DEMONSTRATING CLOUD-BASED
CLINICAL DECISION SUPPORT AT
SCALE:
THE CLINICAL DECISION SUPPORT
CONSORTIUM
Brian E. Dixon, MPA, PhD, FHIMSS Marilyn D. Paterno, MBI
Outline – Part 1
•
Introductions
– Overview of the CDSC and its Aims
•
Theoretical Framework
– Models to accelerate knowledge to practice – Unified theory of CDS
•
Knowledge Representation
– The Four layers – Authoring tools – The KM portal
Outline – Part 2
•
CDS in the Cloud
– The web services approach – CDSC Implementation Sites
– Demo of the Regenstrief system
•
CDS Dashboard
– Assessing CDS effectiveness
•
Future Directions
Spectrum of CDS Systems
Clinical
Reminder
Clinical
Alert
Corollary
Order
Population
Health
CDS Grand Challenges
• Summarize patient-level information • Prioritize recommendations to users
• Combine recommendations for patients with co-morbidities • Improve the human-computer interface
• Use free text information in clinical decision support • Manage large clinical knowledge databases
• Create a internet-accessible, clinical decision support repository • Prioritize CDS content development and implementation
• Disseminate best practices
• Create an architecture for sharing executable CDS modules • Mine large clinical databases to create new CDS
CDS Consortium:
Goal and Significance
•
Goal
: To assess, define, demonstrate, and evaluate best
practices for knowledge management and CDS in
health care IT at scale – across multiple ambulatory
care settings and EHR technology platforms
•
Significance
: The CDS Consortium will carry out a
variety of activities to improve knowledge about
decision support, with the ultimate goal of supporting
and enabling widespread sharing and adoption of CDS.
1. Knowledge Management Life Cycle
2. Knowledge Specification
3. Knowledge Portal and Repository
4. CDS Public Services and Content
5. Evaluation Process for each CDS Assessment and Research Area 6. Dissemination Process for each Assessment and Research Area
5-Year Timeline
2008 • October 2009 2010 2011 2012 2013 • July Content, KM, KT Development Analysis of Best PracticesPilots, Demonstrations Expand CDSC Membership Expand CDSC Content Evaluation ARRA/HITECH Passed MU Stage 2 Final
Three Models to Accelerate
Knowledge -> Practice
•
Current paper-based approach
•
Knowledge artifact import into EMR
•
Cloud-based clinical decision support services
EMR Guideline
Computer Interpretable Guideline
Web Services
CCD/VMR Patient Data Object Decision Support
Clinical
Knowledge Knowledge Structured
Implementable/ Executable Knowledge Service EHR EHR EHR CDSC “L2” GEM CDSC “L4” CDSC “Action – Recommendation” CDSC “L3” CDSC “CCD+”
Knowledge Translation and
Specification: Four-Layer Model
Initial evaluation results: Structured recommendation (L3) was considered more implementable than the semi-structured recommendation (L2).
derived from derived from
Level 1 Unstructured Format : . jpeg , . html , . doc , . xl Level 2 Semi - structured Format : xml Level 3 Structured Format : xml Level 4 Machine Execution Format : any derived from
+ metadata + metadata + metadata + metadata
Boxwala, A.A., et al. A multi-layered framework for disseminating knowledge for computer-based decision support. JAMIA 2011.doi:10.1136/amiajnl-2011-000334
Published Guideline
Semi-structured Recommendation
Structured
Recommendation Executable Rules
Order Sets in CPOE system
Narrative Guideline Screening for High Blood Pressure
Reaffirmation Recommendation Statement U.S. Preventive Services Task Force (USPSTF)
The U.S. Preventive Services Task Force (USPSTF) recommends screening for high blood pressure in adults aged 18 and older. (This is a grade "A" recommendation)
Semi-Structured Recommendation
Meta data
Title: Screening for High Blood Pressure Reaffirmation Recommendation Statement
Developer: U.S. Preventive Services Task Force (USPSTF)
Strength of recommendation: Grade A
Clinical Scenario:
Patient age ≥18 years
Blood pressure not obtained in the last year
Clinical Action:
Obtain and record blood pressure
Structured Recommendation
Meta data
Title: Screening for High Blood Pressure
Developer: CDS Consortium
Derived from: USPSTF BP Screening Semistructured Rec.
Applicable Scenario
Data Mapping: BPRecordedInLastYear: Observation = VitalSign-> select(code.equals(BPLoincCode) and vsDataTime.within(12, months))
Logical Condition: BPRecordedInLastYear->notEmpty()
Recommended Action: VitalSign(code: BPLoincCode)
Arden Syntax Rule
knowledge evoke: …
data:
BPRecordedInLastYear := read last{table=‘RES’, code=‘12345-0’} PCPemail := read {…};
Adult := …;
logic:
if (adult is false) then conclude false;
if (BPRecordInLastYear is null) then conclude true;
action:
Write ‘Patient has not had a blood pressure screening in the last year’ at PCPemail;
L3 Knowledge Module
•
Single knowledge representation approach for
different CDS modalities
–
Order sets, reminders, alerts, documentation
templates
–
Modality features tend to mix-and-match
•
Single representation for different modalities
–
Unified framework for tools development
–
Enables consistency checking
Knowledge Module Structure
Knowledge Module Action Behavior Presentation Metadata Patient DataKnowledge Management Portal
KM Portal
Web Services Approach:
Hypothesis & Goal
•
CDSC CDS Services team hypothesis
– A service-oriented architecture (SOA) approach to decision
support is feasible and will provide benefits in
interoperability, reliability, and reusability of knowledge content used in clinical decision support across multiple sites.
