Cumhuri̇yet’i̇n 100. Yılında Bilgi ve Belge Yönetimi Teknolojisinde Güncel Yaklaşımlar

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DOI :10.26650/B/SS53.2024.015.16   IUP :10.26650/B/SS53.2024.015.16    Tam Metin (PDF)

Belge Yönetiminde İnsan Hatalarını Azaltmada Yapay Zekânın Rolü

Bahattin Yalçınkaya, Mehmet Oytun Cibaroğlu

Otomasyon ve sistem yönetimi mantığının yapay zekâ (YZ) araçları ile buluştuğu çağımızda, artık elektronik ortamda kendini bulan belge yönetimi süreçlerindeki insan kaynaklı potansiyel riskler, kurum ve kuruluşları risk ve hataların azaltılmasında YZ teknolojilerinin kullanımı hakkında daha çok tartışmaya yönlendiriyor. İnsan kaynaklı hataların, veri girişleri, dosyalama ve sınıflandırma gibi çok çeşitli süreçlerde ortaya çıkabileceği göz önüne alındığında, YZ teknolojilerinin belgelerin otomatik sınıflandırılması, dizinlenmesi, metin hatalarını düzeltmesi gibi süreçlerde kullanılabileceği rahatlıkla söylenebilir. İnsan kaynaklı hataların sistem yönetimini etkilediği bir diğer önemli konu ise kurumsal verimliliğin azalması ve kritik hukuki problemlere yol açabilme potansiyelidir. Bu tür problemlerin çözümünde YZ teknolojilerinin çok dikkatli bir şekilde kurumsal süreçlere entegre edilmesi gerekir. Karşılaşılan zorlukların üstesinden gelebilmek için sistematik yaklaşımla strateji ve planlar geliştirilmesi, kurum ve kuruluşların hayati öneme sahip fonksiyonlarını devam ettirmede son derece önemlidir. Bir diğer önemli konu ise kişisel verilerin gizliliği, güvenliği ve etik konular olarak ön plana çıkmaktadır. Hükümetler tarafından oluşturulan çeşitli düzenlemeler veri gizliliği ile etik konular hakkında yol gösterici olmakta; gerektiğinde kuruluşları zorlayıcı tedbirler almaya yöneltmektedir. Yakın gelecekte, daha karmaşık YZ algoritmaları ve modelleri belge yönetimi ve belge yönetiminin tetiklediği diğer ilişkili alanlarda daha iyi analiz yeteneğine sahip olacak ve günümüzdekinden çok daha ileri seviyede çözümler bulabilecektir. Gizlilik endişeleri, iş değiştirme ve insan gözetimi gerektiren süreçler de etik hususları ayrıca beraberinde getirmektedir. Bu çalışmanın amacı, her türlü belge yönetim sisteminde insan kaynaklı hataların azaltılıp sistemin doğruluğunu artırmak için gerekli olan teknoloji ve araçların nasıl ve nerelerde kullanıldığının belirtilmesi ile güncel çözümlerin neler olduğunu okuyucuya anlaşılır bir şekilde iletmektir. Çalışma konusu, alanda son dönemlerin güncel tartışma konularından olan bilgi sistemlerinde doğruluk ve iyileştirmelerin nasıl yapılabileceğinden hareketle seçilmiştir. Konu, çoğunlukla yurtdışı kaynaklı akademik yayınlar ile raporlardan elde edilen içeriklerin analiz edilmesi sonucunda okuyucuya yalın bir şekilde sunulmaktadır. Sonuçta belge yönetim sistemlerinin resmi prosedürlere uygun bir şekilde sürdürülebilmesi için kurum ve kuruluşların, YZ stratejisini kurumsal iş hedefleriyle uyumlu olacak şekilde ayarlaması, kurumu gelecekteki değişikliklere hazırlaması ve rekabet avantajı sağlamak için modern yönetim teorilerini güncel teknolojik araçlarla çevik olarak birleştirebilmesi gerekmektedir.


