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History and Overview

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The evolution of surgical informatics reflects parallel advances in the broader fields of informatics and surgical technology. Early efforts focused on the development of surgical documentation and information management systems, which were soon followed by centralized surgical registries to support collaborative research efforts. Later advances in machine learning paved the way for predictive and generative artificial intelligence models and computer vision.

Year(s) Milestone Notes
1966 1st edition of Current Procedural Terminology codes published Primarily described surgical procedures, foundation for interoperability.
1980s Early EMR adoption Development of first surgical management systems and early clinical decision support.
1991 National VA Surgical Risk Study (NVASRS) initiated NSQIP precursor, first surgical outcomes registry.
2001 ACS-NSQIP registry established Expansion of surgical outcomes registry to private institutions.
2009 HITECH Act and meaningful use requirements Significant acceleration in EHR adoption.
2009-2015 Development and widespread adoption of surgical management systems Integration of operating room management and surgical decision support in the EHR.
2015-2020 Early surgical machine learning applications Predictive models, early computer vision segmentation models.
2020-p. Large language models, computer vision, and augmented / virtual reality Generative AI in clinical practice, real-time intraoperative applications.


Applications of Surgical Informatics

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Information Systems and Data Collection

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Surgical electronic health record (EHR) and electronic medical record (EMR) systems support surgery-specific applications, including surgical scheduling, operating room management, supply and implant tracking, peri- and intraoperative documentation workflows, and consolidated charge capture.

Several EHR vendors have developed integrated surgical management tools, including Epic OpTime, Cerner SurgiNet, and MEDHOST Advanced Perioperative Information Management System (PIMS). Surgical Information Systems is a standalone surgical EMR marketed for use in ambulatory surgery centers and specialty hospitals. Modernizing Medcine offers several EMR versions tailored for specific surgical specialties. Provation Apex Documentation Solutions supports endoscopic procedure documentation.

Clinical Decision Support

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The development of surgical clinical decision support (CDS) parallels advances in EHR documentation, surgical analytics, and predictive artificial intelligence (AI). Surgical CDS has been developed for numerous applications, with a few key examples included in the following table.

Clinical Decision Support Tool Example(s)
Perioperative risk assessment tools ACS NSQIP surgical risk calculator
Revised Cardiac Risk Index (RCRI)[1]: Estimation of cardiac risk for non-cardiac surgery
POTTER calculator[2]: AI-enabled surgical risk calculator
Enhanced Recovery After Surgery (ERAS) protocol integration Order sets and documentation templates to improvement compliance with ERAS guidelines.[3]
Perioperative antibiotic management Prescribing and administration reminders for perioperative antibiotics.[4][5]


Venous thromboembolism (VTE) prophyalxis guidance Risk stratification and ordering guidance.[6]
Perioperative symptom management Opioid stewardship and naloxone prescribing recommendations.[7]
Perioperative pain, postoperative nausea and vomiting, and constipation management.[8]


Procedure Coding and Terminologies

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Procedural coding systems facilitate serve as the foundation for surgical billing, regulatory reporting, and analytics. Two major procedure coding systems are widely used in surgical contexts. The International Classification of Diseases, 10th Revision, Procedure Coding System (ICD-10-PCS) developed by the Centers for Medicare & Medicaid Services (CMS) is primarily used for inpatient surgical procedures. Each surgical procedure is described by a seven-character alphanumeric code that designates body system, operation type, body part, approach, devices used, and any applicable qualifiers.[9]

The Current Procedural Terminology (CPT) system of the American Medical Association (AMA), is more commonly used in outpatient and ambulatory settings. Surgical CPT codes range from 10004 to 69990 and provide standardized written descriptions of procedures for billing and reimbursement.[10]

The Perioperative Nursing Data Set (PNDS), maintained by the Association of periOperative Registered Nurses (AORN), is a standardized, evidence-based terminology system that enables structured documentation of preoperative and intraoperative patient care.[11]


Surgical Registries and Databases

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Surgical registries and databases support large-scale, collaborative research and quality improvement efforts by collecting surgical outcome data across a variety of surgical specialties.


