige Nutzungen berücksichtigen sowie eine angemessene menschliche Aufsicht aufrechterhalten. Legacy-Systeme und inkonsistente Daten können eine weitere Fehlerquelle darstellen, wenn eine Anwendung unvollständige oder schlecht kontextualisierte Informationen erhält.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-healthcare-organizations-need-to-scale-ai":{"id":"title-v2-75d7ce7ff6","additionalClasses":"anchor-title anchor-title--what-healthcare-organizations-need-to-scale-ai","type":"heading2","lines":["Was Unternehmen im Gesundheitswesen benötigen, um KI zu skalieren"],":type":"snowflake-site/components/title-v2"},"text_what-healthcare-organizations-need-to-scale-ai_0":{"id":"text-40872e9712","text":"\u003Cp\u003EDie Ausweitung von KI auf klinische, operative und administrative Aufgaben erfordert eine breitere Grundlage: interoperable Patienten- und Geschäftsdaten, Unterstützung für prädiktive, generative und multimodale Modelle, skalierbare Rechenressourcen, einen gemeinsamen klinischen Kontext, kontinuierliche Evaluierung sowie Governance über Daten, Modelle, Tools und Aktionen hinweg.\u003C/p\u003E\n\u003Ch3\u003ELongitudinaler Kontext von Patient:innen und Versicherten\u003C/h3\u003E\n\u003Cp\u003EPatienteninformationen sind über elektronische Patientenakten (ePA), Abrechnungen, Labore, Apotheken, Bildgebungssysteme, medizinische Geräte und andere Quellen verteilt. Eine longitudinale Akte verbindet diese Ereignisse im Laufe der Zeit und gibt KI-Systemen ein klareres Bild von Diagnosen, Behandlungen, Inanspruchnahme und Ergebnissen.\u003C/p\u003E\n\u003Cp\u003EDie Identitätsauflösung ist für diese Arbeit von zentraler Bedeutung. Namen, Adressen, Versichertenkennungen und Krankenaktennummern können von System zu System variieren, während doppelte oder falsch verknüpfte Datensätze ein erhebliches klinisches Risiko darstellen können. Das Datenfundament muss Datensätze hochpräzise zusammenführen, ohne die darauf angewendeten Datenschutz- und Governance-Kontrollen zu schwächen.\u003C/p\u003E\n\u003Cp\u003EZeitpunkt und Herkunft fügen weiteren Kontext hinzu. Ein vorläufiges Laborergebnis unterscheidet sich von einem Endergebnis, und eine historische Medikation unterscheidet sich von einer, die der:die Patient:in aktuell einnimmt. KI muss nachvollziehen können, woher Informationen stammen, wann sie erfasst wurden und wie sie im Kontext der aktuellen Nutzung zu interpretieren sind.\u003C/p\u003E\n\u003Ch3\u003EInteroperabilität im Gesundheitswesen\u003C/h3\u003E\n\u003Cp\u003EInteroperabilität ermöglicht den Austausch klinischer, administrativer und finanzieller Informationen zwischen Systemen in einem Format, das das empfangende Unternehmen nutzen kann. Standards wie HL7 und \u003Ca rel=\"noopener noreferrer\" href=\"https://ecqi.healthit.gov/fhir/about\" target=\"_blank\"\u003EFast Healthcare Interoperability Resources\u003C/a\u003E (FHIR) bieten Strukturen für den Austausch von Gesundheitsdaten, während gängige Modelle wie OMOP die konsistente Analyse von Beobachtungsdaten unterstützen.\u003C/p\u003E\n\u003Cp\u003EDer rein technische Datenaustausch löst jedoch nur einen Teil der Herausforderung. Organisationen benötigen darüber hinaus eine einheitliche Terminologie für Diagnosen, medizinische Verfahren, Medikation, Laborwerte, Leistungserbringer und Einrichtungen. Ohne diese semantische Abstimmung tauschen zwei Systeme möglicherweise einen Datensatz erfolgreich aus, weisen demselben Feld jedoch unterschiedliche Bedeutungen zu.\u003C/p\u003E\n\u003Cp\u003EFür die KI erweitert die Interoperabilität den Kontext, der einem Modell oder Agenten zur Verfügung steht. Sie kann ein klinisches Ereignis mit vorheriger Behandlung, Versicherungsinformationen und späteren Ergebnissen verknüpfen, anstatt jedes System als separate Sicht auf den:die Patient:in zu betrachten. Aus diesem Grund ist Interoperabilität heute von zentraler Bedeutung für die KI-Strategie im Gesundheitswesen. Cugliotta erklärt: „Interoperabilität ist nicht länger nur ein Häkchen auf der Compliance-Checkliste – sie ist der Motor, der skalierbare KI möglich macht.