Anahtar Kelimeler: Belge Yönetimi, İnsan Hataları, Etik, Gizlilik, Mahremiyet, Yapay Zekâ
DOI :10.26650/B/SS53.2024.015.16   IUP :10.26650/B/SS53.2024.015.16    Tam Metin (PDF)

The Role of Artificial Intelligence on Reducing Human Errors in Records Management

Bahattin Yalçınkaya, Mehmet Oytun Cibaroğlu

In our era where automation and system management logic meet with Artificial Intelligence (AI) tools, the potential human-induced risks in records management processes, which are now found in electronic environment, lead institutions and organizations to more discussions about the use of AI technologies in reducing risks and errors. Considering that human-induced errors can occur in a wide variety of processes such as data entry, filing and classification, it can be easily said that AI technologies can be used in processes such as automatic classification of records, indexing, and correction of text errors. Another important issue that human-induced errors affect system management is the decrease in institutional efficiency and the potential to cause critical legal problems. In solving such problems, AI technologies must be carefully integrated into institutional processes. Developing strategies and plans with a systematic approach in order to overcome the difficulties encountered is extremely important in maintaining the vital functions of institutions and organizations. Another important issue is the privacy, security and ethical issues of personal data. Various regulations created by governments provide guidance on data privacy and ethical issues; directs organizations to take coercive measures when necessary. In the near future, more complex AI algorithms and models will have better analysis capability in records management and other related areas triggered by it, and will be able to find far more advanced solutions than today. Confidentiality concerns also bring ethical considerations in processes that require job change and human oversight. The aim of this study is to clearly convey to the reader what the current solutions are by specifying how and where the technology and tools required to reduce human-induced errors and increase the accuracy of the system in all kinds of records management systems. The subject of the study has been chosen based on how accuracy and improvements can be made in information systems, which is one of the current discussion topics in the field. It is presented to the reader in a simple way as a result of the analysis of the contents obtained from academic publications and reports mostly from foreign-based. As a result, in order for records management systems to be maintained in accordance with official procedures, institutions and organizations should adjust their AI strategy in line with corporate business objectives, prepare the institution for future changes and combine modern management theories with current technological tools agile in order to provide competitive advantage.


Anahtar Kelimeler: Records Management, Human Errors, Ethics, Confidentiality, Privacy, Artificial Intelligence

