Advancements in health informatics for monitoring perioperative patient data
DOI:
https://doi.org/10.18203/2394-6040.ijcmph20241984Keywords:
Perioperative monitoring, Health informatics, EHR, Patient safetyAbstract
Advancements in health informatics are revolutionizing perioperative care by enhancing patient monitoring, improving clinical decision-making, and promoting patient safety. The integration of electronic health records (EHR) facilitates seamless information flow across all perioperative stages, ensuring up-to-date patient data is accessible to the entire healthcare team. This accessibility reduces errors, improves coordination, and enhances overall patient outcomes. EHR systems also support standardized documentation, reducing variability in record-keeping and ensuring comprehensive patient information is consistently captured. Real-time data analytics and predictive modeling are powerful tools in perioperative settings, enabling clinicians to anticipate and manage potential complications proactively. By analyzing vast datasets, predictive models identify patterns and risk factors associated with adverse outcomes, allowing for targeted interventions and personalized care plans. Real-time analytics provide continuous monitoring of patient status during surgery, offering immediate feedback and enabling prompt adjustments to ensure patient stability. Wearable devices and remote monitoring systems further augment real-time data analytics by continuously tracking physiological parameters throughout the surgical process and into the postoperative period. This continuous data stream allows for ongoing assessment of patient status and early detection of complications, enhancing patient safety and outcomes. Health informatics solutions, including automated alerts and decision support tools, minimize human error and ensure adherence to clinical guidelines. Automated reminders for critical actions, such as timely antibiotic administration, significantly reduce the incidence of surgical site infections. Decision support tools provide real-time, evidence-based recommendations at the point of care, supporting clinicians in making informed decisions and improving patient outcomes. Additionally, health informatics facilitates continuous quality improvement by enabling the collection and analysis of performance data, informing targeted interventions and process improvements. Overall, advancements in health informatics are transforming perioperative care, offering numerous benefits in terms of efficiency, accuracy, and patient safety, ultimately leading to better patient outcomes and higher quality of care. Continued innovation in this field will be essential for maintaining and improving the standards of perioperative care.
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References
Kim E-Y. Patient will see you now: The future of medicine is in your hands. Healthcare Informatics Res. 2015;21(4):321-3.
Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010;363(6):501-4.
Donaldson MS, Corrigan JM, Kohn LT. To err is human: building a safer health system. National Academy of Sciences; 2000.
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. 2014;33(7):1123-31.
Wright A, McCoy AB, Hickman TT, Hilaire DS, Borbolla D, Bowes III WA, et al. Problem list completeness in electronic health records: a multi-site study and assessment of success factors. Int J Med Informatics. 2015;84(10):784-90.
Lee CH, Yoon H-J. Medical big data: promise and challenges. Kidney Res Clin Practice. 2017;36(1):3.
Classen DC, Bates DW. Finding the meaning in meaningful use. In. Volume 365: Mass Medical Soc. 2011;855-8.
Miller RA, Waitman LR, Chen S, Rosenbloom ST. The anatomy of decision support during inpatient care provider order entry (CPOE): empirical observations from a decade of CPOE experience at Vanderbilt. J Biomed Informatics. 2005;38(6):469-85.
Kauffman YS, Schroeder AE, Witt DM. Patient specific factors influencing adherence to INR monitoring. Pharmacotherapy. 2015;35(8):740-7.
Kuperman GJ, Gibson RF. Computer physician order entry: benefits, costs, and issues. Ann Int Med. 2003;139(1):31-9.
Berner ES. Clinical decision support systems. Volume 233. Springer; 2007.
Keshtkar L, Rashwan W, Abo-Hamad W, Arisha A. A hybrid system dynamic, discrete event simulation and data envelopment analysis to investigate boarding patients in acute hospitals. Operations Res Health Care. 2020;26:100266.
Fihn SD, McDonell MB, Diehr P, Anderson SM, Bradley KA, Au DH, et al. Effects of sustained audit/feedback on self-reported health status of primary care patients. Am J Med. 2004;116(4):241-8.
Morris AH. Developing and implementing computerized protocols for standardization of clinical decisions. Ann Int Med. 2000;132(5):373-83.
Kuo M-H. Opportunities and challenges of cloud computing to improve health care services. J Med Internet Res. 2011;13(3):e1867.
Mangram AJ, Horan TC, Pearson ML, Silver LC, Jarvis WR, Committee HICPA. Guideline for prevention of surgical site infection, 1999. Infect Control Hospital Epidemiol. 1999;20(4):247-80.
Shekelle PG, Morton SC, Keeler EB. Costs and benefits of health information technology. Evidence Report/Technol Assessment. 2006(132):1-71.
Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293(10):1223-38.
Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc. 2003;10(6):523-30.