Sağlık Hizmetlerinde Dijital Dönüşüm
Özet
Sağlık hizmetlerinde dijital dönüşüm, teşhis ve tedavi süreçlerinde hız ve verimlilik sağlamak amacıyla dijital teknolojilerin entegrasyonunu ifade eder. Bu başlık altında birçok alt dal barındırır. Elektronik sağlık kayıtları, tele-tıp, mobil sağlık uygulamaları, giyilebilir teknoloji, büyük veri, nesnelerin interneti, yapay zeka ve robotik bu alt dallardan en sık karşılaşılanlarıdır. Sağlık hizmetlerinin dijitalleşmesi, hızlı nüfus artışına karşın uzmanların iş yükünü azaltmak ve hastaların kaliteli hizmet almalarını sağlamak için hayati önem taşımaktadır. Elektronik sağlık kayıtlarının dijitalleşmesi ile tıbbi geçmiş, ilaçlar, alerjiler, laboratuvar sonuçları ve tedavi planları gibi geniş bir hasta verisi spektrumunu içerir ve bu veriler, hasta bakımını önemli ölçüde geliştirmiştir. Tele tıp, hastaların artık uzun kuyruklarda beklemek zorunda kalmamasına ve elektronik dosyalar ile hasta bilgilerine daha rahat ve verimli bir şekilde erişebilmesine imkân sunar. Mobil sağlık uygulamaları sayesinde ve giyilebilir teknoloji sayesinde bireylerin sağlık verilerini takip edip, daha temkinli olmalarına olanak tanır. Araştırmacılar, eyleme geçirilebilir bilgi çıkarmak ve sağlık hizmetlerini iyileştirmek için büyük veri analitiğinden yararlanıyor. Nesnelerin interneti, sabit bir masaüstü ortamından etkileşimli bulut ortamlarına yetkilendirme yaklaşımını değiştirerek büyük bir veri ve hizmet akışıdır. Yapay zeka ve robotik sistemler, 3 boyutlu baskı, robotlar, nanoteknoloji vb. teknolojiler sağlık alanında dijitalleşmeye fırsat sunuyor. Kısacası, sağlık hizmetlerinde dijital dönüşüm, bireylerin sağlık hizmetlerine daha kolay ulaşmasını sağlamak amacıyla, sağlık çalışanlarının iş yükünü en alt düzeye indirgeyip, hastaya da en yüksek oranda verim sağlayacak şekilde yapılandırılmasıdır.
Referanslar
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Adeghe, E. P., Okolo, C. A., & Ojeyinka, O. T. (2024). A review of wearable technology in healthcare: Monitoring patient health and enhancing outcomes. Open Access Research Journal of Multidisciplinary Studies, 7(1), 142–148. https://doi.org/10.53022/OARJMS.2024.7.1.0019
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Ayaad, O., Alloubani, A., ALhajaa, E. A., Farhan, M., Abuseif, S., Al Hroub, A., & Akhu-Zaheya, L. (2019). The role of electronic medical records in improving the quality of health care services: Comparative study. International Journal of Medical Informatics, 127. https://doi.org/10.1016/j.ijmedinf.2019.04.014
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Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2021). Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sensors International, 2. https://doi.org/10.1016/J.SINTL.2021.100117
Haleem, A., Javaid, M., Singh, R. P., Suman, R., & Rab, S. (2021). Biosensors applications in medical field: A brief review. In Sensors International (Vol. 2). https://doi.org/10.1016/j.sintl.2021.100100
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Javaid, M., Babu, S., Rab, S., Vaishya, R., & Haleem, A. (2021). Tribological Review of Medical Implants Manufactured by Additive Manufacturing. Tribology and Sustainability, 379–395. https://doi.org/10.1201/9781003092162-24
Javaid, M., Haleem, A., Pratap Singh, R., & Suman, R. (2021). Industrial perspectives of 3D scanning: Features, roles and it’s analytical applications. Sensors International, 2. https://doi.org/10.1016/j.sintl.2021.100114
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Khan, M. A. (2020). An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier. IEEE Access, 8, 34717–34727. https://doi.org/10.1109/ACCESS.2020.2974687
kumar Bhatt, V. K., & Pal, V. K. (2019). An Intelligent System for Diagnosing Thyroid Disease in Pregnant Ladies through Artificial Neural Network. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3382654
