Big Data in Medical Science and Healthcare Management : : Diagnosis, Therapy, Side Effects / / ed. by Peter Langkafel.
Big Data in medical science – what exactly is that? What are the potentials for healthcare management? Where is Big Data at the moment? Which risk factors need to be kept in mind? What is hype and what is real potential? This book provides an impression of the new possibilities of networked data ana...
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Big Data in Medical Science and Healthcare Management : Diagnosis, Therapy, Side Effects / ed. by Peter Langkafel. Berlin ; Boston : De Gruyter, [2015] ©2016 1 online resource (280 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Frontmatter -- Autopilot and “Doctor Algorithm”? -- Contents -- 1. Intro Big Data for Healthcare? -- 2. Information Management for Systems Medicine – on the Next Digital Threshold -- 3. Some Philosophical Thoughts on Big Data -- 4. Big Data from a Health Insurance Company’s Point of View -- 5. Big Data and the Family Doctor -- 6. How Value is Created from Data: Experiences from the Integrated Health Care System, “Gesundes Kinzigtal” (Healthy Kinzigtal) -- 7. Ethics -- 8. The New Data-Supported Quality Assurance of the Federal Joint Committee: Opportunities and Challenges -- 9. Big Data in Healthcare: Fields of Application and Benefits of SAP Technologies -- 10. Big Data – More Risks than Benefits for Healthcare? -- 11. Big Data – An Efficiency Boost in the Healthcare Sector -- 12. Medical Big Data and Data Protection -- 13. Big Data in Healthcare from a Business Consulting (Accenture) Point of View -- 14. Influence of Big Pharma on Medicine, Logistics and Data Technology in a State of Transition -- 15. Semantics and Big Data Semantic Methods for Data Processing and Searching Large Amounts of Data -- 16. Quantified Self, Wearable Technologies and Personal Data -- 17. “For the Benefit of the Patient” ... What Does the Patient Say to That? -- 18. Visualization – What Does Big Data Actually Look Like? -- 19. The Digital Patient? -- Publisher and Index of Authors -- Glossary -- Testimonials restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star Big Data in medical science – what exactly is that? What are the potentials for healthcare management? Where is Big Data at the moment? Which risk factors need to be kept in mind? What is hype and what is real potential? This book provides an impression of the new possibilities of networked data analysis and "Big Data" – for and within medical science and healthcare management.Big Data is about the collection, storage, search, distribution, statistical analysis and visualization of large amounts of data. This is especially relevant in healthcare management, as the amount of digital information is growing exponentially. An amount of data corresponding to 12 million novels emerges during the time of a single hospital stay. These are dimensions that cannot be dealt with without IT technologies. What can we do with the data that are available today? What will be possible in the next few years? Do we want everything that is possible? Who protects the data from wrong usage? More importantly, who protects the data from NOT being used?Big Data is the "resource of the 21st century" and might change the world of medical science more than we understand, realize and want at the moment. The core competence of Big Data will be the complete and correct collection, evaluation and interpretation of data. This also makes it possible to estimate the frame conditions and possibilities of the automation of daily (medical) routine. Can Big Data in medical science help to better understand fundamental problems of health and illness, and draw consequences accordingly?Big Data also means the overcoming of sector borders in healthcare management. The specialty of Big Data analysis will be the new quality of the outcomes of the combination of data that were not related before. That is why the editor of the book gives a voice to 30 experts, working in a variety of fields, such as in hospitals, in health insurance or as medical practitioners. The authors show potentials, risks, concrete practical examples, future scenarios, and come up with possible answers for the field of information technology and data privacy. Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021) Big data. Data mining. Diagnosis Decision making Data processing. Medicine Data processing. Big Data. Datenschutz. Digital Health. Gesundheitsmanagemen. COMPUTERS / Database Management / Data Mining. bisacsh data protection. healthcare