CLINICAL DECISION SUPPORT: Beyond Expert Systems · video cassette. A video tape is valuable in that it can be played back through any standards-conformant machine. The manner in

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CLINICAL DECISION SUPPORT:Beyond Expert Systems

Nick Beard, MB BS MScVice President, Health Informatics

IDX Systems Corporation

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Agenda

Clinical Decision Support Technology Introduction

Expert SystemsApplying Knowledge at the Point of Care

Technology in PracticeSuccesses and Challenges

SAGEA Leading-Edge Consortium

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Clinical Decision Support Technology

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Definition: Clinical Decision Support

The use of information technology to assist clinical staff in interpreting clinical information and in managing patient care.

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Why Do We Offer Decision Support?

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Imperfect Man

“... man is not perfectible. There are limits to man’s capabilities as an

information processor that assurethe occurrence of random errors in

his activities.”

Clem McDonaldNew England Journal of Medicine

1976

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Human Limitations

“The complexity of modern medicine exceeds the inherent limitations of

the unaided human mind.”

David M. Eddy, MD, Ph.D.Clinical Decision MakingJAMA 263:1265-75, 1990

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56% Shaquille O’Neal makes a free throw(Source: 2001-02 Team Statistics, Los Angeles Lakers)

60% Your doctor provides chronic care as indicatedby the medical literature.

70% Your doctor provides acute care as indicatedby the medical literature(Source: RAND Corp., Milbank Quarterly, 1999)

73% Your airline flight arrives on time(Source: DOT Bureau of Transportation Statistics for 2001)

Unaided clinical judgment:What are the odds?

The RAND data referred to her comes from a meta-analysis of the medical literature from 1987 to 1997 in regard to preventive, chronic and acute care.

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Heart attack: Over one quarter of patients do not receive beta blockers.

(Source: Medicare data from Jencks, et al., JAMA, Oct. 4, 2000)

Cancer: “Substantial” numbers of patients do not receive care “known to be effective.”

(Source: IOM National Cancer Policy Board, 1999)

Head injuries: Four out of five trauma centers do not comply with treatment guidelines.

(Source: Brain Trauma Foundation)

Pneumonia: Widespread failure to administer antibiotics quickly.

(Source: Meehan et al., JAMA, Dec. 17, 1997)

Unaided Clinical Judgment:Some grim specifics

The studies referenced here are just a few of those showing the widespread variation in actual adherence to the scientific evidence on best care. Meanwhile, Medicare data on regional variation on a very well accepted therapy -- beta blocker use after heart attacks -- shows states varying from 47 percent to 93 percent.

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Approaches to CDSS

Electronic patient record based:• Retrospective decision support

• Not part of today’s discussions…• ‘Real-time,’ population-based

• “Who needs a vaccination?”• Real-time, patient focused

• “Don’t give that drug!”

Device based:• ECG signal interpretation• Ventilator alarms, etc.

• Not part of today’s discussions…

Our mainfocus today

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Expert Systems

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What are expert systems?

“Expert systems allow a computer program to use expertise to assist in a variety of problems”• Buchanan and Smith, 1989

“Expert systems employ human knowledge to solve problems that ordinarily require human intelligence”• Hayes-Roth, 1983

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Classical model of expert systems

Inferenceengine

Knowledgebase

I/OSeparation ofknowledgefrom ‘reasoningtechnology.’

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An “example”

IF (drug ordered = digoxin)AND (potassium = low)THEN (warning required)

This is the ‘knowledge base’

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Real Time Patient Focused CDSS

Not all about expert systems• In fact, mostly not about expert systems

‘Simpler’ technologies prevail:• Decision tables

• “Drug X interacts with Drug Y”• ‘Stored queries’

• “If Drug X prescribed, get and display lab results p,q,r”• Hard-coded logic

• “If <A, B, C> computes to TRUE, warn…”• (all expressed in C or java or some such language)

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Technology in Practice

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Customer Examples

Montefiore Medical Center, New York

-Dose checking for most pediatric medications based on age, wt. and indication

-Drug-lab interaction warnings (e.g. for Digoxin orders, warn about low Potassium)

-Checking medications for interference with imaging orders

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Customer Examples

Peace Health, North West USA- Creatinine Clearance estimation triggered by orders for drugs that affect kidney function

Chelsea & Westminster, U.K.- OPASS: self selecting order sets for surgical screening based upon pre-admission nursing assessment information

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Decision Support is Harder Than We Think

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The Hard Parts?

