The Evolution of Clinical Decision Support. David McCallie Jr. MD Cerner Corp.

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1 Dr. David McCallie

2 The Evolution of Clinical Decision Support David McCallie Jr. MD Cerner Corp.

3 Agenda General Observations Some Examples

4 Evolution of CDS? Johanna Pung for Wikimedia Deutschland (Wikimedia Deutschland) CC-BY-SA-3.0 ( via Wikimedia Commons

5 Broad Spectrum of CDS Identify Propose Intervene Sepsis Recognition Manage Analyze

6 User Experience Reactive -> interrupt Interactive -> assist Ambient -> guide

7 Knowledge Representation Scripts Rules Algorithms & agents Big data & learning

8 Knowledge Source Locally authored Vendor packages Cloud-based services Third-party via imported content Third-party via interactive plugins

9 Workflow Integration Foreground (triggers) Background (event monitors) Network (multi-agent monitors)

10 Data and Access Ad hoc codes + curly braces (Arden) Value sets + vmr + ECA rules? (HeD) Clinical Element Models + FHIR?

11 Decision Support Goals Avoid harm Guide therapy (Tx) Guide Dx and Tx Prevent Dx

12 Some (random) examples Johanna Pung for Wikimedia Deutschland (Wikimedia Deutschland) CC-BY-SA-3.0 ( via Wikimedia Commons

13 Traditional ADE Interaction Alert

14 erx Guidance + Pre-screened choices

15 Reduction in nuisance ADE alerts

16 Interactive CDS Multi-step VTE Advisor

17 Contextual, ambient, actionable guidance

18 Contextual, ambient, actionable guidance

19 Cloud-based CDS Continuous Sepsis Agent HR Resp. Temp BP Labs CNS Status Etc. Concept Extraction Population EMR Sepsis Agent Alert Routing Data Crawler PowerChart EHR Data Alert Routed to Sepsis Team (and Dashboard)

20 Cloud-based Sepsis Iterative, crowd-sourced Refinement Real-time Monitoring Over 160 facilities ~40K persons per hour ~800K lives per day Version 14 Operating Characteristics Sensitivity 68-91% Specificity % PPV up to 68%

per hour ~800K lives per day Version 14 Operating

21 All Cause Readmission Prediction Model Models C-Stat ACC Model 0.78 Previous Model 0.69 Average models reviewed in JAMA Yale NHHSC/CORE for CMS * Denotes past and present clinical data

22 SMART on FHIR Open Platform Architecture SOA Orchestration mhealth Web Apps OAuth REST API Clinical Element Models & FHIR Data Profiles Health IT Systems

23 FHIR Resource Observation <Observation xmlns=" <name><! CodeableConcept Type of observation (code / type) --></name> <value[x]><! Quantity CodeableConcept Attachment Ratio Period SampledData string Actual result --></value[x]> <interpretation><! CodeableConcept High, low, normal, etc. --></interpretation> <comments value="[string]"/><! Comments about result --> <applies[x]><! datetime Period Physiologically Relevant time/time-period for observation --></applies[x]> <issued value="[instant]"/><! Date/Time this was made available --> <status value="[code]"/><! registered preliminary final amended + --> <reliability value="[code]"/><! ok ongoing early calibrating error <bodysite><! CodeableConcept Observed body part --></bodysite> <method><! CodeableConcept How it was done --></method> <identifier><! Identifier Unique Id for this particular observation -- ></identifier> <subject><! Resource(Patient Group Device Location) Who and/or what --></subject> <specimen><! Resource(Specimen) Specimen used for this observation --></specimen> <performer></performer> <referencerange>  <low></low> <high></high> <meaning><! CodeableConcept Indicates the meaning/use of this range --></meaning> <age><! Range Applicable age range, if relevant --></age> </referencerange> </Observation>

24 FHIR Profile for Blood pressure MessageHeader event: Coding data: Any(0..*) -> List(1..1) List-Bloodpressure subject: Patient > Patient (1..1) (bundled ref) Entry item: Observ. (1..1) (bundled) Entry item: Observ. (1..1) (bundled) Observation-Systolic name: Systolic value: Quantity (value.units mmhg ) Observation-Diastolic name: Diastolic value: Quantity (value.units mmhg )

25 SMART on FHIR Pluggable CDS (Intermountain Healthcare)

26 SMART on FHIR Pluggable CDS (VisualDx)

27 SMART on FHIR Pluggable CDS (VisualDx)

28 Futures for CDS Ambient > interruptive Guidance > interdiction Cloud-based > local Big learning > Big data Community/population data > EHR local data Algorithmic, multi-agent > scripts & rules Multi-disease > single-disease Pluggable apps > imported rules SMART + FHIR + Element Models > DSS + vmr?

29 Thank You! Johanna Pung for Wikimedia Deutschland (Wikimedia Deutschland) CC-BY-SA-3.0 ( via Wikimedia Commons

The HL7 CIMI Workgroup: Creating Detailed Clinical Models to Support FHIR Interoperability. HIMSS March 2, 2016 Las Vegas Stanley M.

The HL7 CIMI Workgroup: Creating Detailed Clinical Models to Support FHIR Interoperability HIMSS March 2, 2016 Las Vegas Stanley M. Huff, MD Current Situation Each EHR vendor uses a proprietary database