Abandoned Inpatient Orders: An Opportunity for Improving CPOE Safety and Efficiency

Abstract

Objectives

Abandoned medication orders—those initiated but not signed—represent a potential safety risk and an indicator of electronic health record (EHR) inefficiency. This study explores inpatient medication abandonment across two large tertiary healthcare systems using different EHRs.

Materials and Methods

Silent alerts were deployed to identify abandoned orders at Site 1 (June 2018–May 2019) and Site 2 (July 2020–May 2023). At Site 1, alerts triggered on all inpatient medication orders. At Site 2, alerts were part of a broader study implementing indication alerts; only orders for study medications triggered alerts. An abandoned order was defined as an order initiated but not signed within 24 hours of initiation. We calculated abandonment rates and rates of reorders, and performed regression to examine the association between abandonment and clinician, patient, and order characteristics. Exponential models were fit to characterize the chronology of reordering.

Results

Among 6.8 million medication orders, abandonment rates were 11.2% at Site 1 and 25.0% at Site 2. Due to fundamental differences in alert configuration and order capture, no direct statistical comparison of abandonment rates between the two sites was conducted. Over half of abandoned orders were reordered within 24 hours (65.3% at Site 1; 54.2% at Site 2). The chronology of reordering was similar at both institutions. Attendings, the most senior clinicians, had the lowest rates of abandonment. Abandonment rates decreased as clinicians placed more orders, but rose as clinicians ordered on more unique patients. Abandonments were higher when ordering for children compared with adults.

Conclusion

Order abandonment is common and varies by patient's age, clinician type, and workload. Abandonment rates declined as house staff providers advanced in training, signifying clinical experience plays a role. Frequent reordering suggests that workflow interruptions or modifications, rather than intentional medication cancellation, may lead to a significant proportion of abandonments. Similarity in the timing of reordering between healthcare systems suggest common reordering processes across sites. Our findings demonstrate significant order abandonment rates, with the potential to use abandonment as a metric to improve computerized provider order entry (CPOE) functionality, clinicians' workflows, and patient safety.

Keywords

medication safety - computerized provider order entry systems - clinical decision support - electronic health records - medication abandonment

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 Columbia University Medical School and Northwestern Medicine Institutional Review Board.

Note

The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. The funding agency had no role in the following research activities: design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Publication History

Received: 22 July 2025

Accepted: 11 January 2026

Article published online:
30 January 2026

• References

• 1 Southern WN, Gray P, Meara E. et al. Clinician experience of electronic health record configurations displaying 1 vs 4 records at a time. [abstract] Med Staff Buffalo 2020 . Accessed at: https://medstaff.chsbuffalo.org/wp-content/uploads/2020/01/Clinician-Experience-of-Electronic-Health-Record-Configurations-Displaying-1-vs-4-Records-at-a-Time.pdf
• 2 Koppel R, Leonard CE, Localio AR, Cohen A, Auten R, Strom BL. Identifying and quantifying medication errors: evaluation of rapidly discontinued medication orders submitted to a computerized physician order entry system. J Am Med Inform Assoc 2008; 15 (04) 461-465
• 3 Burlison JD, McDaniel RB, Baker DK. et al. Using EHR data to detect prescribing errors in rapidly discontinued medication orders. Appl Clin Inform 2018; 9 (01) 82-88
• 4 Devin J, Cullinan S, Looi C, Cleary BJ. Identification of prescribing errors in an electronic health record using a retract-and-reorder tool: a pilot study. J Patient Saf 2022; 18 (07) e1076-e1082
• 5 Adelman JS, Kalkut GE, Schechter CB. et al. Understanding and preventing wrong-patient electronic orders: a randomized controlled trial. J Am Med Inform Assoc 2013; 20 (02) 305-310
• 6 Kern-Goldberger AR, Kneifati-Hayek J, Fernandes Y. et al. Wrong-patient orders in obstetrics. Obstet Gynecol 2021; 138 (02) 229-235
• 7 Grauer A, Kneifati-Hayek J, Reuland B. et al. Indication alerts to improve problem list documentation. J Am Med Inform Assoc 2022; 29 (05) 909-917
• 8 Galanter WL, Hier DB, Jao C, Sarne D. Computerized physician order entry of medications and clinical decision support can improve problem list documentation compliance. Int J Med Inform 2010; 79 (05) 332-338
• 9 Galanter W, Falck S, Burns M, Laragh M, Lambert BL. Indication-based prescribing prevents wrong-patient medication errors in computerized provider order entry (CPOE). J Am Med Inform Assoc 2013; 20 (03) 477-481
• 10 R Core Team. R: A language and environment for statistical computing R Foundation for Statistical Computing, Vienna, Austria;. 2025 . Accessed at: https://www.R-project.org/
• 11 Abraham J, Galanter WL, Touchette D. et al. Risk factors associated with medication ordering errors. J Am Med Inform Assoc 2021; 28 (01) 86-94
• 12 Kannampallil TG, Abraham J, Solotskaya A. et al. Learning from errors: analysis of medication order voiding in CPOE systems. J Am Med Inform Assoc 2017; 24 (04) 762-768
• 13 Galanter WL, Thambi M, Rosencranz H. et al. Effects of clinical decision support on venous thromboembolism risk assessment, prophylaxis, and prevention at a university teaching hospital. Am J Health Syst Pharm 2010; 67 (15) 1265-1273
• 14 Bahl V, Moote MJ, Hu HM, Campbell Jr DA, Morrison M, Englesbe MJ. Impact of clinical decision support with mandatory versus voluntary venous thromboembolism assessment in hospitalized patients. TH Open 2024; 8 (03) e317-e328
• 15 Spyropoulos AC, Goldin M, Koulas I. et al. Universal EHRs clinical decision support for thromboprophylaxis in medical inpatients: a cluster randomized trial. JACC Adv 2023; 2 (08) 100597
• 16 Sorita A, Thongprayoon C, Ahmed A. et al. Frequency and appropriateness of fasting orders in the hospital. Mayo Clin Proc 2015; 90 (09) 1225-1232
• 17 Lambert BL, Schroeder SR, Cohen MR, Paparella S, Doering PL. Beyond mixed case lettering: reducing the risk of wrong drug errors requires a multimodal response. BMJ Qual Saf 2023; 32 (01) 6-9
• 18 Hung PL, Lin PC, Chen JY. et al. Developing an integrated electronic medication reconciliation platform and evaluating its effects on preventing potential duplicated medications and reducing 30-day medication-related hospital revisits for inpatients. J Med Syst 2021; 45 (04) 47
• 19 Abraham J, Kannampallil TG, Jarman A. et al. Reasons for computerised provider order entry (CPOE)-based inpatient medication ordering errors: an observational study of voided orders. BMJ Qual Saf 2018; 27 (04) 299-307
• 20 ECRI Institute. Minimizing distractions and interruptions during medication safety tasks ECRI. Published [ n.d. ][n.d.]. Accessed May 12, 2025. Accessed at: https://home.ecri.org/blogs/ismp-alerts-and-articles-library/minimizing-distractions-and-interruptions-during-medication-safety-tasks
• 21 Epic Systems Corporation. Software. Accessed May 12, 2025 at: https://www.epic.com/software