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Büyük Verinin Sağlık Hizmetlerinde Kullanımında Epistemolojik ve Etik Sorunlar

Yıl 2019, Cilt: 2 Sayı: 2, 80 - 92, 06.09.2019
https://doi.org/10.26650/JARHS2019-616389

Öz

Büyük Veri (big data) dendiğinde geleneksel bilişim sistemlerinin kapasitesinin çok üstünde olan verilerin depolama ve analiz edilerek bilgi üretme süreçlerinin tümünü anlamak mümkündür. “Büyük” ifadesi verinin hacim büyüklüğünden ziyade 5 temel özelliğin büyüklüğünü ifade eder: Hacim, Hız, Çeşitlilik, Doğruluk, Değer. Büyük veri analizleri ile mevcut bilgi ve verilerin ilişkileri tespit edilebilmekte, yeni değişkenler arasındaki bağ hızla tespit edilebilmekte ve böylece gelecekle ilgili sağlık alanında kuvvetli tahminler yapılabilmektedir. Bu bağlamda şimdiye kadar görülmemiş büyüklükteki verinin halk sağlığından klinik bilimlere kadar birçok sağlık hizmeti alanında yeni kullanım ve uygulama imkanları oluşmaktadır. Yeni imkanların yanında tıbbi bilginin oluşmasında epistemolojik olarak önemli farklılıklar ortaya çıkmakta ve bunlar uygulamada bazı sorunları beraberinde getirmektedir. Ayrıca çok sayıda insanın oluşturduğu verilerin güvenli şekilde saklanması gerekmektedir. Burada ise tıbbın kadim etik ilkelerinden olan mahremiyet ve hekimin sır saklama mükellefiyeti ilkelerinin anlamı ve içeriği değişmektedir. Bu bağlamda makalemizde büyük verinin tarihi gelişimi, temel özellikleri ve önemli kavramları açıklanmıştır. Sağlık alanında mevcut ve muhtemel kullanım alanları tartışılarak bu alanlardaki bilgi üretimi ile ilgili epistemolojik sorunlar tespit edilerek eleştirel bir perspektiften ele alınmıştır. Yine bu meyanda ortaya çıkabilecek etik sorunlar ortaya konmuş ve analiz edilmiştir. Meta düzlemde ise bu uygulamaların insan anlayışımız, insan özgürlüğü ve sorumluluğu açısından ne anlama geldiği sorgulanmıştır.

