Using the Electronic Health Record and Artificial Intelligence to Guide Clinical Decision-Making

Abstract

Electronic medical records (EMR) have transformed how clinical information is documented, shared, and utilized over the past 60 years, and the addition of artificial intelligence (AI) has vastly broadened EMR's capabilities. Targeted comprehensive prehabilitation services can be provided to frail patients that mitigate mortality risks. Tools for early detection of sepsis and deterioration are now embedded within the EMR, with predictive models improving their accuracy compared with traditional methodology. The EMR enables protocol-driven care and templated documentation to streamline workflow and improve collaboration amongst providers. Anytime online access, referring to the EMR, enables real-time quality improvement efforts while encouraging patient engagement in their health. Large language models are being utilized to transcribe interactions with patients directly into the EMR, thereby increasing documentation efficiency and enabling more meaningful patient-provider interactions. Advanced data models are empowering patients with safer and more efficient triage of care. These advances are not without drawbacks, including data scarcity in identifying rare complications or pathologies, and a lack of transparency in algorithm development. Nonetheless, the potential of EMR and AI is expansive and will continue to develop and enhance the safety, quality, and efficiency of care while enhancing shared decision-making and provider-patient relationships.

Keywords

EMRs - AI - decision-making

Publication History

Article published online:
31 December 2025

• References

• 1 Gillum RF. From papyrus to the electronic tablet: a brief history of the clinical medical record with lessons for the digital age. Am J Med 2013; 126 (10) 853-857
• 2 Reinus JF. The electronic medical record, Lawrence Weed, and the quality of clinical documentation. Am J Med 2021; 134 (03) e143-e144
• 3 Burde H. Health law the HITECH act-an overview. Virtual Mentor 2011; 13 (03) 172-175
• 4 Alkabbani H, Ahmadian A, Zhu Q, Elkamel A. Machine learning and metaheuristic methods for renewable power forecasting: a recent review. Front Chem Eng 2021 3.
• 5 AI vs. Machine Learning vs. Deep Learning vs. neural networks. IBM. April 17, 2025. Accessed April 24, 2025 at: https://www.ibm.com/think/topics/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks
• 6 Esteva A, Robicquet A, Ramsundar B. et al. A guide to deep learning in healthcare. Nat Med 2019; 25 (01) 24-29
• 7 Singla A, Sukharevsky A, Yee L, Chui M, Hall B. The state of AI: How Organizations are rewiring to capture value. McKinsey & Company. March 12, 2025. Accessed April 1, 2025 at: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
• 8 Zain Z, Almadhoun MKIK, Alsadoun L, Bokhari SFH. Leveraging artificial intelligence and machine learning to optimize enhanced recovery after surgery (ERAS) protocols. Cureus 2024; 16 (03) e56668
• 9 Hii TB, Lainchbury JG, Bridgman PG. Frailty in acute cardiology: comparison of a quick clinical assessment against a validated frailty assessment tool. Heart Lung Circ 2015; 24 (06) 551-556
• 10 Hurria A, Togawa K, Mohile SG. et al. Predicting chemotherapy toxicity in older adults with cancer: a prospective multicenter study. J Clin Oncol 2011; 29 (25) 3457-3465
• 11 Shinall Jr MC, Arya S, Youk A. et al. Association of preoperative patient frailty and operative stress with postoperative mortality. JAMA Surg 2020; 155 (01) e194620
• 12 Varley PR, Buchanan D, Bilderback A. et al. Association of routine preoperative frailty assessment with 1-year postoperative mortality. JAMA Surg 2023; 158 (05) 475-483
• 13 Hall DE, Arya S, Schmid KK. et al. Development and initial validation of the risk analysis index for measuring frailty in surgical populations. JAMA Surg 2017; 152 (02) 175-182
• 14 West MA, Loughney L, Lythgoe D. et al. Effect of prehabilitation on objectively measured physical fitness after neoadjuvant treatment in preoperative rectal cancer patients: a blinded interventional pilot study. Br J Anaesth 2015; 114 (02) 244-251
• 15 Allison II R, Assadzandi S, Adelman M. Frailty: evaluation and management. Am Fam Physician 2021; 103 (04) 219-226