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023). Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of Ambient Intelligence and Humanized Computing, 14(7), 8459. https://doi.org/10.1007/S12652-021-03612-Z
Kumar, Y., & Mahajan, M. (2020). Recent advancement of machine learning and deep learning in the field of healthcare system. In Computational Intelligence for Machine Learning and Healthcare Informatics. https://doi.org/10.1515/9783110648195-005
Kwiatkowska, E. M., & Skórzewska-Amberg, M. (2019). Journal of Management and Business Administration Central Europe Vol. 27, No. 2/2019. Central Europe, 27(2), 48–63. https://doi.org/10.7206/jmba.ce.2450-7814.252
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Mahmood, A., Kedia, S., Wyant, D. K., Ahn, S. N., & Bhuyan, S. S. (2019). Use of mobile health applications for health-promoting behavior among individuals with chronic medical conditions. Digital Health, 5. https://doi.org/10.1177/2055207619882181
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Melton, G. B., McDonald, C. J., Tang, P. C., & Hripcsak, G. (2021). Electronic health records. Biomedical Informatics: Computer Applications in Health Care and Biomedicine: Fifth Edition, 467–509. https://doi.org/10.1007/978-3-030-58721-5_14
Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., & Jamalipour Soufi, G. (2020). Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Medical Image Analysis, 65, 101794. https://doi.org/10.1016/J.MEDIA.2020.101794
Mohd Aman, A. H., Hassan, W. H., Sameen, S., Attarbashi, Z. S., Alizadeh, M., & Latiff, L. A. (2021). IoMT amid COVID-19 pandemic: Application, architecture, technology, and security. In Journal of Network and Computer Applications (Vol. 174). https://doi.org/10.1016/j.jnca.2020.102886
Niazkhani, Z., Toni, E., Cheshmekaboodi, M., Georgiou, A., & Pirnejad, H. (2020). Barriers to patient, provider, and caregiver adoption and use of electronic personal health records in chronic care: a systematic review. BMC Medical Informatics and Decision Making 2020 20:1, 20(1), 1–36. https://doi.org/10.1186/S12911-020-01159-1
Purohit, B., Vernekar, P. R., Shetti, N. P., & Chandra, P. (2020). Biosensor nanoengineering: Design, operation, and implementation for biomolecular analysis. In Sensors International (Vol. 1). https://doi.org/10.1016/j.sintl.2020.100040
Qudah, B., & Luetsch, K. (2019). The influence of mobile health applications on patient - healthcare provider relationships: A systematic, narrative review. In Patient Education and Counseling (Vol. 102, Issue 6). https://doi.org/10.1016/j.pec.2019.01.021
Rudin, R. S., Friedberg, M. W., Shekelle, P., Shah, N., & Bates, D. W. (2020). Getting value from electronic health records: Research needed to improve practice. Annals of Internal Medicine, 172(11), S130–S136. https://doi.org/10.7326/M19-0878/ASSET/IMAGES/M190878TT2_TABLE_2_RECOMMENDATIONS_FOR_RESEARCH_APPROACHES_TO_MAXIMIZE_THE_VALUE_OF_EHRS.JPG
Singh, S., Bhatt, P., Sharma, S. K., & Rabiu, S. (2021). Digital Transformation in Healthcare: Innovation and Technologies. In Blockchain for Healthcare Systems: Challenges, Privacy, and Securing of Data. https://doi.org/10.1201/9781003141471-5
Szinay, D., Jones, A., Chadborn, T., Brown, J., & Naughton, F. (2020). Influences on the uptake of and engagement with health and well-being smartphone apps: Systematic review. In Journal of Medical Internet Research (Vol. 22, Issue 5). https://doi.org/10.2196/17572
Tapuria, A., Porat, T., Kalra, D., Dsouza, G., Xiaohui, S., & Curcin, V. (2021). Impact of patient access to their electronic health record: systematic review. Informatics for Health & Social Care, 46(2), 192–204. https://doi.org/10.1080/17538157.2021.1879810
Tavera Romero, C. A., Ortiz, J. H., Khalaf, O. I., & Prado, A. R. (2021). Business intelligence: business evolution after industry 4.0. In Sustainability (Switzerland) (Vol. 13, Issue 18). https://doi.org/10.3390/su131810026
The Impact of Robotics in Healthcare Surgery: A Revolutionizing Paradigm. (n.d.).