Referanslar

  • Adler-Milstein, J., Chen, J.H. ve Dhaliwal, G. (2021). Next-generation artificial ıntelligence for diagnosis. JAMA, 24(326), 2467-2468, doi: https://doi.org/10.1001/jama.2021.22396 google scholar
  • Akinseye, O.A., Shahreyar, M., Heckle, M.R. ve Khouzam, R.N. (2018). Simultaneous acute cardio-cerebral ınfarction: ıs there a consensus for management? Ann. Transl. Med., 1(6), 1-7, doi: https://doi.org/10.21037/ atm.2017.11.06 google scholar
  • Alothman, A.F. ve Sait, A.R.W. (2022). Managing and retrieving bilingual documents using artificial ıntelli-gence-based ontological framework. Computational Intelligence and Neuroscience, 1-15, doi: https://doi. org/10.1155/2022/4636931 google scholar
  • Aziz, S., Dowling, M. (2019). Machine learning and AI for risk management. Lynn, T., Mooney, J., Rosati, P., Cummins, M. (eds.). Disrupting Finance: Palgrave Studies in Digital Business and Enabling Technologies içinde (ss. 33-50). Palgrave Pivot, Cham, doi: https://doi.org/10.1007/978-3-030-02330-0_3 google scholar
  • Bahri, A.N.B. vd. (2022). Issues and challenges in record management: the adoption of ERMS. International Journal of Accounting, Finance and Business, 7(42), 201-219, doi: 10.55573/IJAFB.074228 google scholar
  • Baothman, F. (2021). Artificial ıntelligence effects on contracts and contracting. M. Shalan ve M. Algarni (Eds.), Innovative and Agile Contracting for Digital Transformation and Industry 4.0 içinde (ss. 149-160). IGI Global, doi: https://doi.org/10.4018/978-1-7998-4501-0.ch008 google scholar
  • Baviskar, D., Ahirrao, D.S., Potdar, V. ve Kotecha, K. (2021). Efficient automated processing of the unstructured documents using artificial ıntelligence: a systematic literature review and future directions. IEEE Access, 99, 1-46, doi: http://dx.doi.org/10.1109/ACCESS.2021.3072900 google scholar
  • Belov, I.I. (2022). Automation of electronic document management systems functions by means of artificial ıntelligence technologies. Herald of an Archivist, 3, 772-783, doi: 10.28995/2073-0101-2022-3-772-783 google scholar
  • Bergdahl, M. vd. (2007). Handbook on data quality assessment methods and tools. European Commission EuroStat. 01.07.2023 tarihinde https://unstats.un.org/unsd/dnss/docs-nqaf/Eurostat-HANDBOOK%20 ON%20DATA%20QUALITY%20ASSESSMENT%20METHODS%20AND%20TOOLS%20%20I.pdf adresinden erişildi. google scholar
  • Bernhardt, W. (2021). The use of artificial ıntelligence in the field of justice, ınternet and new technologies law - nomos-elibrary, 173-196, doi: https://doi.org/10.5771/9783748926979-173 google scholar
  • Brendel, A.B., Mirbabaie, M., Lembcke, T.B. ve Hofeditz, L. (2021). Ethical management of artificial ıntelli-gence Sustainability, 13(4), 1-18, doi: https://doi.org/10.3390/su13041974 google scholar
  • Coşkun, F. ve Gülleroğlu, H.D. (2021). Yapay zekânın tarih içindeki gelişimi ve eğitimde kullanılması. Ankara Üniversitesi Eğitim Bilimleri Fakültesi Dergisi, 54(3), 947-966, doi: 10.30964/auebfd.916220 google scholar
  • Cui, L., Xu, Y., Lv, T. ve Wei, F. (2021). Document AI: benchmarks, models and applications. arXiv, 1-23, doi: https://doi.org/10.48550/arxiv.2111.08609 google scholar
  • Ertürk, Z.K. ve Ertürk, B. (2021). Acil serviste yapay zekâ kullanımı. Aksaray Üniversitesi Tıp Bilimleri Dergisi, 2(2), 39-40. google scholar
  • European Commission. (2020). AI watch - defining artificial intelligence: towards an operational definition and taxonomy of artificial intelligence. JRC Technical Reports, doi: 10.2760/382730 google scholar
  • Fjeld, J., Achten, N., Hilligoss, H., Nagy, A. ve Srikumar, M. (2020). Principled artificial intelligence: mapping consensus in ethical and rights-based approaches to principles for AI. Berkman Klein Center Research Publication No. 2020-1, doi: http://dx.doi.org/10.2139/ssrn.3518482 google scholar
  • Fridman, L., Ding, L., Jenik, B. ve Reimer, B. (2018). Arguing machines: human supervision of black box AI systems that make life-critical decisions. arXiv, 1-11, doi: https://doi.org/10.48550/arXiv.1710.04459 google scholar
  • Godfrey, B., Clapp, T., Takeshi N. ve Chad, S. (2005). Application of healthcare-focused error proofing: princip-les and solution directions for reducing human errors. ASQ World Conference on Quality and Improvement Proceedings, 59, 335-340. google scholar
  • Hardin, J.M., Woodby, L.L., Crawford, M.A., Windsor, R.A. ve Miller, T.M. (2005). Data collection in a multisite project: teleform. Public Health Nursing, 4(22), 366-370, doi: https://doi.org/10.1111/j.0737-1209.2005.220410.x google scholar