• 16 Varley PR, Borrebach JD, Arya S. et al. Clinical utility of the risk analysis index as a prospective frailty screening tool within a multi-practice, multi-hospital integrated healthcare system. Ann Surg 2021; 274 (06) e1230-e1237
• 17 Velazquez-Diaz D, Arco JE, Ortiz A. et al. Use of artificial intelligence in the identification and diagnosis of frailty syndrome in older adults: scoping review. J Med Internet Res 2023; 25: e47346
• 18 PelvEx Collaborative. Predicting outcomes of pelvic exenteration using machine learning. Colorectal Dis 2020; 22 (12) 1933-1940
• 19 Sepsis program activities in Acute Care Hospitals - National Healthcare Safety Network, United States, 2022. Centers for Disease Control and Prevention. August 26, 2023. Accessed April 1, 2025 at: https://www.cdc.gov/mmwr/volumes/72/wr/mm7234a2.htm#:~:text=sepsis%2c%20life%2dthreatening%20organ%20dysfunction%20secondary%20to%20infection%20(1,management%2c%20and%20outcomes%20of%20sepsis
• 20 Haas R, McGill SC. Artificial Intelligence for the Prediction of Sepsis in Adults: CADTH Horizon Scan. Ottawa, ON: Canadian Agency for Drugs and Technologies in Health; 2022
• 21 Chen L, Dubrawski A, Wang D. et al. Using supervised machine learning to classify real alerts and artifact in online multisignal vital sign monitoring data. Crit Care Med 2016; 44 (07) e456-e463
• 22 Patel V, Nguyen H, Davis R. Clinical decision support and early warning systems: impact on rapid response. Crit Care Med 2020; 48 (01) 56-63
• 23 Sepsis Alliance. Electronic alerts for sepsis recognition and treatment. 2021. Accessed March 1, 2025 at: https://www.sepsis.org/
• 24 Arrington T, Estevez E, Birdsall-Fort L, Hill J, Gentry S. Revolutionizing Sepsis Management: Ochsner's Early Detection, Intervention, and Digital Surveillance Approach. HIMSS Davies Award of Excellence Case Study; New Orleans, LA: 2024
• 25 Burdick H, Pino E, Gabel-Comeau D. et al. Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals. BMJ Health Care Inform 2020; 27 (01) e100109
• 26 Stamos MJ, Murrell ZA, Wise PE. et al. Artificial neural networks for predicting anastomotic leak after colorectal surgery. Dis Colon Rectum 2014; 57 (04) 475-483
• 27 Mazaki J, Katsumata K, Ohno Y. et al. A novel predictive model for anastomotic leakage in colorectal cancer using auto-artificial intelligence. Anticancer Res 2021; 41 (11) 5821-5825
• 28 Shao S, Zhao Y, Lu Q, Liu L, Mu L, Qin J. Artificial intelligence assists surgeons' decision-making of temporary ileostomy in patients with rectal cancer who have received anterior resection. Eur J Surg Oncol 2023; 49 (02) 433-439
• 29 Celotto F, Bao QR, Capelli G, Spolverato G, Gumbs AA. Machine learning and deep learning to improve prevention of anastomotic leak after rectal cancer surgery. World J Gastrointest Surg 2025; 17 (01) 101772
• 30 Mayerhoefer ME, Materka A, Langs G. et al. Introduction to radiomics. J Nucl Med 2020; 61 (04) 488-495
• 31 Park SH, Park HM, Baek KR, Ahn HM, Lee IY, Son GM. Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery. World J Gastroenterol 2020; 26 (44) 6945-6962
• 32 Shin J, Seo N, Baek SE. et al. MRI radiomics model predicts pathologic complete response of rectal cancer following chemoradiotherapy. Radiology 2022; 303 (02) 351-358
• 33 Bates DW, Cullen DJ, Laird N. et al; ADE Prevention Study Group. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. JAMA 1995; 274 (01) 29-34
• 34 Rodziewicz TL, Houseman B, Vaqar S, Hipskind JE. Medical Error Reduction and Prevention. In: StatPearls. Treasure Island (FL): StatPearls Publishing; ; February 12, 2024
• 35 Shah K, Lo C, Babich M, Tsao NW, Bansback NJ. Bar code medication administration technology: a systematic review of impact on patient safety when used with computerized prescriber order entry and automated dispensing devices. Can J Hosp Pharm 2016; 69 (05) 394-402
• 36 Mulac A, Mathiesen L, Taxis K, Gerd Granås A. Barcode medication administration technology use in hospital practice: a mixed-methods observational study of policy deviations. BMJ Qual Saf 2021; 30 (12) 1021-1030
• 37 Keohane CA, Bane AD, Featherstone E. et al. Quantifying nursing workflow in medication administration. J Nurs Adm 2008; 38 (01) 19-26
• 38 Rozenblum R, Rodriguez-Monguio R, Volk LA. et al. Using a machine learning system to identify and prevent medication prescribing errors: a clinical and cost analysis evaluation. Jt Comm J Qual Patient Saf 2020; 46 (01) 3-10
