Klinik Karar Destek Sistemleri
Özet
Klinik Karar Destek Sistemleri (KKDS), sağlık sektöründe yönetim, planlama ve tedavi kararlarının verilmesinde hekimlere ve sağlık çalışanlarına yardımcı olan bilgisayar tabanlı yazılım araçlarıdır. Bu sistemler temel olarak bir veri depolama alanı, veri giriş-çıkış sistemi ve öneriler sunan bir tahmin motoru olmak üzere üç bileşenden meydana gelir. Sağlık profesyonellerinin iş yükünü azaltan KKDS; hasta sonuçlarının iyileştirilmesi, verimliliğin artırılması, maliyet tasarrufu ve tıbbi hataların minimuma indirilmesinde önemli avantajlar sunar. Uygulama alanları arasında e-reçete ve ilaç etkileşim kontrolleriyle hasta güvenliği, klinik ve maliyet yönetimi, tıbbi görüntü analizi (CT, MR, PET) ile e-Nabız ve Sağlık-Net gibi hastaya dönük ulusal sistemler yer almaktadır. Bununla birlikte, sistemlerin doğruluğu tamamen sağlanan verinin kalitesine, çeşitliliğine ve senaryo çokluğuna bağımlıdır. Yetersiz veri durumunda hatalı yönlendirmeler oluşabileceği gibi, yapay zekaya aşırı güven hekimlerin kendi klinik deneyimlerini göz ardı etmesine ve bilgi kaybına yol açabilir. Ayrıca veri güvenliği, hasta mahremiyeti ihlalleri ve sistemlerin insani empatiden yoksun olması ciddi riskler teşkil eder. Gelecekte makine öğrenmesi ve derin öğrenme modellerinin gelişimiyle KKDS'lerin tüm vücut hastalıklarını kapsayacak şekilde genişlemesi ve hasta bakım kalitesini daha da optimize etmesi öngörülmektedir.
Clinical Decision Support Systems (CDSS) are computer-based software tools that assist physicians and healthcare professionals in management, planning, and treatment decisions within the healthcare sector. These systems basically consist of three components: a data storage area, a data input-output system, and an inference engine that provides recommendations. Reducing the workload of healthcare professionals, CDSS offers significant advantages in improving patient outcomes, increasing efficiency, saving costs, and minimizing medical errors. Its application areas include patient safety through e-prescriptions and drug-drug interaction checks, clinical and cost management, medical image analysis (CT, MR, PET), and patient-facing national systems such as e-Nabız and Sağlık-Net. However, the accuracy of these systems depends entirely on the quality, diversity, and variety of scenarios of the provided data. In cases of insufficient data, misleading results may occur, and overreliance on artificial intelligence can lead physicians to ignore their own clinical experience, causing a loss of knowledge. Furthermore, data security, violations of patient privacy, and the systems' lack of human empathy pose serious risks. In the future, with the development of machine learning and deep learning models, CDSS is expected to expand to cover whole-body diseases and further optimize the quality of patient care.
Referanslar
I. Sim vd., “Clinical Decision Support Systems for the Practice of Evidence-based Medicine”, Journal of the American Medical Informatics Association, c. 8, sy 6, ss. 527-534, 2001.
R. T. Sutton, D. Pincock, D. C. Baumgart, D. C. Sadowski, R. N. Fedorak, ve K. I. Kroeker, “An overview of clinical decision support systems: benefits, risks, and strategies for success”, npj Digit. Med., c. 3, sy 1, s. 17, 2020.
K. Suzuki ve Y. Chen, Ed., Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging, c. 140. Cham: Springer International Publishing, 2018.
A. Persidis ve A. Persidis, “Medical Expert Systems: An Overview”, Journal of Management in Medicine, c. 5, sy 3, ss. 27-34, 1991.
M. A. Musen, Y. Shahar, ve E. H. Shortliffe, “Clinical Decision-Support Systems”, içinde Biomedical Informatics, E. H. Shortliffe ve J. J. Cimino, Ed. New York, NY: Springer New York, ss. 698-736. 2006.
