Implementation and Adoption of an Order-Based Surgical Case Request Tool across Subspecialty Clinics

Abstract

Background

While computerized provider order entry (CPOE) has become standard for medication, laboratory, referral, and imaging ordering, use in surgical case requests has not been well-described. At a large county hospital, many surgical clinics used a variety of workflows for case requests, leading to data duplication and data storage outside of the electronic health record (EHR).

Objective

We hypothesized that a provider-entered order-based case request (OBCR) tool would improve data entry efficiency and provide a more comprehensive EHR audit trail.

Methods

We implemented an OBCR tool across surgical clinics at a large safety-net hospital system. The existing workflow, whereby clinic managers created operative cases within the EHR after provider communication, remained available. We analyzed all cases requested via old or new workflows for 6 months after the go-live of the tool.

Results

From 2022 to 2023, managers created 7,226 operative case requests across 19 surgical clinics, 158 faculty surgeons, and 1,737 procedure combinations. Most cases (4,585, 63%) were created via OBCR. Clinic OBCR use ranged from 2 to 97% of created cases. With OBCR, case information was entered earlier, resulting in significantly increased time from case creation to scheduling, 12.0 versus 0.7 days, respectively (p < 0.001). Concordantly, mean time from creation to completion increased from 35.4 to 54.6 days (p < 0.001). Rates of “voided cases” decreased in the new workflow (1.9 vs. 4.5%, p < 0.001).

Conclusion

Most surgical clinics at our institution adopted the OBCR tool, facilitating earlier operative case entry with lower void rates. This streamlined case request approach improves preoperative planning and reduces data entry redundancy. The OBCR system also enabled the data collection needed for robust reporting and identification of clinics in need of support or workflow optimization.

Keywords

surgical case request - surgery scheduling - CPOE - surgical informatics

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the UT Southwestern and Parkland Health and Hospital System Institutional Review Boards.

Publication History

Received: 13 November 2024

Accepted: 21 March 2025

Accepted Manuscript online:
24 March 2025

Article published online:
23 July 2025

• References

• 1 Khanna R, Yen T. Computerized physician order entry: promise, perils, and experience. Neurohospitalist 2014; 4 (01) 26-33
• 2 Slight SP, Berner ES, Galanter W. et al. Meaningful use of electronic health records: Experiences from the field and future opportunities. JMIR Med Inform 2015; 3 (03) e30
• 3 HealthIT.gov. Computerized Provider Order Entry: The Basics. Accessed February 22, 2024 at: https://www.healthit.gov/faq/what-computerized-provider-order-entry
• 4 Lehmann CU, Kim GR. Computerized provider order entry and patient safety. Pediatr Clin North Am 2006; 53 (06) 1169-1184
• 5 Levick D. Computer Physician Order Entry (CPOE). Virtual Mentor 2004;6(03):virtualmentor.2004.6.3.cprl1-0403
• 6 Kim GR, Miller MR, Ardolino MA, Smith JE, Lee DC, Lehmann CU. Capture and classification of problems during CPOE deployment in an academic pediatric center. AMIA Annu Symp Proc 2007; 2007: 414-417
• 7 Freeman NLB, McGinigle KL, Leese PJ. Using electronic medical records to identify enhanced recovery after surgery cases. EGEMS (Wash DC) 2019; 7 (01) 34
• 8 Wick EC, Pierce L, Conte MC, Sosa JA. Operationalizing the operating room: ensuring appropriate surgical care in the era of COVID-19. Ann Surg 2020; 272 (02) e165-e167
• 9 Jeang A, Chiang AJ. Economic and quality scheduling for effective utilization of operating rooms. J Med Syst 2012; 36 (03) 1205-1222
• 10 Brooke J. SUS: A quick and dirty usability scale. In: Usability Evaluation in Industry. 1995. Vol. 11/30, p. 189
• 11 Hyzy M, Bond R, Mulvenna M. et al. System usability scale benchmarking for digital health apps: meta-analysis. JMIR Mhealth Uhealth 2022; 10 (08) e37290
• 12 UIUXTrend. Measuring and Interpreting the System Usability Scale (SUS). Accessed March 5, 2025 at: https://uiuxtrend.com/measuring-system-usability-scale-sus/#:~:text=SUS%20score%20will%20be%20able,as%20a%20percentage%20or%20percentile
• 13 Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med 2003; 163 (12) 1409-1416
• 14 Brooks BS, Barman J, Ponce BA, Sides A, Vetter TR. An electronic surgical order, undertaking patient education, and obtaining informed consent for regional analgesia before the day of surgery reduce block-related delays. Local Reg Anesth 2016; 9: 59-64
• 15 Webb AL, Flagg RL, Fink AS. Reducing surgical site infections through a multidisciplinary computerized process for preoperative prophylactic antibiotic administration. Am J Surg 2006; 192 (05) 663-668
• 16 Liu SA, Chiu YT, Lin WD, Chen SJ. Using information technology to reduce the inappropriate use of surgical prophylactic antibiotic. Eur Arch Otorhinolaryngol 2008; 265 (09) 1109-1112
• 17 Dyas AR, Kelleher AD, Erickson CJ. et al. Development of a universal thoracic enhanced recover after surgery protocol for implementation across a diverse multi-hospital health system. J Thorac Dis 2022; 14 (08) 2855-2863
• 18 Levine WC, Dunn PF. Optimizing operating room scheduling. Anesthesiol Clin 2015; 33 (04) 697-711
• 19 Zaribafzadeh H, Webster WL, Vail CJ. et al. Development, deployment, and implementation of a machine learning surgical case length prediction model and prospective evaluation. Ann Surg 2023; 278 (06) 890-895
• 20 Kendale S, Bishara A, Burns M, Solomon S, Corriere M, Mathis M. Machine learning for the prediction of procedural case durations developed using a large multicenter database: Algorithm development and validation study. JMIR AI 2023; 2: e44909
• 21 Franceschi D, Suarez MM, Ruiz JW, Seo D, Merchant NB. A novel interdisciplinary iterative approach for optimizing the electronic health record to improve perioperative efficiency. Ann Surg 2020; 272 (04) 669-675
• 22 Isch EL, Sarikonda A, Sambangi A. et al. Evaluating the efficacy of large language models in CPT coding for craniofacial surgery: A comparative analysis. J Craniofac Surg 2024; (e-pub ahead of print)
• 23 Zaidat B, Lahoti YS, Yu A, Mohamed KS, Cho SK, Kim JS. Artificially intelligent billing in spine surgery: An analysis of a large language model. Global Spine J 2025; 15 (02) 1113-1120