Demonstrating Cloud-based Clinical Decision Support at ... · demonstrating cloud-based clinical...

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 • Acknowledgements

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

Sittig et al., JBI, 2008

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 Practices

Pilots, 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

Structured Knowledge

Implementable/ Executable Knowledge

Service

EHR EHR

EHR

CDSC “L2”

GEM

CDSC “L4”

CDSC “Action – Recommendation”

CDSC “L3”

CDSC “CCD+”

The Unified Theory for CDS

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;

The Four Layers Illustrated

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 Data

Knowledge Authoring Tool

Knowledge Authoring Tool

Knowledge Authoring Tool

Knowledge Management Portal

http://cdsportal.partners.org/cdscsearch.aspx

KM Portal

Search Results for ‘diabetes’

CDS IN THE CLOUD

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

Service Input Formats

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*

* Estimate

Performance over 90 days*

Site - Guideline # days Type+ Average

Calls/day 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

+ Synchronous / Asynchronous * May 13, 2013 – August 10, 2013

Partners HealthCare @ LMR CDSC Guidelines

Reminders appear on Summary Screen

Partners 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

Contraindication warning

Regenstrief Institute @ Wishard Hospital

DEMONSTRATION CDSC Guidelines at Wishard - Gopher

NextGen @ WVP Health Authority

GE Centricity @ Rutgers Robert Wood Johnson Medical Group

CDS DASHBOARD

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

effectiveness?

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

Epidemiology of CDS: the CDS/Reminder Lifecycle

“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

NNTR 3.87

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)

Number Needed to Remind (NNTR)

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 reminders to implement or enhance

FUTURE PLANS

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: Blackford Middleton, MD, MPH, MSc blackford.middleton@vanderbilt.edu