M. Özata and Ş. Aslan, ‘Klinik karar destek sistemleri ve örnek uygulamalar’, Kocatepe Tıp Dergisi, vol. 5, no. 1, 2004.
E. S. Berner, Ed., Clinical decision support systems: theory and practice, 2nd ed. New York, NY: Springer, 2007.
J. Osheroff vd., Improving outcomes with clinical decision support: an implementer’s guide, Second edition. Boca Raton, FL: HIMSS Publishing, an imprint of Taylor and Francis, 2012.
R. C. Deo, “Machine Learning in Medicine”, Circulation, c. 132, sy 20, ss. 1920-1930,2015
Office for Civil Rights (OCR), ‘HITECH act enforcement interim final rule’, Web Sayfası, Erişim Tarihi: 12/12/2022, Web adresi:https://www.hhs.gov/hipaa/for-professionals/special-topics/HITECH-act-enforcement-interim-final-rule/index.html
P. Vonbach, A. Dubied, S. Krähenbühl, ve J. H. Beer, “Prevalence of drug–drug interactions at hospital entry and during hospital stay of patients in internal medicine”, European Journal of Internal Medicine, c. 19, sy 6, ss. 413-420, 2008.
P. J. Helmons, B. O. Suijkerbuijk, P. V. Nannan Panday, ve J. G. Kosterink, “Drug-drug interaction checking assisted by clinical decision support: a return on investment analysis”, Journal of the American Medical Informatics Association, c. 22, sy 4, ss. 764-772,2015.
V. Koutkias, J. Bouaud, ve Section Editors for the IMIA Yearbook Section on Decision Support, “Contributions from the 2017 Literature on Clinical Decision Support”, Yearb Med Inform, c. 27, sy 01, ss. 122-128,2018.
S. Phansalkar vd., “High-priority drug–drug interactions for use in electronic health records”, J Am Med Inform Assoc, c. 19, sy 5, ss. 735-743, 2012.
P. Cornu vd., “High-priority and low-priority drug–drug interactions in different international electronic health record systems: A comparative study”, International Journal of Medical Informatics, c. 111, ss. 165-171, 2018.
D. S. McEvoy vd., “Variation in high-priority drug-drug interaction alerts across institutions and electronic health records”, Journal of the American Medical Informatics Association, c. 24, sy 2, ss. 331-338, 2017.
I. Cho, J.-H. Lee, J. Choi, H. Hwang, ve D. W. Bates, “National Rules for Drug–Drug Interactions: Are They Appropriate for Tertiary Hospitals?”, J Korean Med Sci, c. 31, sy 12, s. 1887, 2016.
C. D. Mahoney, C. M. Berard-Collins, R. Coleman, J. F. Amaral, ve C. M. Cotter, “Effects of an integrated clinical information system on medication safety in a multi-hospital setting”, American Journal of Health-System Pharmacy, c. 64, sy 18, ss. 1969-1977,2007.
P. Peris-Lopez, A. Orfila, A. Mitrokotsa, ve J. C. A. van der Lubbe, “A comprehensive RFID solution to enhance inpatient medication safety”, International Journal of Medical Informatics, c. 80, sy 1, ss. 13-24,2011.
E. G. Poon vd., “Effect of Bar-Code Technology on the Safety of Medication Administration”, N Engl J Med, c. 362, sy 18, ss. 1698-1707, 2010.
W. van der Veen vd., “Association between workarounds and medication administration errors in bar-code-assisted medication administration in hospitals”, Journal of the American Medical Informatics Association, c. 25, sy 4, ss. 385-392, 2018
S. Eslami, N. F. de Keizer, D. A. Dongelmans, E. de Jonge, M. J. Schultz, ve A. Abu-Hanna, “Effects of two different levels of computerized decision support on blood glucose regulation in critically ill patients”, International Journal of Medical Informatics, c. 81, sy 1, ss. 53-60, 2012.
P. Jia, L. Zhang, J. Chen, P. Zhao, and M. Zhang, “The Effects of Clinical Decision Support Systems on Medication Safety: An Overview,” PLoS ONE, vol. 11, no. 12, p. e0167683, 2016.