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Referanslar
Abernethy, A., Adams, L., Barrett, M., Bechtel, C., Brennan, P., Butte, A., Faulkner, J., Fontaine, E., Friedhoff, S., Halamka, J., Howell, M., Johnson, K., Long, P., McGraw, D., Miller, R., Lee, P., Perlin, J., Rucker, D., Sandy, L., … Valdes, K. (2022). The Promise of Digital Health: Then, Now, and the Future. NAM Perspectives, 6(22). https://doi.org/10.31478/202206E
Adane, K., Gizachew, M., & Kendie, S. (2019). The role of medical data in efficient patient care delivery: A review. In Risk Management and Healthcare Policy (Vol. 12). https://doi.org/10.2147/RMHP.S179259
Adeghe, E. P., Okolo, C. A., & Ojeyinka, O. T. (2024). A review of wearable technology in healthcare: Monitoring patient health and enhancing outcomes. Open Access Research Journal of Multidisciplinary Studies, 7(1), 142–148. https://doi.org/10.53022/OARJMS.2024.7.1.0019
Adekunle Oyeyemi Adeniyi, Jeremiah Olawumi Arowoogun, Rawlings Chidi, Chioma Anthonia Okolo, & Oloruntoba Babawarun. (2024). The impact of electronic health records on patient care and outcomes: A comprehensive review. World Journal of Advanced Research and Reviews, 21(2). https://doi.org/10.30574/wjarr.2024.21.2.0592
Ayaad, O., Alloubani, A., ALhajaa, E. A., Farhan, M., Abuseif, S., Al Hroub, A., & Akhu-Zaheya, L. (2019). The role of electronic medical records in improving the quality of health care services: Comparative study. International Journal of Medical Informatics, 127. https://doi.org/10.1016/j.ijmedinf.2019.04.014
Benson, T., & Grieve, G. (2021). Why Interoperability Is Hard. https://doi.org/10.1007/978-3-030-56883-2_2
Chandra, P. (2020). Miniaturized label-free smartphone assisted electrochemical sensing approach for personalized COVID-19 diagnosis. Sensors International, 1. https://doi.org/10.1016/j.sintl.2020.100019
Chatterjee, S., Das, S., Ganguly, K., & Mandal, D. (2024). Advancements in robotic surgery: innovations, challenges and future prospects. In Journal of Robotic Surgery (Vol. 18, Issue 1). https://doi.org/10.1007/s11701-023-01801-w
Chioma Anthonia Okolo, Oloruntoba Babawarun, Jeremiah Olawumi Arowoogun, Adekunle Oyeyemi Adeniyi, & Rawlings Chidi. (2024). The role of mobile health applications in improving patient engagement and health outcomes: A critical review. International Journal of Science and Research Archive, 11(1). https://doi.org/10.30574/ijsra.2024.11.1.0334
Chouliaras, N., Kittes, G., Kantzavelou, I., Maglaras, L., Pantziou, G., & Ferrag, M. A. (2021). Cyber ranges and testbeds for education, training, and research. Applied Sciences (Switzerland), 11(4). https://doi.org/10.3390/app11041809
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0217-0
Debon, R., Coleone, J. D., Bellei, E. A., & De Marchi, A. C. B. (2019). Mobile health applications for chronic diseases: A systematic review of features for lifestyle improvement. In Diabetes and Metabolic Syndrome: Clinical Research and Reviews (Vol. 13, Issue 4). https://doi.org/10.1016/j.dsx.2019.07.016
Gouda, W., & Yasin, R. (2020). COVID-19 disease: CT Pneumonia Analysis prototype by using artificial intelligence, predicting the disease severity. Egyptian Journal of Radiology and Nuclear Medicine, 51(1), 1–11. https://doi.org/10.1186/S43055-020-00309-9/FIGURES/7
Grundy, Q. (2022). A Review of the Quality and Impact of Mobile Health Apps. In Annual Review of Public Health (Vol. 43). https://doi.org/10.1146/annurev-publhealth-052020-103738
Gupta, P., Haleem, A., & Javaid, M. (2019). Designing of a carburettor body for ethanol blended fuel by using CFD analysis tool and 3D scanning technology. Journal of Scientific and Industrial Research, 78(7).
Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2021). Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sensors International, 2. https://doi.org/10.1016/J.SINTL.2021.100117
Haleem, A., Javaid, M., Singh, R. P., Suman, R., & Rab, S. (2021). Biosensors applications in medical field: A brief review. In Sensors International (Vol. 2). https://doi.org/10.1016/j.sintl.2021.100100
Iribarren, S. J., Akande, T. O., Kamp, K. J., Barry, D., Kader, Y. G., & Suelzer, E. (2021). Effectiveness of mobile apps to promote health and manage disease: Systematic review and meta-analysis of randomized controlled trials. In JMIR mHealth and uHealth (Vol. 9, Issue 1). https://doi.org/10.2196/21563
Ishfaq, R., & Raja, U. (2015). Bridging the Healthcare Access Divide: A Strategic Planning Model for Rural Telemedicine Network. Decision Sciences, 46(4), 755–790. https://doi.org/10.1111/DECI.12165
Javaid, M., Babu, S., Rab, S., Vaishya, R., & Haleem, A. (2021). Tribological Review of Medical Implants Manufactured by Additive Manufacturing. Tribology and Sustainability, 379–395. https://doi.org/10.1201/9781003092162-24
Javaid, M., Haleem, A., Pratap Singh, R., & Suman, R. (2021). Industrial perspectives of 3D scanning: Features, roles and it’s analytical applications. Sensors International, 2. https://doi.org/10.1016/j.sintl.2021.100114
Kerleau, M., & Pelletier-Fleury, N. (2002). Restructuring of the healthcare system and the diffusion of telemedicine. The European Journal of Health Economics : HEPAC : Health Economics in Prevention and Care, 3(3), 207–214. https://doi.org/10.1007/S10198-002-0131-8
Khan, M. A. (2020). An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier. IEEE Access, 8, 34717–34727. https://doi.org/10.1109/ACCESS.2020.2974687
kumar Bhatt, V. K., & Pal, V. K. (2019). An Intelligent System for Diagnosing Thyroid Disease in Pregnant Ladies through Artificial Neural Network. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3382654
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023). Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of Ambient Intelligence and Humanized Computing, 14(7), 8459. https://doi.org/10.1007/S12652-021-03612-Z
Kumar, Y., & Mahajan, M. (2020). Recent advancement of machine learning and deep learning in the field of healthcare system. In Computational Intelligence for Machine Learning and Healthcare Informatics. https://doi.org/10.1515/9783110648195-005
Kwiatkowska, E. M., & Skórzewska-Amberg, M. (2019). Journal of Management and Business Administration Central Europe Vol. 27, No. 2/2019. Central Europe, 27(2), 48–63. https://doi.org/10.7206/jmba.ce.2450-7814.252
Larsen, S. B., Sørensen, N. S., Petersen, M. G., & Kjeldsen, G. F. (2016). Towards a shared service centre for telemedicine: Telemedicine in Denmark, and a possible way forward. Health Informatics Journal, 22(4), 815–827. https://doi.org/10.1177/1460458215592042
Mahmood, A., Kedia, S., Wyant, D. K., Ahn, S. N., & Bhuyan, S. S. (2019). Use of mobile health applications for health-promoting behavior among individuals with chronic medical conditions. Digital Health, 5. https://doi.org/10.1177/2055207619882181