Kaynakça

  • 1. Cox M., Ellsworth D. (1997); Application Controlled Demand Paging for Out-of-Core Visualization; Proceedings. Visualization ‘97 (Cat. No. 97CB36155).
  • 2. Mashey R. (1998); Big Data and the Next Wave of InfraStress Problems, Solutions, Opportunities; USENIX Annual Technical Conference, http://bit. ly/2Y0nwPZ (30.04.2019).
  • 3. Schwardmann U. (1993): Parallelization of a Multigrid Solver On The Ksr1; J-Supercomputer 10(3) S.4-12.
  • 4. World Economic Forum (2012); Big Data, Big Impact: New Possibilities for International Development, http://bit.ly/2vA6qMl (30.04.2019). 5. Gantz J.; Reinsel D. (2010); The Digital Universe Decade – Are You Ready?; IDC, http://bit.ly/2VzWj9b (30.04.2019).
  • 6. Rotella P. (2012); Is Data The New Oil? , Forbes, http://bit.ly/2V6Z6au (30.04.2019). 7. Merriam Webster Dictionary, “Datum” maddesi, https://www.merriam-webster.com/dictionary/ datum (30.04.2019).
  • 8. Numanoğlu N., Eynehan M.E. (2016): Türkiye’nin küresel rekabetçiliği için bir gereklilik olarak sanayi 4.0; Tüsiad, İstanbul.
  • 9. Bayrakçı S. (2015); Sosyal Bilimlerdeki Akademik Çalışmalarda Büyük Veri Kullanımı; Yüksek Lisans Tezi; Marmara Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul.
  • 10. Mauro A. , Greco M., Grimaldi M. (2014): What is Big Data? A Consensual Definition and a Review of Key Research Topics; 4th International Conference on Integrated Information, Madrid.
  • 11. Ffoulkes P. (2017): inside Bigdata Guide to Use of Big Data on an Industrial Scale Report; insideBIGDATA.
  • 12. Jacobson R. (2013): 2.5 quintillion bytes of data created every day. How does CPG & Retail manage it?, IBM Inc., https://ibm.co/2vqNHTF (30.04.2019).
  • 13. Reinsel D, Gantz J, Rydning J; Data Age 2025 The Evolution of Data to Life-Critical; IDC White Paper; 2017.
  • 14. Omnicore Agency (2019): Facebook by the Numbers: Stats, Demographics & Fun Facts, https:// www.omnicoreagency.com/facebook-statistics/ (30.04.2019).
  • 15. Smith C. (2013): Facebook Users Are Uploading 350 Million New Photos Each Day; Business Insider, http://www.businessinsider.com/facebook-350million-photos-each-day-2013-9?IR=T Erişim Tarihi: (30.04.2019).
  • 16. Soderbery R. (2013): How Many Things Are Currently Connected To The “Internet of Things” (IoT)?, Forbes, http://bit.ly/2GPfD91 (30.04.2019).
  • 17. Kitchin R.(2016): The ethics of smart cities and urban science. Philos Trans A Math Phys Eng Sci. 28;374(2083).
  • 18. IBM Inc. (2019): What is MapReduce?, https://www. ibm.com/analytics/hadoop/mapreduce (30.04.2019).
  • 19. D’Avolio L. (2016); Hype and Disappointment on the Road to Healthcare’s Promised Land; BigData and Healthcare Analytics Forum, http://bit.ly/2WenjrS (30.04.2019).
  • 20. Schönberger M., Cukier K. (2013); Büyük Veri - Yaşama, Çalışma ve Düşünme Şeklimizi Dönüştürecek Bir Devrim; Çev. Banu Erol; Paloma; İstanbul.
  • 21. Uçar A. (2018); IBM Watson ile Sağlığa Arttırılmış bir Bakış; SD Platform; 46: 6-11.
  • 22. Chen H.Y., Chuang C.H., Yang Y.J., Wu T.P. (2011); Exploring the risk factors of preterm birth using data mining; Expert Systems with Applications 38: 5384– 5387.
  • 23. Twitter Blog (2014): Twitter #DataGrants selections, https://blog.twitter.com/engineering/en_us/a/2014/ twitter-datagrants-selections.html (30.04.2019).
  • 24. Vitality Inc. (2019): Britain’s Healthiest Workplace (BHW) Privacy Policy, https://www.vitality.co.uk/ business/healthiest-workplace/faqs/ (30.04.2019).
  • 25. NIH: About the ‘All of Us’ Research Program, https:// allofus.nih.gov/about/about-all-us-research-program (30.04.2019).
  • 26. Makary A., Daniel M. (2016): Medical error— the third leading cause of death in the US; BMJ 3;353:i2139.
  • 27. Deutscher Ethikrat (2017): Big Data und Gesundheit – Datensouveränität als informationelle Freiheitsgestaltung, Berlin.
  • 28. Karakaş M. (2016): Büyük Veri, Endüstriyel İnternet ve Sağlık Alanındaki Uygulamaları, İstanbul: BETİM.
  • 29. Lipworth W., Mason P.H., Kerridge I., Ioannidis J.P.A. (2017): Ethics and Epistemology in Big Data Research. J Bioeth Inq. 14(4):489-500.
  • 30. Lipworth W., Mason P.H., Kerridge I. (2017): Ethics and Epistemology of Big Data. J Bioeth Inq. 14(4):485488.
  • 31. Hand D.J. (2018): Aspects of Data Ethics in a Changing World: Where Are We Now? ; Big Data; 6(3):176-190.
  • 32. Weichert T. (2014): Big Data, Gesundheit und der Datenschutz; Datenschutz und Datensicherheit, 38 (12), 831-838.
  • 33. Fisher C.B., Layman D.M. (2018): Genomics, Big Data, and Broad Consent: a New Ethics Frontier for Prevention Science; Prev Sci. 19(7):871-879.
  • 34. Polonetsky J.; Tene O. (2013): Privacy and Big data: Making Ends Meet; Stanford Law Review, 66(25).
  • 35. Spiegel Online (2013), Frauenarzt muss ins Gefängnis, http://bit.ly/2ULaDHj (30.04.2019). 3
  • 6. Equifax Inc. (2017); Equifax Announces Cybersecurity Incident Involving Consumer Information, https://investor.equifax.com/news-and-events/ news/2017/09-07-2017-213000628 (30.04.2019).
  • 37. Humer C., Finkle J. (2014): Your medical record is worth more to hackers than your credit card, Reuters News, https://reut.rs/2J3Z9g0 (30.04.2019).
  • 38. McNeal G.S. (2015): Health Insurer Anthem Struck By Massive Data Breach, Forbes, http://bit.ly/2VaKTtf (30.04.2019).
  • 39. Vodafone Institute for Society and Communication (2016); Big Data, A Europen Survey on the Opportunities and Risks of Data Analytics, http://bit. ly/2W9QRa1 (30.04.2019).
  • 40. Buchner, B. (2006): Informationelle Selbstbestimmung im Privatrecht. ; Tübingen.
  • 41. Chadwick R., Berg K. (2001):Solidarity and equity: new ethical frameworks for genetic databases. Nat Rev Genet., 2(4):318-321.
  • 42. Townend D. (2018): Conclusion: harmonisation in genomic and health data sharing for research: an impossible dream? Hum Genet. 137(8):657-664.
  • 43. Prainsack B.; Buyx A (2017): Solidarity in Biomedicine and Beyond. Cambridge.