“\u003C/p\u003E\n\u003Ch3\u003EMultimodale klinische Daten\u003C/h3\u003E\n\u003Cp\u003EKI im Gesundheitswesen arbeitet mit strukturierten Datensätzen, klinischen Notizen, Bildern, Audio, Wellenformen, Genomdaten und Dokumenten. Jedes Format erfasst einen anderen Teil der Patienten- oder Betriebsgeschichte.\u003C/p\u003E\n\u003Cp\u003EEine Anwendung für die medizinische Bildgebung kann beispielsweise den eigentlichen Scan, den Befund des Radiologen sowie die relevante Vorgeschichte benötigen. Ambient Documentation beginnt mit Audioaufnahmen und erzeugt Text, der mit strukturierten Falldaten übereinstimmen muss. Ein Forschungsmodell kann Laborwerte, Genommerkmale und longitudinale Ergebnisse kombinieren.\u003C/p\u003E\n\u003Cp\u003EDie Unterstützung dieser Kombinationen erfordert mehr als nur die Speicherung verschiedener Dateitypen. Die Grundlage muss die Beziehungen zwischen ihnen bewahren – welches Bild zu welcher Studie gehört, welche Notiz welchen Fall beschreibt und welche Beobachtung aktuell war, als eine Empfehlung generiert wurde.\u003C/p\u003E\n\u003Ch3\u003EKlinischer und semantischer Kontext\u003C/h3\u003E\n\u003Cp\u003EEin Feldname erfasst selten die volle klinische Bedeutung eines Wertes. Eine Diagnose kann vermutet, bestätigt, historisch oder für die Abrechnung eingegeben worden sein. Ein Medikament kann verschrieben, ausgegeben, verabreicht oder abgesetzt worden sein. Ein Behandlungsfall kann eine stationäre Aufnahme, eine Beobachtung, eine Notfallbehandlung oder einen ambulanten Besuch darstellen.\u003C/p\u003E\n\u003Cp\u003EDer semantische Kontext liefert diese Unterscheidungen durch genehmigte Definitionen, Terminologie-Mappings, klinische Logik und Beziehungen. Er unterstützt auch verwaltete Metriken wie Wiederaufnahme, Aufenthaltsdauer, Versorgungslücken und Qualitätsmaßstäbe, deren Berechnung von detaillierten Inklusions- und Exklusionsregeln abhängen kann.\u003C/p\u003E\n\u003Cp\u003EBei generativer KI und Agenten hilft der semantische Kontext dabei, Fragen, die in der vertrauten Sprache des Gesundheitswesens formuliert sind, in die richtigen Daten und Logiken zu übersetzen. Das System kann die richtigen Datensätze abrufen und gleichzeitig die damit verbundene Bedeutung bewahren.\u003C/p\u003E\n\u003Ch3\u003EKI-Dienste, Compute-Ressourcen und Evaluierung\u003C/h3\u003E\n\u003Cp\u003EAnwendungen im Gesundheitswesen stellen unterschiedliche Anforderungen an die KI-Umgebung. Prädiktive Modelle trainieren möglicherweise mit großen longitudinalen Datensätzen, Computer Vision erfordert Bildverarbeitung und GPU-Rechenressourcen, und Anwendungen für generative KI benötigen das Parsen von Dokumenten, Retrieval und Inferenz. Agenten fügen Orchestrierung, Werkzeugzugriff und Aufzeichnungen mehrstufiger Aktivitäten hinzu.\u003C/p\u003E\n\u003Cp\u003EDie Evaluierung sollte auf den Anwendungsfall abgestimmt sein. Ein klinisches Risikomodell sollte systematisch auf Sensitivität, Spezifität, Kalibrierung und Performance über verschiedene Patienten-Subgruppen hinweg evaluiert werden. Ein Dokumentationsassistent sollte auf Auslassungen, nicht unterstützte Hinzufügungen und Auswirkungen auf die Codierung überprüft werden. Ein Agent muss daraufhin evaluiert werden, ob er die richtigen Werkzeuge auswählt, Berechtigungen beachtet und den Workflow fehlerfrei abschließt.\u003C/p\u003E\n\u003Cp\u003EDas Monitoring wird nach der Bereitstellung fortgesetzt. Evaluierung, Lineage und Versionierung helfen Teams dabei, diese Änderungen zu erkennen und festzustellen, ob ein Update erforderlich ist.