Going With The (Knowledge) Flow: The Future Of Decision Making In Improving Outcomes

Going With The (Knowledge) Flow: The Future Of Decision Making In Improving Outcomes

Ian Z. Chuang, MD, MS, CCFP

SVP, Healthcare Informatics and Chief Medical Officer

Dennis Morrison, PhD

Chief Clinical Officer

CARERECORDTM: A platform providing a

framework for EMR, billing, scheduling, and clinical workflows

CARECONNECTTM_{: Beyond}

interoperability, focused on integration, a messaging bus

ensuring an open and free flowing network

CAREVIEWTM_{: Providing specific}

needs to extend capabilities,

connect disparate communities and optimize user experiences

CAREPATHWAYSTM_{: The use}

of data to drive optimal outcomes and operational efficiencies

CAREMANAGERTM_{: Coordinating}

care to ensure healthcare access, coordination, affordability and outcomes

PLEXUSTM: On demand services,

technology and delivery providing predictable results as needed, when needed

CAREPOINTETM_{: Person-centric solutions to}

enable and empower the consumer,

providing a seamless integrated connection in the pursuit of recovery, health and

wellness.

CARERECORDTM: A platform providing a

framework for EMR, billing, scheduling, and clinical workflows

CARECONNECTTM: Beyond

interoperability, focused on integration, a messaging bus

ensuring an open and free flowing network

CAREPOVTM_{: Providing specific}

needs to extend capabilities,

connect disparate communities and optimize user experiences

CAREPATHWAYSTM_{: The use}

of data to drive optimal outcomes and operational efficiencies

CAREMANAGERTM: Coordinating

care to ensure healthcare access, coordination, affordability and outcomes

PLEXUSTM_{: On demand services,}

technology and delivery providing predictable results as needed, when needed

CAREPOINTETM_{: Person-centric solutions to}

enable and empower the consumer, providing a seamless integrated connection in the pursuit of recovery, health and wellness

We are our own worst enemies

Social Services MH/SU Medical

Recipients of Services Clients Consumers Families People Individuals Client Consumer Patient Patient Providers of

Services Direct Service Providers Social/Case Workers

Clinicians Therapists Providers Doctor Nurse Provider

Interventions _Supports Services Treatment _Therapy Treatment _Therapy

Intervention Documentation

Service Plan Permanency Plan Individualized Service Plan

Treatment Plan

MH/SU

Medical

Family &

Social

Services

Key Point about CDSS

An EHR is NOT an electronic

recapitulation of a paper record

The EHR can and should do things for

you that the paper record can’t.

It’s not just about EHRs anymore

CDSS 1.0

Why do we need CDS?

It is now humanly impossible for unaided

healthcare professionals to possess all the

knowledge needed to deliver medical care

with the efficacy and safety made possible

by current scientific knowledge.

Definition Clinical Decision Support

Systems

Clinical Decision Support Systems are:

“…active knowledge systems which use

two or more

items of patient

data

to

generate case-specific

advice

"

Definition Clinical Decision Support

Systems

Put Another Way:

If

X

is true and

Y

is true:

CDSS 2.0

Who gets the decision support information?

Health care and Social Services need to be

transformed

Less expensive professionals

to do

more sophisticated things

in

The Least Expensive Provider is

CONSUMERS ARE ALREADY

DOING DECISION SUPPORT

Data Deluge

What’s the new role of care

providers when consumers have

more data?

Eric Topol, MD

“…[the doctor’s] role will be progressively

morphed into providing guidance, wisdom,

experience on

how to transform data and information to

knowledge and judgment.”

Netsmart Clinical Model

Pre-Intake _Intake Service

Plan ProgNotes Outcomes

Clinician Decision Support

Consumer/Family Decision Support

DECISION SUPPORT

FUNCTIONALITY

Meaningful Use Opens the Door for CDS

• CDS = Clinical Decision Support • Meaningful Use = a start

– Phasing: Stage 1 vs Stage 2 – Functional capabilities focused

• Necessary but NOT sufficient

– Success is based on implementation of specific CDS interventions tied to Clinical Quality Measures

(CQMs)

What is a Clinical Decision Support System?

• Information technology – based functionality

designed to improve clinical decision-making

– System generated information based on user data input and/or available data

– Presented back to the user – Functionality Focused

• How a decision support will work within an EHR system?

• How will the clinician user interact with the decision support functionality?

Clinical Decision Support: The Bigger Picture

• Objective Focused

– Process – Outcome

• Knowledge-Driven Care Process

– What are we trying to achieve? Desired outcomes? – How can an intelligent system prompt, guide or

influence the clinician user to the desired outcomes

• Knowledge Flow is key

Clinical Decision Support as a Process

• Referencing the way the nervous system works:

– Alerts and information display interventions are the efferent arms

• Based on the available data, specific message is pushed to the

clinician user at the point-of-care

– Dashboard, reports, and benchmark are the afferent arms

• Data from the point-of-care come back into the central data base • Analysis for knowledge-driven process improvement

• Both perspectives are necessary to close the loop

CareGuidance

• A broad set of integrated decision support

capabilities (not just alerts) that interact with the right user at the right time to have the most

impact of making optimal decisions and actions

– At the point-of-care, the caregiver is the target user – At home, and in the community, the consumer, their

proxy, health coaches are the target users

– Care Managers and Operations Managers are target users at a system and population level

Clinician Workflow Opportunities for CareGuidance

Pre-Intake Intake Service Plan Progress Note Outcomes

• Risk identification • Assessment • Reference • Work routing • Progress • Performance • Assessment • Progress • Compliance • Treatment Guideline • Therapy • Test • Medication • Reference • Targeted cohort • Scheduling • Hallmark events

Components of CareGuidance

• Knowledgebase

– Reference data in computer-usable form

• Codesets • Criteria • Metadata

• Trigger

– Event

– Date/time vs time interval – Data

• Software algorithm • Notification output

– Recommendation/guidance/content output

Information Based Decision Support

• Four levels of information detail

– Identify – Inform – Educate – Guide

• Different levels of functional intervention

– Passive info display

– Workflow alerts/interruption

– Workflow interruption with action

Decision Support imbedded into Workflow Functionality

• Reference information or guidelines are actionable content with the EMR

• Complex set of decision support functionality structured like EMR functional components, such as Order Entry

– Order sets

Interactive Forms/Diagnostic Tool

• Interactive functionality for real-time decision support

• Clinician provides data as requested by the tool • Upon completion of data input, the tool runs an

algorithm to generate an output

– Clinician uses the information

– The output may be something that is actionable and ready for user selection

• One advantage is the ability to run different scenarios

Online Reference

• Digital reference libraries

– Research paper – Textbooks

– Info aggregators, such as Up-to-Date

• Functionality

– Info on the side

Prognosis/Prediction

• Predicting health risk or events, such as risk of death

– Framingham – APACHE

Challenges and Obstacles

• Information overload

• Lack of precision/lack of relevance • Lack of time

• Topically correct, but wrong user audience

• Decision support is only as good as the data that is available/inputted

The Data Matters for CareGuidance

• Garbage in-garbage out

– Erroneous data

• “cold” as in feeling cold or a viral respiratory infection?

– Imprecise data

• “diabetes”, meaning diabetes mellitus or diabetes insipidus

– If diabetes mellitus, which one? Type I, Type II, Gestational?

– Data omission

• Is the absence of a specific data interpreted as “not present” or

“not stated”? Does it matter?

• Rules-based CDS can be biased towards specificity (more

precision), or sensitivity (more inclusion but higher rates of false positive) depending on how they are designed

CareGuidance Data Strategy

• Structure or codify the data needed for CareGuidance in pre-defined ways

– Support synonym

– Support related concepts described at different levels of granularity via concept identifiers and data

dictionary

• “anemia” “folate deficiency anemia”, and “severe iron deficiency

anemia” are related if we don’t care about the type of anemia

• String match won’t work so easily

• Same decision support rule can support different but related data

Additional Requirements for CDSS

• Data codified for software algorithm

• Data codified for monitoring and reporting • Recording decision support actions for

monitoring and reporting

• Data codified to track outcomes

Two Sides of Knowledge-Driven Care Process

• If we want more evidence-based practice, we need more practice-based evidence.

Evidence-Based Practice Guidance/ Suggestion

Practice-Based Evidence

“do that for this reason”

When All is Said and Done

“If it was your mom receiving care in the middle of a complex health system, given the option,

wouldn’t you prefer to have a decision support system checking on optimal care as a back-up?”

Q&A

Additional questions can be directed to:

[email protected]