Epistemological and Ethical Issues of Big Data Use in Healthcare

Yıl 2019, Cilt: 2 Sayı: 2, 80 - 92, 06.09.2019
https://doi.org/10.26650/JARHS2019-616389

Öz

The concept of “Big Data” covers all processes of storing and analyzing information that is beyond the capacity of traditional information systems. “Big” describes the magnitude of five major characteristics of data and not volume alone: Volume, Velocity, Variety, Veracity, Value. With the help of big data analysis, it is possible to identify the relationships between existing medical information and data, to determine the link between new variables and to make strong predictions about the future healthcare. In this context, new usage and application opportunities arise in various healthcare services ranging from public health to clinical sciences. In addition to new opportunities, big data analysis also presents new epistemological differences in the formation of medical knowledge, possibly engendering new controversies. Furthermore, the data generated by a large number of people need to be safely stored. The challenge of data security is set to alter the meaning and content of two well-established principles of medicine, “patient-privacy” and “physician-patient confidentiality”. In this context, we explain the historical development of big data, its basic features and important related concepts. The article further treats the current and potential uses of big data analysis in the field of health, and identifies and critically analyzes epistemological problems related to medical knowledge production. We also discuss the ethical problems that may arise. At the metalevel, we ask how the potential applications of big data analysis can impact our understanding of humanhood, human freedom and responsibility. 