management. Baumann, Timo, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Brunner, Thomas, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Dittmann, Birger, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Eberhardt, Werner, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Engelhorn, Michael, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Fischer, Alexander, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Grüning, Thilo, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Hildebrandt, Helmut, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Höchstetter, Renate, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Josef, Schepers, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Kaczynski, Anika, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Kamps, Harald, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Krolop, Sebastian, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Langkafel, Peter, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Langkafel, Peter, editor. edt http://id.loc.gov/vocabulary/relators/edt Laslo, Peer, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Mühlbacher, Axel, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Müller, Albrecht von, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Peuker, Martin, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Pimperl, Alexander, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Pötter-Kirchner, Karola, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Röhrig, Rainer, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Schaper, Hartmut, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Schulte, Timo, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Schumacher, Florian, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Souchon, Henri, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Wehmeier, Axel, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Weichert, Thilo, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Weigand, Markus A., contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Wendel, Pascal, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Wetzel, Martin, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Zimmermann-Rittereiser, Marcus, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Title is part of eBook package: De Gruyter DG Plus eBook-Package 2016 9783110701005 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2015 9783110439687 ZDB-23-DGG Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2015 9783110438772 ZDB-23-DEI print 9783110445282 https://doi.org/10.1515/9783110445749 https://www.degruyter.com/isbn/9783110445749 Cover https://www.degruyter.com/cover/covers/9783110445749.jpg |
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author_facet |
Baumann, Timo, Baumann, Timo, Brunner, Thomas, Brunner, Thomas, Dittmann, Birger, Dittmann, Birger, Eberhardt, Werner, Eberhardt, Werner, Engelhorn, Michael, Engelhorn, Michael, Fischer, Alexander, Fischer, Alexander, Grüning, Thilo, Grüning, Thilo, Hildebrandt, Helmut, Hildebrandt, Helmut, Höchstetter, Renate, Höchstetter, Renate, Josef, Schepers, Josef, Schepers, Kaczynski, Anika, Kaczynski, Anika, Kamps, Harald, Kamps, Harald, Krolop, Sebastian, Krolop, Sebastian, Langkafel, Peter, Langkafel, Peter, Langkafel, Peter, Langkafel, Peter, Laslo, Peer, Laslo, Peer, Mühlbacher, Axel, Mühlbacher, Axel, Müller, Albrecht von, Müller, Albrecht von, Peuker, Martin, Peuker, Martin, Pimperl, Alexander, Pimperl, Alexander, Pötter-Kirchner, Karola, Pötter-Kirchner, Karola, Röhrig, Rainer, Röhrig, Rainer, Schaper, Hartmut, Schaper, Hartmut, Schulte, Timo, Schulte, Timo, Schumacher, Florian, Schumacher, Florian, Souchon, Henri, Souchon, Henri, Wehmeier, Axel, Wehmeier, Axel, Weichert, Thilo, Weichert, Thilo, Weigand, Markus A., Weigand, Markus A., Wendel, Pascal, Wendel, Pascal, Wetzel, Martin, Wetzel, Martin, Zimmermann-Rittereiser, Marcus, Zimmermann-Rittereiser, Marcus, |
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author_sort |
Baumann, Timo, |
title |
Big Data in Medical Science and Healthcare Management : Diagnosis, Therapy, Side Effects / |
spellingShingle |
Big Data in Medical Science and Healthcare Management : Diagnosis, Therapy, Side Effects / Frontmatter -- Autopilot and “Doctor Algorithm”? -- Contents -- 1. Intro Big Data for Healthcare? -- 2. Information Management for Systems Medicine – on the Next Digital Threshold -- 3. Some Philosophical Thoughts on Big Data -- 4. Big Data from a Health Insurance Company’s Point of View -- 5. Big Data and the Family Doctor -- 6. How Value is Created from Data: Experiences from the Integrated Health Care System, “Gesundes Kinzigtal” (Healthy Kinzigtal) -- 7. Ethics -- 8. The New Data-Supported Quality Assurance of the Federal Joint Committee: Opportunities and Challenges -- 9. Big Data in Healthcare: Fields of Application and Benefits of SAP Technologies -- 10. Big Data – More Risks than Benefits for Healthcare? -- 11. Big Data – An Efficiency Boost in the Healthcare Sector -- 12. Medical Big Data and Data Protection -- 13. Big Data in Healthcare from a Business Consulting (Accenture) Point of View -- 14. Influence of Big Pharma on Medicine, Logistics and Data Technology in a State of Transition -- 15. Semantics and Big Data Semantic Methods for Data Processing and Searching Large Amounts of Data -- 16. Quantified Self, Wearable Technologies and Personal Data -- 17. “For the Benefit of the Patient” ... What Does the Patient Say to That? -- 18. Visualization – What Does Big Data Actually Look Like? -- 19. The Digital Patient? -- Publisher and Index of Authors -- Glossary -- Testimonials |