“Open Learning Initiative: Welcome”. Web Sayfası, Erişim Tarihi: 10/12/2022, Web adresi: https://oli.cmu.edu/jcourse/webui/login/required.do?redirect=%2Fjcourse%2Fworkbook%2Factivity%2Fpage%3Fcontext%3De6f7c0b180020ca600c0f4e5957d6f8c&
C. A. Algaze, M. Wood, N. M. Pageler, P. J. Sharek, C. A. Longhurst, ve A. Y. Shin, “Use of a Checklist and Clinical Decision Support Tool Reduces Laboratory Use and Improves Cost”, Pediatrics, c. 137, sy 1, s. e20143019, 2016.
M. G. Pruszydlo, S. U. Walk-Fritz, T. Hoppe-Tichy, J. Kaltschmidt, ve W. E. Haefeli, “Development and evaluation of a computerised clinical decision support system for switching drugs at the interface between primary and tertiary care”, BMC Med Inform Decis Mak, c. 12, sy 1, s. 137, 2012.
C. M. Bell, A. Jalali, ve E. Mensah, “A Decision Support Tool for Using an ICD-10 Anatomographer to Address Admission Coding Inaccuracies: A Commentary”, OJPHI, c. 5, sy 2,2013.
M. M. Segal vd., “Experience with Integrating Diagnostic Decision Support Software with Electronic Health Records: Benefits versus Risks of Information Sharing”, eGEMs, c. 5, sy 1, s. 23, 2017.
R. Kunhimangalam, S. Ovallath, ve P. K. Joseph, “A Clinical Decision Support System with an Integrated EMR for Diagnosis of Peripheral Neuropathy”, J Med Syst, c. 38, sy 4, s. 38, 2014.
A. I. Martinez-Franco vd., “Diagnostic accuracy in Family Medicine residents using a clinical decision support system (DXplain): a randomized-controlled trial”, Diagnosis, c. 5, sy 2, ss. 71-76, 2018.
S. Razzaki vd., “A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis”, , arXiv preprint arXiv:1806.10698, 2018.
‘IBM researchers bring AI to radiology at RSNA 2016’, IBM Research Blog, Web Sayfası, Erişim Tarihi: 13/12/2022, Web adresi: https://www.ibm.com/blogs/research/2016/11/ai-radiology/
K. Suzuki ve Y. Chen, Ed., Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging, c. 140. Cham: Springer International Publishing, 2018.
‘IBM Watson Health’, IBM Watson Health, Web Sayfası, Erişim Tarihi: 09/12/2022, Web adresi:https://www.ibm.com/tr-tr/watson-health
V. Gulshan vd., “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs”, JAMA, c. 316, sy 22, s. 2402, 2016.
C. Ozdemir, M. A. Gedik, ve Y. Kaya, “Age Estimation from Left-Hand Radiographs with Deep Learning Methods”, TS, c. 38, sy 6, ss. 1565-1574, 2021.
Y. Kaya, Z. Yiner, M. Kaya, ve F. Kuncan, “A new approach to COVID-19 detection from x-ray images using angle transformation with GoogleNet and LSTM”, Meas. Sci. Technol., c. 33, sy 12, s. 124011, 2022.
‘(10.04.2018) Karar Sınırı (Eşik Değer), Kritik Değer (Panik Değer) ve Ölçüm Birimlerinin Harmonizasyonu Hakkında (Tüm SBYS Üreticilerine)’. Web Sayfası, Erişim Tarihi: 10/12/2022, Web adresi:https://kayittescil.saglik.gov.tr/TR-34401/10042018-karar-siniri-esik-deger-kritik-deger-panik-deger-ve-olcum-birimlerinin-harmonizasyonu-hakkinda-tum-sbys-ureticilerine.html
M. Ataş, C. Özdemi̇R, I. Ataş, B. Ak, ve E. Özeroğlu, “Biometric identification using panoramic dental radiographic images withfew-shot learning”, Turkish Journal of Electrical Engineering and Computer Sciences, c. 30, sy 3, ss. 1115-1126, 2022.
P. Spyridonos, D. Cavouras, P. Ravazoula, ve G. Nikiforidis, “A computer-based diagnostic and prognostic system for assessing urinary bladder tumour grade and predicting cancer recurrence”, Medical Informatics and the Internet in Medicine, c. 27, sy 2, ss. 111-122, 2002.