Kaynakça

  • 1. Cox M., Ellsworth D. (1997); Application Controlled Demand Paging for Out-of-Core Visualization; Proceedings. Visualization ‘97 (Cat. No. 97CB36155).
  • 2. Mashey R. (1998); Big Data and the Next Wave of InfraStress Problems, Solutions, Opportunities; USENIX Annual Technical Conference, http://bit. ly/2Y0nwPZ (30.04.2019).
  • 3. Schwardmann U. (1993): Parallelization of a Multigrid Solver On The Ksr1; J-Supercomputer 10(3) S.4-12.
  • 4. World Economic Forum (2012); Big Data, Big Impact: New Possibilities for International Development, http://bit.ly/2vA6qMl (30.04.2019). 5. Gantz J.; Reinsel D. (2010); The Digital Universe Decade – Are You Ready?; IDC, http://bit.ly/2VzWj9b (30.04.2019).
  • 6. Rotella P. (2012); Is Data The New Oil? , Forbes, http://bit.ly/2V6Z6au (30.04.2019). 7. Merriam Webster Dictionary, “Datum” maddesi, https://www.merriam-webster.com/dictionary/ datum (30.04.2019).
  • 8. Numanoğlu N., Eynehan M.E. (2016): Türkiye’nin küresel rekabetçiliği için bir gereklilik olarak sanayi 4.0; Tüsiad, İstanbul.
  • 9. Bayrakçı S. (2015); Sosyal Bilimlerdeki Akademik Çalışmalarda Büyük Veri Kullanımı; Yüksek Lisans Tezi; Marmara Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul.
  • 10. Mauro A. , Greco M., Grimaldi M. (2014): What is Big Data? A Consensual Definition and a Review of Key Research Topics; 4th International Conference on Integrated Information, Madrid.
  • 11. Ffoulkes P. (2017): inside Bigdata Guide to Use of Big Data on an Industrial Scale Report; insideBIGDATA.
  • 12. Jacobson R. (2013): 2.5 quintillion bytes of data created every day. How does CPG & Retail manage it?, IBM Inc., https://ibm.co/2vqNHTF (30.04.2019).
  • 13. Reinsel D, Gantz J, Rydning J; Data Age 2025 The Evolution of Data to Life-Critical; IDC White Paper; 2017.
  • 14. Omnicore Agency (2019): Facebook by the Numbers: Stats, Demographics & Fun Facts, https:// www.omnicoreagency.com/facebook-statistics/ (30.04.2019).
  • 15. Smith C. (2013): Facebook Users Are Uploading 350 Million New Photos Each Day; Business Insider, http://www.businessinsider.com/facebook-350million-photos-each-day-2013-9?IR=T Erişim Tarihi: (30.04.2019).
  • 16. Soderbery R. (2013): How Many Things Are Currently Connected To The “Internet of Things” (IoT)?, Forbes, http://bit.ly/2GPfD91 (30.04.2019).
  • 17. Kitchin R.(2016): The ethics of smart cities and urban science. Philos Trans A Math Phys Eng Sci. 28;374(2083).
  • 18. IBM Inc. (2019): What is MapReduce?, https://www. ibm.com/analytics/hadoop/mapreduce (30.04.2019).
  • 19. D’Avolio L. (2016); Hype and Disappointment on the Road to Healthcare’s Promised Land; BigData and Healthcare Analytics Forum, http://bit.ly/2WenjrS (30.04.2019).
  • 20. Schönberger M., Cukier K. (2013); Büyük Veri - Yaşama, Çalışma ve Düşünme Şeklimizi Dönüştürecek Bir Devrim; Çev. Banu Erol; Paloma; İstanbul.
  • 21. Uçar A. (2018); IBM Watson ile Sağlığa Arttırılmış bir Bakış; SD Platform; 46: 6-11.
  • 22. Chen H.Y., Chuang C.H., Yang Y.J., Wu T.P. (2011); Exploring the risk factors of preterm birth using data mining; Expert Systems with Applications 38: 5384– 5387.
  • 23. Twitter Blog (2014): Twitter #DataGrants selections, https://blog.twitter.com/engineering/en_us/a/2014/ twitter-datagrants-selections.html (30.04.2019).