  • Haston, E., Cubey, R. ve Harris, D.J. (2012). Data concepts and their relevance for data capture in large scale digitisation of biological collections. International Journal of Humanities and Arts Computing, 1-2(6), 111119, doi: https://doi.org/10.3366/ijhac.2012.0042 google scholar
  • Henttonen, P. (2015). Dimensions of contextual records management classifications. Knowledge Organization, 72(4), 477-485, doi: http://dx.doi.org/10.5771/0943-7444-2015-7-477 google scholar
  • Hupont, I., Micheli, M., Delipetrev, B., Gomez, E. ve Garrido, J.S. (2023). Documenting high-risk AI: a Euro-pean regulatory perspective, Computer, 56(5), 18-27, doi: 10.1109/MC.2023.3235712 google scholar
  • IEAE. (1990). Human error classification and data collection. Teknik Rapor, International Atomic Energy Agen-cy. 18.07.2023 tarihinde https://www-pub.iaea.org/MTCD/Publications/PDF/te_538_web.pdf adresinden erişildi. google scholar
  • Kanzi, N. (2010). An ınvestigation of the role of records management with specific reference to amathole district municipality. Yüksek Lisans Tezi. Nelson Mandela Metropolitan Üniversitesi, Güney Afrika. 15.07.2023 tarihinde https://core.ac.uk/download/pdf/145052807.pdf adresinden erişildi. google scholar
  • Krafft, P.M. (2021). An action-oriented AI policy toolkit for technology audits by community advocates and ac-tivists. FAccT’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, Mart 2021, 772-781, doi: https://doi.org/10.1145/3442188.3445938 google scholar
  • Lawton, P., Ingraham, J. ve Blickensderfer, B. (2021). Best Practices for Reducing Interface Errors in Electronic Medical Records. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 65(1), 904907. google scholar
  • Layer, L. vd. (2020). Automatic log analysis with NLP for the CMS workflow handling. EPJ Web Conference, 245, 1-7, doi: https://doi.org/10.1051/epjconf/202024503006 google scholar
  • Lindberg, A., Nickerson, J.V., Saltz, J. ve Seidel, S. (2021). AI, Organizing, and management. Proceedings of the 54th Hawaii International Conference on System Sciences, 5493-5494, doi: https://doi.org/10.24251/ hicss.2021.667 google scholar
  • Lore, F., Basile, P., Appice, A., Gemmis, M., Malerba ,D. ve Semeraro, G. (2023). An AI Framework to sup-port decisions on GDPR compliance. Journal of Intelligent Information Systems, 1-28, doi: https://doi. org/10.1007/s10844-023-00782-4 google scholar
  • Matheny, M.E., Whicher, D. ve Israni, S.T. (2019). Artificial ıntelligence in health care: a report from the national academy of medicine, JAMA, 323(6), 509-510, doi: 10.1001/jama.2019.21579 google scholar
  • McCarthy, K.P. ve Wellens, U. (1988). Measurements of 900 mhz propagation for microcellular communications systems design,” 8th European Conference on Electrotechnics, Conference Proceedings on Area Commu-nication, Stockholm, Sweden, 297-300, doi: 10.1109/EURCON.1988.11161 google scholar
  • Mitchel, J. vd. (2011). Evaluation of data entry errors and data changes to an electronic data capture clinical trial database. Drug Information Journal, 45(4), 421-430, doi: http://dx.doi.org/10.1177/009286151104500404 google scholar
  • Naranji, E., Sarkani, S., ve Mazzuchi, T. (2015). Reducing human/pilot errors in aviation using augmented cognition and automation systems in aircraft cockpit. AIS Transactions on Human-Computer Interaction, 7(2), 71-96. google scholar
  • Negash, S. (2004). Business intelligence. Communications of the Association for Information Systems, 13, 177195, doi: https://doi.org/10.17705/1CAIS.01315 google scholar
  • Nizam, V. ve Aslekar, A. (2021). Challenges of applying AI in healthcare in India. Journal of Pharmaceutical Research International, 33(36B), 203-209. doi: 10.9734/jpri/2021/v33i36B31969 google scholar
  • Odintsov, O., Ilchenko, N. ve Kryzhanivska, L. (2021). Introduction of electronic document technologies in public administration. Proceedings of Scientific Works of Cherkasy State Technological University Series Economic Sciences, 60, 108-116, doi: https://doi.org/10.24025/2306-4420.1.60.2021.229175 google scholar
  • Öztemiz, S. (2019). Türkiye Cumhuriyeti Sağlık Bakanlığı Elektronik Belge Yönetim Sisteminin (EBYS) kullanılabilirliği, Türk Kütüphaneciliği, 33(4), 282-295, doi: 10.24146/tk.576165 google scholar
  • Paulsen, A., Overgaard, S. ve Lauritsen, J.M. (2012). Quality of data entry using single entry, double entry and automated forms processing: an example based on a study of patient-reported outcomes. PLoS ONE, 4(7), 1-6, doi: https://doi.org/10.1371/journal.pone.0035087 google scholar