Knowledge Acquisition

Knowledge Coding

Knowledge Deployment

Knowledge Maintenance

NOTTHIS!

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User Interface Considerations

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Problem #1

Real-World experience of CDSS is limited:• Yet, the case for its use is widely considered to have been made.

There are very few successful expert system deployments:• Consequently:

• …‘standards-based distributable knowledge’ is barely available,• …the wheel must be re-invented at many sites.

Arden Syntax is not the solution:• Inadequate temporal representations• ‘Curly brackets’ problem

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Problem #2

Most clinical information systems provide only rudimentary support for clinical guidelines. Insofar as guidelines are supported, guidelines must be ‘hand crafted’ for the clinical information systems. Modifying existing systems to incorporate new guidelines is expensive and time-consuming. Result: the ‘Holy Grail’ of [appropriate] use of guidelines at the point of care is very rare.

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S A G E

We will now take a closer look at SAGE, a research project to develop guidelines technology

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SAGE Overview

What is SAGE about?

Progress

Industry significance

In this overview of SAGE, we will cover three topics: first, what is SAGE; second, what is the status of the research, and finally what is the broader industry significance – what might ‘the world look like’ once SAGE is ‘done.’

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What is SAGE about?‘Working Knowledge’

IDX Systems, in partnership with Stanford Medical Informatics, Mayo Clinic, Intermountain Health Care, Nebraska Health Systems, and Apelon, Inc. has formed a collaborative research effort to develop informatics technology solutions in the area of computer-based clinical guidelines. We have been awarded funding for this large-scale, multi-site research project from the National Institute of Standards (NIST) Advanced Technology Program (ATP).

There is growing evidence that evidence-based, patient-specific care guidelines, integrated appropriately into care workflow of clinical information systems, have tremendous potential to augment clinical decision-making, thereby improving national standards of care and reducing costs of care. While recent research has provided a foundation of knowledge in these areas, the informatics technologies required to support the creation, representation, universal sharing, and wide-spread deployment of computer-interpretable care guidelines are simply not yet available. The combined expertise of our research group, in the areas of medical informatics, guideline modeling, clinical practice, and advanced clinical information systems will be focused on the above problem. The purpose of our project is the development of technologies to enable the authoring, representation, distribution, and deployment of active clinical guidelines to standards-conforming computerized patient care systems. Our ultimate goal is to produce and make available technologies for creation and dissemination of clinical knowledge in the form of computable, interoperable clinical guidelines.

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Clinical Knowledge Delivery

Traditional clinical ‘knowledge delivery methods’ (books, journals) are failing.

People hardly use guidelines today.

A significant unsolved problem in our industry:• “How can we get ‘best practice’ into the hands of

clinical practitioners ASAP?”

Why is SAGE important? What are the motivations for the project? In a nutshell –to enable a ‘quantum leap’ in clinical knowledge management.

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Active GuidelinesType 2

Diabetes

EvaluationIf

Needed

NeedsStabilization?

yes

no

Recommend self-managementprogram:Nutrition therapyPhysical ActivityEducation for self-managementFoot care

Set individualized treatment goals:Glycemic control: HbA1c < 7%Lipid levels: LDL " 130 mg/dlBP control: BP " 130/85 mm HgASA unless contraindicatedTobacco cessation if indicated

no

Treatment goals notmet:• Modify treatmentbased on appro-priate guidelineand/or• See GlycemicControl Algorithmand/or• Consider referralto diabetes healthteam or specialists

AreTreatmentGoals Met?

yes

See OngoingManagement

Algorithmfor maintainingtreatment goals

and complicationprevention

AD

AD

AD

AD

AD

Initialstabilization

for outpatientsrequiring

immediateinsulin

treatment

Text-books became active, offering targeted, relevant guidance at the point of care?