title_sub |
Diagnosis, Therapy, Side Effects / |
title_full |
Big Data in Medical Science and Healthcare Management : Diagnosis, Therapy, Side Effects / ed. by Peter Langkafel. |
title_fullStr |
Big Data in Medical Science and Healthcare Management : Diagnosis, Therapy, Side Effects / ed. by Peter Langkafel. |
title_full_unstemmed |
Big Data in Medical Science and Healthcare Management : Diagnosis, Therapy, Side Effects / ed. by Peter Langkafel. |
title_auth |
Big Data in Medical Science and Healthcare Management : Diagnosis, Therapy, Side Effects / |
title_alt |
Frontmatter -- Autopilot and “Doctor Algorithm”? -- Contents -- 1. Intro Big Data for Healthcare? -- 2. Information Management for Systems Medicine – on the Next Digital Threshold -- 3. Some Philosophical Thoughts on Big Data -- 4. Big Data from a Health Insurance Company’s Point of View -- 5. Big Data and the Family Doctor -- 6. How Value is Created from Data: Experiences from the Integrated Health Care System, “Gesundes Kinzigtal” (Healthy Kinzigtal) -- 7. Ethics -- 8. The New Data-Supported Quality Assurance of the Federal Joint Committee: Opportunities and Challenges -- 9. Big Data in Healthcare: Fields of Application and Benefits of SAP Technologies -- 10. Big Data – More Risks than Benefits for Healthcare? -- 11. Big Data – An Efficiency Boost in the Healthcare Sector -- 12. Medical Big Data and Data Protection -- 13. Big Data in Healthcare from a Business Consulting (Accenture) Point of View -- 14. Influence of Big Pharma on Medicine, Logistics and Data Technology in a State of Transition -- 15. Semantics and Big Data Semantic Methods for Data Processing and Searching Large Amounts of Data -- 16. Quantified Self, Wearable Technologies and Personal Data -- 17. “For the Benefit of the Patient” ... What Does the Patient Say to That? -- 18. Visualization – What Does Big Data Actually Look Like? -- 19. The Digital Patient? -- Publisher and Index of Authors -- Glossary -- Testimonials |
title_new |
Big Data in Medical Science and Healthcare Management : |
title_sort |
big data in medical science and healthcare management : diagnosis, therapy, side effects / |
publisher |
De Gruyter, |
publishDate |
2015 |
physical |
1 online resource (280 p.) |
contents |
Frontmatter -- Autopilot and “Doctor Algorithm”? -- Contents -- 1. Intro Big Data for Healthcare? -- 2. Information Management for Systems Medicine – on the Next Digital Threshold -- 3. Some Philosophical Thoughts on Big Data -- 4. Big Data from a Health Insurance Company’s Point of View -- 5. Big Data and the Family Doctor -- 6. How Value is Created from Data: Experiences from the Integrated Health Care System, “Gesundes Kinzigtal” (Healthy Kinzigtal) -- 7. Ethics -- 8. The New Data-Supported Quality Assurance of the Federal Joint Committee: Opportunities and Challenges -- 9. Big Data in Healthcare: Fields of Application and Benefits of SAP Technologies -- 10. Big Data – More Risks than Benefits for Healthcare? -- 11. Big Data – An Efficiency Boost in the Healthcare Sector -- 12. Medical Big Data and Data Protection -- 13. Big Data in Healthcare from a Business Consulting (Accenture) Point of View -- 14. Influence of Big Pharma on Medicine, Logistics and Data Technology in a State of Transition -- 15. Semantics and Big Data Semantic Methods for Data Processing and Searching Large Amounts of Data -- 16. Quantified Self, Wearable Technologies and Personal Data -- 17. “For the Benefit of the Patient” ... What Does the Patient Say to That? -- 18. Visualization – What Does Big Data Actually Look Like? -- 19. The Digital Patient? -- Publisher and Index of Authors -- Glossary -- Testimonials |
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9783110445749 9783110701005 9783110439687 9783110438772 9783110445282 |
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R - Medicine |
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R852 |
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R 3852 B5513 42016 |
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https://doi.org/10.1515/9783110445749 https://www.degruyter.com/isbn/9783110445749 https://www.degruyter.com/cover/covers/9783110445749.jpg |
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600 - Technology |
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610 - Medicine & health |
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610 - Medicine & health |
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610.72/4 |
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doi_str_mv |
