Can Automated Retrieval of Data from Emergency Department Physician Notes Enhance the Imaging Order Entry Process?

Abstract

Background When a paucity of clinical information is communicated from ordering physicians to radiologists at the time of radiology order entry, suboptimal imaging interpretations and patient care may result.

Objectives Compare documentation of relevant clinical information in electronic health record (EHR) provider note to computed tomography (CT) order requisition, prior to ordering of head CT for emergency department (ED) patients presenting with headache.

Methods In this institutional review board-approved retrospective observational study performed between April 1, 2013 and September 30, 2014 at an adult quaternary academic hospital, we reviewed data from 666 consecutive ED encounters for patients with headaches who received head CT. The primary outcome was the number of concept unique identifiers (CUIs) relating to headache extracted via ontology-based natural language processing from the history of present illness (HPI) section in ED notes compared with the number of concepts obtained from the imaging order requisition.

Results Our analysis was conducted on cases where the HPI note section was completed prior to image order entry, which occurred in 23.1% (154/666) of encounters. For these 154 encounters, the number of CUIs specific to headache per note extracted from the HPI (median = 3, interquartile range [IQR]: 2–4) was significantly greater than the number of CUIs per encounter obtained from the imaging order requisition (median = 1, IQR: 1–2; Wilcoxon signed rank p < 0.0001). Extracted concepts from notes were distinct from order requisition indications in 92.9% (143/154) of cases.

Conclusion EHR provider notes are a valuable source of relevant clinical information at the time of imaging test ordering. Automated extraction of clinical information from notes to prepopulate imaging order requisitions may improve communication between ordering physicians and radiologists, enhance efficiency of ordering process by reducing redundant data entry, and may help improve clinical relevance of clinical decision support at the time of order entry, potentially reducing provider burnout from extraneous alerts.

Keywords

electronic health records - computerized physician order entry - natural language processing - emergency medicine - radiology