K. Kaplan, Y. Kaya, M. Kuncan, ve H. M. Ertunç, “Brain tumor classification using modified local binary patterns (LBP) feature extraction methods”, Medical Hypotheses, c. 139, s. 109696, 2020.
‘E-Nabız Hakkında’. Web Sayfası, Erişim Tarihi: 12/12/2022, Web adresi: https://enabiz.gov.tr/Yardim/Index
H. C. Ates vd., “End-to-end design of wearable sensors”, Nat Rev Mater, c. 7, sy 11, ss. 887-907,2022.
D. R. Seshadri vd., “Wearable sensors for monitoring the physiological and biochemical profile of the athlete”, npj Digit. Med., c. 2, sy 1, s. 72,2019.
‘Bugünden Yarına Giyilebilir Teknolojiler, Web Sayfası, Erişim Tarihi: 12/12/2022, Web adresi: https://www.megasan.com/images/thumbs/0172075_bugunden-yarina-giyilebilir-teknolojiler_800.jpeg
D. Dowding, N. Mitchell, R. Randell, R. Foster, V. Lattimer, ve C. Thompson, “Nurses’ use of computerised clinical decision support systems: a case site analysis”, Journal of Clinical Nursing, c. 18, sy 8, ss. 1159-1167, 2009.
‘Global clinical Decision Support Systems (CDSS) Market’. Web Sayfası, Erişim Tarihi: 1/12/2022, Web adresi: https://bisresearch.com/industry-report/global-clinical-decision-support-systems-market.html
J. Pestian, M. Spencer, P. Matykiewicz, K. Zhang, S. Vinks, ve T. Glauser, “Personalizing Drug Selection Using Advanced Clinical Decision Support”, Biomed Inform Insights, c. 2, s. BII.S2506, 2009.
D. Rossille, J.-F. Laurent, ve A. Burgun, “Modelling a decision-support system for oncology using rule-based and case-based reasoning methodologies”, International Journal of Medical Informatics, c. 74, sy 2-4, ss. 299-306, 2005.
J. Varghese, M. Kleine, S. I. Gessner, S. Sandmann, ve M. Dugas, “Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic review”, Journal of the American Medical Informatics Association, c. 25, sy 5, ss. 593-602, 2018.
Q. C. Van Est, J. Gerritsen, and L. Kool, Human rights in the robot age: Challenges arising from the use of robotics, artificial intelligence, and virtual and augmented reality. 2017.
H. M. Obeidat, H. S. Gheeshan, O. Y. Malkhawi, and I. A. Khalaf, ‘Computerized clinical decision support systems and their clinical application in health care delivery system’, Jordan Medical Journal, vol. 43, no. 4, pp. 267–273, 2009.
M. Khalifa and I. Zabani, ‘Improving utilization of clinical decision support systems by reducing alert fatigue: Strategies and recommendations’, Stud. Health Technol. Inform., vol. 226, pp. 51–54, 2016.
L. S.K., S. N. Mohanty, S. R. S., S. Krishnamoorthy, U. J., ve K. Shankar, “Online clinical decision support system using optimal deep neural networks”, Applied Soft Computing, c. 81, s. 105487, 2019.
A. Masood vd., “Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT”, IEEE J. Transl. Eng. Health Med., c. 8, ss. 1-13, 2020.
G. Medic vd., “Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review”, F1000Res, c. 8, s. 1728, 2019.
B. Vasey vd., “Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems: A Systematic Review”, JAMA Netw Open, c. 4, sy 3, s. e211276, 2021.
I. Atas, C. Ozdemir, M. Atas, ve Y. Dogan, “Forensic Dental Age Estimation Using Modified Deep Learning Neural Network”, arXiv preprint arXiv: 2208.09799,2022.
I. Ataş, “Human Gender Prediction Based on Deep Transfer Learning from Panoramic Dental Radiograph Images”, TS, c. 39, sy 5, ss. 1585-1595, 2022.
‘Sağlık.NET Hakkında’, Web Sayfası, Erişim Tarihi: 15/12/2022, Web adresi:https://esaglik.gov.tr/TR,6212/sagliknet-hakkinda.html
