Slawomirski L, Auraaen A, Klazinga N. The economics of patient safety. Paris: Organisation for Economic Co-Operation and Development; 2017.
Aydemir, A., & Koç, Z. (2023). Patient safety culture and attitudes among emergency care unit nurses in Türkiye. Eastern Mediterranean Health Journal, 29(3):195–204. https://doi.org/10.26719/emhj.23.026
Griffiths P, Dall’Ora C. Nurse staffing and patient safety in acute hospitals: Cassandra calls again? BMJ Quality and Safety. 2023;32(5):241–3. https://doi.org/10.1136/bmjqs-2022-015578.
Hodkinson A, Tyler N, Ashcroft DM, Keers RN, Khan K, Phipps D, Abuzour A, Bower P, Avery A, Campbell S, Panagioti M. Preventable medication harm across health care settings: a systematic review and meta-analysis. BMC Med. 2020;18(1):1–13. https://doi.org/10.1186/s12916-020-01774-9.
Newman-Toker DE, Nassery N, Schaffer AC, Yu-Moe CW, Clemens GD, Wang Z, Zhu Y, Saber Tehrani AS, Fanai M, Hassoon A, Siegal D. Burden of serious harms from diagnostic error in the USA. BMJ Qual Saf. 2023;33(2):109–20. https://doi.org/10.1136/bmjqs-2021-014130.
WHO. (2022). Medication Without Harm – Global Patient Safety Challenge.
Churpek MM, Adhikari R, Edelson DP. The value of vital sign trends for detecting clinical deterioration on the wards. Resuscitation. 2016;102:1–5. https://doi.org/10.1016/j.resuscitation.2016.02.005.
Astier A, Carlet J, Hoppe-Tichy T, Jacklin A, Jeanes A, McManus S, Pletz MW, Seifert H, Fitzpatrick R. What is the role of technology in improving patient safety? A French, German and UK healthcare professional perspective. Journal of Patient Safety and Risk Management. 2020;25(6):219–24.
Flott K, Maguire J, Phillips N. Digital safety: the next frontier for patient safety. Future Healthcare Journal. 2021;8(3):e598–601.
Schneider EC, Ridgely MS, Meeker D, Hunter LE, Khodyakov D, Rudin RS. Promoting patient safety through effective health information technology risk management. Rand Health Quarterly. 2014;4(3):7.
Chang Y-H, Lin Y-C, Huang F-W, Chen D-M, Chung Y-T, Chen W-K, Wang CCN. Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department. BMC Emerg Med. 2024;24(1):237. https://doi.org/10.1186/s12873-024-01152-1.
Choi A, Choi SY, Chung K, Chung HS, Song T, Choi B, Kim JH. Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department. Sci Rep. 2023;13(1):1–10. https://doi.org/10.1038/s41598-023-35617-3.
Porto BM, Fogliatto FS. Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review. BMC Emerg Med. 2024;24(1):219. https://doi.org/10.1186/s12873-024-01135-2.
Eloranta S, Boman M. Predictive models for clinical decision making: Deep dives in practical machine learning. J Intern Med. 2022;292(2):278–95. https://doi.org/10.1111/joim.13483.
Tyler, S., Olis, M., Aust, N., Patel, L., Simon, L., Triantafyllidis, C., Patel, V., Lee, D. W., Ginsberg, B., Ahmad, H., & Jacobs, R. J. (2024). Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review. Cureus, 16(5). https://doi.org/10.7759/cureus.59906
Da’Costa A, Teke J, Origbo JE, Osonuga A, Egbon E, Olawade DB. AI-driven triage in emergency departments: A review of benefits, challenges, and future directions. International Journal of Medical Informatics. 2025;197:105838. https://doi.org/10.1016/j.ijmedinf.2025.105838.