  • 24. Vitality Inc. (2019): Britain’s Healthiest Workplace (BHW) Privacy Policy, https://www.vitality.co.uk/ business/healthiest-workplace/faqs/ (30.04.2019).
  • 25. NIH: About the ‘All of Us’ Research Program, https:// allofus.nih.gov/about/about-all-us-research-program (30.04.2019).
  • 26. Makary A., Daniel M. (2016): Medical error— the third leading cause of death in the US; BMJ 3;353:i2139.
  • 27. Deutscher Ethikrat (2017): Big Data und Gesundheit – Datensouveränität als informationelle Freiheitsgestaltung, Berlin.
  • 28. Karakaş M. (2016): Büyük Veri, Endüstriyel İnternet ve Sağlık Alanındaki Uygulamaları, İstanbul: BETİM.
  • 29. Lipworth W., Mason P.H., Kerridge I., Ioannidis J.P.A. (2017): Ethics and Epistemology in Big Data Research. J Bioeth Inq. 14(4):489-500.
  • 30. Lipworth W., Mason P.H., Kerridge I. (2017): Ethics and Epistemology of Big Data. J Bioeth Inq. 14(4):485488.
  • 31. Hand D.J. (2018): Aspects of Data Ethics in a Changing World: Where Are We Now? ; Big Data; 6(3):176-190.
  • 32. Weichert T. (2014): Big Data, Gesundheit und der Datenschutz; Datenschutz und Datensicherheit, 38 (12), 831-838.
  • 33. Fisher C.B., Layman D.M. (2018): Genomics, Big Data, and Broad Consent: a New Ethics Frontier for Prevention Science; Prev Sci. 19(7):871-879.
  • 34. Polonetsky J.; Tene O. (2013): Privacy and Big data: Making Ends Meet; Stanford Law Review, 66(25).
  • 35. Spiegel Online (2013), Frauenarzt muss ins Gefängnis, http://bit.ly/2ULaDHj (30.04.2019). 3
  • 6. Equifax Inc. (2017); Equifax Announces Cybersecurity Incident Involving Consumer Information, https://investor.equifax.com/news-and-events/ news/2017/09-07-2017-213000628 (30.04.2019).
  • 37. Humer C., Finkle J. (2014): Your medical record is worth more to hackers than your credit card, Reuters News, https://reut.rs/2J3Z9g0 (30.04.2019).
  • 38. McNeal G.S. (2015): Health Insurer Anthem Struck By Massive Data Breach, Forbes, http://bit.ly/2VaKTtf (30.04.2019).
  • 39. Vodafone Institute for Society and Communication (2016); Big Data, A Europen Survey on the Opportunities and Risks of Data Analytics, http://bit. ly/2W9QRa1 (30.04.2019).
  • 40. Buchner, B. (2006): Informationelle Selbstbestimmung im Privatrecht. ; Tübingen.
  • 41. Chadwick R., Berg K. (2001):Solidarity and equity: new ethical frameworks for genetic databases. Nat Rev Genet., 2(4):318-321.
  • 42. Townend D. (2018): Conclusion: harmonisation in genomic and health data sharing for research: an impossible dream? Hum Genet. 137(8):657-664.
  • 43. Prainsack B.; Buyx A (2017): Solidarity in Biomedicine and Beyond. Cambridge.
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Eczacılık ve İlaç Bilimleri
Bölüm Derleme
Yazarlar

Abdullah Uçar 0000-0002-0220-3720

İlhan İlkılıç Bu kişi benim 0000-0002-4250-8676

Yayımlanma Tarihi 6 Eylül 2019
Gönderilme Tarihi 6 Mayıs 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 2 Sayı: 2

Kaynak Göster

MLA Uçar, Abdullah ve İlhan İlkılıç. “Büyük Verinin Sağlık Hizmetlerinde Kullanımında Epistemolojik Ve Etik Sorunlar”. Sağlık Bilimlerinde İleri Araştırmalar Dergisi, c. 2, sy. 2, 2019, ss. 80-92, doi:10.26650/JARHS2019-616389.