• References

• 1 Sentinel Event Alert. 2015. Available at: www.jointcommission.org . Accessed February 11, 2016
• 2 Obara P, Sevenster M, Travis A, Qian Y, Westin C, Chang PJ. Evaluating the referring physician's clinical history and indication as a means for communicating chronic conditions that are pertinent at the point of radiologic interpretation. J Digit Imaging 2015; 28 (03) 272-282
• 3 Leslie A, Jones AJ, Goddard PR. The influence of clinical information on the reporting of CT by radiologists. Br J Radiol 2000; 73 (874) 1052-1055
• 4 Munkvold G, Ellingsen G, Koksvik H. Formalising work - reallocating redundancy. Paper presented at: Proceedings of the ACM 2006 conference on CSCW. Banff, Alberta, Canada; 2006:59–68. Available at: http://www.idi.ntnu.no/grupper/su/publ/glenn/4_2006_CSCW.pdf . Accessed December 5, 2016
• 5 Gupta A, Raja AS, Khorasani R. Examining clinical decision support integrity: is clinician self-reported data entry accurate?. J Am Med Inform Assoc 2014; 21 (01) 23-26
• 6 Bates DW, Kuperman GJ, Wang S. , et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003; 10 (06) 523-530
• 7 Khorasani R, Hentel K, Darer J. , et al. Ten commandments for effective clinical decision support for imaging: enabling evidence-based practice to improve quality and reduce waste. Am J Roentgenol 2014; 203 (05) 945-951
• 8 Boonn WW, Langlotz CP. Radiologist use of and perceived need for patient data access. J Digit Imaging 2009; 22 (04) 357-362
• 9 Edlow JA, Panagos PD, Godwin SA, Thomas TL, Decker WW. ; American College of Emergency Physicians. Clinical policy: critical issues in the evaluation and management of adult patients presenting to the emergency department with acute headache. Ann Emerg Med 2008; 52 (04) 407-436
• 10 Choosing Wisely: An Initiative of the ABIM Foundation. Available at: http://choosingwisely.org . Accessed February 11, 2016
• 11 Hanna TN, Rohatgi S, Shekhani HN, Dave IA, Johnson JO. Clinical information available during emergency department imaging order entry and radiologist interpretation. Emerg Radiol 2017; 24 (04) 361-367
• 12 Garla VN, Brandt C. Ontology-guided feature engineering for clinical text classification. J Biomed Inform 2012; 45 (05) 992-998
• 13 Garla V, Lo Re III V, Dorey-Stein Z. , et al. The Yale cTAKES extensions for document classification: architecture and application. J Am Med Inform Assoc 2011; 18 (05) 614-620
• 14 MetamorphoSys Help. 2009. Available at: https://www.nlm.nih.gov/research/umls/implementation_resources/metamorphosys/help.html . Accessed February 8, 2016
• 15 National Cancer Informatics Program. Terminology. Available at: https://cbiit.nci.nih.gov/ncip/biomedical-informatics-resources/interoperability-and-semantics/terminology . Accessed February 4, 2016
• 16 Cutrer FM. Evaluation of the adult with nontraumatic headache in the emergency department. UpToDate, Post TW (Ed). Available at: https://www.uptodate.com/contents/evaluation-of-the-adult-with-nontraumatic-headache-in-the-emergency-department . Accessed February 4, 2019
• 17 Rothman RE, Keyl PM, McArthur JC, Beauchamp Jr NJ, Danyluk T, Kelen GD. A decision guideline for emergency department utilization of noncontrast head computed tomography in HIV-infected patients. Acad Emerg Med 1999; 6 (10) 1010-1019
• 18 Edlow JA, Caplan LR. Avoiding pitfalls in the diagnosis of subarachnoid hemorrhage. N Engl J Med 2000; 342 (01) 29-36
• 19 Friedman BW, Lipton RB. Headache emergencies: diagnosis and management. Neurol Clin 2012; 30 (01) 43-59 , vii
• 20 Swadron SP. Pitfalls in the management of headache in the emergency department. Emerg Med Clin North Am 2010; 28 (01) 127-147
• 21 Practice parameter: the utility of neuroimaging in the evaluation of headache in patients with normal neurologic examinations (summary statement). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 1994; 44 (07) 1353-1354
• 22 Ramirez-Lassepas M, Espinosa CE, Cicero JJ, Johnston KL, Cipolle RJ, Barber DL. Predictors of intracranial pathologic findings in patients who seek emergency care because of headache. Arch Neurol 1997; 54 (12) 1506-1509
• 23 Locker TE, Thompson C, Rylance J, Mason SM. The utility of clinical features in patients presenting with nontraumatic headache: an investigation of adult patients attending an emergency department. Headache 2006; 46 (06) 954-961
• 24 Perry JJ, Stiell IG, Sivilotti MLA. , et al. High risk clinical characteristics for subarachnoid haemorrhage in patients with acute headache: prospective cohort study. BMJ 2010; 341: c5204-c5204
• 25 Perry JJ, Stiell IG, Sivilotti MLA. , et al. Clinical decision rules to rule out subarachnoid hemorrhage for acute headache. JAMA 2013; 310 (12) 1248-1255
• 26 Morgenstern LB, Huber JC, Luna-Gonzales H. , et al. Headache in the emergency department. Headache 2001; 41 (06) 537-541
• 27 Goldstein JN, Camargo Jr CA, Pelletier AJ, Edlow JA. Headache in United States emergency departments: demographics, work-up and frequency of pathological diagnoses. Cephalalgia 2006; 26 (06) 684-690
• 28 Douglas AC, Franz WJ, Broderick DF. , et al. American College of Radiology ACR Appropriateness Criteria®. 1996. Available at: https://acsearch.acr.org/docs/69482/Narrative/ . Accessed October 26, 2018
• 29 Huber TC, Krishnaraj A, Patrie J, Gaskin CM. Impact of a commercially available clinical decision support program on provider ordering habits. J Am Coll Radiol 2018; 15 (07) 951-957
• 30 Sanders DL, Miller RA. The effects on clinician ordering patterns of a computerized decision support system for neuroradiology imaging studies. Proc AMIA Symp 2001; 583-587
• 31 Gupta A, Ip IK, Raja AS, Andruchow JE, Sodickson A, Khorasani R. Effect of clinical decision support on documented guideline adherence for head CT in emergency department patients with mild traumatic brain injury. J Am Med Inform Assoc 2014; 21 (e2): e347-e351
• 32 Ip IK, Raja AS, Gupta A, Andruchow J, Sodickson A, Khorasani R. Impact of clinical decision support on head computed tomography use in patients with mild traumatic brain injury in the ED. Am J Emerg Med 2015; 33 (03) 320-325
• 33 Dean NC, Jones BE, Ferraro JP, Vines CG, Haug PJ. Performance and utilization of an emergency department electronic screening tool for pneumonia. JAMA Intern Med 2013; 173 (08) 699-701
• 34 Protecting Access to Medicare Act of. 2014 (PL 113–93) Section 218(b). 2014. Available at: https://www.congress.gov/113/plaws/publ93/PLAW-113publ93.pdf . Accessed May 2, 2016
• 35 Celi LA, Marshall JD, Lai Y, Stone DJ. Disrupting electronic health records systems: the next generation. JMIR Med Inform 2015; 3 (04) e34
• 36 Demner-Fushman D, Chapman WW, McDonald CJ. What can natural language processing do for clinical decision support?. J Biomed Inform 2009; 42 (05) 760-772
• 37 Kummer BR, Lerario MP, Navi BB. , et al. Clinical information systems integration in New York City's first mobile stroke unit. Appl Clin Inform 2018; 9 (01) 89-98
• 38 Tai-Seale M, Olson CW, Li J. , et al. Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine. Health Aff (Millwood) 2017; 36 (04) 655-662
• 39 Zulman DM, Shah NH, Verghese A. Evolutionary pressures on the electronic health record: caring for complexity. JAMA 2016; 316 (09) 923-924
• 40 Babbott S, Manwell LB, Brown R. , et al. Electronic medical records and physician stress in primary care: results from the MEMO Study. J Am Med Inform Assoc 2014; 21 (e1): e100-e106