Böhm-Hustede, A. K., Lubasch, J. S., Hoogestraat, A. T., Buhr, E., & Wulff, A. (2025). Barriers and facilitators to the implementation and adoption of computerised clinical decision support systems: an umbrella review protocol. Systematic Reviews, 14(1). https://doi.org/10.1186/s13643-024-02745-4
Jones C, Thornton J, Wyatt JC. Artificial intelligence and clinical decision support: Clinicians’ perspectives on trust, trustworthiness, and liability. Med Law Rev. 2023;31(4):501–20. https://doi.org/10.1093/medlaw/fwad013.
Altmann-Richer, L. (2018). Using Predictive Analytics to Improve Health Care Demand Forecasting. November.
Niu S, Ma J, Yin Q, Wang Z, Bai L, Yang X. Modelling Patient Longitudinal Data for Clinical Decision Support: A Case Study on Emerging AI Healthcare Technologies. Inf Syst Front. 2024. https://doi.org/10.1007/s10796-024-10513-x.
Duwalage KI, Burkett E, White G, Wong A, Thompson MH. Forecasting daily counts of patient presentations in Australian emergency departments using statistical models with time-varying predictors. Emerg Med Australas. 2020;32(4):618–25. https://doi.org/10.1111/1742-6723.13481.
Fan B, Peng J, Guo H, Gu H, Xu K, Wu T. Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model Development and Performance Evaluation. JMIR Med Inform. 2022;10(7):e34504. https://doi.org/10.2196/34504.
Zhao X, Lai JW, Ho AFW, Liu N, Ong MEH, Cheong KH. Predicting hospital emergency department visits with deep learning approaches. Biocybernetics and Biomedical Engineering. 2022;42(3):1051–65. https://doi.org/10.1016/j.bbe.2022.07.008.
Guttmann A, Schull MJ, Vermeulen MJ, Stukel TA. Association between waiting times and short term mortality and hospital admission after departure from emergency department: Population based cohort study from Ontario, Canada. Bmj. 2011;342:7809. https://doi.org/10.1136/bmj.d2983.
Morley C, Unwin M, Peterson GM, Stankovich J, Kinsman L. Emergency department crowding: A systematic review of causes, consequences and solutions. PLoS ONE. 2018;13(8):e0203316. https://doi.org/10.1371/journal.pone.0203316.
Amankwah-Amoah J, Khan Z, Wood G, Knight G. COVID-19 and digitalization: The great acceleration. J Bus Res. 2021;136:602–11. https://doi.org/10.1016/j.jbusres.2021.08.011.
Scarlat C, Stănciulescu GD, Panduru DA. COVID-19 pandemic as accelerator: opportunity for digital acceleration. Journal of Internet and E-Business Studies. 2022;2022:1–14.
Osipov, V. S., & Skryl, T. V. (2021). Impact of digital technologies on the efficiency of healthcare delivery. In IoT in healthcare and ambient assisted living (pp. 243–261). Springer.
Alotaibi, Y. K., & Federico, F. (2017). The impact of health information technology on patient safety. Saudi Medical Journal, 38(12):1173–1180. https://doi.org/10.15537/smj.2017.12.20631
Adesina, A., Iyelolu, T., & Paul, P. (2024). Leveraging predictive analytics for strategic decision-making: Enhancing business performance through data-driven insights. World Journal of Advanced Research and Reviews, 22:1927–1934. https://doi.org/10.30574/wjarr.2024.22.3.1961
Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019;380(14):1347–58. https://doi.org/10.1056/nejmra1814259.
Bates DW, Evans RS, Murff H, Stetson PD, Pizziferri L, Hripcsak G. Detecting adverse events using information technology. Journal of the American Medical Informatics Association : JAMIA. 2003;10(2):115–28. https://doi.org/10.1197/jamia.m1074.
Giannini HM, Ginestra JC, Chivers C, Draugelis M, Hanish A, Schweickert WD, Fuchs BD, Meadows L, Lynch M, Donnelly PJ, Pavan K, Fishman NO, Hanson CW 3rd, Umscheid CA. A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice. Crit Care Med. 2019;47(11):1485–92. https://doi.org/10.1097/CCM.0000000000003891.
Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs (Project Hope). 2014;33(7):1123–31. https://doi.org/10.1377/hlthaff.2014.0041.
Choi A, Lee K, Hyun H, Kim KJ, Ahn B, Lee KH, Hahn S, Choi SY, Kim JH. A novel deep learning algorithm for real-time prediction of clinical deterioration in the emergency department for a multimodal clinical decision support system. Sci Rep. 2024;14(1):30116. https://doi.org/10.1038/s41598-024-80268-7.
Porto BM. Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review. BMC Emerg Med. 2024;24(1):219. https://doi.org/10.1186/s12873-024-01135-2.
Fernandes, M., Vieira, S. M., Leite, F., Palos, C., Finkelstein, S., & Sousa, J. M. C. (2020). Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review. Artificial Intelligence in Medicine, 102(November 2019), 101762. https://doi.org/10.1016/j.artmed.2019.101762
Chen, Z., Liang, N., Zhang, H., Li, H., Yang, Y., Zong, X., Chen, Y., Wang, Y., & Shi, N. (2023). Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart, 10(2). https://doi.org/10.1136/openhrt-2023-002432
Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. Npj Digital Medicine. 2020;3(1):1–10. https://doi.org/10.1038/s41746-020-0221-y.
Muhiyaddin R, Abd-Alrazaq AA, Househ M, Alam T, Shah Z. The impact of Clinical Decision Support Systems (CDSS) on physicians: A scoping review. Studies in Health Technology and Informatics. 2020;272:470–3. https://doi.org/10.3233/SHTI200597.
Syrowatka, A., Motala, A., Lawson, E., & Shekelle, P. (2023). Computerized Clinical Decision Support To Prevent Medication Errors and Adverse Drug Events: Rapid Review. Making Healthcare Safer IV: A Continuous Updating of Patient Safety Harms and Practices.
Zikos D, DeLellis N. CDSS-RM: a clinical decision support system reference model. BMC Med Res Methodol. 2018;18(1):137. https://doi.org/10.1186/s12874-018-0587-6.
Robertson J, Moxey AJ, Newby DA, Gillies MB, Williamson M, Pearson S-A. Electronic information and clinical decision support for prescribing: state of play in Australian general practice. Fam Pract. 2011;28(1):93–101. https://doi.org/10.1093/fampra/cmq031.
Singh H, Thomas EJ, Mani S, Sittig D, Arora H, Espadas D, Khan MM, Petersen LA. Timely follow-up of abnormal diagnostic imaging test results in an outpatient setting: are electronic medical records achieving their potential? Arch Intern Med. 2009;169(17):1578–86. https://doi.org/10.1001/archinternmed.2009.263.
Hailey, D., Roine, R., & Ohinmaa, A. (2002). Systematic review of evidence for the benefits of telemedicine. Journal of Telemedicine and Telecare, 8(1_suppl), 1–7. https://doi.org/10.1258/1357633021937604
Filip, R., Gheorghita Puscaselu, R., Anchidin-Norocel, L., Dimian, M., & Savage, W. K. (2022). Global Challenges to Public Health Care Systems during the COVID-19 Pandemic: A Review of Pandemic Measures and Problems. Journal of Personalized Medicine, 12(8). https://doi.org/10.3390/jpm12081295
Macnamara, B. N., Berber, I., Çavuşoğlu, M. C., Krupinski, E. A., Nallapareddy, N., Nelson, N. E., Smith, P. J., Wilson-Delfosse, A. L., & Ray, S. (2024). Does using artificial intelligence assistance accelerate skill decay and hinder skill development without performers’ awareness? Cognitive Research: Principles and Implications, 9(1). https://doi.org/10.1186/s41235-024-00572-8
Harada, T., Miyagami, T., Kunitomo, K., & Shimizu, T. (2021). Clinical decision support systems for diagnosis in primary care: A scoping review. International Journal of Environmental Research and Public Health, 18(16).
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