A Comparison of One- and Four-Open-Chart Access: No Change in Computerized Provider Order Entry Error Rates

Abstract

Objective To assess changes in computerized provider order entry error rates among providers who with less than 24-hour notice were switched from four-chart access to one-chart-only access.

Methods An interrupted time series analysis of emergency medicine providers, hospitalists, and maternal child health providers was performed with pairwise comparison of computerized provider order entry error rates within and between specialties. This retrospective snapshot consisted of four phases. Phase 1 was the baseline 2 weeks where providers were privileged to work with up to four charts open. Phase 2 was the 2-week period where providers were limited to one-chart access. Phase 3 was the 2-week period where providers were returned to four-chart access. And phase 4 was a 2-week period 3 months following the end of phase 3.

Results Analysis of the overall and specialty-stratified cohorts revealed no statistically significant differences in median computerized provider order entry error rates across the four phases (Wilcoxon signed-rank test, α = 0.05). However, statistically significant differences in median computerized provider order entry error rates were detected between the three specialties within each phase of the study (Kruskal–Wallis, p < 0.001).

Conclusion Allowing providers in select specialties to have access to four charts simultaneously does not increase their computerized provider order entry error rates. Significant differences in error rates between specialties suggest the need for further study of the use of standardized order sets, charting, and workflow variations.

Keywords

open charts - electronic health records - health information technology - patient safety

Protection of Human and Animal Subjects

This study was submitted to the Dignity Health Research Integrity Office (eIRB) and deemed a continuous quality improvement project sponsored by Dignity Health conducted by Health Informatics. As such, it was exempt from IRB review.

• References

• 1 Ash J, Singh H, Sittig D. Computerized provider order entry with decision support. Safety assurance factors for EHR resilience (SAFER), November 2016 . Available at: https://www.healthit.gov/sites/default/files/safer/guides/safer_cpoe.pdf . Accessed February 5, 2019
• 2 The Joint Commission. Sentinel event alert. Issue 54, March 31, 2015 . Available at: https://www.jointcommission.org/assets/1/18/SEA_54.pdf . Accessed February 5, 2019
• 3 Paparella SF. Accurate patient identification in the emergency department: meeting the safety challenges. J Emerg Nurs 2012; 38 (04) 364-367
• 4 Office of the National Coordinator for Health Information Technology. Anticipating unintended consequences of health information technology and health information Exchange: how to identify and address unsafe conditions associated with health IT. 2013 . Available at: https://www.healthit.gov/sites/default/files/How_to_Identify_and_Address_Unsafe_Conditions_Associated_with_Health_IT.pdf . Accessed February 28, 2019
• 5 Office of the National Coordinator for Health Information Technology. Self-assessment: patient identification: general instructions for the SAFER self-assessment guides. Available at: https://www.healthit.gov/sites/default/files/safer/guides/safer_patient_identification.pdf . Published September 2016.Accessed May 27, 2019
• 6 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
• 7 Green RA, Hripcsak G, Salmasian H. , et al. Intercepting wrong-patient orders in a computerized provider order entry system. Ann Emerg Med 2015; 65 (06) 679.e1-686.e1
• 8 Hyman D, Laire M, Redmond D, Kaplan DW. The use of patient pictures and verification screens to reduce computerized provider order entry errors. Pediatrics 2012; 130 (01) e211-e219
• 9 Middleton B, Bloomrosen M, Dente MA. , et al; American Medical Informatics Association. Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J Am Med Inform Assoc 2013; 20 (e1): e2-e8
• 10 Adelman JS, Applebaum JR, Schechter CB. , et al. Effect of restriction of the number of concurrently open records in an electronic health record on wrong-patient order errors: a randomized clinical trial. JAMA 2019; 321 (18) 1780-1787
• 11 Levin HI, Levin JE, Docimo SG. “I meant that med for Baylee not Bailey!”: a mixed method study to identify incidence and risk factors for CPOE patient misidentification. AMIA Annu Symp Proc 2012; 2012: 1294-1301
• 12 Kannampallil TG, Manning JD, Chestek DW. , et al. Effect of number of open charts on intercepted wrong-patient medication orders in an emergency department. J Am Med Inform Assoc 2018; 25 (06) 739-743
• 13 Adelman JS, Berger MA, Rai A. , et al. A national survey assessing the number of records allowed open in electronic health records at hospitals and ambulatory sites. J Am Med Inform Assoc 2017; 24 (05) 992-995
• 14 Koppel R, Metlay JP, Cohen A. , et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA 2005; 293 (10) 1197-1203
• 15 Lowry SZ, Quinn MT, Ramaiah M. , et al. Technical evaluation, testing, and validation of the usability of electronic health records. Available at: https://www.nist.gov/publications/nistir-7804-technical-evaluation-testing-and-validation-usability-electronic-health . Accessed September 17, 2019
• 16 Harrison MI, Koppel R, Bar-Lev S. Unintended consequences of information technologies in health care--an interactive sociotechnical analysis. J Am Med Inform Assoc 2007; 14 (05) 542-549
• 17 Ash JS, Sittig DF, Poon EG, Guappone K, Campbell E, Dykstra RH. The extent and importance of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc 2007; 14 (04) 415-423
• 18 Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc 2006; 13 (05) 547-556
• 19 Ratwani RM, Fong A, Puthumana JS, Hettinger AZ. Emergency physician use of cognitive strategies to manage interruptions. Ann Emerg Med 2017; 70 (05) 683-687
• 20 McLeod AI, Vingilis ER. Power computations in time series analyses for traffic safety interventions. Accid Anal Prev 2008; 40 (03) 1244-1248
• 21 Rozario NL, Moore CG, McWilliams A. Interrupted time series power calculation using DO loop simulations. Available at: http://support.sas.com/resources/papers/proceedings17/1339-2017.pdf . Accessed May 28, 2019
• 22 Zhang F, Wagner AK, Ross-Degnan D. Simulation-based power calculation for designing interrupted time series analyses of health policy interventions. J Clin Epidemiol 2011; 64 (11) 1252-1261