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Infectious Disease Rapid CDS Deployment: A Zika Case Study
Session 56, March 6, 2018
Stephanie H. Hoelscher, Chief Clinical Analyst, Texas Tech University
Dwayne Hoelscher, IT Clinical Core Analyst, University Medical Center
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Stephanie H. Hoelscher, MSN RN-BC CPHIMS CHISP
Dwayne Hoelscher, MSN RN CPHIMS
Has no real or apparent conflicts of interest to report.
Conflict of Interest
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Agenda
Objectives
Background
Technical Strategy
Maintenance
Next Steps
Conclusion
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UMC
Served
+300,000
Inpatient 501 beds
4,645 Employees
Most Wired
Best Place to Work in
Texas
Best Trauma Center Texas
TTUHSC
Campuses 6
Telemedicine Sites 14
Service 108
counties
Research
EHR Integrated Simulation
Center
Programs 5
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Objectives
Define
Define the process for development and build of a clinical decision support workflow
Identify
Identify the risks and benefits of a CDS used for treatment of patients exposed to an infectious disease, such as Zika
Evaluate
Evaluate the usefulness of a CDS within an EHR, in regards to the demonstrated Zika process
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West Nile
Hantavirus
AIDS/HIV Zika
PertussisTuberculosis
MeaslesEbola
Background
• 2012 West Nile
• 2014 Hantavirus
• 2014 Ebola
• 2016 Zika
• Next?
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Zika History• Uganda
– Monkeys 1947
– Humans 1952
• Resurgence 1960’s – 1980’s
– Africa
– Asia
• First LARGE Outbreak 2007
– Island of Yap (Federated States of Micronesia)
• First Severe Pathogenicity
– Brazil, October 2015
– Microcephaly connection made
(Image Source: bing.com/maps) (WHO, 2016)
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Zika History
(CDC, 2016; Wang et al., 2016)
• Brazil Outbreak 2015 – 2016
– Severe pathogenicity now noted
• Microcephaly
• Intercranial calcifications
• Guillain-Barré syndrome
– Transmission changes
• Sexual transmission
• Maternal-fetal transmission
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Identified Issues
• Misused or overused tool
• Situational awareness of upcoming
threats
• Slow reaction time to develop and
build new ID processes in the EHR
• Lack of evidence and maintenance in
the existing design
• No governance
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Development Framework
• The RIGHT information
• To the RIGHT people
• Through the RIGHT channels
• In the RIGHT format
• At the RIGHT point in the workflow
(AHRQ, 2013; Osheroff et al., 2012) (Images Source: Hoelscher, 2017)
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Resources
• Subject matter experts (SMEs)
• Local infectious disease providers
• Scholarly literature review
• All-Hazards Workgroup
• Office of National Coordinator (ONC)
• U.S. Department of Health and Human Services
• Zika EHR Preparedness Workgroup
• Nursing Informaticists
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• RIGHT Channels
• Most impactful
• Least intrusive
• No existing CDS
• For infectious disease
• Prior to Ebola
• After Zika
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Technical Strategy
CDS
Ebola
Hantavirus
TB
MERS
SARS
Zika
West Nile
Measles• Assessment of current state
• Evidence review– Literature
– CDC current guidelines
– SMEs
• CDS Design– Usable and functional
– Non-intrusive as possible
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• Nursing Intake Forms– Conducted at all points of entry
• Evidence Review– Integration of travel timeframe
– Updates of current CDC travel hotspots
– Updates of symptomology
– Integration of pregnancy and sexual partner travel status
• Enhanced Functionality– Required fields
– Event set hierarchy changes
(Adapted from Cerner, 2017; Posted with Cerner Permission)
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• Open chart pop up
• Providers only (end user role)
• Females only
• Ages between 10 and 55 years
• Not previously exposed to Zika (charted)
• Pregnancy status of pregnant or unsure
• Patient or partner travel
– Identified locations
Alert CriteriaZika Example Case
(Adapted from Cerner, 2017; Posted with Cerner Permission)
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Implementation
• Implementation is cyclical
– Dynamic, never static
• Rapid Deployment Model Development
– Base model developed
• Alert model
• Form model
– Designed for quick turn-around
– Don’t forget about maintenance
EB Inquiry
Build Revision
Validate
EducateGo-Live
Maintain
Reassess
Hoelscher
CDS Rapid
Deployment
Model
(Images Source: Hoelscher, 2017)
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Validation
• Multiple possible scenarios within EHR
• Rapid deployment, have test patients ready
• Does the alert verbiage make sense?
