Efficiency of Emergency Physicians: Insights from an Observational Study using EHR Log Files

Abstract

Objective With federal mandates and incentives since the turn of this decade, electronic health records (EHR) have been widely adopted and used for clinical care. Over the last several years, we have seen both positive and negative perspectives on its use. Using an analysis of log files of EHR use, we investigated the nature of EHR use and their effect on an emergency department's (ED) throughput and efficiency.

Methods EHR logs of time spent by attending physicians on EHR-based activities over a 6-week period (n = 2,304 patients) were collected. For each patient encounter, physician activities in the EHR were categorized into four activities: documentation, review, orders, and navigation. Four ED-based performance metrics were also captured: door-to-provider time, door-to-doctor time, door-to-disposition time, and length of stay (LOS). Association between the four EHR-based activities and corresponding ED performance metrics were evaluated.

Results We found positive correlations between physician review of patient charts, and door-to-disposition time (r = 0.43, p < 0.05), and with LOS (r = 0.48, p < 0.05). There were no statistically significant associations between any of the other performance metrics and EHR activities.

Conclusion The results highlight that longer time spent on reviewing information on the EHR is potentially associated with decreased ED throughput efficiency. Balancing these competing goals is often a challenge of physicians, and its implications for patient safety is discussed.

Keywords

physician performance - EHR logfiles - meaningful use - emergency department

• References

• 1 Blumenthal D. Stimulating the adoption of health information technology. N Engl J Med 2009; 360 (15) 1477-1479
• 2 Dick RS, Steen EB. The Computer-Based Patient Record: An Essential Technology for Health Care. Washington, DC: Institute of Medicine; 1991
• 3 Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform 2008; 77 (05) 291-304
• 4 Patel VL, Kannampallil TG. Human factors and health information technology: current challenges and future directions. Yearb Med Inform 2014; 9 (01) 58-66
• 5 Patel VL, Kannampallil TG, Shortliffe EH. Role of cognition in generating and mitigating clinical errors. BMJ Qual Saf 2015; 24 (07) 468-474
• 6 Patel VL, Kaufman DR, Kannampallil TG. Diagnostic reasoning and decision making in the context of health information technology. Rev Hum Factors Ergon 2013; 8 (01) 149-190
• 7 Geisler BP, Schuur JD, Pallin DJ. Estimates of electronic medical records in U.S. emergency departments. PLoS One 2010; 5 (02) e9274
• 8 Kannampallil TG, Jones LK, Patel VL, Buchman TG, Franklin A. Comparing the information seeking strategies of residents, nurse practitioners, and physician assistants in critical care settings. J Am Med Inform Assoc 2014; 21 (e2): e249-e256
• 9 Shapiro JS, Mostashari F, Hripcsak G, Soulakis N, Kuperman G. Using health information exchange to improve public health. Am J Public Health 2011; 101 (04) 616-623
• 10 Ford EW, Menachemi N, Phillips MT. Predicting the adoption of electronic health records by physicians: when will health care be paperless?. J Am Med Inform Assoc 2006; 13 (01) 106-112
• 11 Hersh W. Health care information technology: progress and barriers. JAMA 2004; 292 (18) 2273-2274
• 12 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
• 13 Simon SR, Kaushal R, Cleary PD. , et al. Correlates of electronic health record adoption in office practices: a statewide survey. J Am Med Inform Assoc 2007; 14 (01) 110-117
• 14 Wright A, Henkin S, Feblowitz J, McCoy AB, Bates DW, Sittig DF. Early results of the meaningful use program for electronic health records. N Engl J Med 2013; 368 (08) 779-780
• 15 Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004; 11 (02) 104-112
• 16 Park SY, Lee SY, Chen Y. The effects of EMR deployment on doctors' work practices: a qualitative study in the emergency department of a teaching hospital. Int J Med Inform 2012; 81 (03) 204-217
• 17 Ben-Assuli O, Ziv A, Sagi D, Ironi A, Leshno M. Cost-effectiveness evaluation of EHR: simulation of an abdominal aortic aneurysm in the emergency department. J Med Syst 2016; 40 (06) 141
• 18 Ben-Assuli O. Electronic health records, adoption, quality of care, legal and privacy issues and their implementation in emergency departments. Health Policy 2015; 119 (03) 287-297
• 19 Caine K, Hanania R. Patients want granular privacy control over health information in electronic medical records. J Am Med Inform Assoc 2013; 20 (01) 7-15
• 20 Calvitti A, Hochheiser H, Ashfaq S. , et al. Physician activity during outpatient visits and subjective workload. J Biomed Inform 2017; 69: 135-149
• 21 Patel VL, Kannampallil TG. Cognitive informatics in biomedicine and healthcare. J Biomed Inform 2015; 53: 3-14
• 22 Malhotra S, Jordan D, Shortliffe E, Patel VL. Workflow modeling in critical care: piecing together your own puzzle. J Biomed Inform 2007; 40 (02) 81-92
• 23 Abraham J, Kannampallil TG, Reddy M. , Eds. Peripheral Activities during EMR Use in Emergency Care: A Case Study. American Medical Informatics Association (AMIA) Annual Symposium; 2009; San Francisco, CA
• 24 Kannampallil TG, Abraham J, Patel VL. Methodological framework for evaluating clinical processes: a cognitive informatics perspective. J Biomed Inform 2016; 64: 342-351
• 25 Kannampallil T, Li Z, Zhang M. , et al. Making sense: sensor-based investigation of clinician activities in complex critical care environments. J Biomed Inform 2011; 44 (03) 441-454
• 26 Vankipuram M, Kahol K, Cohen T, Patel VL. Toward automated workflow analysis and visualization in clinical environments. J Biomed Inform 2011; 44 (03) 432-440
• 27 Zheng K, Hanauer DA, Weibel N, Agha Z. Computational Ethnography: Automated and Unobtrusive Means for Collecting Data In Situ for Human–Computer Interaction Evaluation Studies. Cognitive Informatics for Biomedicine: Human Computer Interaction. London, United Kingdom: Springer; 2015: 111-140
• 28 Hripcsak G, Vawdrey DK, Fred MR, Bostwick SB. Use of electronic clinical documentation: time spent and team interactions. J Am Med Inform Assoc 2011; 18 (02) 112-117
• 29 Zheng K, Padman R, Johnson MP, Diamond HS. An interface-driven analysis of user interactions with an electronic health records system. J Am Med Inform Assoc 2009; 16 (02) 228-237
• 30 McHugh M, Tanabe P, McClelland M, Khare RK. More patients are triaged using the Emergency Severity Index than any other triage acuity system in the United States. Acad Emerg Med 2012; 19 (01) 106-109
• 31 Denton C, Shapiro JS, Nimo G, Kannampallil TG, Patel VL. , Eds. Perceived Effects of Meaningful Use of Electronic Health Records on Clinical Workflow. AMIA Annual Symposium; 2016; Chicago, IL
• 32 McDonald CJ, Callaghan FM, Weissman A, Goodwin RM, Mundkur M, Kuhn T. Use of internist's free time by ambulatory care Electronic Medical Record systems. JAMA Intern Med 2014; 174 (11) 1860-1863
• 33 Sinsky C, Colligan L, Li L. , et al. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Ann Intern Med 2016; 165 (11) 753-760