Patients were evaluated against proven guidelines -automatically?

Key data were presented at critical decision points -automatically?

The “guideline” here is a mere fragment of a guideline from ICSI. It is a very high quality guideline – but the full content of the guideline runs to over 50 pages. The precise details on the flowchart are not relevant – it is merely an example of the kind of ‘structure’ typical of such flowcharts.

What kind of infrastructure would be needed to support the widespread use clinical guidelines – active guidelines that really aid patient care, by interceding with recommendations at the right time? A great deal of infrastructure – as illustrated in the next slide.

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Creationof Clinical Knowledge

Representationof Clinical Knowledge

Deploymentof Clinical Knowledge

Clinical KnowledgeWorkbench

Clinical KnowledgeGeneric Library

• If . . .• Then . . .

LW Site A

LW Site B

IDX Site D

Non-IDX Site

EvaluatoryFeedback

Authoring

Consensus

Editing

• Standards-based• Indexed• Transportable• Interoperable

LW Site C

• If . . .• Then . . .

Local Library

ProtocolsOrdersetsOrdersEMR

• If . . .• Then . . .

Patient-specificguidelines

Workflow• Alerts• Scheduling• Views

Problem linkedEMR coupled

• “National” repository• Internet-shared• Process support

Clinical Guidelines

LocalWorkbench

OutcomesAnalysis

This is the infrastructure we envisioned – to answer the question posed by the previous slide. You will be glad to know I am not intending to explain it all in detail here and now… instead, let’s use an analogy.

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1975:• Sony:

Betamax 1976:

• JVC:VHS

Video now a multi-billion dollar industry.

Analogy

Looking back around 25 years, video technology was introduced. The ability to film, to record and store moving images, and the ability to play them back on demand.

Now, video is a vast industry. The developers of video technology created an infrastructure that enabled the creation of an entirely new form of publishing industry.

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Analogy

How was that ‘movie’ first distributed?

When the “movie” we just saw (the moonshot clip) was first recorded – how was it viewed? How did people get to see it at home?

It could only be seen in people’s homes when it was broadcast there – via a signal, received through a TV aerial.

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Analogy

SAGEMACHINEGuideline

File(s)

Type 2 Diabetes

Evaluation If

Needed

Needs Stabilization?

yes

no

Recommend self-management program:Nutrition therapyPhysical ActivityEducation for self-managementFoot care


no


Are Treatment Goals Met?

yes

See Ongoing Management

Algorithmfor maintaining treatment goals

and complication prevention

AD

AD

AD

AD

AD

AD

Initial stabilization

for outpatients requiring

immediate insulin

treatment

The TV set is the ‘equivalent’ of the CIS. As video anticipated little need to change TV set, SAGE anticipates that the main infrastructures of extant CIS technology will remain intact. The video cassette player is the analog of the SAGE machine – the ‘playback technology’ for the guideline files: themselves being analogous to the video cassette.

A video tape is valuable in that it can be played back through any standards-conformant machine. The manner in which it was created is irrelevant – except insofar as there are many, many really bad movies in existence.

Thus the analog of the ‘video camera’ in this architecture is the ‘workbench,’ and specialized software tool with which guidelines may created – or be rendered into distributable electronic form.

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A Quick History

November 2000• Jim Campbell, Paul Clayton & Nick Beard meet...• Initial ideas formulated…

January 2001 - August 2001• ‘Gate 1’ Technical Proposal• ‘Gate 2’ Commercialization Proposal• Oral Review at NIST, Washington DC

September 2001• Award (contingent) granted

October 2001• Contracts finalized

This is a brief history of the project initiation.