• URLs up-to-date, working
Deep-Dive Validation
• Alert firing correctly
• Random chart review
Data Review
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Data
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Maintenance
• Update based on CDC guidelines (primary)
– Algorithms, in general
– Travel alerts
• SME recommendations and modifications
– Tightening rule as needed (i.e., age modification)
– Seasonal? Declined at this time
• Governance requests
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Governance
• Development of governance process
• Alignment of technology to strategic plan
• Formalize process
• Increased end-user adoption/participation
• Increased usability from end-user testing
• Ensure effective management
• Establish measurement framework to ensure delivery as expected
Image Source: https://its.yale.edu
(Adapted from Ward, 2011)
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GovernanceLocal Hierarchy
• Interprofessional
• Multiple infectious disease champions
• Executive buy-in
• Evidence-based
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Limitations to the Zika Case
• Initial buy-in reluctance from end-users, SMEs
• Alert fatigue issues with any kind of CDS
• Keeping up with interim (not final) CDC guidelines
• Unique challenge with pregnancy and sexual partner integration
• Unable to place order from open chart alert
• Consistent and appropriate use of diagnosis and problem list
• Multiple places to update information (orders, forms, and rules)
• Single vendor
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Next Steps
• Unable to place order from open chart alert
• Discuss better “channel” in the workflow for order integration
• Link appropriate patient education to the process
• Multiple places to update information (i.e., forms, rules, orders)
• Further rule modification (i.e., gender, infant)
• Validation
• Current state only included informaticist testing
• Future state plan for “testing” group to rapidly test prior to roll out
• Better development of integration testing scripts
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Conclusion
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• Stephanie H. Hoelscher MSN RN-BC CPHIMS CHISP
• Email: [email protected]
• Twitter: @StephHoelcsher
• Dwayne Hoelscher MSN RN CPHIMS
• Email: [email protected]
• Twitter: @DwayneHoelscher
Do not forget to complete the online session evaluation, thanks!
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ReferencesCenters for Disease Control and Prevention. (2016). About Zika. Retrieved from https://www.cdc.gov/zika/about/index.html
Duffy, M. R., Chen, T., Hancock, W. T., Powers, A. M., Kool, J. L., Lanciotti, R. S.,…Hayes, E. B. (2009). Zika virus
outbreak on Yap Island, Federated States of Micronesia. The New England Journal of Medicine, 360, 2536-2543.
doi:10.1056/NEJMoa0805715
Eisenberg, F. (2016). All hazards approach: The business case. [Business case in preparation].
Osheroff, J. A., Teich, J. M., Levick, D., Saldana, L., Velasco, F., Sittig, D.,…Jenders, R. (2012). Improving outcomes with
clinical decision support: An implementer’s guide (2nd ed.). Chicago, IL: HIMSS Publishing
Wang, L., Valderramos, S. G., Wu, A., Ouyang, S., Li, C., Brasil, P.,…Cheng, G. (2016). From mosquitos to humans: Genetic
evolution of Zika virus. Cell Host & Microbe, 19(5), 561-565. doi: http://dx.doi.org/10.1016/j.chom.2016.04.006
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ReferencesWard, P. (2011). SharePoint governance [PowerPoint slides]. Retrieved from
https://www.slideshare.net/Peter1020/sharepoint-governance
World Health Organization. (2016). Zika virus. Retrieved from http://www.who.int/mediacentre/factsheets/zika/en/
Upadhyay, D. K., Sittig, D. F., & Singh, H. (2014). Ebola US patient zero: Lessons on misdiagnosis and effective use of
electronic health records. Diagnosis 2014, 1(4), 283-287. doi 10.1515/dx-2014-0064