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Intro Big Data for Healthcare? -- </subfield><subfield code="t">2. Information Management for Systems Medicine – on the Next Digital Threshold -- </subfield><subfield code="t">3. Some Philosophical Thoughts on Big Data -- </subfield><subfield code="t">4. Big Data from a Health Insurance Company’s Point of View -- </subfield><subfield code="t">5. Big Data and the Family Doctor -- </subfield><subfield code="t">6. How Value is Created from Data: Experiences from the Integrated Health Care System, “Gesundes Kinzigtal” (Healthy Kinzigtal) -- </subfield><subfield code="t">7. Ethics -- </subfield><subfield code="t">8. The New Data-Supported Quality Assurance of the Federal Joint Committee: Opportunities and Challenges -- </subfield><subfield code="t">9. Big Data in Healthcare: Fields of Application and Benefits of SAP Technologies -- </subfield><subfield code="t">10. Big Data – More Risks than Benefits for Healthcare? -- </subfield><subfield code="t">11. Big Data – An Efficiency Boost in the Healthcare Sector -- </subfield><subfield code="t">12. Medical Big Data and Data Protection -- </subfield><subfield code="t">13. Big Data in Healthcare from a Business Consulting (Accenture) Point of View -- </subfield><subfield code="t">14. Influence of Big Pharma on Medicine, Logistics and Data Technology in a State of Transition -- </subfield><subfield code="t">15. Semantics and Big Data Semantic Methods for Data Processing and Searching Large Amounts of Data -- </subfield><subfield code="t">16. Quantified Self, Wearable Technologies and Personal Data -- </subfield><subfield code="t">17. “For the Benefit of the Patient” ... What Does the Patient Say to That? -- </subfield><subfield code="t">18. Visualization – What Does Big Data Actually Look Like? -- </subfield><subfield code="t">19. The Digital Patient? -- </subfield><subfield code="t">Publisher and Index of Authors -- </subfield><subfield code="t">Glossary -- </subfield><subfield code="t">Testimonials</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="a">restricted access</subfield><subfield code="u">http://purl.org/coar/access_right/c_16ec</subfield><subfield code="f">online access with authorization</subfield><subfield code="2">star</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Big Data in medical science – what exactly is that? What are the potentials for healthcare management? Where is Big Data at the moment? Which risk factors need to be kept in mind? What is hype and what is real potential? This book provides an impression of the new possibilities of networked data analysis and "Big Data" – for and within medical science and healthcare management.Big Data is about the collection, storage, search, distribution, statistical analysis and visualization of large amounts of data. This is especially relevant in healthcare management, as the amount of digital information is growing exponentially. An amount of data corresponding to 12 million novels emerges during the time of a single hospital stay. These are dimensions that cannot be dealt with without IT technologies. What can we do with the data that are available today? What will be possible in the next few years? Do we want everything that is possible? Who protects the data from wrong usage? More importantly, who protects the data from NOT being used?Big Data is the "resource of the 21st century" and might change the world of medical science more than we understand, realize and want at the moment. The core competence of Big Data will be the complete and correct collection, evaluation and interpretation of data. This also makes it possible to estimate the frame conditions and possibilities of the automation of daily (medical) routine. Can Big Data in medical science help to better understand fundamental problems of health and illness, and draw consequences accordingly?Big Data also means the overcoming of sector borders in healthcare management. The specialty of Big Data analysis will be the new quality of the outcomes of the combination of data that were not related before. That is why the editor of the book gives a voice to 30 experts, working in a variety of fields, such as in hospitals, in health insurance or as medical practitioners. The authors show potentials, risks, concrete practical examples, future scenarios, and come up with possible answers for the field of information technology and data privacy.</subfield></datafield><datafield tag="538" ind1=" " ind2=" "><subfield code="a">Mode of access: Internet via World Wide Web.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">In English.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Description based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Big data.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Data mining.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Diagnosis</subfield><subfield code="x">Decision making</subfield><subfield code="x">Data processing.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Medicine</subfield><subfield code="x">Data processing.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Big Data.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Datenschutz.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Digital Health.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gesundheitsmanagemen.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Database 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