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SAGE is an R&D project to develop the technology infrastructure to enable computable clinical guidelines, shareable and interoperable across multiple clinical information system platforms

Scope: 3 year, $18.5 M, multi-site, collaborative project

Funding: NIST Advanced Technology Program

Overview

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Partners in the project:Apelon, Inc. Stanford Medical InformaticsIntermountain HealthcareUniversity of Nebraska Medical CenterMayo Clinic

IDXIDX

IHCIHC

UNMCUNMC MayoMayo

SMISMI

ApelonApelon

Overview

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The National Institute of Standards and Technology (NIST), an arm of the U.S. Department of Commerce, funds “high risk” research through its Advanced Technology Program (ATP).

The mission of the NIST/ATP program is “To accelerate the development of innovative technologies for broad national benefit through partnerships with the private sector”.

NIST/ATP projects must entail research that ‘leads to significant national benefits.’

The SAGE project is partially funded by NIST/ATP Cooperative Agreement Number 70NANB1H3049

“A word from our sponsors”

The NIST ATP program funds many interesting and important R&D projects. SAGE is the largest health informatics program yet funded by NIST.

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Analogy

SAGEMACHINEGuideline

File(s)

Type 2 Diabetes

Evaluation If

Needed


yes

no



no



yes




AD

AD

AD

AD

AD

AD



immediate insulin

treatment

Three main deliverables

Interoperable guideline model

Interoperable guideline workbench

Guideline deployment technology

Let’s take a closer look at the guideline file format work.

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GuidelineMapping Module

GuidelineFile .EGF

Admin. User or Clinical

Superuser

GuidelineLocalEditor

EditedGuidelineFile .EGF

Client for Execution

Server

Commands

Reports

Mapping Resources

CIS Rule Execution

System

CISOther Modules

CISOrder Entry

ExecutionServer

Guideline Compiler

Resource Manager

Local CIS Libraries

.

.

.

AdministrationSubsystem

Execution Subsystem

Guideline Deployment Software

Local Clinical Information System (CIS)

Clinical User

SAGE machine

C.I.S.

ankle.ankle.egfegf

Step 1: Guideline setup

Step 2: Guideline-based care

Guideline Deployment

‘Deployment’ is expected to be a 2-step process: “setup,” where various local ‘bindings’ and other preparations would be undertaken; and then the actual use of the guideline to support care.

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Adding a problemAdding a problem--record to a patient’s record record to a patient’s record is a potential ‘trigger’ for guideline proposal.is a potential ‘trigger’ for guideline proposal.

Diabetes mellitus

Would you like to enroll this patient on the Diabetes

Management Guideline?

Would you like to see more information on the Diabetes

Management Guideline?

Activated guidelines could remind clinicians of their availability by detecting appropriate Problem List entries.

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LastWord supports order sets LastWord supports order sets -- which could be the basis which could be the basis for ‘early’ implementation of guidelines.for ‘early’ implementation of guidelines.

Order sets are already susceptible to effective Order sets are already susceptible to effective ‘automated reasoning’ ‘automated reasoning’ -- for example at C&W in for example at C&W in London.London.

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Flexible, highly Flexible, highly contextcontext--sensitive sensitive problemproblem--specific specific feedback will be feedback will be critical to effective critical to effective communication of communication of ‘guideline conclusions’ ‘guideline conclusions’ to clinical staff.to clinical staff.

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Guideline progress tracking and decision flow could Guideline progress tracking and decision flow could be displayed to the clinician in real time be displayed to the clinician in real time

Lastly – important to provide clinicians with visual reference and context about progress along a care pathway or protocol.

This particularly example illustrates (early and primitive) ability to transform guideline KB content into diagrammatic flow of actions and decisions.

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Industry SignificanceWhy Should You Care?

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What will the World look like?

A step-through of the ‘big picture’ once SAGE is done.

What might the process of medical knowledge distribution look like once the SAGE project is totally successful?

The presentation that follows describes the multiple steps that would be enabled by the SAGE infrastructure. They are not ‘steps’ that any individual would follow, but rather the sequence of ‘events’ that encompass the process of guideline creation, encoding, distribution, use and evaluation.

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Step 1:Collect the Evidence

Guideline author collects source material required for the guideline. This may comprise textbooks, research papers, textual guidelines, paper-based flowcharts.

Type 2 Diabetes

Evaluation If

Needed


yes

no



no



yes




AD

AD

AD

AD

AD

AD



immediate insulin

treatment

Guidelines exist – in various states. Some are thorough, systematic, logical and flowcharted guidelines, fully referenced and capable of being moved “quickly” to practice. Others are ‘implicit’ in textbooks, research articles, and – even less accessible – ‘undocumented’ clinical practice in the heads of physicians.

In order to begin the electronic encoding of a guideline, the information required to describe and define it is first collected and collated. Arguably, ‘Step 0’ in this process is the selection of the problem to be encoded.

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Use a “guideline workbench” to encode electronic versions of guidelines.

The workbench provides assistance, such as highlighting logical inconsistencies or workflow ‘dead ends.’

Step 2:Build the Guideline

Guidelines are encoded using a ‘workbench.’ The workbench provides many capabilities/services. It includes access to standardized ‘objects’ from which clinical guidelines may be built, standardized vocabularies and –perhaps – codes for enterprise resources (“this guideline requires access to an echocardiogram”) and clinical information system capabilities.

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Step 3:Publish the Guideline

InteroperableGuideline

WorkbenchSoftware

The encoded guideline will be stored on a website –perhaps managed by a not-for-profit, perhaps by a commercial organization.

GuidelinesGuidelines

Inc.Inc.

Type 2 Diabetes

Evaluation If

Needed


yes

no



no



yes




AD

AD

AD

AD

AD

AD



immediate insulin

treatment

Guideline

File(s)

How guidelines are eventually published and/or distributed need not be presumed here. For the purposes of this illustration, we shall presume an imaginary organization called Guidelines Inc., which makes encoded guidelines available for download on “www.guidelines.com” (or adopts some other method of distribution). Security, commercial arrangements (if any), cataloguing methods, etc. are not explored further in this presentation.

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Clinical practice specialists at a specific healthcare delivery organization would ‘download’ the guidelines.

Step 4:Download the Guideline

The guideline would be acquired – maybe on a CD, or a computer tape, but most likely downloaded from a website. The guidelines would be ideally clearly comprehensible to ‘computer-disinterested’ clinicians, and free of the gratuitous hieroglyphics that sometimes plague computer-based representations of clinical knowledge. The dependencies of the guideline on specific enterprise resources (e.g., 24 hour access to emergency MRI or cardiac echo services) that may render the guideline difficult to implement in, for example, remote rural locations would be explicit. Two ‘levels’ of localization are envisaged:First, adaptation of the guideline ‘language’ to local parlance where necessary (e.g. guideline speaks generic drug names, hospital A does not; hosp B speaks of “BUN,” hosp C uses “Urea and electrolytes”). This is “minimally medically-epistemologically controversial,” and is likely to slowly diminish as in significance as standard terms are increasingly adopted.Second, true local change to the guideline clinical content – either to match local ‘lore,’ or to accommodate standardization of clinical practice in the context of inconclusive evidence (e.g. a serum porcelain screening test is required every 2 or 4 years, depending on which review article is consulted).It is mostly the second of these that will intersect with the efforts of ‘Step 5’ overleaf.

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Upon local approval of the guideline, it may need to be adapted prior to deployment.

This may entail substantive changes to clinical content.

Type 2 Diabetes

Evaluation If

Needed


yes

no



no



yes




AD

AD

AD

AD

AD

AD



immediate insulin

treatment

Local Workbench

Medical Practice Committee

Step 5:Develop Local Consensus

The overall process presumes that a guideline will usually require “local modification.” This is presumed here to occur through use of a ‘special version’ of the workbench originally used to encode the guideline.

It may also entail adaptation of nomenclature (e.g., use of trade names for drugs rather than generic names) or specific local terms for laboratory investigations. In addition, adaptation to the particular local clinical information services may be required. For example, the local CIS may not support features anticipated by the guideline, such as automatic messaging to pagers, or physician order entry. Local adaptation of guidelines would be undertaken using a tool similar to the workbench used for the ‘central, initial’guideline creation; adapted to support certain local requirements, such as the maintenance of ‘libraries’ of local terminologies etc. Upon conclusion of the local adaptation of the guideline, the guideline would be ‘installable’ into the local CIS environment.

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The guideline will be imported into the local C.I.S.

EG: IDX Carecast, Cerner Milleneum, Siemens Soarian...

GuidelineMapping Module

GuidelineFile .EGF

Admin. User or Clinical

Superuser

GuidelineLocalEditor

EditedGuidelineFile .EGF

Client for Execution

Server

Commands

Reports

Mapping Resources

CIS Rule Execution

System

CISOther Modules

CISOrder Entry

ExecutionServer

Guideline Compiler

Resource Manager

Local CIS Libraries

.

.

.

AdministrationSubsystem

Execution Subsystem

Guideline Deployment Software

Local Clinical Information System (CIS)

Clinical User

Compiler

C.I.S.

Step 6:Import the Guideline

• Clinical pathways• Problem-linked

order sets• Expert systems• Flowcharts

ankle.ankle.egfegf

This is the ‘magical,’ critical step! Unless we accomplish this, the project will not have succeeded. The diagram assumes that the guideline is ‘compiled’ directly into executable objects/structures that directly reside in/or are run by the CIS. Alternative architectures are being considered, such as the notion of a standard API to enable the guideline to be compiled and run on ‘a separate box,’ which in turn communicatesinstructions to the CIS. It is not intended to resolve this question in this presentation.

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START:Guideline deployed…

Surveillance:Is this patient a candidate?

ActivationGuideline instantiated for patient

Selection by Clinician

Recommendations for tests

Drug-dose guidance

Standard Order-sets

Discharge planning

Step 7:Guidelines in Practice

After upload, guideline(s) are deployed.

Upon successful localization and deployment in the local CIS infrastructure, the guideline may be used to guide care. The illustration above includes merely a few examples of the various things that the guideline might now ‘do.’ [Illustrated above as ‘recommendations for tests,’ or ‘drug-dose guidance,’ or ‘standard order-sets.’]

Ideally, the guidelines would be deployed in a manner that automatically recorded each time the guideline ‘intervened,’ and the consequences of the intervention (recommendations accepted, ignored, etc) to facilitate subsequent guideline evaluation.

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Local Workbench

Guideline impact must be evaluated.

Step 8:Evaluating the Guidelines

NY16-Aug-03ARF – 844 – ACC#L3

YN07-Aug-03AMI – 229 – ngch#J7

YY03-Jul-03CCF – 403 – ICSI#B2

Result?Accept?Activation date

Guideline

Our expectation is that each time a guideline is invoked and that it “provides feedback,” a log of its recommendations would be recorded to facilitate guideline evaluation. IDX has experience of knowledge-base behavior logging as part of expert system deployment and evaluation.

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GuidelinesGuidelines

Inc.Inc.

Guideline evaluations will be reported to the ‘central’organization.

Step 9:Consolidated feedback to central library

Ideally, all the organizations gaining experience of guideline effectiveness (or otherwise) will be able to centrally consolidate the evaluations to the “central library.”

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Markets for SAGE technologies

Guidelines.com Guideline technology company Clinical trials management Other markets?

There are several possible applications of SAGE technologies. The success of the project will enable a new class of “publishing company,” akin to how video technology enables organizations like Blockbusters to come into business (as well as creating a new channel for existing movie companies).

The technology of guideline deployment will represent a different business. Commercialization of that work is being planned.

It is hopeful that the work will succeed in also enabling progress towards combining the work of guideline-based care with clinical trials. These are different, but the overlap is significant.

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SAGE - Summary

SAGE is about:

• Solving technological problems

• Creating infrastructure

• Making a market

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Questions?

Documents

CLINICAL DECISION SUPPORT: Beyond Expert Systems · video cassette. A video tape is valuable in that it can be played back through any standards-conformant machine. The manner in