National Surgical Quality Improvement Program (NSQIP)

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The National Veterans Administration Surgical Risk Study (NVASRS) was launched in 1991 in an effort to improve the quality of surgical care delivery in VA hospital. The program tracked risk-adjusted outcome data in order to identify and implement surgical quality improvement initiative within the VA hospital system. Research VA. (2025). Improving Surgical Outcomes Through Data. Retrieved from https://www.research.va.gov/research_in_action/Improving-surgical-outcomes-through-data.cfm Upon nationwide expansion in 1994, the program was renamed as the National Surgical Quality Improvement Program (NSQIP), and subsequently rebranded as the VASQIP ( ) upon merging with the VA cardiac surgery outcome database in 2006. Between 1992 to 2006, the program was credited with reducing VA hospital perioperative morbidity and mortality by 43% and 47%, respectively. (Research VA, 2025)

Under direction of the American College of Surgeons (ACS), a parallel program was established for private hospitals in 2001 (ACS-NSQIP), followed by the inclusion of pediatric patients in 2010. American College of Surgeons. (2025). ACS NSQIP History. Retrieved from https://www.facs.org/quality-programs/data-and-registries/acs-nsqip/history/ The ACS-NSQIP collects data on >130 variables, including preoperative risk factors, intraoperative variables, and postoperative outcomes. These data have served as the basis for numerous large-scale research and quality improvement studies.


Other Surgical Outcome Registries

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Organizations

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Resources

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References

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  1. ^ Andersson, C., Wissenberg, M., Jørgensen, M. E., Hlatky, M. A., Mérie, C., Jensen, P. F., Gislason, G. H., Køber, L., Torp-Pedersen, C. (January 2015). "Age-Specific Performance of the Revised Cardiac Risk Index for Predicting Cardiovascular Risk in Elective Noncardiac Surgery". Circulation: Cardiovascular Quality and Outcomes. 8 (1). Ovid Technologies (Wolters Kluwer Health): 103–108. doi:10.1161/circoutcomes.114.001298. ISSN 1941-7705 1941-7713, 1941-7705. {{cite journal}}: Check |issn= value (help)
  2. ^ Bertsimas, D., Dunn, J., Velmahos, G. C., Kaafarani, H. M. A. (October 2018). "Surgical Risk Is Not Linear: Derivation and Validation of a Novel, User-friendly, and Machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator". Annals of Surgery. 268 (4): 574–583. doi:10.1097/SLA.0000000000002956. ISSN 1528-1140.
  3. ^ Liu, V. X., Rosas, E., Hwang, J. C., Cain, E., Foss-Durant, A., Clopp, M., Huang, M., Mustille, A., Reyes, V. M., Paulson, S. S., Caughey, M., Parodi, S. (September 2017). "The Kaiser Permanente Northern California Enhanced Recovery After Surgery Program: Design, Development, and Implementation". The Permanente Journal. 21 (3). The Permanente Federation: 17–003. doi:10.7812/TPP/17-003.
  4. ^ Larsen RA, Evans RS, Burke JP, Pestotnik SL, Gardner RM, Classen DC. Improved perioperative antibiotic use and reduced surgical wound infections through use of computer decision analysis. Infect Control Hosp Epidemiol. 1989;10(7):316–320. doi:10.1086/646035.
  5. ^ Lie SA, Lee KYA, Goh MH, Harikrishnan S, Poopalalingam R. Achieving 100 percent compliance to perioperative antibiotic administration: a quality improvement initiative. Singapore Med J. 2019;60(3):130–135. doi:10.11622/smedj.2018039.
  6. ^ Borab ZM, Lanni MA, Tecce MG, Pannucci CJ, Fischer JP. Use of Computerized Clinical Decision Support Systems to Prevent Venous Thromboembolism in Surgical Patients: A Systematic Review and Meta-analysis. JAMA Surg. 2017;152(7):638-645. doi:10.1001/jamasurg.2017.0131
  7. ^ Nguyen M, Ledan S, Cheng C, et al. Impact of Clinical Decision Support Within the Electronic Medical Record on Opioid Prescribing and Dispensing. Perm J. 27(4):64-71. doi:10.7812/TPP/23.063
  8. ^ Noll E, Noll-Burgin M, Bonnomet F, et al. Knowledge-based, computerized, patient clinical decision support system for perioperative pain, nausea and constipation management: a clinical feasibility study. J Clin Monit Comput. 2024;38(4):907-913. doi:10.1007/s10877-024-01148-z
  9. ^ Centers for Medicare & Medicaid Services. "ICD-10-PCS Official Guidelines for Coding and Reporting." https://www.cms.gov/medicare/icd-10/2024-icd-10-pcs
  10. ^ American Medical Association. "CPT® (Current Procedural Terminology)." https://www.ama-assn.org
  11. ^ AORN. "Perioperative Nursing Data Set (PNDS)." Association of periOperative Registered Nurses. https://www.aorn.org
  12. ^ "Surgeon Specific Registry". American College of Surgeons. 2025.



Submitted by Taylor N. Anderson, MD

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