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  <doc>
    <id>7873</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>9</issue>
    <volume>150</volume>
    <type>article</type>
    <publisherName>Springer Science and Business Media LLC</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Systematic development of a patient-reported ONCOlogical-ROUTinE-Screening (ONCO-ROUTES) procedure at the University Cancer Center Regensburg</title>
    <abstract language="eng">Purpose&#13;
The evaluation of treatment success and progression in oncology patient-reported outcomes (PROs) is playing an increasingly important role. Meanwhile, PROs are a component of the certification requirements of the German Cancer Society for oncology centers. PROs are used to provide supportive therapy. There is currently no instrument that fully covers the requirements. At the University Hospital Regensburg (UKR), a digital ONCOlogical-ROUTinE-Screening (ONCO-ROUTES) procedure was developed in order to assess the need for supportive therapy in a standardized way and to provide patients with supportive interventions tailored to their needs.&#13;
 &#13;
Methods&#13;
On the basis of current requirements and guidelines, the development of ONCO-ROUTES was supported by experts in focus groups and interviews, and digitalization was carried out in connection with the IT infrastructure.&#13;
 &#13;
Results&#13;
A Needs-based, Quality-of-life (QoL) and Symptoms Screening (NQS2) tool already established in the routine at the UKR was further developed into ONCO-ROUTES, which is made up of the domains therapy phase, nutrition, tobacco use, alcohol use, quality of life, general condition/functional status, physical activity, psychooncology, social services, and further support needs. By linking the digitized questionnaire to the hospital information system, the results are available for immediate use in routine operations and thus for the referral of patients for further supportive therapy.&#13;
 &#13;
Conclusion&#13;
The digital PRO application ONCO-ROUTES is designed to involve patients in monitoring additional supportive needs and thus, improves supportive interdisciplinary treatment.</abstract>
    <parentTitle language="eng">Journal of Cancer Research and Clinical Oncology</parentTitle>
    <identifier type="issn">1432-1335</identifier>
    <identifier type="doi">10.1007/s00432-024-05955-4</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Julia Maurer</author>
    <author>Anna Saibold</author>
    <author>Katharina Gerl</author>
    <author>Michael Koller</author>
    <author>Oliver Kölbl</author>
    <author>Tobias Pukrop</author>
    <author>Sandra Windschuettl</author>
    <author>Sabine Einhell</author>
    <author>Anne Herrmann-Johns</author>
    <author>Georgios Raptis</author>
    <author>Karolina Mueller</author>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor eHealth (eH)</collection>
  </doc>
  <doc>
    <id>7866</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>312</pageFirst>
    <pageLast>321</pageLast>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>28</volume>
    <type>article</type>
    <publisherName>Thieme</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-12-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">KI-gestützte Untersuchung in der nicht-operativen Versorgung symptomgebender Erkrankungen des Kniegelenks – ein multiprofessionelles Konzept (KINEESIO)</title>
    <abstract language="deu">Beschwerdebilder am Kniegelenk aufgrund muskuloskelettaler degenerativer oder verletzungsbedingter Erkrankungen sind häufig, nehmen im Alter zu und sind mit der steigenden Inanspruchnahme ärztlicher und therapeutischer Behandlungsmaßnahmen verbunden. Einer erfolgreichen Therapie gehen oft notwendige zeit- und ressourcenaufwendige Untersuchungen zur Erkennung und Differenzierung der patient*innenspezifischen Problematik voraus. Im Zusammenhang mit der nicht-operativen Versorgung des Kniegelenks hat ein sektorübergreifendes multiprofessionelles Forschungsteam ein Konzept entwickelt, um künstliche neuronale Netze so zu trainieren, dass sie bei der ärztlichen und physiotherapeutischen Untersuchung unterstützend Einsatz finden können. Denn gerade in der Erfassung und Auswertung umfassender Datenmengen liegen große Potenziale in der Künstlichen Intelligenz (KI) im Gesundheitswesen. Das Projekt KINEESIO trainiert und testet KI-gestützte Screening- Tools zur Untersuchung von Patient*innen mit Kniegelenkerkrankungen. Diese unterstützen die Abläufe zwischen Leistungserbringern und Patient*innen, tragen zu einer verbesserten Differenzierung individueller Beschwerdebilder bei und dienen Entscheidungsprozessen für eine adäquate Versorgung. Dadurch sollen Ressourcen im Gesundheitswesen geschont und eine qualitativ hochwertige Therapie ausreichend ermöglicht werden.</abstract>
    <abstract language="eng">Complaints regarding knee joints resulting from musculoskeletal degenerative or injury-related conditions are common, increase with age, and are associated with rising demand for treatments by physicians and therapists. The success of a therapeutic intervention is frequently contingent upon necessary time- and resource-intensive examinations to identify and differentiate the patient’s specific issues. In connection with non-surgical knee joint treatment, a crosssectoral, multidisciplinary research team has developed a concept for training artificial neural networks to assist in medical and physiotherapeutic examinations. After all, there is great potential for artificial intelligence (AI) in healthcare, particularly in collecting and analyzing large amounts of data. The KINEESIO project is training and testing screening tools supported by artificial intelligence for the assessment of patients with knee joint disorders. These tools support the workflows between healthcare providers and patients, improve the differentiation of individual symptoms and serve the decisionmaking process towards appropriate care. This should conserve healthcare resources and enable sufficient high-quality treatment.</abstract>
    <parentTitle language="deu">MSK – Muskuloskelettale Physiotherapie</parentTitle>
    <identifier type="doi">10.1055/a-2402-9982</identifier>
    <identifier type="issn">2701-6986</identifier>
    <enrichment key="BegutachtungStatus">begutachtet</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Elke Schulze</author>
    <author>Christoph Palm</author>
    <author>Maximilian Kerschbaum</author>
    <author>Roman Seidel</author>
    <author>Lars Lehmann</author>
    <author>Michael Koller</author>
    <author>Andrea Pfingsten</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Physiotherapie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Diagnose</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Kniegelenk</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="FakSoz">Fakultät Sozial- und Gesundheitswissenschaften</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="persons" number="pfingstenlphpub">Pfingsten, Andrea (Prof. Dr.), Publikationen  - Labor Physiotherapie</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Physiotherapie (LPh)</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
</export-example>
