Translating Clinical Guidelines

into Knowledge-Guided

Decision SupportBlackford Middleton, MD, MPH, MSc, FACP, FACMI, FHIMSS

Corporate Director, Clinical Informatics Research & Development

Chairman, Center for Information Technology Leadership

Harvard Medical School

Overview

• The Evidence for CDS

• The Value Potential of CDS

• Current examples and R&D Projects

• The Clinical Decision Support Consortium

Flexner Report

Abraham Flexner,

Medical Education in the United States and

Canada. Boston: Merrymount Press, 1910

"...The curse of medical education is the

excessive number of schools. The

situation can improve only as weaker

and superfluous schools are

extinguished."

“Society reaps at this moment

but a small fraction of the

advantage which current

knowledge has the power to

confer.”

The Evidence for CDS

• CDS yields increased adherence to guideline-based care, enhanced

surveillance and monitoring, and decreased medication errors

• (Chaudhry et al., 2006)

• CDS, at the time of order entry in a computerized provider order entry

system can help eliminate overuse, underuse, and misuse.

• (Bates et al., 2003; Austin et al., 1994; Linder, Bates and Lee, 2005; Tierney et

al., 2003)

• For expensive radiologic tests and procedures this guidance at the point of

ordering can guide physicians toward ordering the most appropriate and

cost effective, radiologic tests.

• (Bates et al., 2003; Khorasani et al., 2003)

• Showing the cumulative charge display for all tests ordered, reminding

about redundant tests ordered, providing counter-detailing during order

entry, and reminding about consequent or corollary orders may also

impact resource utilization

• (Bates and Gawande, 2003; Bates, 2004; McDonald et al., 2004).

The Value of Ambulatory CDS

• Savings potential: $44 billion

• reduced medication, radiology, laboratory, and ADE-

related expenses

• Advanced CDS systems

• Savings potential only with advanced CDS

• cost five times as much as basic CDS

• generate 12 times greater financial return

• A potential reduction of more than 2 million

adverse drug events (ADEs) annually

Johnston et al., 2003

http://www.citl.org

Serious Medication Error

Rates Before and After CPOE

Bates et. al. JAMA 1998.

CAD/DM Smart Form

Smart View: Data Display

Smart Assessment, Orders, and Plan

Assessment and recommendations generated from rules engine

Smart Documentation

• Lipids

• Anti-platelet therapy

• Blood pressure

• Glucose control

• Microalbuminuria

• Immunizations

• Smoking

• Weight

• Eye and foot examinations

CAD/DM Smart Form

Medication Orders

Lab Orders

Referrals

Handouts/Education

Preliminary Results:

Smart Form On Treatment Analysis

0% 10% 20% 30% 40% 50% 60% 70% 80%

Up-to-date BP result

Change in BP therapy if above goal

Up-to-date height and weight

Change in therapy if A1C above goal

Up-to-date foot exam documented

Up-to-date eye exam documented

# of deficiencies addressed

Smart Form Used Control

<0.001

<0.001

<0.001

<0.001

<0.001

0.05

0.004

0.006

CAD Quality Dashboard

Targets are 90th percentile for HEDIS or for Partners providers

Zero defect care: • Aspirin• Beta-blockers• Blood pressure• Lipids

Red, yellow, and green indicators show adherence with targets

Discrepancy

Details

Patient Journal Causes

Provider Activation

Grant RW et al. Practice-linked Online Personal Health Records for Type 2

Diabetes: A Randomized Controlled Trial. Arch Intern Med. 2008 Sep

8;168(16):1776-82. .

More medication changes in visits after diabetes journal submission:

A Roadmap for National Action on Clinical Decision Support

“to ensure that optimal, usable and effective clinical decision support is widely available to providers,

patients, and individuals where and when they need it to make health care decisions.”

Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. J. Am. Med. Inform.

Assoc. 2007;14(2):141-145.

CDS Consortium Goal

To assess, define, demonstrate, and

evaluate best practices for knowledge

management and clinical decision support in

healthcare information technology at scale –

across multiple ambulatory care settings and

EHR technology platforms.www.partners.org/cird/cdsc

Guideline Model Chronology

1980 1990 2000

ONCOCIN EON(T-Helper) GLIF2

Arden

MBTA

GEODE-CM

EON2

GLIF3

Asbru

Oxford System

of MedicineDILEMMA PROforma

PRESTIGE

PRODIGY

Decision Tables GEM

PRODIGY3

P. L. Elkin, M. Peleg, R. Lacson, E. Bernstam, S. Tu, A. Boxwala, R. Greenes, & E. H. Shortliffe.

Toward Standardization of Electronic Guidelines. MD Computing 17(6):39-44, 2000

Narrative Recommendation layer

Narrative text of the recommendation from the published guideline.Semi-Structured Recommendation layer

Breaks down the text into various slots such as those for applicable clinical scenario, the recommended intervention, and evidence basis for the recommendation

Standard vocabulary codes for data and more precise criteria (pseudocode)

Abstract Representation layer

Structures the recommendation for use in particular kinds of CDS tools

• Reminder and alert rules

• Order sets

A recommendation could have several different artifacts created in this layer, one for each kind of CDS tool

Machine Executable layer

Knowledge encoded in a format that can be rapidly integrated into a CDS tool on a specific HIT platform

E.g., rule could be encoded in Arden Syntax

A recommendation could have several different artifacts created in this layer, one for each of the different HIT platforms

CDSC Multilayered

Knowledge Representation

Narrative Guideline

Semistructured Recommendation

Abstract Representation

Machine Execution

Enterprise CDS Framework

Run Rules

Controller

O

R

C

H

E

S

T

R

A

T

O

R

Supporting Services

CCD

Factory

CCD

Translation

Services

Classification

Services

Pt Data

Access

ECRS

Metadata

Query

CDS Consumers

External to

PHS

Vendor,

nonCache

Internal,

Cache

Vendor Products

Rule

Authoring

Patient DataReference

Data

Rule Execution

Server

Rule DB

Action

Patient

Factory

An external repository of clinical

content with web-based viewer

Search Criteria

Content Type…

Specialty

“I conclude that though the individual physician is not perfectible, the system of care is, and that the computer will play a major part in the perfection of future care systems.”

Clem McDonald, MD NEJM 1976Thank you!Blackford Middleton, MDbmiddleton1@partners.org

Where are we?