\u003C/p\u003E\n\u003Ch3\u003EGovernance über Daten, Modelle und Agenten hinweg\u003C/h3\u003E\n\u003Cp\u003EGovernance im Gesundheitswesen beginnt beim Zugriff auf geschützte Gesundheitsdaten (PHI), endet aber nicht dort. Richtlinien sollten die Rolle der Nutzer:innen, den Zweck der Arbeit und die minimal erforderlichen Informationen widerspiegeln. Einwilligungen, vertragliche Einschränkungen und Forschungsgenehmigungen können die Datennutzung weiter einschränken.\u003C/p\u003E\n\u003Cp\u003EModelle erfordern die Überwachung von Trainingsdaten, des vorgesehenen Verwendungszwecks, der Performance in Untergruppen und der Veränderungen im Laufe der Zeit. Anwendungen für generative KI benötigen Source Grounding und eine Möglichkeit, abgerufene Belege von generierten Inhalten zu unterscheiden. Bei Agenten erstreckt sich die Governance auf Werkzeugberechtigungen und Aktionen: Das Lesen eines Anspruchs, das Entwerfen eines Widerspruchs und die Ausstellung eines ablehnenden Bescheids stellen unterschiedliche Befugnisebenen dar.\u003C/p\u003E\n\u003Cp\u003EAuditierbarkeit verbindet diese Kontrollen. Unternehmen im Gesundheitswesen sollten in der Lage sein, festzustellen, auf welche Daten eine KI-Anwendung zugegriffen hat, welches Modell und welche Version eine Ausgabe erzeugt hat, welche Werkzeuge ein Agent aufgerufen hat und wo eine Person das Ergebnis überprüft oder genehmigt hat.\u003C/p\u003E\n\u003Ch3\u003EVerwaltete Zusammenarbeit im Gesundheitswesen\u003C/h3\u003E\n\u003Cp\u003EDie Gesundheitsversorgung und -forschung umfassen Leistungserbringer, Kostenträger, Apotheken, Labore, Gesundheitsbehörden und Life-Sciences-Unternehmen. Viele KI-Anwendungsfälle hängen daher von Daten ab, die außerhalb des Unternehmens gespeichert sind, das die Anwendung entwickelt.\u003C/p\u003E\n\u003Cp\u003EKontrolliertes Data Sharing und Data Clean Rooms können gemeinsame Analysen unterstützen und gleichzeitig einschränken, was die einzelnen Teilnehmer:innen sehen können. So könnten beispielsweise Kostenträger und Leistungserbringer Behandlungsergebnisse auswerten oder Forscher:innen eine Kohorte institutionsübergreifend analysieren, ohne uneingeschränkte rohe PHI auszutauschen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_what-healthcare-organizations-need-to-scale-ai_0":{"id":"text-a9b507acda","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EKURZER TIPP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EEine longitudinale Patientenakte ist nicht automatisch ein KI-bereiter Patientenkontext. Das System muss auch festhalten, wann Informationen erfasst wurden, woher sie stammen, wie sie definiert wurden und ob sie für die aktuelle klinische, operative oder forschungsbezogene Nutzung geeignet sind.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-snowflake-supports-ai-in-healthcare":{"id":"title-v2-352d9d68fe","additionalClasses":"anchor-title anchor-title--how-snowflake-supports-ai-in-healthcare","type":"heading2","lines":["Wie Snowflake KI im Gesundheitswesen unterstützt"],":type":"snowflake-site/components/title-v2"},"text_how-snowflake-supports-ai-in-healthcare_0":{"id":"text-87b43c407e","text":"\u003Cp\u003ESnowflake hilft Unternehmen im Gesundheitswesen dabei, klinische, Abrechnungs-, Bildgebungs-, Labor-, Betriebs- und Forschungsdaten in \u003Ca href=\"https://www.snowflake.com/de/solutions/industries/healthcare-and-life-sciences/\"\u003ESnowflakes AI Data Cloud für Gesundheitswesen und Life Sciences\u003C/a\u003E zusammenzuführen. Die Plattform unterstützt Interoperabilitätsmuster im Gesundheitswesen und multimodale Daten. So können Teams Informationen über die gesamte Patient Journey hinweg verknüpfen und gleichzeitig die Governance über sensible Akten wahren. Snowflake unterstützt Compliance-Programme, die für regulierte Workloads im Gesundheitswesen relevant sind, einschließlich HIPAA und HITRUST, während jedes Unternehmen weiterhin dafür verantwortlich ist, seine Umgebung in Übereinstimmung mit seinen eigenen Verpflichtungen zu konfigurieren und zu betreiben.