Clinical Decision Support Services
Tonya Hongsermeier, MD, MBA
CMIO
Agenda
• About Clinical Decision Support Services
• Experience of the Clinical Decision Support Consortium
• Standards Efforts Underway to Make Them Widely Available
• Opportunities and Challenges
About Clinical Decision Support Services (CDSS)
Outside the Cloud Inside the Cloud
CDSS Firewall
Data Normalization and Classification
Services
Cloud-based Clinical
Decision Support Services
CDSS EHR
Consumer
PATIENT DATA
ASSESSMENTS and
RECOMMENDATIONS
1) Externalizes application of CDS Logic that can provide assessments and guidance
2) Externalizes curation of clinical knowledge to a CDSS provider or their respective content supplier
3) EHR vendor is responsible for making it possible to send data in the appropriate workflow context and receive assessments and recommendations from CDSS
4) EHR vendor is responsible for making it possible to insert CDSS guidance into the appropriate EHR workflow context
5) Implementing consumer still needs to determine insertion, support ongoing semantic harmonization
Clinical Decision Support Consortium (CDSC)
1. Knowledge Management Life Cycle
2. Knowledge
Specification
3. Knowledge Portal and
Repository
4. CDS Public Services
and Dashboard
5. Evaluation Process for each CDS Assessment and Research Area
6. Dissemination Process for each Assessment and Research Area
• Knowledge management lifecycle • Knowledge specification • Knowledge Portal and Repository • CDS Knowledge Content and Public Web Services • Evaluation • Dissemination
Led by Dr. Blackford Middleton AHRQ funded from 2008-2013
CDSC Conceptual Approach
CDSC Evidence-based Guidelines (e.g., DM, HTN, CAD)
Level 1
Translation
DisseminationLevel 2 and Level 3
Specifications
CDS Services
Provider
Dashboard
Developer
Dashboard
KM Portal and
Repository
EMR End user
accessRefinement
Performance Measures
Collaboration Collaboration
NextGen Centricity Regenstrief Partners LMR
Reminders
Partners HealthCare EHR Regenstrief Medical Record System®
Two Examples of CDSC Implementation of
CDS services
©CDS Consortium
Legal Agreements Developed to Address Liability Points of Failure
• CDS manufacturing defect
– Software does not perform as designed
– i.e. alerts fail to notify due to gap in software or service Device
– CDS supplier must be able to audit/trace all guidance provided
• CDS implementation defect
– Customer implementation of software results in defective functioning
– i.e. alerts fail to fire because customer has incorrectly implemented services insertion or failed to notify CDS supplier that their dictionary changed
• CDS user error
– Software performs as designed, customer has implemented correctly, however user does not utilize correctly
– i.e. user ignores alert, turns off alerts, fails to notice alert
– Blurred distinctions here because users typically blame CDS manufacturer or implementation team for creating unusable CDS.
– Legal precedent to date still renders the Provider accountable for determining if the CDS guidance is appropriate because the Provider has the richer context of the patient to interpret the relevance of the guidance
CDSC Services Rule Building Blocks
Problem Classes Drug Classes Classes of Observations
And Test Results
Indication State
Inferences
Goal State
Inferences
Contraindication State
Inferences
Infobutton Knowledge
Access
Diagnostic Testing
Care Management
Observation Dictionaries
LOINC or SNOMED
Order Classes
Drug Dictionaries
RX Norm
Order Catalogues
Patient
Assessment
Problem Dictionaries SNOMED
Risk State
Inferences
Recommendations
Guideline Context
Family History
Patient Preference
Phenotypic State
Genotypic State
Classes Of
Raw Data
Inferred Patient Context (Clinical State
Rules)
Patient Education
Qualifying for ACEI
ARB Drug Class
ACEi Drug Class
Contra Indication
To ACEi/ARB
Allergy to ACEi/ARB
Clinical State Rule
Pregnant Clinical State
Rule
Pregnant Prob
Class Subset
Preg. Complications
Prob Class Subset
Low BP Clinical State
Rule
Low BP Prob
Class Subset
Hyperkalemia Disease
State Rule
Hyperkalemia Prob
Class Subset
NOT NOT NOT Non-Gest
DM Disease State Rule
DM Disease State Rule
DM Prob Class Subset
DM Complications Prob Class Subset
Gest DM Disease
State Rule
Gest DM Prob Class Subset
NOT
Simple Rules are NOT Simple:
IF in non-gestational DM pt with non-ESRD qualifying for ACEi
Key:
___ AND
. . . OR Non-ESRD
CRF Disease State Rule
CRF Prob
Class Subset
Creat >2 w/in 12 mo Calc Rule
GFR<50 Calc Rule
Proteinuria Disease State
Rule
Malb/cre>30 Calc Rule
Proteinuria Prob Class
Subset
ESRD Disease
State Rule
ESRD Prob Class Subset
Dialysis Comp Prob
Class Subset
&NOT O
R
CDSC, ACDS, HL7 and Other Standards
ONC S&I Health eDecisions Use Case 1
– Data Model for CDS Artifact
Authoring – Knowledge Representation for
CDS Artifacts – Artifacts can be value sets
(groupers), rules, order sets, documentation templates, etc
– Enable interoperable knowledge sharing
CDS Artifact Sharing Use Case
FR & Data Elements
VMR GEM
eRECS CDSC
L3
HL7 Order
Set Model
SHARP ARDEN
Inputs
Use Case 1: CDS Artifact Sharing
HeD Artifact Sharing
Standard
Harmonization and Modeling for 3 Artifact
Types
CREF
CDSC, Open CDS, HL7
ONC S&I Health eDecisions Use Case 2
• Use Case 2: CDS Guidance Services
– CDS Services Insertion
– Patient Data Output by EHR/PHR system to CDS Guidance Service
CDS Guidance Services Use Case
FR & Data Elements
HL7
Consolidated CDA
Other
Standards
HL7 DSS
Services
Inputs
HeD CDS Guidance Services
Standards
Harmonization and Modeling
HL7 Context Aware
Information Retrieval
Opportunities and Challenges
• EHR vendors can’t expect their customers to curate all this knowledge inside their EHR walls Immunizations, Genetics, Genomics, Personalized Medicine
• Few EHR CDS systems can actually execute the kind of inferencing required for personalized medicine
• Hence, Memorial Sloan Kettering is training IBM Watson Personalized Cancer Rx service
• We need to advocate for EHR vendors to move beyond “walled gardens of simple CDS”