1. Can Emergency Department Provider Notes Help Achieve More Dynamic
Clinical Decision Support?
Informatics Insights for Clinical Documentation
S03
Justin Rousseau
Brigham and Women’s Hospital, Harvard Medical School
Dell Medical School at the University of Texas at Austin
Twitter: #AMIA2017

2. Disclosure
Research was funded by National Library of Medicine grant #T15LM007092
No bearing on study design, data collection, analysis, or interpretation
I and my spouse/partner have no other relevant relationships with commercial interests to disclose.
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3. Learning Objectives
After participating in this session the learner should be better able to:
Identify and assess unique sources of clinical data in the EHR to use to augment interaction with clinical decision support tools.
Assess opportunities for the use of informatics tools to enhance the order entry process to deliver the right clinical decision support for the right person at the right time.
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4. Imaging Clinical Decision Support (CDS)
 An application of health information technology to inform clinical decision- making at the point of care regarding the need for imaging or the optimal diagnostic study based on the best available evidence
Khorasani R, et al. Ten commandments for effective clinical decision support for imaging: enabling evidence-based practice to improve quality and reduce waste. Am J Roentgenol Oct 23;203(5):945–51.
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5. Order Requisition Computerized physician order entry system (CPOE)
Ip IK, Khorasani R. Decision Support in Diagnostic Radiology. In: Evidence-Based Neuroimaging Diagnosis and Treatment.
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6. Imaging CDS – “Activation”
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7. Imaging CDS
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8. Cervical Spine CDS
National Emergency X-Radiography Utilization Study (NEXUS)
Assists clinicians in ruling out cervical spine injury
Reduces unnecessary c-spine imaging
Well validated and in clinical use
CDS Displays when CT C-spine or 3 View X-ray C-spine AND Hx = Trauma
Hoffman JR, et al. Selective cervical spine radiography in blunt trauma: methodology of the National Emergency X-Radiography Utilization Study (NEXUS). Ann Emerg Med 1998;32:461–9.
Hoffman JR, et al. Validity of a Set of Clinical Criteria to Rule Out Injury to the Cervical Spine in Patients with Blunt Trauma. N Engl J Med 2000;343:94–9.
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9. Cervical Spine CDS
National Emergency X-Radiography Utilization Study (NEXUS)
C-spine radiography indicated if any of the clinical criteria are met
Posterior midline c-spine tenderness
Evidence of intoxication
Decreased level of alertness
Focal neurologic deficit
Painful distracting injuries
Hoffman JR, et al. Ann Emerg Med 1998;32:461–9.
Hoffman JR, et al. N Engl J Med 2000;343:94–9.
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10. Identifying Gaps Areas for Improvement of CDS Impacts
Timing of delivery
Specificity
Alert fatigue
Sensitivity
Missed opportunities
Redundant data entry
Incomplete, Inaccurate, or conflicting data
Impacts
Quality of care
Secondary use of data
Research and monitoring adherence to guidelines
Bates DW. Ten Commandments for Effective Clinical Decision Support. JAMIA 2003;10:523–30.
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11. Patient Selection
85,916 of Emergency Department encounters over 18 months
3,155 encounters with chief complaint of Fall/Trauma/Bicycle Accident that did not alert trauma team
438 encounters with both fall/trauma/bicycle accident and c- spine imaging
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12. Methods
Outcomes:
Proportion of encounters with at least one c-spine-specific CDS rule attribute in clinical notes at time of imaging order
Agreement between CDS attributes in clinical notes and data entered in CDS tool in the CPOE
Manual review by two physicians (internal medicine attending and neurologist) of most recent of all note sections submitted prior to image ordering
NEXUS Criteria
NEXUS Exclusion Criteria
Canadian C-spine CDS Rule Exclusion Criteria
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13. Results
438 Encounters with fall/trauma/bicycle accident where imaging of c-spine was performed
At least one note section was complete in 42% (184/438) of encounters
HPI (35%), A/P (11%), Attending note (8%), Head and Neck Exam, nml (8%), Neuro Exam, nml (7%), HEENT Comments (5%), Neuro Exam Comments (3%), Neuro ROS comment (1%),
NEXUS CDS activated in 27% (117/438)
48% (56/117) had note sections submitted prior to image ordering
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14. CDS Attributes in encounters with notes prior to imaging
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15. Agreement between Notes and CDS
Kappa
Agreement
<0
Less than chance
Slight
Fair
Moderate
Substantial
Almost perfect
McNemar; p=0.23
Viera AJ, Garrett JM. Understanding interobserver agreement: the kappa statistic. Fam Med 2005;37:360–3.