\u003C/p\u003E\n\u003Cp\u003ESnowflake Openflow kann Batch-, Streaming-, strukturierte und unstrukturierte Daten aus Systemen des Gesundheitswesens aufnehmen. Für die Verarbeitung von HL7 v2- und FHIR-Nachrichten stehen Entwicklerleitfäden zur Verfügung, die Teams bei der Arbeit mit weit verbreiteten Austauschformaten im Gesundheitswesen unterstützen.\u003C/p\u003E\n\u003Cp\u003EFür prädiktives maschinelles Lernen und medizinische Bildgebung können Teams Daten aufbereiten und Modelle in der Nähe der von ihnen genutzten kontrollierten Datensätze entwickeln. Snowflake Notebooks und Container Runtime unterstützen fortschrittliche Data Science und GPU-basierte Workloads; zu den aktuellen Ressourcen von Snowflake für das Gesundheitswesen gehört ein verteilter Workflow zur Verarbeitung medizinischer Bilder, der mit MONAI erstellt wurde.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/product/features/cortex/\"\u003ECortex AI\u003C/a\u003E unterstützt generative und multimodale Anwendungen für strukturierte und unstrukturierte Daten im Gesundheitswesen. Cortex AI-Funktionen können Text oder Bilder extrahieren, klassifizieren und zusammenfassen, während Cortex Search relevante Passagen aus klinischen, Richtlinien- oder Forschungsdokumenten abruft. Cortex Analyst bietet über semantische Modelle einen natürlichsprachlichen Zugang zu kontrollierten strukturierten Daten. Zusammen können diese Dienste eine Anwendung sowohl auf die Patienten- oder Betriebsakte als auch auf die zu deren Interpretation erforderlichen Dokumente stützen.\u003C/p\u003E\n\u003Cp\u003EBei Workflows, die mehrere Schritte umfassen, können \u003Ca href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/\"\u003ECortex Agents\u003C/a\u003E Schlussfolgerungen aus strukturierten und unstrukturierten Daten ziehen und genehmigte Tools verwenden. Klinische und folgenschwere Entscheidungen können weiterhin einer menschlichen Überprüfung unterliegen, wobei der Agent innerhalb definierter Berechtigungen agiert.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E bietet Mitarbeiter:innen im Gesundheitswesen eine dialogorientierte Arbeitsumgebung, die auf Unternehmenskontext und Agenten basiert. Es kann Nutzer:innen dabei helfen, Fragen über Akten, Abrechnungen, reale und andere Daten hinweg zu untersuchen und dann genehmigte Arbeiten in verbundene Anwendungen zu übertragen.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E bietet Governance, Kontext und Richtliniendurchsetzung für Daten und KI. Richtlinien, die über die Query Engine von Snowflake durchgesetzt werden, gelten konsistent, unabhängig davon, ob der Aufrufer ein:e Analyst:in, ein Business Intelligence-Tool oder ein KI-Agent ist, während Lineage und Discovery Teams dabei helfen, zu verstehen, wie Informationen genutzt werden.\u003C/p\u003E\n\u003Cp\u003EIm gesamten Ökosystem des Gesundheitswesens unterstützen Snowflake Secure Data Sharing und \u003Ca href=\"https://www.snowflake.com/de/product/features/data-clean-rooms/\"\u003ESnowflake Data Clean Rooms\u003C/a\u003E eine kontrollierte Zusammenarbeit, ohne auf den wiederholten Austausch statischer Datenkopien angewiesen zu sein. Leistungserbringer, Kostenträger, Forscher:innen und Life-Sciences-Unternehmen können genehmigte Daten gemeinsam analysieren und gleichzeitig die Kontrolle über PHI und andere sensible Informationen behalten.