  • Pirim, A.G.H. (2006). Yapay zekâ. Yaşar Üniversitesi e-Dergisi, 1(1), 81-93. google scholar
  • Raj, A., Sharma, S., Singh, J. ve Singh, A. (2023). Revolutionizing data entry: An in-depth study of optical cha-racter recognition technology and its future potential. International Journal for Research in Applied Science & Engineering Technology, 11(2), 645-653, doi: https://doi.org/10.22214/ijraset.2023.49108 google scholar
  • Rasal, A., Kulkarni, A., Patle, A., Kalbhor, A., Kapare, S. ve Pagare, R. (2022). Office productivity enhancement using an online appointment manager app. International Journal for Research in Applied Science & Engi-neering Technology, 10(2), 864-869, doi: https://doi.org/10.22214/ijraset.2022.40403 google scholar
  • Rees, C. ve Müller, B. (2022). All that glitters is not gold: trustworthy and ethical AI principles. AI Ethics, 2022, 1-14, doi: https://doi.org/10.1007/s43681-022-00232-x google scholar
  • Russell, S., ve Norvig, P. (2020). Artificial intelligence: a modern approach (4th ed.). Boston: Pearson. google scholar
  • Sahu, A.H., Nimbalkar, P.S., Kamble, A.Y. ve Bagal, N.S. (2023). Integration of OCR and the ticket creation system with IBot. International Research Journal of Modernization in Engineering Technology and Science, 5(3), doi: https://www.doi.org/10.56726/IRJMETS34597 google scholar
  • Salmen, A. (2022). Employing RPA and AI to automize order entry process with ındividual and small-sized stru-ctures: a SME business case study. Acta Academica Karviniensia, 22(2), 78-96, doi: 10.25142/aak.2022.017 google scholar
  • Sambetbayeva, M., Kuspanova, I., Yerimbetova, A., Serikbayeva, S., ve Bauyrzhanova, S. (2022). Develop-ment of ıntelligent electronic document management system model based on machine learning methods. Eastern-European Journal of Enterprise Technologies, 1-2(115), 68-76, doi: https://doi.org/10.15587/1729-4061.2022.251689 google scholar
  • Schiff, D.S., Biddle, J., Borenstein, J. ve Laas, K. (2019). What’s next for AI ethics, policy, and governance? A Global Overview. SocArXiv, 1-6, doi: https://doi.org/10.1145/3375627.3375804 google scholar
  • Sherchan, W. vd. (2019). Assessing regulatory risk in personal financial advice documents: a pilot study. arXiv, 1-8, doi: https://doi.org/10.48550/arXiv.1910.12580 google scholar
  • Spring, S.F. vd. (2007). Automated documentation error detection and notification ımproves anesthesia billing performance. Anesthesiology, 106, 157-163, doi: https://doi.org/10.1097/00000542-200701000-00025 google scholar
  • Touray, R. (2021). A review of records management in organisations. Open Access Library Journal, 8, 1-23, doi: https://doi.org/10.4236/oalib.1108107 google scholar
  • Tsai, C.F. (2007). On classifying digital accounting documents. The International Journal of Digital Accounting Research, 7(13), 53-71, doi: https://doi.org/10.4192/1577-8517-v7_3 google scholar
  • Tsymbaliuk, S., Vasylyk, A. ve Bılyk, O. (2022). Artificial intelligence in HR: practices and prospects of the spread in Ukraine. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 17, 152160, doi: 10.55549/epstem.1176076 google scholar
  • Uhm, D., Ryu, J.B. ve Jun, S. (2020). Patent data analysis of artificial ıntelligence using bayesian ınterval esti-mation. Applied Sciences, 10(2), 1-12, doi: http://dx.doi.org/10.3390/app10020570 google scholar
  • WHO. (2015). Guidance on good data and record management practices. Working Document QAS/15.624, 1-35. 19.07.2023 tarihinde http://www.it-asso.com/gxp/files/Guidance-on-good-data-management-practi-ces_QAS15-624_16092015.pdf adresinden erişildi. google scholar
  • Wu, J. vd. (2014). citeSeerX: AI in a digital library search engine. Proceedings of the AAAI Conference on Artificial Intelligence, 28(2), 2930-2937, doi: https://doi.org/10.1609/aaai.v28i2.19019 google scholar
  • Xu, Z. vd. (2023). Artificial intelligence for anterior segment diseases: a review of potential developments and clinical applications. Ophthalmol Ther, 3(12), 1439-1455, doi: https://doi.org/10.1007/s40123-023-00690-4 google scholar
  • Yusuf, S.K. ve Adekoya, O.M. (2021). Trends in contemporary record management. Handbook of Research on Information and Records Management in the Fourth Industrial Revolution, 326-343, doi: 10.4018/978-1-7998-7740-0.ch021 google scholar
  • Zhutyaeva, S.A. ve Lysova, T.A. (2021). Trends in the development of electronic document management: prospects, problems, opportunities. Economics and Management, 12(27), 963-970, doi: https://doi. org/10.35854/1998-1627-2021-12-963-970 google scholar


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