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16. Conclusions
Provider EHR documentation is an underutilized information resource that contains CDS attributes and exclusion criteria and is available in a significant percentage of encounters at the point of C-spine image ordering
Harvesting CDS attributes and exclusion criteria from EHR notes when available may:
Allow pre-population of CDS tools, reducing redundant data entry
Allow suppression of CDS rules not applicable to the current patient, potentially reducing workflow interruption and alert fatigue
CDS is not activated as often as expected
There is only fair agreement of CDS rule attributes comparing notes to the CDS tool in the CPOE
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17. Future Work
Evaluate the quality and quantity of clinical data documented in encounters that are less acute Evaluate performance of automated extraction of attributes using text mining or natural language processing
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18. Questions
What of the following ten commandments for effective clinical decision support for imaging1 is most impacted by the identification of clinical decision support (CDS) criteria in clinical notes prior to image ordering?
Respect the ordering provider’s workflow
Evidence must be current
Clinical recommendations must be brief, unambiguous, and actionable
Imaging CDS must enable measurement of its impact
1. Khorasani R, Hentel K, Darer J, Langlotz C, Ip IK, Manaker S, et al. Ten commandments for effective clinical decision support for imaging: enabling evidence-based practice to improve quality and reduce waste. Am J Roentgenol Oct 23;203(5):945–51.
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19. Answer
Respect the ordering provider’s workflow
Evidence must be current
Clinical recommendations must be brief, unambiguous, and actionable
Imaging CDS must enable measurement of its impact
Explanation: Considering the ordering provider’s workflow includes considering every click of the provider and an effort to reduce or eliminate redundant data entry. Identifying CDS criteria in clinical notes prior to image ordering would provide the opportunity to prepopulate these criteria into CDS tools, reducing redundant data entry and reducing time spent by the provider interacting with the CDS tool. The most immediate impact of identifying CDS criteria in clinical notes is on the provider’s workflow.
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20. Questions
Alert fatigue results from excess alerts or notifications that are not relevant to the current context of the situation. What is the most likely opportunity to reduce alert fatigue from interacting with Clinical Decision Support (CDS) tools for imaging?
Identifying CDS criteria in providers notes to prepopulate into CDS tools
Identifying inclusion criteria in the EHR to suggest CDS when applicable for an imaging study that has not been ordered
Identifying exclusion criteria in the EHR to suppress CDS when not applicable for an imaging study that has been ordered
Reconfigure the production rules to identify candidate cases to deliver imaging CDS
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21. Answer
Identifying CDS criteria in providers notes to prepopulate into CDS tools
Identifying inclusion criteria in the EHR to suggest CDS when applicable for an imaging study that has not been ordered
Identifying exclusion criteria in the EHR to suppress CDS when not applicable for an imaging study that has been ordered
Reconfigure the production rules to identify candidate cases to deliver imaging CDS
Explanation: The goal to reduce alert fatigue would be to eliminate inappropriate exposure to a CDS tool. The most effective way to address this of the options above is to maximize the specificity of delivery of the tool by identifying exclusion criteria that are already known or present in the EHR in order to suppress CDS when it is not relevant to the patient context due to the presence of an exclusion criterion for the evidence supporting the CDS rule.
Lin C-P, Payne TH, Nichol WP, Hoey PJ, Anderson CL, Gennari JH. Evaluating clinical decision support systems: monitoring CPOE order check override rates in the Department of Veterans Affairs’ Computerized Patient Record System. J Am Med Inform Assoc JAMIA Oct;15(5):620–6.
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22. AMIA is the professional home for more than 5,400 informatics professionals, representing frontline clinicians, researchers, public health experts and educators who bring meaning to data, manage information and generate new knowledge across the research and healthcare enterprise.
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23. Thank you! Email me at: justin.rousseau@austin.utexas.edu