• 39 Chen Y, Wong M. Integration of ERAS protocols in colorectal surgery via EMRs. Surg Clin North Am 2018; 98 (06) 1201-1212
• 40 Smith R, Johnson L, Patel M. The impact of electronic medical records on healthcare quality. JAMA 2020; 324 (06) 553-560
• 41 Lee TH, Cosgrove T. Engaging doctors in the health care revolution. Harv Bus Rev 2014; 92 (06) 104-111 , 138
• 42 Office of the National Coordinator for Health Information Technology. Patient engagement playbook. 2022. Accessed April 10, 2025 at: https://www.healthit.gov/
• 43 Rotenstein L, Melnick ER, Iannaccone C. et al. Virtual scribes and physician time spent on electronic health records. JAMA Netw Open 2024; 7 (05) e2413140
• 44 Shah SJ, Crowell T, Jeong Y. et al. Physician perspectives on ambient AI scribes. JAMA Netw Open 2025; 8 (03) e251904
• 45 Martin SS, Quaye E, Schultz S. et al. A randomized controlled trial of online symptom searching to inform patient generated differential diagnoses. NPJ Digit Med 2019; 2: 110
• 46 Riboli-Sasco E, El-Osta A, Alaa A. et al. Triage and diagnostic accuracy of online symptom checkers: systematic review. J Med Internet Res 2023; 25: e43803
• 47 AI Healthcare Agent for Patient Access & Operational Solutions. AI Healthcare Agent for Patient Access & Operational Solutions. 2023. Available at: https://www.clearstep.health/ . Accessed February 1, 2025
• 48 Sears K, Belbin S, Rashno E. et al. Implementing triage-bot: supporting the current practice for triage nurses. Surg Technol Int 2024; 44: 61-65
• 49 Shanafelt TD, Dyrbye LN, Sinsky C. et al. Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction. Mayo Clin Proc 2016; 91 (07) 836-848
• 50 Friedberg MW, Chen PG, Van Busum KR. et al. Factors affecting physician professional satisfaction and their implications for patient care, health systems, and health policy. Rand Health Q 2014; 3 (04) 1
• 51 Dimou FM, Eckelbarger D, Riall TS. Surgeon burnout: a systematic review. J Am Coll Surg 2016; 222 (06) 1230-1239
• 52 Moalem J. Burnout in surgery. American College of Surgeons. Accessed March 20, 2025 at: https://www.facs.org/for-medical-professionals/news-publications/journals/rise/articles/burnout/
• 53 Arndt BG, Beasley JW, Watkinson MD. et al. Tethered to the EHR: primary care physician workload and burnout. Ann Fam Med 2017; 15 (05) 419-426
• 54 Peckham C. . Medscape Survey Lifestyle Report 2016: Bias and Burnout. Accessed March 20, 2025 at: http://www.medscape.com/features/slideshow/lifestyle/2016/general-surgery
• 55 Robeznieks A. Five physician specialties that spend the most time in the EHR. American Medical Association. September 11, 2024. Accessed April 20, 2025 at: https://www.ama-assn.org/practice-management/digital/five-physician-specialties-spend-most-time-ehr
• 56 Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg 2018; 268 (01) 70-76
• 57 Institute of Medicine (US) Committee on Ethical and Legal Issues Relating to the Inclusion of Women in Clinical Studies. Mastroianni AC, Faden R, Federman D, eds. Women and Health Research: Ethical and Legal Issues of Including Women in Clinical Studies: Volume I. Washington, DC: National Academies Press; 1994. NIH Revitalization Act of 1993, Public Law 103–43. Accessed April 4, 2025 at: https://www.ncbi.nlm.nih.gov/books/nbk236531/
• 58 Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med Inform 2020; 8 (07) e18599
• 59 Giuffrè M, Shung DL. Harnessing the power of synthetic data in healthcare: innovation, application, and privacy. NPJ Digit Med 2023; 6 (01) 186
• 60 Jiang Y, Chen H, Loew M, Ko H. COVID-19 CT image synthesis with a conditional generative adversarial network. IEEE J Biomed Health Inform 2021; 25 (02) 441-452
• 61 Turkbey B, Harmon SA. The Good, the Bad, and the Unexplained: Five Tips for Evaluating an AI Product. National Cancer Institute Center for Biomedical Informatics and Information Technology. June 17, 2024. Accessed February 1, 2025 at: https://datascience.cancer.gov/news-events/blog/good-bad-and-unexplained-five-tips-evaluating-ai-product
• 62 Awati R, Yasar K. What is black box AI?. Definition from TechTarget. What is? October 16, 2024. Accessed May 6, 2025 at: https://www.techtarget.com/whatis/definition/black-box-ai#:~:text=black%20box%20ai%20is%20any,the%20model%20susceptible%20to%20cyberattacks