Mehta, A., Cheng Ng, J., Andrew Awuah, W., Huang, H., Kalmanovich, J., Agrawal, A., Abdul-Rahman, T., Hasan, M. M., Sikora, V., & Isik, A. (2022). Embracing robotic surgery in low- and middle-income countries: Potential benefits, challenges, and scope in the future. Annals of Medicine and Surgery (2012), 84. https://doi.org/10.1016/J.AMSU.2022.104803
Melton, G. B., McDonald, C. J., Tang, P. C., & Hripcsak, G. (2021). Electronic health records. Biomedical Informatics: Computer Applications in Health Care and Biomedicine: Fifth Edition, 467–509. https://doi.org/10.1007/978-3-030-58721-5_14
Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., & Jamalipour Soufi, G. (2020). Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Medical Image Analysis, 65, 101794. https://doi.org/10.1016/J.MEDIA.2020.101794
Mohd Aman, A. H., Hassan, W. H., Sameen, S., Attarbashi, Z. S., Alizadeh, M., & Latiff, L. A. (2021). IoMT amid COVID-19 pandemic: Application, architecture, technology, and security. In Journal of Network and Computer Applications (Vol. 174). https://doi.org/10.1016/j.jnca.2020.102886
Niazkhani, Z., Toni, E., Cheshmekaboodi, M., Georgiou, A., & Pirnejad, H. (2020). Barriers to patient, provider, and caregiver adoption and use of electronic personal health records in chronic care: a systematic review. BMC Medical Informatics and Decision Making 2020 20:1, 20(1), 1–36. https://doi.org/10.1186/S12911-020-01159-1
Purohit, B., Vernekar, P. R., Shetti, N. P., & Chandra, P. (2020). Biosensor nanoengineering: Design, operation, and implementation for biomolecular analysis. In Sensors International (Vol. 1). https://doi.org/10.1016/j.sintl.2020.100040
Qudah, B., & Luetsch, K. (2019). The influence of mobile health applications on patient - healthcare provider relationships: A systematic, narrative review. In Patient Education and Counseling (Vol. 102, Issue 6). https://doi.org/10.1016/j.pec.2019.01.021
Rudin, R. S., Friedberg, M. W., Shekelle, P., Shah, N., & Bates, D. W. (2020). Getting value from electronic health records: Research needed to improve practice. Annals of Internal Medicine, 172(11), S130–S136. https://doi.org/10.7326/M19-0878/ASSET/IMAGES/M190878TT2_TABLE_2_RECOMMENDATIONS_FOR_RESEARCH_APPROACHES_TO_MAXIMIZE_THE_VALUE_OF_EHRS.JPG
Singh, S., Bhatt, P., Sharma, S. K., & Rabiu, S. (2021). Digital Transformation in Healthcare: Innovation and Technologies. In Blockchain for Healthcare Systems: Challenges, Privacy, and Securing of Data. https://doi.org/10.1201/9781003141471-5
Szinay, D., Jones, A., Chadborn, T., Brown, J., & Naughton, F. (2020). Influences on the uptake of and engagement with health and well-being smartphone apps: Systematic review. In Journal of Medical Internet Research (Vol. 22, Issue 5). https://doi.org/10.2196/17572
Tapuria, A., Porat, T., Kalra, D., Dsouza, G., Xiaohui, S., & Curcin, V. (2021). Impact of patient access to their electronic health record: systematic review. Informatics for Health & Social Care, 46(2), 192–204. https://doi.org/10.1080/17538157.2021.1879810
Tavera Romero, C. A., Ortiz, J. H., Khalaf, O. I., & Prado, A. R. (2021). Business intelligence: business evolution after industry 4.0. In Sustainability (Switzerland) (Vol. 13, Issue 18). https://doi.org/10.3390/su131810026
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