\u003C/p\u003E\n\u003Cp\u003EKI im Gesundheitswesen umfasst Modelle zur Risikovorhersage, Systeme zur Interpretation von Bildern und Sprache sowie Agenten, die die Arbeit über Anwendungen hinweg koordinieren. Eine gemeinsame Grundlage gibt diesen Technologien Zugang zu dem klinischen und operativen Kontext, den sie benötigen, und bewahrt gleichzeitig die Aufsicht, die erforderlich ist, wenn KI in die Pflege, den Versicherungsschutz, die Forschung oder die Bezahlung einfließt.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-snowflake-supports-ai-in-healthcare_0":{"id":"text-ea35af2f6e","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EWICHTIGSTE ERKENNTNIS\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EKI im Gesundheitswesen liefert den größten Mehrwert, wenn sie auf vernetzten, vertrauenswürdigen Daten basiert und mit klarer Governance bereitgestellt wird. Unternehmen, die longitudinalen Kontext, Interoperabilität, kontinuierliche Evaluierung und menschliche Aufsicht kombinieren, können KI in den Bereichen Pflege, Betrieb und Forschung skalieren und gleichzeitig klinische, finanzielle und datenschutzrechtliche Risiken managen.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_how-snowflake-supports-ai-in-healthcare_1":{"id":"text-efdd7b1dbd","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/de/artificial-intelligence/industries/\"\u003EErfahren Sie, wie KI in anderen Branchen eingesetzt wird \u003E\u003C/a\u003E\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EEntdecken Sie den Hub für KI in verschiedenen Branchen:\u003C/i\u003E\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-advertising\"\u003EKI in der 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aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-12ebbc54ff",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text_943981956_copy_":{"id":"text-6fdad86620","additionalClasses":"eyebrow-text","text":"\u003Cp\u003EIn diesem Leitfaden\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-7646309be9","additionalClasses":"page-toc","text":"\u003Cul\u003E\u003Cli data-anchor=\"how-ai-supports-clinical-care\"\u003EWie KI die klinische Versorgung unterstützt\u003C/li\u003E\u003Cli data-anchor=\"how-ai-supports-patient-access-and-care-management\"\u003EWie KI den Patientenzugang und das Care Management unterstützt\u003C/li\u003E\u003Cli 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Fragen"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-ef8c22214e","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003EIhre häufigen Fragen zu KI-Compliance – beantwortet von Snowflake-Expert:innen.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-3dea7b019d","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"Welche Vorteile bietet KI im Gesundheitswesen?","richText":"\u003Cp\u003EKI kann Unternehmen im Gesundheitswesen dabei helfen, Risiken früher zu erkennen, den Verwaltungsaufwand zu reduzieren, den Patientenzugang 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Sie kann Teams auch dabei helfen, Informationen aus fragmentierten Systemen zusammenzuführen, was es einfacher macht, die Vorgeschichte von Patient:innen zu verstehen, die Pflege zu koordinieren und Ergebnisse über Populationen hinweg zu analysieren.\u003C/p\u003E"},{"title":"Welche Risiken birgt KI im Gesundheitswesen?","richText":"\u003Cp\u003EZu den Hauptrisiken gehören ungenaue Ergebnisse, Bias, Datenschutzverletzungen, mangelnde Transparenz, eine zu starke Abhängigkeit von Automatisierung und Performance-Änderungen nach der Bereitstellung. Diese Risiken sind besonders wichtig, wenn KI die klinische Versorgung, Bezahlung, Versicherungsdeckung, den Zugang zu Dienstleistungen oder die Patientenkommunikation beeinflusst. 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Diese Tools sind am nützlichsten, wenn sie auf vertrauenswürdigen Quelldaten basieren und so konzipiert sind, dass Nutzer:innen überprüfen können, woher die Informationen 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