•
Interoperability goal – A web service that is
– External to the application and/or system – Agnostic to the technology of the calling site
– Capable of being called from inside or outside its firewall – Supports / makes use of emerging standards
Web Services Approach:
Service Flexibility
Web Services Approach:
Flexible yet Standard Output
•
Action – Recommendation Model
–
Utilizes HL7 Datatypes R1 standard
• Makes the decomposing task easier
• Mappable to local EHRs that use coded data
–
Actionable for clinical use, as in:
• Create Orders – Medications, Procedures • Record Observations – Problems
• Display Messages – Text-based Alerts
• Provide Knowledge Assets – Patient Education Material • Recommend Encounters – Referrals
Message Observation Encounter Knowledge Asset Message
Sample
WVP Health Authority Salem,Oregon Wishard Hospital Indianapolis,IN RWJ Medical Group New Brunswick,NJ PHS Host Boston, MA
Web Services Approach:
Implementations
Current Status
Site - Guideline Clinics Providers
Partners - CDSC 2 48
Partners - Immunization 4 40
Regenstrief - CDSC 2 72
NextGen - CDSC 4 10
GE – CDSC (Expects to start late August) 1 10*
Performance over 90 days
*Site - Guideline # days Type+ Calls/dayAverage Avg time (secs)
Partners - CDSC 90 S 1,573 0.98
Partners -
Immunization 80 S 526 0.82
Regenstrief - CDSC 64 A 113 1.06
NextGen - CDSC 87 A 80 1.68
Partners HealthCare @ LMR
CDSC Guidelines
Reminders appear on Summary ScreenPartners HealthCare @ LMR
CDSC Guidelines
And on Reminder Screen
Partners HealthCare @ LMR
CDSC Guidelines
And at time of signing
Partners HealthCare @ LMR
CDC Immunizations
Partners HealthCare @ LMR
CDC Immunizations
Regenstrief Institute @ Wishard
Hospital
DEMONSTRATION
CDSC Guidelines at Wishard - GopherGE Centricity @ Rutgers Robert
Wood Johnson Medical Group
Purpose
•
Develop performance reporting tools and
CDS dashboards to
–
review adherence to CDS Consortium guidelines
–
assess effectiveness of CDS on patient care
outcomes
•
How does the dashboard help assess CDS
Putting Reminders in Context
Patient becomes member of eligible population Reminder logic becomes true Reminder displayed Reminder accepted Right action documented Clinical outcome“Prevalence” “Logic” “Display”“Acknowledged”“Performance” “Outcome”
Measurement Period T0 T1 Patients with Type2 DM Overdue for A1 C Test Reminder displayed to user User clicks on reminder and chooses coded response A1 C test result documented A1 C < =7. 0
“How well are the reminders working?”
•
To measure the Effectiveness of CDS, dashboard uses
–
Display (Counts)
–
Acknowledged (%)
–
Performance (“Right” action taken)
–
Contribution to Clinical Performance
•
Number Needed to Remind (NNTR)
– the number of reminders needed to be displayed to
a provider for that provider to take the recommended action
Dashboard – CDS Designer View
Total patients 47,782 Performing total 28,476 Patients where reminders displayed 2,757 Total count of displays 14,944
Reminders in Context
CDS
Reminders Displayed
NNTR is:
2757 patients
with reminder displayed divided by 713 patients
who had reminder displayed and then had aspirin
added to the Med List
= 3.87
(If look at total #reminder
displays rather than #patients, then NNTR is 20.96)
Less effective More patients Less effective Fewer patients More effective Fewer patients More effective More patients
CDS Reminder Effectiveness
Uses for Dashboard Results
•
Systematically monitor and evaluate
effectiveness of clinical decision support
•
Prioritize which decision support guidelines or
Future Directions for the CDSC
•
Current demonstrations
– Complete site trials currently running – Publish multi-site trial results, analysis
•
Continuing to expand
– Technology development (e.g., support for DSS, SMART) – Clinical content offerings (e.g., pharmacogenomic, other
rules in development)
•
Relocating Administrative Support
– Vanderbilt University Medical Center
FOR MORE INFORMATION
CDSC Blackford Middleton blackford.middleton@vanderbilt.edu
Four-Layer Process and Knowledge Authoring Tool
Aziz Boxwala aziz.boxwala@meliorix.com KM Portal Tonya Hongsermeier tonya.hongsermeier@gmail.com ECRS Web Service Howard Goldberg hgoldberg@partners.org Action-Recommendation Model Beatriz Rocha brocha@partners.org CDS Dashboard Jonathan Einbinder jseinbinder@partners.org
THANK YOU
Presenters:Brian E. Dixon, MPA, PhD, FHIMSS bedixon@regenstrief.org
Marilyn D. Paterno, MBI mdpaterno@partners.org
Contact:
