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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: steph.hoelscher@ttuhsc.edu

• Twitter: @StephHoelcsher

• Dwayne Hoelscher MSN RN CPHIMS

• Email: dwayne.hoelscher@umchealthsystem.com

• 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